Data philanthropy refers to the practice of private companies donating corporate data. This data is usually donated to nonprofits or donation-run organizations that have difficulty keeping up with expensive data collection technology. The concept was introduced through the United Nations Global Pulse initiative in 2011 to explore corporate data assets for humanitarian, academic, and societal causes. For example, anonymized mobile data could be used to track disease outbreaks, or data on consumer actions may be shared with researchers to study public health and economic trends. == Definition == A large portion of data collected from the internet consists of user-generated content, such as blogs, social media posts, and information submitted through lead generation and data forms. Additionally, corporations gather and analyze consumer data to gain insight into customer behavior, identify potential markets, and inform investment decisions. United Nations Global Pulse director Robert Kirkpatrick has referred to this type of data as "massive passive data" or "data exhaust." == Challenges == While data philanthropy can enhance development policies, making users' private data available to various organizations raises concerns regarding privacy, ownership, and the equitable use of data. Different techniques, such as differential privacy and alphanumeric strings of information, can allow access to personal data while ensuring user anonymity. However, even if these algorithms work, re-identification may still be possible. Another challenge is convincing corporations to share their data. The data collected by corporations provides them with market competitiveness and insight regarding consumer behavior. Corporations may fear losing their competitive edge if they share the information they have collected with the public. Numerous moral challenges are also encountered. In 2016, Mariarosaria Taddeo, a digital ethics professor at the University of Oxford, proposed an ethical framework to address them. == Sharing strategies == The goal of data philanthropy is to create a global data commons where companies, governments, and individuals can contribute anonymous, aggregated datasets. The United Nations Global Pulse offers four different tactics that companies can use to share their data that preserve consumer anonymity: Share aggregated and derived data sets for analysis under nondisclosure agreements (NDA) Allow researchers to analyze data within the private company's own network under NDAs Real-Time Data Commons: data pooled and aggregated among multiple companies of the same industry to protect competitiveness Public/Private Alerting Network: companies mine data behind their own firewalls and share indicators == Application in various fields == Many corporations take part in data philanthropy, including social networking platforms (e.g., Facebook, Twitter), telecommunications providers (e.g., Verizon, AT&T), and search engines (e.g., Google, Bing). Collecting and sharing anonymized, aggregated user-generated data is made available through data-sharing systems to support research, policy development, and social impact initiatives. By participating in such efforts, these organizations contribute to causes regarded as beneficial to society, allowing institutions to give back meaningfully. With the onset of technological advancements, the sharing of data on a global scale and an in-depth analysis of these data structures could mitigate the effects of global issues such as natural disasters and epidemics. Robert Kirkpatrick, the Director of the United Nations Global Pulse, has argued that this aggregated information is beneficial for the common good and can lead to developments in research and data production in a range of varied fields. === Digital disease detection === Health researchers use digital disease detection by collecting data from various sources—such as social media platforms (e.g., Twitter, Facebook), mobile devices (e.g., cell phones, smartphones), online search queries, mobile apps, and sensor data from wearables and environmental sensors—to monitor and predict the spread of infectious diseases. This approach allows them to track and anticipate outbreaks of epidemics (e.g., COVID-19, Ebola), pandemics, vector-borne diseases (e.g., malaria, dengue fever), and respiratory illnesses (e.g., influenza, SARS), improving response and intervention strategies for the spread of diseases. In 2008, Centers for Disease Control and Prevention collaborated with Google and launched Google Flu Trends, a website that tracked flu-related searches and user locations to track the spread of the flu. Users could visit Google Flu Trends to compare the amount of flu-related search activity versus the reported numbers of flu outbreaks on a graphical map. One drawback of this method of tracking was that Google searches are sometimes performed due to curiosity rather than when an individual is suffering from the flu. According to Ashley Fowlkes, an epidemiologist in the CDC Influenza division, "The Google Flu Trends system tries to account for that type of media bias by modeling search terms over time to see which ones remain stable." Google Flu Trends is no longer publishing current flu estimates on the public website; however, visitors to the site can still view and download previous estimates. Current data can be shared with verified researchers. A study from the Harvard School of Public Health (HSPH), published in the October 12, 2012 issue of Science, discussed how phone data helped curb the spread of malaria in Kenya. The researchers mapped phone calls and texts made by 14,816,521 Kenyan mobile phone subscribers. When individuals left their primary living location, the destination and length of journey were calculated. This data was then compared to a 2009 malaria prevalence map to estimate the disease's commonality in each location. Combining all this information, the researchers could estimate the probability of an individual carrying malaria and map the movement of the disease. This research can be used to track the spread of similar diseases. === Humanitarian aid === Calling patterns of mobile phone users can determine the socioeconomic standings of the populace, which can be used to deduce "its access to housing, education, healthcare, and basic services such as water and electricity." Researchers from Columbia University and Karolinska Institute used daily SIM card location data from both before and after the 2010 Haiti earthquake to estimate the movement of people both in response to the earthquake and during the related 2010 Haiti cholera outbreak. Their research suggests that mobile phone data can provide rapid and accurate estimates of population movements during disasters and outbreaks of infectious disease. Big data can also provide information on looming disasters and can assist relief organizations in rapid-response and locating displaced individuals. By analyzing specific patterns within this 'big data', governments and NGOs can enhance responses to disruptive events such as natural disasters, disease outbreaks, and global economic crises. Leveraging real-time information enables a deeper understanding of individual well-being, allowing for more effective interventions. Corporations utilize digital services, such as human sensor systems, to detect and solve impending problems within communities. This is a strategy used by the private sector to anonymously share customer information for public benefit, while preserving user privacy. === Impoverished areas === Poverty still remains a worldwide issue, with over 2.5 billion people currently impoverished. Statistics indicate the widespread use of mobile phones, even within impoverished communities. Additional data can be collected through Internet access, social media, utility payments and governmental statistics. Data-driven activities can lead to the accumulation of 'big data', which in turn can assist international non-governmental organizations in documenting and evaluating the needs of underprivileged populations. Through data philanthropy, NGOs can distribute information while cooperating with governments and private companies. === Corporate === Data philanthropy incorporates aspects of social philanthropy by allowing corporations to create profound impacts through the act of giving back by dispersing proprietary datasets. The public sector collects and preserves information, considered an essential asset. Companies track and analyze users' online activities to gain insight into their needs related to new products and services. These companies view the welfare of the population as key to business expansion and progression by using their data to highlight global citizens' issues. Experts in the private sector emphasize the importance of integrating diverse data sources—such as retail, mobile, and social media data—to develop essential solutions for global challenges. In Data Philanthropy:
SMART Health Card
The SMART Health Card framework is an open source immunity passport program designed to store and share medical information in paper or digital form. It was initially launched as a vaccine passport during the COVID-19 pandemic, but is envisioned for use for other infectious diseases. SMART Health Cards include a QR code which can be scanned and verified using the official SMART Health Card Verifier mobile app, supported by Apple and Android. It was rolled out by the Vaccination Credential Initiative (VCI) based on technology developed at Boston Children's Hospital, and standards set by Health Level Seven International (HL7) and the World Wide Web Consortium (W3C). It is recognized by the International Organization for Standardization. == History == === Founding === In February 2009, United States president Barack Obama signed an economic stimulus package which included $19 billion in funds for investment in health information technology. The following month, researchers from Boston Children's Hospital and Harvard Medical School, Kenneth Mandl and Isaac Kohane, published an article in The New England Journal of Medicine calling for the modernization of electronic health records through API integrations on mobile devices. In April 2010, the pair secured a $15 million grant through the Office of the National Coordinator for Health Information Technology's Strategic Health IT Advanced Research Projects (SHARP) program. With this federal funding, the researchers began development of an interoperable healthcare IT platform they called "Substitutable Medical Applications and Reusable Technologies" (SMART). The first iteration of the platform API was previewed later that year, and "SMART Classic" was released in 2011. In 2013, SMART adopted the open-source Fast Health Interoperability Resources (FHIR) standard developed by Health Level Seven International (HL7). The newly named SMART on FHIR platform was debuted in February 2014 at the Health Information Management Systems Society conference. === 21st Century Cures Act === According to SMART Health IT, Mandl successfully lobbied for the inclusion of a universal API requirement in the 21st Century Cures Act, signed into law on December 13, 2016. The team also advocated for a federal rule establishing SMART as the universal API. In 2019, the Office of the National Coordinator for Health Information Technology published the "final rule" specifying the SMART framework as the standard to satisfy the requirements of the 21st Century Cures Act; the rule was implemented in June 2020. === COVID-19 === The SMART Health Card framework was deployed as a "de facto standard" for vaccine passports in the COVID-19 pandemic in the United States and other international jurisdictions. On January 14, 2021, the Mitre Corporation announced the launch of a new public–private partnership called the Vaccination Credential Initiative (VCI) alongside the CARIN Alliance, Cerner, Change Healthcare, The Commons Project Foundation, Epic Systems, Evernorth, Mayo Clinic, Microsoft, Oracle, Safe Health, and Salesforce. VCI's purpose was to employ the SMART Health Card framework in order to create a unified proof-of-vaccination system for COVID-19 vaccines.The California Department of Public Health introduced a Digital Covid-19 Vaccine Record portal in June 2021, allowing individuals to verify their vaccination status using the SMART Health Card reader. On August 5, 2021, New York Governor Andrew Cuomo announced the introduction of the "Excelsior Pass Plus" which would expand its Excelsior Pass program into other states and internationally by connecting it to the SMART Health Card system. As of August 27, 2021, 415,000 citizens of Louisiana had added their COVID-19 vaccination status to their state-run, SMART Health Card enabled LA Wallet. On September 8, 2021, Hawaii governor David Ige announced the rollout of the state's Hawaiʻi SMART Health Card. County-level health departments across the United States partnered with VaccineCheck to issue SMART Health Cards by verifying vaccine cards provided by the Centers for Disease Control and Prevention. The Government of Canada spent CAD$4.6 million to develop a proof-of-vaccination credential on the SMART Health Card framework, enabling its ArriveCAN travel application to store, recognize and verify credentials from every province, territory and foreign country. Since October 2021, Canadian provinces and territories used the SMART Health Card format as a requirement by the federal government, including British Columbia, Newfoundland and Labrador, the Northwest Territories, Nova Scotia, Nunavut, Ontario, Quebec, Saskatchewan and the Yukon. On October 13, 2021, the American Immunization Registry Association (AIRA) published a statement encouraging adoption of SMART Health Cards as a common standard "where allowed by local law and policy." "SMARTHealth.Cards" was listed as a supporting member of AIRA through the VCI. A SMART Health Cards Global Forum was held on October 28, 2021. The event featured keynote speakers Andy Slavitt (former Senior Pandemic Advisor to President Joe Biden’s COVID-19 pandemic response team) and Mike Leavitt (former United States Secretary of Health and Human Services). On December 20, 2021, Japan's Ministry of Health, Labour and Welfare launched its COVID-19 Vaccination Certificate Application using the SMART Health Card. By January 2022, about 80% of Americans who had received a COVID-19 vaccine had access to a SMART Health Card through their state governments, local businesses, universities and healthcare systems. == Participants == === Developers === SMART Health IT is based out of the Computational Health Informatics Program (CHIP) at the Boston Children's Hospital. CHIP's related projects include Apache cTAKES, Genomic Information Commons, HealthMap, and VaccineFinder. The SMART Health Card's project sponsor is HL7 International's Public Health Work Group, consisting of representatives from Allscripts, the Altarum Institute, Tennessee Department of Health and Washington State Department of Health. === Issuers === Official registries of authorized SMART Health Card issuers are maintained by SMART Health IT, the Vaccination Credential Initiative, and the CommonTrust Network. Authorized issuers include:
R2ML
The REWERSE Rule Markup Language (R2ML) is developed by the REWERSE Working Group I1 for the purpose of rules interchange between different systems and tools. == Scope == An XML based rule language; Support for: integrity rules, derivation rules, production rules and reaction rules; Integrate functional languages (such as OCL) with Datalog languages (such as SWRL); Serialization and interchange of rules by specific software tools; Integrating rule reasoning with actual server side technologies; Deploying, publishing and communicating rules in a network. == Design principles == Modeled using MDA; Rule concepts defined with the help of MOF/UML; Required to accommodate: Web naming concepts, such as URIs and XML namespaces; The ontological distinction between objects and data values; The datatype concepts of RDF and user-defined datatypes; Actions (following OMG PRR submission); Events; EBNF abstract syntax; XML based concrete syntax validated by an XML Schema; Allowing different semantics for rules.
User modeling
User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing. == Background == A user model is the collection and categorization of personal data associated with a specific user. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling. Therefore, it is the basis for any adaptive changes to the system's behavior. Which data is included in the model depends on the purpose of the application. It can include personal information such as users' names and ages, their interests, their skills and knowledge, their goals and plans, their preferences and their dislikes or data about their behavior and their interactions with the system. There are different design patterns for user models, though often a mixture of them is used. Static user models Static user models are the most basic kinds of user models. Once the main data is gathered they are normally not changed again, they are static. Shifts in users' preferences are not registered and no learning algorithms are used to alter the model. Dynamic user models Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models Stereotype based user models are based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. The application therefore can make assumptions about a user even though there might be no data about that specific area, because demographic studies have shown that other users in this stereotype have the same characteristics. Thus, stereotype based user models mainly rely on statistics and do not take into account that personal attributes might not match the stereotype. However, they allow predictions about a user even if there is rather little information about him or her. Highly adaptive user models Highly adaptive user models try to represent one particular user and therefore allow a very high adaptivity of the system. In contrast to stereotype based user models they do not rely on demographic statistics but aim to find a specific solution for each user. Although users can take great benefit from this high adaptivity, this kind of model needs to gather a lot of information first. == Data gathering == Information about users can be gathered in several ways. There are three main methods: Asking for specific facts while (first) interacting with the system Mostly this kind of data gathering is linked with the registration process. While registering users are asked for specific facts, their likes and dislikes and their needs. Often the given answers can be altered afterwards. Learning users' preferences by observing and interpreting their interactions with the system In this case users are not asked directly for their personal data and preferences, but this information is derived from their behavior while interacting with the system. The ways they choose to accomplish a tasks, the combination of things they takes interest in, these observations allow inferences about a specific user. The application dynamically learns from observing these interactions. Different machine learning algorithms may be used to accomplish this task. A hybrid approach which asks for explicit feedback and alters the user model by adaptive learning This approach is a mixture of the ones above. Users have to answer specific questions and give explicit feedback. Furthermore, their interactions with the system are observed and the derived information are used to automatically adjust the user models. Though the first method is a good way to quickly collect main data it lacks the ability to automatically adapt to shifts in users' interests. It depends on the users' readiness to give information and it is unlikely that they are going to edit their answers once the registration process is finished. Therefore, there is a high likelihood that the user models are not up to date. However, this first method allows the users to have full control over the collected data about them. It is their decision which information they are willing to provide. This possibility is missing in the second method. Adaptive changes in a system that learns users' preferences and needs only by interpreting their behavior might appear a bit opaque to the users, because they cannot fully understand and reconstruct why the system behaves the way it does. Moreover, the system is forced to collect a certain amount of data before it is able to predict the users' needs with the required accuracy. Therefore, it takes a certain learning time before a user can benefit from adaptive changes. However, afterwards these automatically adjusted user models allow a quite accurate adaptivity of the system. The hybrid approach tries to combine the advantages of both methods. Through collecting data by directly asking its users it gathers a first stock of information which can be used for adaptive changes. By learning from the users' interactions it can adjust the user models and reach more accuracy. Yet, the designer of the system has to decide, which of these information should have which amount of influence and what to do with learned data that contradicts some of the information given by a user. == System adaptation == Once a system has gathered information about a user it can evaluate that data by preset analytical algorithm and then start to adapt to the user's needs. These adaptations may concern every aspect of the system's behavior and depend on the system's purpose. Information and functions can be presented according to the user's interests, knowledge or goals by displaying only relevant features, hiding information the user does not need, making proposals what to do next and so on. One has to distinguish between adaptive and adaptable systems. In an adaptable system the user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options. Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system itself, based on the built user model. Thus, an adaptive system needs ways to interpret information about the user in order to make these adaptations. One way to accomplish this task is implementing rule-based filtering. In this case a set of IF... THEN... rules is established that covers the knowledge base of the system. The IF-conditions can check for specific user-information and if they match the THEN-branch is performed which is responsible for the adaptive changes. Another approach is based on collaborative filtering. In this case information about a user is compared to that of other users of the same systems. Thus, if characteristics of the current user match those of another, the system can make assumptions about the current user by presuming that he or she is likely to have similar characteristics in areas where the model of the current user is lacking data. Based on these assumption the system then can perform adaptive changes. == Usages == Adaptive hypermedia: In an adaptive hypermedia system the displayed content and the offered hyperlinks are chosen on basis of users' specific characteristics, taking their goals, interests, knowledge and abilities into account. Thus, an adaptive hypermedia system aims to reduce the "lost in hyperspace" syndrome by presenting only relevant information. Adaptive educational hypermedia: Being a subdivision of adaptive hypermedia the main focus of adaptive educational hypermedia lies on education, displaying content and hyperlinks corresponding to the user's knowledge on the field of study. Intelligent tutoring system: Unlike adaptive educational hypermedia systems intelligent tutoring systems are stand-alone systems. Their aim is to help students in a specific field of study. To do so, they build up a user model where they store information about abilities, knowledge and needs of the user. The system can now adapt to this user by presenting approp
Versata
Versata is a privately held software company, one of several business units under the ESW Capital umbrella. Versata acquires underperforming or financially struggling enterprise software companies, integrates them into their portfolio, and makes operational changes to improve the viability and performance of the companies. == History == === Early years (1991–2000) === This company was founded in 1991 with the name Image Innovations; Naren Bakshi was co-founder and president, Kevin Fletcher Tweedy was vice president of technology, and they sold a development tool set named Image Application WorkBench that worked with Plexus Software's imaging platform. In 1997, the company name changed to Vision Software. They sold a small suite of software: Vision Builder for accelerated coding; and Vision StoryBoard Pro for creating software documentation. In 1998, their flagship product was a Java development tool named Vision JADE. In January 2000, the company changed names again, this time to Versata, and their e-business automation system, Versata Logic Suite, had three components: Versata Logic Server to host business rules written in Java, Versata Studio for developing the business rules, and Versata Connectors for connecting the logic server to IBM database servers. === Public company (2000–2006) === They went public in March 2000 during the dot-com bubble, raising about $94 million and reaching a market capitalization of over $2.5 billion despite reporting just $13 million in revenue and a $21 million loss in the prior year. In November 2000, Versata expanded into the business workflow area with the acquisition of Verve, Inc. and its workflow management system by the same name. From early 2001 through mid-2003, Versata's revenues were in quarter-over-quarter decline until Alan Baratz took over as CEO. Five consecutive quarters of growth followed until early 2005, when revenues once again took a downward plunge. In mid-2005, the company was notified by NASDAQ that it no longer met NASDAQ's requirements for continued listing, related to maintenance of a minimum amount of shareholder's equity, market value, or net income. In July 2005, Versata was delisted from NASDAQ and publicly traded on the OTC (also known as the Pink Sheets). == Versata, a business unit of ESW Capital == In January 2006, Austin-based Trilogy, Inc. acquired the company and took it private. Trilogy then proceeded to merge portions of Trilogy, specifically, Trilogy Technology Group, into Versata and began acquiring further companies, reorganizing dramatically and offshoring most technical positions to its office in Bangalore, India. From 2006 to 2008, Versata continued to make acquisitions mostly in US. Most of the employees in the acquired companies were laid -off with the majority work being offshored to its India office in Bangalore. In early 2009, Versata made another major overhaul of its business model when it asked all its employees in India to work as contractors through oDesk for a gDev which is an entity incorporated by Trilogy to manage its outsourcing activities. The only employees left in Versata were the ones in US. == Acquisitions == a Corizon was acquired by Metatomix, while Metatomix was part of Versata. b Infopia was acquired by Everest Software, while Everest Software was part of Versata. c Symphony Commerce was acquired by Quantum Retail, while Quantum Retail was part of Versata. == Legal disputes == === Patent infringement and "poison pill" lawsuits with Selectica === The legal disputes with Selectica began in 2004 (before Trilogy acquired Versata in January 2006) and lasted until 2010. While there were many suits and counter-suits, they largely centered around three issues: 2004–2006: Patent infringement in configure, price, and quote (CPQ) software 2005–2007: Patent infringement in contract lifecycle management (CLM) software 2008–2010: The "poison pill" lawsuit In 2004, Selectica and Trilogy had competing CPQ software: Selectica sold Solutions Advisor and Deal Optimization, while Trilogy sold Selling Chain. In April of that year, Trilogy Software sued Selectica for patent infringement. In 2005, before the court ruling, Trilogy made several offers to buy Selectica, but the board rejected them. In January 2006, the court ordered Selectica to pay Trilogy $7.5 million in damages. Four days after the January 2006 judgment in the first lawsuit, Trilogy announced its acquisition of Versata for an undisclosed amount. In 2005, Selectica had acquired the Determine CLM software platform, which included features that overlapped with some offered by Versata. In October 2006, Versata filed a second patent infringement lawsuit. The case was settled in 2007, with Selectica agreeing to pay Trilogy and Versata $10 million, plus up to $7.5 million in additional contingent payments. In 2008, Versata began acquiring Selectica stock. By December, Selectica's board amended its shareholder rights plan to adopt a "poison pill" with an unusually low trigger threshold: if any shareholder acquired more than 4.99% of company stock, their ownership would be diluted. The board explained that the move was meant to protect Selectica's net operating losses (NOLs), which were tax-deductible if the company returned to profitability. Under IRS Section 382, a significant change in stock ownership could cause those NOLs to be disqualified. Versata intentionally triggered the poison pill and also offered to sell back the stocks at a profit (greenmailing them), which prompted a legal dispute over whether Selectica's board had the authority to set such a low threshold and whether defending NOLs justified triggering shareholder dilution. The case ultimately reached the Delaware Supreme Court, which upheld the poison pill in October 2010, ruling in favor of Selectica. === Intellectual property lawsuit over joint development with Sun Microsystems === In 1998, Sun Microsystems hired Trilogy to help Sun's developers in California create a software configurator (later named the WC5 Configurator) that Sun's customers could use to modify products they wanted to buy, customizing products to have the features they wanted. Trilogy worked on the WC5 Configurator for several years, then Sun transferred the work to Oracle to finish. Trilogy believed that they owned the copyright to the work they'd done for Sun, and in 2006 after the merger with Versata they sued Sun for more than $100 million in damages. In April 2009, a jury ruled in favor of Sun and rejected Versata's claims. === Patent lawsuit and ruling on patents of abstract ideas with SAP === SAP developed Pricing Engine, a component in their enterprise resource planning (ERP) system. It competed with an older Trilogy product called Pricer, which was part of Trilogy's Selling Chain platform in the mid-1990s before they merged with Versata. In April 2007—the year after Trilogy acquired Versata—Versata filed a lawsuit against SAP for patent infringement. In August 2009, the jury agreed with Versata and awarded them $139 million. The court granted a new trial on damages and in September 2011, in the retrial, the jury awarded Versata $345 million. This then went to the US Court of Appeals, which in May 2013 affirmed the $345 million damages award, plus interest that had accumulated. In October 2014, Versata and SAP settled their litigation for an undisclosed amount of money. With the dispute between Versata and SAP settled, in June 2013 the Patent Trial and Appeal Board (PTAB) reviewed the validity of the patent itself, and issued a decision in a Covered Business Method (CBM) review, stating that the disputed items were abstract ideas and thus under the US patent law not patentable. In July 2015, the Federal Circuit agreed with PTAB's decision that the challenged items were not patentable. === Trade secrets and damages dispute with Internet Brands === Internet Brands was formerly known as CarsDirect and AutoData Solutions. Like Trilogy, they made software for automakers that helped customers compare vehicles online. In the late 1990s, Trilogy and Internet Brands tried to combine their products but failed to do so, and after a December 1999 lawsuit they made a settlement agreement in May 2001. In 2008, Versata sued Internet Brands claiming they had violated the settlement agreement by making presentations to potential clients stating they had a license from Versata to use and sell Versata technical solutions; and doing so had cost Versata business with Chrysler. Internet Brands' countersuit argued that Versata had misappropriated trade secrets and asked the jury to use Versata's business relationship with Toyota—including revenue from Toyota contracts—as a benchmark to calculate damages. The jury agreed and used that data to determine a $2 million damages award in favor of Internet Brands’ subsidiary, AutoData Solutions. Versata appealed the decision, and in January 2014 the court upheld the $2 million award to Internet Brands. === Patent challenges a
Racter
Racter is an artificial intelligence program that generates English language prose at random. It was published by Mindscape for IBM PC compatibles in 1984, then for the Apple II, Mac, and Amiga. An expanded version of the software, not the one released through Mindscape, was used to generate the text for the published book The Policeman's Beard Is Half Constructed. == History == Racter, short for raconteur, was written by William Chamberlain and Thomas Etter. Racter's initial creation was the short story Soft Ions, which appeared in the October 1981 issue of Omni (magazine). The publication's editors bought the story in January 1980, before it had even been written. In exchange for the rights, the editors offered financial support to Chamberlain and Etter so the two could refine Racter. In 1983, Racter produced a book called The Policeman's Beard Is Half Constructed (ISBN 0-446-38051-2). The program originally was written for an OSI which only supported file names at most six characters long, causing the name to be shorted to Racter and it was later adapted to run on a CP/M machine where it was written in "compiled ASIC on a Z80 microcomputer with 64K of RAM." This version, the program that allegedly wrote the book, was not released to the general public. The sophistication claimed for the program was likely exaggerated, as could be seen by investigation of the template system of text generation. In 1984, Mindscape released an interactive version of Racter, developed by Inrac Corporation, for IBM PC compatibles, and it was ported to the Apple II, Mac, and Amiga. The published Racter was similar to a chatterbot. The BASIC program that was released by Mindscape was far less sophisticated than anything that could have written the fairly sophisticated prose of The Policeman's Beard. The commercial version of Racter could be likened to a computerized version of Mad Libs, the game in which you fill in the blanks in advance and then plug them into a text template to produce a surrealistic tale. The commercial program attempted to parse text inputs, identifying significant nouns and verbs, which it would then regurgitate to create "conversations", plugging the input from the user into phrase templates which it then combined, along with modules that conjugated English verbs. By contrast, the text in The Policeman's Beard, apart from being edited from a large amount of output, would have been the product of Chamberlain's own specialized templates and modules, which were not included in the commercial release of the program. == Reception == The Boston Phoenix called the story Soft Ions "schematic nonsense. But the scheme is obvious enough and the nonsense accessible enough to an attentive reader that one can almost believe Chamberlain when he predicts that before long Racter will be ready to write for the pulp-reading public." PC Magazine described some of Policeman's Beard's scenes as "surprising for their frankness" and "reflective". It concluded that the book was "whimsical and wise and sometimes fun". Computer Gaming World described Racter as "a diversion into another dimension that might best be seen before paying the price of a ticket. (Try before you buy!)" A 1985 review of the program in The New York Times notes that, "As computers move ever closer to artificial intelligence, Racter is on the edge of artificial insanity." It also states that Racter's "always-changing sentences are grammatically correct, often funny and, for a computer, sometimes profound." The article includes examples showing interaction with Racter, most often Racter asking the user questions. == Reviews == Jeux & Stratégie #47
Writesonic
Writesonic is an AI visibility and generative engine optimization (GEO) platform used by enterprises, digital agencies, direct-to-consumer (D2C) companies, and fast-growing brands to understand and improve how they are represented in AI-generated search and answer systems. The platform analyzes how brands appear in AI answers, compares their visibility and citations against competitors, and provides tools to create and optimize on-site content and secure mentions across third-party sources, discussion forums, and user-generated platforms that influence AI outputs. == History == Writesonic was founded by Samanyou Garg in October 2020 in San Francisco, California. The company initially operated as Magicflow before adopting its current name. In its seed round, the company raised $2.5 million from investors including Y-Combinator, HOF Capital, and Soma Capital. The company began with AI-powered content generation tools. In 2023, it expanded into AI-enhanced search engine optimization. In 2024, the company launched an AI agent specifically designed for SEO tasks, with integrations to platforms including Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console. This was among the first specialized AI agents developed for SEO automation. Around the same time, Writesonic expanded its product line into Generative engine optimization (GEO), developing tools to analyze and improve how brands are represented in AI-generated search and answer environments. However, it is currently being challenged in the market with competitors such as Profound (known for their dashboards) and Meridian (known for their execution). == Technology and features == In 2024, the company introduced an artificial intelligence agent designed to automate search engine optimization (SEO) tasks. The agent integrates with platforms such as Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console to conduct technical audits, perform keyword research, carry out competitive analysis, and assist in strategy development. It is capable of identifying content gaps, suggesting optimization measures, and generating SEO strategies using real-time data from the integrated platforms. The platform also includes features for content strategy, optimization, and management. It makes use of large language models such as GPT-5, Claude Opus 4.1, and Claude Sonnet 4.5, in combination with proprietary workflows for fact-checking, internal linking, and content structure optimization.