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Out of three questions, first two questions are answered here Surbhi Tripathi's answer to What is the Nipah virus, and how is it transmitted?The last question which is most important one to answer is about government's measures on NiV. As India is one of the member state of World Health Organisation so the preparedness for NiV is recommended by WHO will be apt to apply in India:Surveillance, Prevention and Control ofNipah Virus Infection: A Practical HandbookPage No. 18–354. OUTBREAK OR EMERGENCY PREPAREDNESS AND RESPONSE FOR NiV4.1. PreparednessPreparedness in terms of technical and logistical management of a Nipah outbreak is essential in countries with recurrent outbreaks. The best response to a Nipah outbreak is being able to detect cases as early as possible and prevent further infections.4.1.1. Enhancing surveillance during the NiV transmission seasonSurveillance should be intensified during the Nipah season from January through May, when most Nipah outbreaks have been identified. This will increase the possibility of identifying NiV infection and understanding the characteristics of the virus. Blood, CSF, urine and throat swabs are collected from suspected patients and sent to the reference laboratories.4.1.2. Awareness building in hospitals and raising community awareness• Encourage and train health-care workers to maintain standard infection control precautions, e.g., personal hygiene, use of personal protective equipment(PPE), and manage encephalitis or neurological patients appropriately.• Disseminate information to communities through multimedia, leaflets, posters and meetings (group, community and market) encouraging people:o to stop consumption of raw date palm sap;o not to eat fruit partially eaten by bats;o cover the mouth and nose while caring for unconscious patients;o wash hands with soap and water before and after feeding and taking care of patients.4.1.3. Infection control in health-care settings should be in place• Implement standard infection control precautions.• Acquire and maintain PPE stock and other equipment needed in epidemiological investigations and outbreak response.4.1.4. Planning for outbreak response: some major components4.1.4.1. Formation of a multisectoral team 20Since NiV infection is a zoonosis and outbreaks may be associated with multiple factors such as animal reservoirs, sociocultural practices, food habits and possible human-to-human transmission, a multidisciplinary team is needed, and preparation should be done for pre-outbreak, outbreak and post-outbreak phases.A multisectoral team should be built up at national and local levels for the monitoring, evaluation and response to unusual acute public health events and outbreak response, including Nipah outbreaks. The team should have a holistic, multidisciplinary approach consisting of public health personnel, clinicians and laboratory personnel. The multisectoral team may consist of the following professionals (depending on the evolving and country-specific situation) who would bring relevant expertise in outbreak investigation and response:• epidemiologist• microbiologist• anthropologist and/or social scientist• veterinarian• ecologist.National or subnational level – Rapid Response Team (NRRT): The NRRT should be assigned from institutes at the national/provincial level and partner institutes.District/provincial level – District Rapid Response Team (DRRT): The DRRT consists of the head of health services at the district/provincial level and clinical and laboratory expertise, and other expertise from the public health department.4.1.4.2. Evaluate and ensure the supplies for sample collection, storage and shipment of samples:• Assess PPE in stock;• Assess sample collection instruments;• Assess sample storage capacity in the laboratory;• Evaluate laboratory capacity for NiV testing (e.g., biosafety, quality, skills, human resources and consumables for NiV virus testing);• Evaluate hospital capacities for isolation facilities and ability to treat Nipah patients in Nipah-prone areas.4.2. Alert and outbreak investigationThe outbreak investigation should lead to formulation of an appropriate public health intervention as soon as the source and mode of transmission are known. In the meantime, control measures mitigating known risk factors should be implemented as soon as NiV transmission is suspected.4.2.1. Investigation of a suspected case or cluster of suspect cases:4.2.1.1. Standard Operating Procedures (SOPs) for sample collection and transportation in place:• Surveillance physician will take verbal consent from patient or patient’s family member;• Collect 5 ml venous blood;• If possible, collect 3 ml extra-CSF when appropriate;• Aliquot 1 ml serum and 1 ml CSF samples in 1.8 ml cryovial tube. Try to aliquot serum and CSF samples in three cryovial tubes;• Label the cryovial tube with: type of samples (serum/CSF), patient name and identification number, and date of sample collection;• Store the serum and CSF samples in liquid nitrogen if possible, or −20°C freezer for short-term storage if liquid nitrogen is not available;• Ship samples in liquid nitrogen tank or ice pack to assigned centre for laboratory diagnosis;• Store samples in −70°C freezer for longer-term storage;• A list of potential national or international reference laboratories should be pre-established. There can be several for different purposes: a frontline laboratory would be the WHO Collaborating Centre for laboratory diagnosis of viral diseases with BSL 3 or BSL 4 facilities (see list of WHO Collaborating Centres and other institutions for laboratory diagnosis, surveillance and response in Appendix 4).4.2.1.2. Templates of data collection instruments pre-developed and in place for quick useThese templates should include the following:• line listing of all cases;• case reporting form;• questionnaire for case-control studies or other relevant studies;• forms for sample collection.4.2.1.3. SOP for activating and conducting outbreak investigation teamsThis SOP is commonly country-specific as the process relies on the administrative structures and capacity or resources of a given country. Therefore a country-based manual or protocol for outbreak investigations should be in place in at-risk countries forNipah outbreaks. A more generalized national SOP manual for all emerging or re-emerging infectious diseases of international concern could be developed focusing on a mechanism of response and roles and responsibilities of different parties.The following are some of the key components to prepare a team for outbreak investigation:1) National or Subnational Rapid Response Team (RRT) Should an outbreak of NiV virus disease be suspected and/or reported, the National RRT should be activated and should meet together to:(1) Plan and conduct the investigation;(2) Request further technical support if needed (e.g., further analysis and interpretation, risk communication, initiate control).2) Administrative SOP for field work in place: administrative clearance, organize supplies, travel arrangements:• approval/permission from competent authority;• arrangement for accommodation;• arrangement for security, if needed;• arrange vehicle;• supplies:o medicineso sample collection instrumentso PPEo disinfectants, hand sanitizero basic medical and investigation equipment, e.g., stethoscope, thermometer, GPS instrument, etc.3) SOP for rapid mobilization of additional or experts teamsIf the NiV outbreak is confirmed, an experienced Nipah outbreak investigation team comprising an epidemiologist, clinician, veterinarian and anthropologist or social scientist can move to the field within 24 hours of outbreak reporting.4.2. 1.4. Nipah outbreak investigationThe overall objective of investigating Nipah outbreaks is to control the outbreak and prevent future outbreaks. Any Nipah (or suspicion of) outbreaks should be investigated as the disease is of public health concern with potentially devastating consequences.The specific objectives include the following:• to determine the extent of the outbreak;• to characterize the populations at greatest risk and to identify specific risk factors;• to provide practical recommendations to strengthen control and prevention measures.Key steps when conducting Nipah outbreak investigationStep 1: Activate preparation plan for outbreak investigation (details above).Step 2: Confirm the outbreak.One of the first tasks of the initial investigation team is to verify that a suspected cluster of cases is indeed a real outbreak with common cause. Some will be unrelated cases of the same disease, and others will turn out to be real cases of AES or ALRI but of unrelated diseases. This step consists of confirming the diagnosis through visiting the outbreak affected areas to (1) examine the patients and/or review the medical charts to describe and understand the clinical presentation; (2) collect blood, CSF and throat swab samples at the time of admission/ first contact, and follow-up serum samples 2 weeks after the onset of illness for testing.A Nipah outbreak is defined as the identification of at least one laboratory-confirmed case.Step 3: Define and identify cases.The investigators should develop or adapt standardized case definitions appropriate to the outbreak context (see details in standard case definitions). Testing for NiV infection should be performed when there are: (i) clusters of AES due to an unknown agent or (ii) patients with AES due to an unknown agent living in or near NiV zones.Patients with AES should also be tested for NiV infection when they are exposed to a cluster of unexplained neurological/pulmonary illness in animals, such as horses and pigs.Step 4: Case-findingIn many outbreaks, including Nipah outbreaks, the first cases that are recognized are usually a small proportion of the total number. Retrospective and prospective case-findings are crucial to determine the true magnitude and geographical extent of the outbreak.Active case-finding should be conducted:Among close contacts:• A close contact is defined as “a patient or the person who came in contact with a Nipah case (confirmed or probable cases) AND stayed in the room or veranda or vehicle for at least 15 minutes”.• Record contacts for potential follow-up if need be. They are to be followed up in case of occurrence of illness (up to 18 days). Serum specimens should be collected in case of symptom onseto in high-risk groups or in groups exposed to the sourceo through enhancing surveillance in the outbreak area and the at-risk areas for case-finding in the communityStep 5: Evaluate the outbreak in relation to ‘time, place and person’• establish a line-list of current and previous cases;• draw an epidemic curve;• analyse and interpret the data to identify potential sources of transmission.Step 6: Develop and evaluate hypothesesOnce step 5 has been done, investigators should have some hypotheses regarding the source and/or mode of transmission and the exposures that caused the disease. These hypotheses should be compared with established facts.Step 7: Refine hypotheses and carry out additional studiesIf step 6 is not conclusive, these hypotheses can be refined to look for new modes or vehicles of transmission and be evaluated through conducting case–control studies.Step 8: Implement control and prevention measures (see response section below)Step 9: Communicate findings and information about risks (i.e., outbreak report)• Develop an outbreak report and disseminate to concerned authorities.• Learning from the outbreak includes detailing:o new findingso major limitations during outbreak investigation• Resume the activities of pre-outbreak phase.4.3. Additional considerations with respect to Nipah outbreaksWhen the Nipah outbreak is confirmed, the investigation team needs to:• Immediately inform the local, regional and national authorities.• Inform the partners/stakeholders (notably those involved at local level): treating hospitals, patients’ relatives.• Declare the Nipah outbreak to WHO under the International HealthRegulation 2005 (IHR) via National IHR focal points (see detail below in the response section).Notification and assessment of Nipah outbreak and/or cases to WHO should be based on the following four criteria described in Annex II of IHR 2005. A "yes" to any of the four criteria would lead to notifying WHO under Article 6 of the IHR.• Is the public health impact of the Nipah outbreak and/or cases serious?• Are the Nipah outbreak and/or cases unusual or unexpected?• Is there a significant risk of international spread?• Is there a significant risk of international travel or trade restrictions?4.3.1. Conduct rapid risk assessmentSome of the major risk assessment questions should include the following:• What is the risk of occurrence of further cases from the detected outbreak?• What is the risk of spread of the infection?• What is the risk of major impact of the current outbreak on the health-care system?4.3.2. Evaluate the impact of control measuresEach outbreak should be thoroughly investigated, and lessons learnt from each outbreak should be evaluated and documented so that control measures can be reviewed and modified as required.4.3. 3. Develop further research with the objective of identifying determinants of infection or severity and determining modes and dynamics of infectionThe populations to be investigated would be those exposed to NiV:4.3.3.1. Health-care workers (HCWs)There is evidence of nosocomial transmission in India and Bangladesh, and one nurse was positive to Nipah IgM antibody in Malaysia (3, 4). HCWs are to be trained for infection control and prevention (see below). Surveillance should be in place to detect any suspected cases among HCWs. In addition, a study should be conducted to identify asymptomaticcases among HCWs who provided service to Nipah patients. Among these, positive cases should be subsequently compared with negative ones to determine risk factors for infection and understand the dynamics of transmission. Some components of the study could include:• Make a list of HCWs who provided care to Nipah patients.• Take consent from HCWs.• Interview at-risk HCWs using an exposure questionnaire, about 3 weeks after the last exposure to NiV-infected patients.• Collect 5 ml of blood for serology testing about 3 weeks after the last exposure to NiV-infected patients.4.3.3.2. Communities potentially exposed to NiVThe investigation should encourage involvement of multidisciplinary and multisectoral team using a one-health approach. For instance, investigators should have the support of microbiologists and their laboratories to conduct community-based seroprevalence surveys (detection of recent antibody response) to determine the extent of the outbreak via detecting subclinical and/or asymptomatic cases. Asymptomatic cases could be further compared with controls to identify risk factors for infection.Anthropologists or other social scientists with extensive community-based experience could help propose additional behaviour risk factors to be tested in a case–control study. Anthropologists should work with communication/health promotion specialists to develop communication messages combining both local explanatory models and biomedical models using local terms and languages, and deliver the message in such a way that it is meaningful to the community.Veterinarians and eco-health specialists should join the investigation to conduct studies collecting specimens from animals and the environment in the outbreak settings.Zoonotic and environmental investigations during an NiV outbreak primarily aim to determine the primary reservoir, likely source of the virus, route of transmission and the extent of the spread of the virus in animals. Georeferenced positive specimens could be analysed with positive human cases to better understand the dynamics of transmission.4.4. ResponseAs soon as a Nipah outbreak is confirmed, national authorities should implement control measures based on known risk factors. The interventions should be based on a multisectoral approach and include/understand the following strategic objectives:1. Establishment of a coordination committee for outbreak prevention, and control activities and resources mobilization; the role of this committee is to ensure the general coordination of operations. It must clearly define the responsibilities of the various teams and the route of information during outbreak response operations.2. Setting up partnerships with the media to ensure media monitoring and better risk communication.3. Formation of a referral system with the principal objective of easing transfer of cases to the appropriate case-management health-care settings.a. Active detection for new Nipah cases and their transfer to the case-management ward.b. Follow up all contacts during 18 days after their last unprotected exposure to Nipah patient(s) or infected animal or tissue (e.g., laboratory) and their transfer to the case-management ward if they fall sick.4. Set up a social mobilization and medical education programme whose principal role is to inform the public and promote practices that decrease community transmission of the disease.5. At the foci zone, the medical team should ensure safe case management of Nipah patients by complying with the following guidelines:a. Respect patients and their families’ dignity and rights, in particular their right for information on disease and treatment,b. Set up a specific Nipah case-management ward that ensures biosafety of in-patient care,c. Set up infection prevention and control measures for safe patient care,d. Organize the safe transport of patients from their residence to the ward,e. The express consent of patients is necessary for any hospitalization. In the event of patient’s refusal to be hospitalized, the medical team should organize, temporarily, a patient’s care at home with his/her family support.f. Organize safe burials while respecting the funeral ceremony,g. Set up psychosocial support (patients, family, HCWs).6. Outside the foci zone, to prevent secondary foci, the medical team should reinforce standard infection prevention and control measures in health care in all health centres of the affected district and all hospitals catering to the outbreak zone.7. Establishment of links with the animal health sector to:a. Continue monitoring the cause of disease and death in domestic animals and wildlife.b. Test samples and alert public health authorities as needed.c. Control slaughtering/butchering activities of domestic animals and wildlife, at home, and in markets and slaughterhouses.8. Media and communicationa. Designate a spokesperson in the outbreak team.b. Designate a spokesperson at the national level who communicates with national media.c. Regularly update reports to be sent to assigned authority.d. Conduct regular meetings with press and community.e. Distribute information, education and communication material.4.5. In the aftermath of the outbreak (evaluation)4.5.1. Declare the end of the outbreakThe health ministry declares the end of the outbreak. The date of outbreak end is equal to twice the mean incubation period for Nipah counted from the last infectious contact with a confirmed or probable case.The national authorities should use the announcement of the end of the outbreak to acknowledge national and international field teams as well as the media. They should also formally present their solidarity and their empathy to the victims, their families and the affected populations.4.5.2. Writing a final report of the outbreak control activitiesThe report objective is to describe the activities undertaken during the epidemic as well as constraints and difficulties encountered. It should include technical aspects (final epidemiological analysis, clinical investigations, etc.), as well as administrative and financial aspects. The report should be published to achieve wider dissemination of findings and lessons learnt.4.5.3. Archive outbreak documents and files• Gather all the reports, files, photographs, videos and other documents related to the outbreak management.• Store all the documents in a place accessible for their later use.4.5.4. Evaluate the management of the outbreakThe evaluation of the management of the outbreak response will review the performance of the various components of the strategy: coordination, relationship to the media, surveillance system, social mobilization programme, clinical management and logistics.The aim of the evaluation is to determine lessons learnt to improve the future management of epidemics. This evaluation should be led by a team comprising national and technical partners.4.5.5. To resume activities of the pre-outbreak period

AI in Law: Current Applications – Insights Up FrontBased on our assessment of the companies and offerings in the legal field, current applications of AI appear to fall in six major categories:Due diligence – Litigators perform due diligence with the help of AI tools to uncover background information. We’ve decided to include contract review, legal research and electronic discovery in this section.Prediction technology – An AI software generates results that forecast litigation outcome.Legal analytics – Lawyers can use data points from past case law, win/loss rates and a judge’s history to be used for trends and patterns.Document automation – Law firms use software templates to create filled out documents based on data input.Intellectual property – AI tools guide lawyers in analyzing large IP portfolios and drawing insights from the content.Electronic billing – Lawyers’ billable hours are computed automatically.Next, we’ll explore the major areas of current AI applications in law, individually and in-depth:(We’ve done our best to place companies into the category that best represents their product offering, but it’s important to note that there is overlap on many of the groupings we’ve chosen.)Due DiligenceOne of the primary tasks that lawyers perform on behalf of their clients the confirmation of facts and figures, and thoroughly assessing a legal situation. This due diligence process is required for intelligently advising clients on what their options are, and what actions they should take.While extensive due diligence can positively impact long-term shareholder returns (according to a study by the City University of London), the process can also be very time-consuming and tedious. Lawyers need to conduct a comprehensive investigation of meaningful results. As such, lawyers are also prone to mistakes and inaccuracy when doing spot checks.Kira SystemsNoah Waisberg, a former M&A lawyer who founded the software company Kira Systems, thinks that due diligence errors by junior lawyers often occur for a number of reasons. These include working very late at night or on the eve of a weekend, forgetting to perform due diligence before the end of the workweek, and failing to act on it when a deal structure is completely revised.He adds, “Many associates are in a certain negative mood about the efficacy of manual due diligence. Lawyers, being human, get tired and cranky, with unfortunate implications for voluminous due diligence in M&A.”Kira Systems asserts that its software is capable of performing a more accurate due diligence contract review by searching, highlighting, and extracting relevant content for analysis. Other team members who need to perform multiple reviews of the content can search for the extracted information with links to the original source using the software. The company claims that its system can complete the task up to 40 per cent faster when using it for the first time, and up to 90 per cent for those with more experience.LEVERTONLEVERTON, an offshoot of the German Institute for Artificial Intelligence, also uses AI to extract relevant data, manage documents and compile leases in real estate transactions. The cloud-based tool is said to be capable of reading contracts at high speeds in 20 languages.In 2015, IT firm Atos sought the help of real estate firm Colliers International, which used LEVERTON in performing due diligence of a company that the former was about to acquire. Through the use of LEVERSON’s AI, information such as payable rent, maintenance costs and expiration dates were extracted from thousands of documents and then organized on a spreadsheet.eBreviaHowever, lawyers can be burdened by reviewing multiple contracts and they may miss important edits that result in legal issues later on. This is the same problem that Ned Gannon and Adam Nguyen, co-founders of eBrevia, experienced when they were still working as junior associates. They built a startup in partnership with Columbia University with the intention of shortening the document review process.eBrevia claims to use natural language processing and machine learning to extract relevant textual data from legal contracts and other documents to guide lawyers in analysis, due diligence and lease abstraction. A lawyer would have to customize the type of information that needs to be extracted from scanned documents, and the software will then convert it to searchable text. The software will summarize the extracted documents into a report that can be shared and downloaded in different formats.JPMorganOther organizations such as JPMorgan in June 2016 have tapped AI by developing in-house legal technology tools. JP Morgan claims that their program, named COIN (short for Contract Intelligence), extracts 150 attributes from 12,000 commercial credit agreements and contracts in only a few seconds.This is equivalent to 36,000 hours of legal work by its lawyers and loan officers according to the company. COIN was developed after the bank noticed an annual average of 12,000 new wholesale contracts with blatant errors.ThoughtRiverOther AI industry players includeThoughtRiver, which handles contracts, portfolio reviews and investigations for improved risk management. Its Fathom Contextual Interpretation Engine was developed together with machine learning experts authorities at Cambridge University.The company states that it designed the product to automate summaries of high-volume contract reviews. While users read content extracts, they can also read the meanings of clauses provided by the AI. The system is also said to be capable of flagging risky contracts automatically. The company provides a brief tour of their product in the 3-minute video below, including a detailed look at the user interface and basic functions of the software:LawGeexLawGeex claims that its software validates contracts if they are within predefined policies. If they fail to meet the standards, then the AI provides suggestions for editing and approval. It does this by combining machine learning, text analytics, statistical benchmarks and legal knowledge by lawyers according to the company.In this video, LawGeex CEO Noory Bechor further explains how his product can cater to legal services.The company also claims that with their tool, law firms can cut costs by 90 per cent and reduce contract review and approval time by 80 per cent (though these numbers don’t seem to be coupled with any case studies). The firm lists Deloitte and Sears among some of its current customers.Legal RobotOn the other hand, Legal Robot, a San Francisco-based AI company, currently offers Contract Analytics, its answer to the growing contract review software market. Currently, in beta, the company states that its software is capable of changing legal content into numeric form and raising issues on the document through machine learning and AI.A video presenting how the software works state that it builds a legal language model from thousands of documents. This knowledge is used to score the contract based on language complexity, legal phrasing, and enforceability. With the issues flagged by the software, it then provides suggestions on improving the contract’s compliance, consistency, and readability by evaluating it on best practices, risk factors and differences in the jurisdiction.Ross IntelligenceEvery lawsuit and court case requires diligent legal research. However, the number of links to open, cases to read and information to note, can overwhelm lawyers who have limited time doing research. Lawyers can take advantage of the natural language search capability of the ROSS Intelligence software by asking questions, and receiving information such as recommended readings, related case law and secondary resources.BakerHostetler, in what seemed like a break in tradition, employed ROSS in its bankruptcy department, 100 years after the law firm’s founding. The law firm’s chairman, Steven Kestner, explained in an interview that they decided to employ the software to work on 27 terabytes of data. A Forbes report describes ROSS’ function in the law firm’s operations: “ROSS will be able to quickly respond to questions after searching through billions of documents.The company claims that lawyers can ask ROSS questions in plain English such as “what is the Freedom of Information Act?” and the software will respond with references and citations. Like most machine learning systems, ROSS purportedly improves with use.CasetextOn the other hand, Casetext’sCARA claims to allow lawyers to forecast an opposing counsel’s arguments by finding opinions that were previously used by lawyers. Users can also detect cases that have been negatively treated and flagged as something that lawyers may deem unreliable.Casetext claims large law firms such as DLA Piper and Ogletree Deakins as its clients.Other Assorted ApplicationsOther software products also combine machine learning and legal analysis to assist lawyers with legal research but with limited coverage. For example:Loom features win/loss rates and the judge ruling information but only for civil cases in select Canadian provinces. In an interview with Stanford Law School, Mona Datt (co-founder of Loom Analytics) elaborates:“Instead of performing open text searches looking for personal injury precedents, a lawyer could use Loom’s system to see all personal injury decisions that were published in a given time span and then break them down by the outcome.Instead of combing through individual decisions looking for ones written by a particular judge, Loom’s system can show all decisions authored by that particular judge and provide an at-a-glance snapshot of their ruling history. In short, we’re providing quantitative metrics on Canadian case law.”Judicata, on the other hand, only serves California state law as of this writing. Its software, Clerk, is said to be capable of reading and analyzing legal briefs. It also evaluates their pros and cons and then assigns a score for each brief based on arguments, drafting and context.The recommendations intended to reduce content mistakes are listed as part of the action items for the user. In the company’s blog, Product Manager Beth Hoover explains, “Clerk helps reduce these errors by identifying the quotations in a brief and cross-checking them against the cited case to ensure the text is identical and the page numbers are correct.”Potential Bias ConcernsIn a recent paper by Susan Nevelow Mart of the University of Colorado Law School tested if online legal case databases would return the same relevant search results. She found out that engineers who design these search algorithms for case databases such as Casetext, Fastcase, Google Scholar, Lexis Advance, Ravel, and Westlaw have biases on what would be a relevant case that their respective algorithms will show to the user.For example, newer databases such as Fastcase and Google Scholar have generated less relevant search results compared to older databases such as Westlaw and Lexis. Mart argues that search algorithms should be able to generate redundant results on whatever legal online database is used since lawyers need only the most relevant cases. However, because these engineers have biases and assumptions when developing their algorithms, users are recommended to use multiple databases in order to find out the cases that fit their needs.By no means does a single paper (or a dozen papers) imply that legal AI tools shouldn’t be used, but rather that their pros and cons are yet to be weighed out thoroughly. Bias is by no means specific to the legal field, as machine learning systems are always influenced by the data that they’re trained on.Readers with an interest in AI bias and the ethical considerations of discrimination by machines may benefit from reading our recent article in collaboration with the IEEE: Should Business Leaders Care About AI Ethics?EverlawThere has been a growth in the number of e-discovery product manufacturers that harness AI and machine learning. Everlaw uses its predictive coding feature to create prediction models based on at least 300 documents that were classified before as relevant or irrelevant by the user.The AI looks into the contents and metadata and uses such information to classify other documents. The company claims that the prediction model’s results can help users easily identify which documents are most relevant. It also recommends actions on the part of the user on how to improve the software’s predictive accuracy of the model.You can check a demonstration of Everlaw’s Prediction Technology feature in this video:DISCODISCO claims to deliver faster results using its cloud technology for document search on large data volumes. Similar to Everlaw, it also employs prediction technology to suggest which documents are most likely to be relevant or irrelevant to the user.The AI works by assigning scores on tags (on a scale of -100 to +100) in order to improve its prediction results. The software displays its search results with each document’s score and suggests which material is most likely useful for the reader. In the promotional video below, Dr Alan Lockett (DISCO’s Head of Data Science) explains the company’s technology in simple terms:CatalystDenver-based Catalyst markets its Automated Redaction product to help lawyers and legal reviewers remove sensitive and confidential information on documents. “Manual redaction”, as the company claims, is cumbersome considering the amount of time that a reviewer spends on locating content on a digital document and then applying black boxes on these statements.Their tool allows users to convert a document to digital format and then perform multiple sets of redactions for a single document by searching for a word or phrase. Users can also set patterns such as social security numbers on the software to be redacted. An overview of the Automated Redaction feature for e-Discovery introduces its uses.ExterroExterro’s WhatSun claims to combine the functions of a project management software with the capabilities of performing e-Discovery. In other words, users can perform their legal research and then collaborate with others using the software.According to one of Exterro’s law firm clients, they were able to cut down on redundant workers from 100 lawyers down to 5 when they started to employ the system. The software was able to perform the e-Discovery tasks of the lawyers at a 95 per cent cost savings according to the law firm. The company claims AOL, Microsoft, and Target among its marquee customers.Brainspace DiscoveryBrainspace Discovery clusters and sorts documents to match closely to a user’s document search. When finding documents, the AI employs concept search (searching for documents that are similar in concept but not necessarily in words or phrases), term or phrase extension (instructing the software to remove terms incorrectly associated with the results), and classification (specifying another category to refine the search). The topic of document digitization and search is explored further in our article on Document Digitization in Finance. The company claims that by combining these three features, the software can better deliver document search results closer to a user’s needs.Other AI-powered contract review platforms that cater to due diligence for legal professionals include:iManage’s RAVN whose M&A Due Diligence Robot is designed for M&A documents to automate the review process and extract data from cluster sets;LitIQ, which capitalizes on computational linguistics technology to reduce contract-related disputes (Gary Sangha, founder of LitIQ shares his thoughts on the relationship of machine learning and law in this interview);LegalSifter, which claims to cut time and financial costs through its AI software that looks for specific concepts in documents such as general terms and conditions and confidentiality agreements;Seal, whose software is used by Dropbox, PayPal and Experian, was able to reduce the time spent to 48 hours from 255 days by a utility company by searching for thousands of contracts with specific clauses according to their case study; andLuminance, which claims to be the only tool that searches and ranks unusual and anomalous documents and clauses for lawyers.While there has been a growth in the use of e-Discovery tools, its application has become a public issue in states such as California. In 2015, the State Bar released an amended Proposed Formal Opinion, requiring lawyers to have a decent knowledge on the to use the e-Discovery system or they will warrant discipline after being proven to have committed intentional or reckless acts. The State Bar also suggests that if a lawyer is incompetent on the facility, he should learn the skill, hire someone who’s knowledgeable, or just simply decline representation.Prediction TechnologyIn 2004, a group of professors from Washington University tested their algorithm’s accuracy in forecasting Supreme Court decisions on all 628 argued cases in 2002. They compared their algorithm’s results against a team of experts’ findings. The statistical model by the researchers proved to be a better predictor by correctly forecasting 75 per cent of the outcomes compared to the expert’s 59 per cent accuracy. Although in a separate industry with its own separate problems numerous additional use-cases of predictive analytics can be found in our article on Predictive Analytics in Banking. This article illuminates AI programs utilizing predictive analytics to solve real-world issues.Expanding the coverage from 1816 to 2015, Prof. Daniel Katz of Michigan State University and his two colleagues achieved a 70.2 per cent accuracy on case outcomes of the Supreme Court in their 2017 study. Similarly, Nikolaos Aletras of University College London and his team used machine learning to analyze case text of the European Court of Human Rights and reported a 79 per cent accuracy on their outcome prediction.Prof. Daniel Kantz, in his 2012 paper, stated, “Quantitative legal prediction already plays a significant role in certain practice areas and this role is likely to increase as greater access to appropriate legal data becomes available.”IntraspexionIndeed, several AI companies have ventured into this field such as Intraspexion, which has patented software systems that claim to present early warning signs to lawyers when the AI tool detects threats of litigation.The system works by searching for high-risk documents and displays them according to the level of risk that the AI has determined. When a user clicks on a document, risk terms as identified by subject matter experts through the algorithm are highlighted. According to the company, users can which documents put them at risk for litigation when they use the software.Ravel LawAnother tool, Ravel Law, is said to be able to identify outcomes based on relevant case law, judge rulings and referenced language from more than 400 courts. The product’s Judge Dashboard feature contains cases, citations, circuits and decisions of a specific judge that is said to aid lawyers in understanding how the judge is likely to rule on a case.The firm’s CEO, Daniel Lewis, affirms such claim in this interview when he explained that the Ravel Law can aid in litigation strategy by providing information on how judges make decisions.Lex MachinaLex Machina’s Legal Analytics Platform has a variety of features that are said to assist lawyers in their legal strategy. For example, the Timing Analytics feature uses AI to predict an estimated time when a case goes to trial before a specific judge.The Party Group Editor, on the other hand, allows users to select lawyers and analyze their experience before a judge or a court and the number of lawsuits they were involved in before, among others. In the video below, the product’s user interface is featured and sample analytics results are presented:PremonitionFinally, Premonition, which claims to be the world’s largest litigation database, asserts to have invented the concept of predicting a lawyer’s success by analyzing his win rate, case duration and type, and his pairing with a judge at an accuracy of 30.7 per cent average case outcome. According to the company, the product can also aid in looking at different cases and how long they’re going to take for each attorney.But similar to any analytics platform, AI tools that deal with predictive technology need a lot of data in the form of case documents to fully work according to Kantz. In an article, the model is described as “exceptionally complicated.” That’s because it needs almost 95 variables (with almost precise values up to four decimal places) supported by almost 4,000 randomized decision trees to predict a judge’s vote. Kratz admits that a database that will fully support his product is still not readily available except for a few ones that charge access fees to obtain data.Legal AnalyticsCase documents and docket entries provide supplementary insights during litigation by lawyers. Current AI tools claim that today’s software products are able to extract key data points from these documents to support arguments.Lex MachinaHogan Lovells litigation attorney Dr Chris Mammen uses Lex Machina’s Legal Analytics software to find out “who is the plaintiff, who is their counsel, who have they represented, and who else have they sued.”The software generates data that can be used to analyze an opposing counsel’s likelihood of winning or losing a case. He claims to save time through the analytics results when creating a narrative. “You send an email to the research department, get first results, iterate the process – that usually takes a day or more.”Intellectual property lawyer Huong Nguyen also used the software when she represented a generic pharma company. Using the Legal Analytics tool,, she found out that the judge’s history of ruling cases tends to favour cases like hers. Both parties settled in the end, which was a better resolution according to Nguyen.Ravel LawThe data can also be used in pitching a law firm’s services to potential clients by providing intelligence on the opposing counsel, generating values on the probability of winning the case and identifying litigation trends to use in their marketing campaigns.Apart from prediction technology, Ravel Law’s software also claims to provide lawyers with judges’ data on cases, circuits and ruling on their dashboard, which can be used in landing new clients. Currently, the company is bolstering its data minefield by working with Harvard Law School in digitizing the faculty’s US case law library to be made available on its tech platform.Settlement AnalyticsIt must be noted that legal analytics still has its limitations. Robert Parnell, CEO of SettlementAnalytics, explains in his paper that there is uncertainty in producing results with a high level of accuracy with this kind of technology. This comes in very small sample sizes after filtering, cognitive biases, the tendency to interpret random trends as valid patterns, and having a lot of data noise.Parnell opines, “Overall, the quantitative analysis of legal data is much more challenging and error-prone than is generally acknowledged. Although it is appealing to view data analytics as a simple tool, there is a danger of neglecting science in what is basically data science. The consequences of this can be harmful to decision making.”Document AutomationA McKinsey & Company report estimates that knowledge work automation will most likely be one of the top disruptors in the global economy.A screenshot from McKinsey’s report cited above. See the orange bar to the right of “2. Automation of knowledge work”Some law firms are also beginning to adopt such technology by drafting documents through automated software. Many such software companies claim that the final document, which could take days by manual human drafting, is generated in a matter of minutes. This technology is utilized in finance today; discover real-world banking-sector examples of the concepts outlined in this article.PerfectNDANeota Logic System claims that it's software PerfectNDA shortens the nondisclosure agreement (NDA) process by offering templates selected by AI according to a user’s scenario. The user answers questions and a pre-filled template is then generated. In addition, the software also features document filing and integrated e-signatures to streamline related manual processes involved in NDA drafting.Intellectual PropertySecuring patents, copyrights and trademarks is often best left to a lawyer’s expertise. However, the entire patent application process can be long and arduous. Traditional trademark and patent search, for example, involves looking into hundreds, if not thousands, of results through manual research. This takes so much time, which is ironic considering that patent applications are time-sensitive.According to US patent attorney Patrick Richards, “You only have one year from the first time the invention is publicly disclosed [i.e. sold] to file a patent application; if as a business owner you launched a product within the last year, you need to talk to a patent attorney right away to make sure it is protected.”TrademarkNowTrademarkNow is a company taking on some of the manual knowledge work of intellectual property application with AI. It uses a complex algorithm that is said to shorten weeklong searches for patents, registered products and trademark using the Trademark Clearance platform, which returns search results in less than 15 seconds according to the company’s claims.The system analyzes the results and ranks them according to relevance to the user as identified by the algorithm. As this many e-discovery applications, the solution promises efficiency of spent attention for legal teams.ANAQUA StudioThe cloud-based ANAQUA Studio, on the other hand, is specifically designed for drafting patents and prosecution. The company’s datasheet states that it’s the first patent application-drafting tool for lawyers that save four hours on provisional patent application and 20 hours and non-provisional types. The system is said to be able to detect document errors, circular claim references and formatting defects aside from automatically generating literal claims support.SmartShellSeattle-based TurboPatent released SmartShell to support paralegals performing document reviews, drafting, formatting and identifying issues on patent applications. The software uses AI and natural language processing to assist in creating legal claims.In a case study listed on TurboPatent’s website, two paralegals from the Pacific Patent Group used the software to perform document retrieval, bibliographic data research, examiner remarks review and rejection issues discovery. TurboPatent claims that Pacific’s paralegals were 500-800% more productive in their tasks when using SmartShell (thought the case study isn’t clear what exact tasks were relevant for the software, and which weren’t – we can assume that many paralegal tasks aren’t currently improvable with AI).A brief overview of how the product’s functions and value proposition can be seen in the 1-minute video below:Electronic BillingElectronic Billing platforms provide an alternative to paper-based invoicing with the goal of reducing disputes online items, more accurate client adjustments, (potentially) more accurate reporting and tracking, and reduced paper costs. Firms in the healthcare space are also utilizing AI for medical billing; this concept is further explained in our article Artificial Intelligence for Medical Billing and Coding.BrightflagBrightflag offers centralized legal pricing software that automatically adjusts line-by-line items. It also allows users to centralize the invoice review so that all documents submitted are routed directly to the correct approver. In addition, the AI provides analytics features by tracking and categorizing all pricing data to determine alternative fee arrangements (AFA) and budgets.The company claims that its average client reduces administrative costs related to payment management by 8 to 12 per cent by using the platform’s assisted review feature. The company lists telecom giant Telstra and ride-hailing company Uber among its current marquee clients.SmokeballSmokeball’s cloud-based legal practice management tool automates the recording of time and activities by law firms. One major feature of this tool is the capability to track all activities including emails that are valid for billing. It claims to have automated more than 600,000 forms and managed over 10 million documents according to its website. The video below explains Smokeball’s software: