全球化研究报告-PDF版

璞跃（Plug and Play）：2022年基于数据、分析和人工智能的旅游行为深度研究报告（英文版）（18页）.pdf

1Deep Diving into Travel Behavior with Data,Analytics,and AIWe help you in every step of your innova.

2022-11-30 18页 5星级

Clifford Chance：欧盟人工智能新规对金融行业企业的影响（英文版）（17页）.pdf

IMPACT OF THE NEW EU AI REGULATION ON FINANCIAL SECTOR FIRMS IMPACT OF THE NEW EU AI REGULATION ON F.

2022-11-30 17页 5星级

IBM商业价值研究院：人工智能对金融功能的量化影响（英文版）（36页）.pdf

IBM Institute for Business Value|Benchmark InsightsAIs quantified impact on the finance functionImpr.

2022-11-30 36页 5星级

亚太经合组织（APEC）：医疗生态系统数字化转型的人工智能(AI)政策建议-案例研究报告（英文版）（16页）.pdf

Artificial Intelligence(AI)Policy Recommendation on Digital Transformation for Healthcare Ecosystem-.

2022-11-30 16页 5星级

亚太经合组织（APEC）：人工智能在经济决策中的应用（2022）（英文版）（11页）.pdf

APEC Policy Support Unit POLICY BRIEF No.52 November 2022 Artificial Intelligence in Economic Polic.

2022-11-29 11页 5星级

德勤（Deloitte）：后疫情时代的网络安全-聚焦金融服务（英文版）（12页）.pdf

Cybersecurity in a post-pandemic world:A focus on financial services2Cybersecurity in a post-pandemi.

2022-11-28 12页 5星级

Intranel：人工智能驱动的道路安全网络白皮书（英文版）（26页）.pdf

The AI-Powered Road Safety NetworkAn innovative approach to enabling safer roads in New Zealand through Emerging T2About the authorAdam is an entrepreneur and an Emerging Technology leader.Through his role with Intranel Consulting,he enables business partners to accelerate technology delivery using a lean,business-driven approach.Email:LinkedIn:adamlynessAdam LynessFounder and Business Development Manager at Intranel Consulting Services31.The case for a smart AI-powered safety network Why do we need to do something different?What is the current intelligent transport landscape?So what are the gaps?How to fill these gaps?Why should we use AI for road safety?Adaptive Safe Path technology and digital convergence2.The wait for autonomous vehicles Why build a network into road infrastructure?The role of the government and manufactures3.An AI-powered solution SmartSafe-Concept design How AI prevents accidents Human factors The value of data Privacy and social acceptance Costs and return on investment4.Summary456778101213141515182021222425Contents4The potential of maturing Artificial Intelligence(AI)and Internet-of-Things(IoT)technologies to enable rapid improvements in road safety has been largely overlooked.On one hand,roading spend remains focused on traditional physical infrastructure,while on the other,technologists are distracted by a space race to build autonomous vehicles.Autonomous technology is difficult and it will likely take 20-30 years to mature and substantially replace existing fleets.Unaddressed,this gap will result in a lot of unnecessary social harm on our roads.What can we do in the meantime?Fortunately,there is an opportunity to utilise existing tech,and AI in particular to significantly reduce harm in the short term by retro-fitting smart safety technology to road networks and existing fleets.This challenge should be led by technologists and government outside the vehicle manufacturing sector as manufacturers goal of maximising new vehicle sales aligns poorly with delivering social good from optimal road safety technology.The case for a smart AI-powered safety networkSolution:SmartSafe Network Technology In this whitepaper,we will present an approach for using current technologies to build intelligent road safety networks.We call this SmartSafe Network technology.SmartSafe works at the infrastructure level and operates with the existing vehicle fleet,delivering a leap forward in road safety at a cost of a few hundred dollars per vehicle,within a 5-year time frame.This is feasible with existing technologies,affordable,and politically tenable.5Improved road safety has a direct impact on saving lives.In the New Zealand context(reflected in many OECD countries),the road toll has remained fairly static despite many campaigns around driver behaviour.These are expensive and slow to shift the needle because the major underlying factor is human fallibility.Why do we need to do something different?Anecdotally,just about everyone admits to having made a mistake that could have seriously hurt someone when driving so its not simply a“good”vs“bad”driver issue.“Vision Zero”road safety strategies acknowledge human factors but planning is very evolutionary in nature(best practice=“do more of the same”)and doesnt really explore the potential for revolutionary new technologies to shortcut the process.The social cost of this is huge,estimated in New Zealand(with a population of 5 million)as$4.8 billion in 2017 across 378 fatalities and 3,000 serious injuries.Many OECD countries have extensive networks of high-risk roads and older vehicle fleets,making a strong case for retrofitted AI-powered safety infrastructure when compared against the costs and time-frames for replacing physical infrastructure or the entire vehicle fleets.Graph 1.Number of road deaths in New Zealand,2010-2019.Source:The Ministry of Transport6What is the current intelligent transport landscape?Smart roads and safety systems fall under the umbrella of Intelligent Transport Systems(ITS),encompassing emerging tech as well as now-standard technologies such as automated speed cameras,road tolling,dynamic signage and traffic management.AI is used for a variety of tasks such as managing traffic density,and monitoring intersections for“near misses”.Several Connected Vehicle pilots are operational which draw on existing vehicle-to-everything(V2x)communication standards and have the potential to form the basis for complete safety-oriented communication protocols.There is a broad range of active R and D projects and commercial products on offer,highlighting that the technological“building blocks”required to underpin an intelligent road safety network have moved out of the lab and are commercially mature in most cases.Huge investment is going into high profile self-driving car projects like Googles Waymo with competing projects from Uber,Tesla,and several others.Ancillary technologies such as 5G networks are being used to test“vehicle to everything”(V2X)communication and Bluetooth 5.0-based Bicycle to Vehicle(B2V)protocols are being explored to encompass vulnerable road users.Powerful AI-based computer vision models can now run on inexpensive,low power hardware.Trimbles RTX technology provides centimetre accurate GPS positioning.In summary7So what are the gaps?Recent paradigm shifts in AI technology,enabling computer vision models to run on inexpensive,low-power hardware are yet to be exploited as an enabling technology for wide-area road safety networks.“Smart road”technology projects tend to be fragmented across small“demonstrators”which dont generate sufficient data to build a clear-cut case for retro-fitting safety technology at scale to existing vehicle fleets and road networks.While pilot systems have been deployed to small numbers of test vehicles,there hasnt been an effort to build a comprehensive,safety-focused system at a scale that can benefit from network effects and generate enough hard data to justify significant investment.In fairness,this is hard as it requires bringing multiple parties together-government,transport agencies,technologists,funders,as well as end-users.How to fill these gaps?Applying a“lean start-up”mindset will help to bridge these gaps.Imagine an angel investor has just provided$5M of seed funding with the goal of reducing the roll toll in New Zealand by 30%in 5 years.What would happen next?A typical process would identify an initial group of users(“customers”)for whom you can solve a compelling problem and generate“traction”to attract further investment.This group could be“frequent drivers on a dangerous rural commuter route”.These customers would largely reside in the same community with road safety already an area of significant concern and discussion.A prototype built around a single,well-trafficked route could achieve both community“buy-in”and a high degree of coverage for those users by focusing on their daily commuter route.Given time,the system can collect hard data on risk factors,accident rates,“near misses”,and the impact of safety interventions.Strong data and a reduction in accident rates will support further investment rounds and the enterprise can grow and reduce road accident harm.8“The latest generation of AI devices is really good at tracking multiple objects reliably and cheaply on low-cost,low-power hardware,without needing a cloud connection.Why should we use AI for road safety?On a fundamental level,a distributed road safety network needs to know the location and velocity of moving objects on the road,as well as the topology of the road itself.If the network has this information on a continuous basis it can then assess risks and apply interventions to reduce the potential for an accident.The latest generation of AI devices can derive velocity and position data solely from cameras without requiring Light Detection and Ranging(LIDAR)or other expensive sensors.If that data is communicated to an in-vehicle user interface,risks can be dynamically assessed and drivers provided with advance warning to avoid accidents before they happen.Earlier computer vision techniques struggled with variations in lighting,orientation,and background but current algorithms are vastly better and would enable“plug and play”nodes to be added to a network where required,without any complex tuning.While vision-based systems may not work as well in heavy fog or driving snow when compared to other technologies,a supplementary safety network doesnt need to be active everywhere,all the time to deliver its core benefits-90%or higher“uptime”is achievable and sufficient.9Googles beta Coral accelerator hardware in combination with a Raspberry Pi and camera module.10Adaptive Safe Path technology and digital convergenceThe SmartSafe network has a novel“Adaptive Safe Path”feature that learns what constitutes a“safe”driving envelope on any given stretch of road by observing how vehicles have recently navigated it,with the assumption that most vehicles,most of the time,are driving safely for the conditions.As an example,imagine a 400m stretch of windy road in rainy conditions.The system will create a model of a safe driving“line”for the conditions based on the median approach(position and velocity)taken by recent vehicles.Should a new vehicle deviate significantly from that line(either through excessive speed or road position)then risk indicators may be triggered.This approach enables the network to adapt to road conditions,road hazards,intersections or road works fairly quickly without depending on a central database for road topology.AI can also be used by in-vehicle modules to detect a sleepy or distracted driver or erratic driving,both major causes of accidents.Again this is no longer a major technical stretch and can run on cheap hardware,providing older vehicles many of the key safety benefits of newer high-end vehicles.Adaptive Safe Path enables a network to adapt to road conditions and hazards without depending on a central database for road topology.11Other technologies are also converging to support the case for intelligent safety networks,including IoT,solar,battery and communications tech,and improved GPS systems.5G tech specifically supports this type of application,including“device to device”modes enabling vehicles and roadside infrastructure to communicate without the need for cellular coverage(Cellular-V2X(C-V2X).The majority of supporting technologies are mature enough that implementation and deployment risks are well understood.Modern,inexpensive AI hardware can be trained to recognise and track any type of object.This example built by Intranels AI team is running on$150 worth of hardware at 50 frames per second.Training the model to recognise different scooter brands only took a few hours.Learn more about Intranels AI project.Intranel is using AI to track and identify scooters.12In talking about a road safety network it does raise the question of why we need smarter roads if we can just wait for smarter cars?If“smarter cars”mean fully autonomous vehicles,the wait might be 20-30 years in most places before the technology becomes ubiquitous and the existing fleet is replaced.That equates to a lot of(preventable)harm through road accidents.A key difference between fully autonomous vehicles and a supplementary road safety network like SmartSafe is that the latter doesnt need to be right all the time to deliver on its core purpose-to warn drivers of potential hazards.In fact,it may even be better to not be“always-on”to prevent over-reliance or complacency when driving.Reliable autonomous vehicles are orders of magnitude more difficult to achieve than a supplementary safety system as the acceptable failure rate is close to zero.Most people wont embrace computers that occasionally glitch and kill people.“A“Smarter roads”approach enables retrofitting the existing fleet and fills the gap between current technology and the ubiquity of autonomous vehicles.The wait for autonomous vehicles“Smarter cars”also encompasses newer vehicles equipped with advanced safety features,including automated braking and collision warning and avoidance systems and Advanced driver-assistance systems(ADAS)that can take over routine driving tasks in some highway situations.Research indicates that Forward Collision Avoidance technology(FCAT)systems may prevent 30-50%of injury crashes,although publicly available data is scarce.This strengthens the argument for retrofitting some of these features into existing networks and vehicles.13There are fundamental performance and adoption benefits in integrating tracking into the road network itself and exposing public API interfaces that inexpensive equipment can access.Proprietary in-vehicle systems are currently limited by what their own sensors can detect,and cant exchange data with other vehicles or a network to receive comprehensive information about moving objects in their vicinity.While pure“vehicle to vehicle”(V2V)plays with high accuracy GPS,equipped vehicles can mitigate the need for roadside nodes.They do require a very high percentage of drivers to opt-in in order to be effective,and covering vulnerable road users like cyclists and pedestrians becomes much harder.An integrated tracking approach with roadside nodes means all vehicles can be tracked and all road users can receive basic visible warning signals from roadside nodes,regardless of whether they have an in-vehicle module.Early adopters with vehicle modules in coverage areas receive an even better experience from a system that can track everything without gaps from non-participating vehicles.As an example,a network-based approach would enable all approaching vehicles to be warned of an oncoming reckless driver,even if that driver occupies a non-connected vehicle without an in-vehicle module.Retrofitting a single dashboard device to an existing vehicle that can communicate with a SmartSafe road safety network is much less expensive than upgrading an existing vehicle with a comprehensive sensor suite.Autonomous vehicles and other driver assistance tools work better when supported by an integrated road network.Smart road infrastructure has broad benefits outside the SmartSafe network,including accelerating the safe deployment of autonomous vehicles.Tracking everything on the road provides key additional data to self-driving vehicles,boosting both safety and reliability.Open protocols for vehicles to access road networks and share data will significantly enhance the performance of any self-driving technology.Why build a network into road infrastructure?14The role of government and manufacturersWhile the NZ government is encouraging ITS(Intelligence Transport Systems)innovation,current transport agency spending is very much on physical infrastructure and conventional“proven”technology,with the development of high-tech safety systems primarily left to vehicle manufacturers.Manufacturers ultimately want to maximise new vehicle sales,a goal which is poorly aligned with safety infrastructure as a social good-i.e.retro-fitting safety tech to an existing fleet doesnt sell new vehicles.Government,as the major funder of infrastructure,should play a role in collaborating on high-tech road safety systems operating at the infrastructure level,and supporting the development and adoption of protocols and standards around this.The potential for huge reductions in harm from leveraging existing technology means smart road safety infrastructure should be a strategic focus for public sector funding,alongside other ITS priorities.The private sector cannot be relied on to fill this gap due to current incentives focusing investment primarily on new vehicles.The government also has a key role in ensuring that data generated by new technologies is used appropriately,in line with local privacy protections and expectations.15SmartSafe-Concept designThe core of the SmartSafe concept network is based around roadside tracking nodes that know their location in relation to the road and can collaboratively track the location and velocity of vehicles,pedestrians,and cyclists in visual range.The tech within the roadside node is familiar,consisting of a digital camera,AI engine,GPS module,wireless connectivity(DSRC or similar protocol over 5G),and two redundant powered Ethernet connections to different base stations.Each node includes a high brightness display panel to provide basic visible alerts to drivers without in-vehicle modules.Roadside nodes work together to share tracking vectors for individual vehicles to get better location and velocity estimates and minimise the effects of occlusion from other vehicles.Base stations are spaced every 1km and power a 500m string of 10 nodes in either direction.They can draw power from the grid or a solar/battery combination.Cloud connectivity is through 4G/5G,a LoRa IoT connection,trunked radio,or satellite internet.That connection can be low bandwidth given AI functions occur in local hardware.In-vehicle modules receive data from the road network on vehicle trajectories and road topology and calculate potential risks to drivers.If these exceed a threshold,there are several tiers of audio and visual alerts depending on the risk level.The vehicle module can act as a“black box”recorder with both accelerometers and road and driver-facing cameras.It can also use AI to generate alerts around driver fatigue or distraction and feed data back to the network if a driver or vehicle represents a hazard to other road users.An AI-powered solution16Basic topology of how an automated road safety system could be structured.The system is focused on safety rather than automation or traffic management,but it would complement those functions.17Nodes are modular and redundant and will self-adapt to their location,enabling a roading crew to drop in a replacement quickly and easily.At the network level,the SmartSafe system uses 5G wireless V2i channels to provide an augmented driving service to which any equipped vehicle module(or roadside node)can subscribe via API.Real-time safety-oriented intelligence is delivered via edge compute AI devices embedded within the nodes rather than a centralised service in order to enhance privacy,reliability,and low latency.This can be visualised as an edge compute“bubble”where the network within a 1km radius of a vehicle delivers velocity and position of other road users as well as data on road topology and potential hazards.The“bubble”can operate independently of cloud services or any other infrastructure,greatly enhancing overall system reliability and privacy.SmartSafe-Concept design18Assuming a road safety network can track vehicles reliably,the question becomes how can this be used to reduce accidents?For retrofitted systems,it isnt practical for a system to take control of a vehicle,so the AIs job is to give drivers advance warnings of hazards,whether they originate from the drivers behaviour or another road user.If we look at common accident factors on the right of Table 1 below and match them with detectable events on the left of the table we can see intuitively that advance warning will avoid or mitigate accidents caused by these factors.Evidence from existing(and more basic)in-vehicle advanced safety systems indicates this conclusion is sound,but a lack of publicly available data to date makes quantifying benefits accurately very difficult without building a comprehensive prototype.The crash factors on the right make up a key causative factor in the vast majority of serious accidents this is data that is publicly available in many countries,including through NZTAs Crash Analysis System.AI WarningCrash FactorsYoure falling asleep/are distractedFatigue/DistractionYoure driving dangerouslySpeed,loss of controlYoure not slowing at your normal rate for an upcoming intersectionFailure to give wayNot safe to enter an intersection(oncoming traffic)Failure to give wayUpcoming hazardRoad damage/careless drivingUpcoming pedestrian/cyclistDidnt see vulnerable road usersNot safe to overtakeHead on collisionDangerous driver/police pursuit in your vicinitySpeed,loss of controlOther vehicle(s)not slowing for intersectionFailure to give wayTable 1.Detectable events matched against related crash factors.How AI prevents accidents19How AI prevents accidentsHeuristics around alerts can be fairly simple for instance,if a driver pulls out to overtake and there is an oncoming vehicle at a dangerous closing velocity,audio,and visual alerts will be issued.Metrics around what constitutes“dangerous driving”are more complex and might involve a combination of weather conditions,speed and acceleration data,and crossing the centre line as well as whether a vehicle deviates substantially from the“Adaptive Safe Path”generated from previous vehicles.AI predictive models can improve over time by feeding back data from accidents or“near misses”.A network-based approach has the advantage that safety messages can be micro-targeted to a particular area for instance,if an accident has blocked a road or if police nearby have initiated a pursuit of a dangerous driver.An example of predictive metrics.20Any safety warning system comes with behavioural challenges.If users feel overwhelmed with unnecessary warnings,they get sick of them and pull the plug.AI has a major advantage in being able to recognise and adapt to different drivers and prioritise risks.As an example,if the AI learns a driver typically approaches an intersection at a fast rate before braking,it will adjust when it issues a warning appropriately.This learning mechanism helps ensure that warnings will be less likely to be ignored.For some drivers,there is a risk that the absence of a warning could be taken as a green light to initiate a dangerous maneuver,e.g.overtaking on a blind corner if an“oncoming vehicle”alert isnt triggered when they pull out.This can be mitigated by system design,e.g.recognising the maneuver as dangerous due to road topology and issuing the same warning as for an oncoming vehicle(or any other major imminent hazard).Keeping drivers aware that the safety system may not always be present or“always-on”,may minimise over-reliance or complacency.Interestingly,work by SAE indicates that existing in-vehicle Advanced Driver-Assistance Systems(ADAS)are(so far)not encouraging driver inattentiveness.Being accountable for reckless driving identified by the system would also go some way to encouraging better practices but also has privacy implications and risks reducing uptake-drivers will not want to be“spied”on and any non-anonymised reporting of behaviour will have to be negotiated with communities,reviewed by a human,and reserved for the most serious cases.“The first thing many New Zealand drivers do on acquiring an imported vehicle is disable the automated speed warning that beeps over 110km/h.Human factors21The value of dataA huge benefit of a road safety tracking network is that,even on a small scale(less than a few thousand vehicles),it yields comprehensive data not just on the lead up to accidents but also“near misses”,which currently dont get recorded and probably outnumber accidents 10 to 1.Driving data that precedes accidents or near misses can be captured to continuously train better AI and risk models and enable better targeting of risky behaviours.This targeting can be much more nuanced than say instantaneous speed cameras and can have an educative focus,with personalised feedback available to drivers.At the policy level,strong data enables better safety interventions and informs transport policy and strategy development.An example of improving road safety through data is ERoads Guardian system that monitors driving and fatigue to provide feedback to professional drivers.AI-Powered road safety networks like SmartSafe generate comprehensive data to build and improve risk models.22Privacy concerns are legitimate with any network that tracks movements of vehicles or people.Opinions will vary,ranging from an expectation of almost complete privacy to the view that driving is a privilege and drivers dont necessarily have a right for their behaviour to be outside the purview of the state.The status quo sits most closely with the latter stance,with tacit acceptance of broad deployment of enforcement measures and traffic cameras.Within developed urban centres,licence plate recognition technology capable of tracking an individual vehicle around a city is already common.Privacy protections lie not with the absence of such technologies(they already exist)but with legal protections restricting how data is gathered and used,and transparency and oversight around this.Privacy can be managed with system design,especially where AI happens on local nodes and video data isnt routinely sent to a central server.From a privacy risk perspective,theres a big difference between hoovering up every licence plate and sending it to a central server versus local roadside nodes receiving a list of stolen vehicle plates to look out for.For some drivers,enhanced monitoring by insurance companies,parents,or employers may be appropriate as part of the conditions to access a vehicle.Centralising non-anomymised data can be triggered only in the event of a serious accident,as would accessing in-vehicle module data.This is similar to the situation now with some drivers installing cameras for their own peace of mind.Privacy and social acceptance23“The threshold for non-anonymised reporting of risky or illegal driving behaviour should be negotiated with communities to ensure“buy-in”is present,and human review applied to reports,prior to sanctions being applied.Politically,significant public backlash is less likely if initiatives are community-led,and the focus on safety and saving lives is strictly maintained.The proposed system is designed to protect participating drivers,without uptake needing to be universal in pilot areas-effectively an“opt-in”system.The initial appeal is likely to lie with parents of younger drivers and regular commuters on dangerous routes,with the insurance industry another clear beneficiary.24Costs and return on investmentCosts will vary between regions and geographies but modelling in the New Zealand context indicates an ROI over a 20-year lifespan of 700%,meaning for every dollar spent on the network$7 in measured“social harm reduction”will be generated through reduced injuries and fatalities.Installation is assumed to be primarily on state highways which are responsible for a higher portion of harm per kilometre and present less complex environments for monitoring.Social costs of accidents and injuries use New Zealand Transport Agency figures.25Right now new technology development in the transport space is focused on a space race to build autonomous vehicles.This is laudable,but the 20-30 year time frame to fully replace existing fleets means theres a 15 to a 20-year gap where road accident harm will continue at similar levels to now.This gap can be addressed in the short-term by retro-fitting intelligent road safety systems to existing infrastructure,built around AI and other proven technologies.Systems like the proposed SmartSafe network concept will generate raw data to enable safety interventions as well as supporting a myriad of in-vehicle systems,including accelerating the roll-out of autonomous vehicles.The challenge is about reducing social harm through optimal use of high-tech public infrastructure and as such government and transport agencies should play a major role.Vehicle manufacturers are valuable stakeholders but their focus on new vehicle sales means they cannot be relied on to the lead delivery of optimal solutions.A SmartSafety network can be built quickly relative to autonomous vehicle tech in that it doesnt attempt to replace the driver,but rather has a supplementary role in providing advance warning prior to a risk manifesting as an accident.This delivers major benefits without needing to be 100curate at all times and in all conditions.A razor-sharp focus on safety and application of lean business principles to building a beta system at a modest scale will generate measurable harm reduction and a valuable data set to build risk models.This will inform data-driven policy around interventions and help train AI-powered“driver assistants”to mitigate risk prior to an accident occurring.Hard data and an“open IP”approach will facilitate subsequent investment and roll-out,with the potential to meaningfully reduce harm at the national level within 5 years.Geographies like New Zealand with a large network of high-risk highways,high accident rates,and older vehicle fleets are especially well-placed to benefit from this technology.While there are known risks in managing human factors and privacy concerns,the challenge is an engineering one and not“blue-sky”research.Lives can be saved with existing technologies in a way that is both affordable and politically tenable.SummaryGet in touch with Intranel Contact us to discuss how AI and Emerging Tech can help overcome your challenges and transform your business.Email us at Or fill the form at

2022-11-28 26页 5星级

麦肯锡（McKinsey）：人工智能在生物制药研究中的应用-集中精力和扩大规模的时机已至（英文版）（11页）.pdf

Life Sciences PracticeAI in biopharma research:A time to focus and scaleBy focusing on specific scie.

2022-11-25 11页 5星级

毕马威（KPMG）：毕马威未来银行2.0-如何打造数字化时代的智链公司银行（2022）（英文版）（22页）.pdf

Future of commercial bankingSeptember 2022home.kpmgThe customer first in digitalThe commercial banki.

2022-11-25 22页 5星级

SANS：2022年及未来工业控制系统（ICS）于运营技术（OT）网络安全现状报告（英文版）（20页）.pdf

SurveyThe State of ICS/OT Cybersecurity in 2022 and BeyondWritten by Dean ParsonsOctober 20222022 SA.

2022-11-24 20页 5星级

Perkins Coie：人工智能系统可以做出专利发明吗？（2022）（英文版）（6页）.pdf

z Can Artificial Intelligence Systems Make Patented Inventions?BABAK TEHRANCHI|PARTNER PERKINS COIE LLP Patent Proscecution JINGYUAN HUANG|ASSOCIATE PERKINS COIE LLP Patent Proscecution 2 Can Artificial Intelligence Systems Make Patented Inventions?INTRODUCTION In 2018,a team led by Ryan Abbott,a University of Surrey law professor,and Stephen Thaler,a computer scientist from Missouri,started the Artificial Inventor Projecta project seeking intellectual property rights for inventions generated by an artificial intelligence(AI)device without a traditional human inventor.1 In challenging the status quo,which requires listing a person as the inventor,Abbott and Thaler filed two patent applications related to a food container and a neural flame internationally,both listing an AI machine as the inventor.2 In doing so,they brought an interesting question to the forefront:Can a patent be issued with an AI machine as the inventor?The answer has been an almost unanimous no,with rare exceptions in some countries.For example,the worlds first patent with an AI machine as the inventor was issued in South Africa in 2021.Other countries,however,have adopted different stances in considering AI inventorship.This paper reviews the status of these applications in several major jurisdictions and discusses additional issues in AI technology that may not be sufficiently addressed by the existing patent laws.CONVENTIONAL INVENTORSHIP OF PATENTS The United States Patent and Trademark Office(USPTO)In the United States,the exclusive right to new discoveries for limited times is granted by the Congress in the form of patent rights.3 The inventors“whoever invents or discovers any new and useful process,machine,manufacture,or composition of matter,or any new and useful improvement thereof”“may obtain a patent therefor,subject to the c onditions and requirements of this title.”4 The use of the pronoun“whoever”in 35 U.S.C.101 indicates that the inventors should be natural persons,not machines.Indeed,“the threshold question in determining inventorship is who conceived the invention,”not what conceived the invention,because“conception is the formation in the mind of the inventor,of a definite and permanent idea of the complete and operative invention.”5 The human requirement of inventorship is further elucidated in the procedural rules put in place to ensure the proper identification of inventorship for each patent.For example,an inventor is required to execute documents identifying himself or herself as the inventor.6 The person who executes the oath or declaration for an application must have reviewed and understood the contents of the application,including the claims.7 In the case of the inventor or joint inventor assigning the rights to a company,the assignment can“serve as an oath or declaration if the assignment as executed includes the information and statements”required for the oath or declaration and“a copy of the assignment is recorded.”8 The inventor 1 See THE ARTIFICIAL INVENTOR,https:/ See Patents and Applications,THE ARTIFICIAL INVENTOR,https:/ See U.S.CONST.art.I,8,cl.8.4 35 U.S.C.101.5 Fiers v.Revel,984 F.2d 1164,1168(Fed.Cir.1993);see also Burroughs Wellcome Co.v.Barr Labys,Inc.,40 F.3d 1223,122728(Fed.Cir.1994).6 See 37 C.F.R.1.63(a).7 See id.1.63(c).8 Id.1.63(e).3 Can Artificial Intelligence Systems Make Patented Inventions?also bears the duty to disclose to the USPTO all known information that is material to patentability of the invention,such as other patents or publications that describe similar or related concepts.9 The inability of the current patent system to accommodate non-human inventors is further illustrated by Dr.Thalers creative attempts to fulfill the formal U.S.patent filing requirements.For example,Dr.Thaler and his team filed two applicationsU.S.Application Nos.16/524,350 and 16/524,532on July 29,2019,listing a single inventor in the Application Data Sheet(ADS)with the first name“Device for the Autonomous Bootstrapping of Unified Sentience(DABUS)”and the family name“Invention generated by artificial intelligence.”10 In lieu of the requisite declaration or oath,Dr.Thaler executed a substitute statement,which is allowed under 35 U.S.C.l15(d),to state that the inventor DABUS was“under legal incapacity in view of the fact that the sole inventor is a Creativity Machine(i.e.,an artificial intelligence),with no legal personality or capability to execute this substitute statement.”11 Dr.Thaler further identified himself as the assignee of the entire right,title,and interest in the invention and signed the assignment document.12 The USPTO responded by indicating that the ADS did not identify each inventor by his or her legal name as required by the pertinent procedures.13 Dr.Thaler then filed two consecutive petitions,requesting supervisory review and reconsideration of the issue.14 The petitions were denied one after another,followed by a final written decision denying the request for reconsideration.15 In the final written decision,the USPTO pointed to the use of pronouns that are specific to natural persons in various statutes.16 The USPTO also highlighted the Federal Circuit decisions that have clarified that,to perform the mental act of inventing,inventors must be natural persons.17 In conclusion,the USPTO stated that a machine does not qualify as an inventor under the U.S.patent laws.18 The USPTOs final written decision was appealed to the District Court of the Eastern District of Virginia.The district court,after reviewing the statutory language(and also relying on the Supreme Courts construction of the term“individual”in the Torture Victim Protection Act),affirmed the conclusion that inventors must be natural persons.19 An appeal to the Federal Circuit is now pending.9 See id.1.63(c).10 In re Application of Application No.:16/524,350.11 In re Application of Application No.:16/524,350.12 Id.13 See id.14 See id.15 See id.16 See id.17 See id.18 See id.19 Memorandum Opinion,Thaler v.Iancu,Civil Action No.20-cv-903(E.D.Va.Sept.2,2021),ECF No.33,appeal docketed sub nom.Thaler v.Hirshfeld,No.2021-2347(Sept.24,2021).4 Can Artificial Intelligence Systems Make Patented Inventions?The European Patent Office The requirements for inventorship in Europe are not as onerous as the requirements in the United States,but certain procedural rules are still required.For example,in accordance with Rule 19(1)of the European Patent Convention(EPC),a designation of the inventor shall state the family name,given names,and full address of the inventor.20 Drs.Thaler and Abbott also filed their patent applications with the European Patent Office(EPO).In the request for grant of a European patent,the inventor field was initially left empty,but a later-filed designation of the inventor listed DABUS as the inventor.21 The EPO noted that the legal framework of the EPC provides for natural persons,legal persons,and bodies equivalent to legal persons acting in certain capacities but does not provide for non-persons as applicants,as inventors,or in any other role in the patent grant proceedings.22 In particular,where non-natural persons are concerned,legal personality is given only on the basis of legal fictions.23 These legal fictions are either directly created by legislation or developed through consistent jurisprudence.24 Therefore,Al systems or machines have no rights at present because they have no legal personality comparable to natural or legal persons.25 The Intellectual Property Office of the United Kingdom The patent applications suffered a similar fate after they were filed with the Intellectual Prope rty Office of the United Kingdom on October 17,2018,and November 7,2019,respectively.26 The filings stated that the inventor was an AI machine called DABUS and that Dr.Thaler had acquired the right to grant of the patents in question by“ownership of the creativity machine DABUS.”27 Similar to its U.S.and European counterparts,Section 7 of the United Kingdoms Patents Act 1977 states that“any person may make an application for a patent either alone or jointly with another”and“inventor in relation to an invention means the actual deviser of the invention and joint inventor shall be construed accordingly.”28 Here,the concept of the“actual deviser”is similar to the concept of“conception”in the United Statesthe word“actual”means that the natural person who“came up with the inventive concept”is the inventor.29 Furthermore,in seeking consistency with the EPO,the British High Court concluded that“DABUS is not a person,whether natural or legal.”30 In particular,“DABUS is not a legal person because(unlike corporations)it has not had conferred upon it legal personality by operation of law.”31 The Court of Appeal also affirmed the ruling of the High Court and dismissed the appeal.32 20 Rule 19(1)EPC.21 See In Re EP 18 275 163;see also In Re 18 275 174.22 Id.23 Id.24 Id.25 Id.26 See Thaler v Comptroller-General of Patents,Designs and Trade Marks 2020 EWHC 2412(Pat)(21 Sept.2020).27 Id.28 Thaler 2020 EWHC 2412(Pat).29 See id.30 Id.31 Id.32 Thaler v Comptroller-General of Patents,Designs and Trade Marks 2021 EWCA Civ 1374.5 Can Artificial Intelligence Systems Make Patented Inventions?FAVORABLE OUTCOME FOR DABUS The South African Patent Office The worlds first patent application listing DABUS as the inventor was granted by the Companies and Intellectual Property Commission in South Africa.33 One of the contributing factors of the grant was that,in South Africa,patent applications are not formally examinedthey can be granted if they meet the required formalities at a minimum.34 The South African patent laws also lack a specific definition of“inventor.”35 The Australian Patent Office Similar to the South African patent laws,the Australian Patents Act 1990 has no definition of“inventor.”36 The Federal Court of Australia found that none of the relevant provisions in the Patents Act 1990 or the Paris Convention excludes an inventor from being a non-human AI device or system.37 Furthermore,the Patents Act 1990 focuses on the inventive step,which“uses a hypothetical and objective construct that is not at all concerned with the inventors mental processes as such.”38 The Australian court thus concluded that an AI system can be an inventor but cannot be a patent applicant or be granted a patent.39 AI SYSTEMS VERSUS OUTPUTS OF AI SYSTEMS The DABUS AI machine is a form of neurocomputing that allows machines to generate new concepts along with their anticipated consequences.40 In general,such AI systems work by ingesting large amounts of training data,analyzing the data for correlations and patterns based on a set of predetermined rules,and using these patterns to make predictions about future states.The outputs of such systems,such as the food container or the neural flame sought to be patented by Drs.Abbott and Thaler,can certainly be seen as ideas formed by the AI systems.However,the formation of the ideasor rather,the analysis of correlations using existing datacannot exist without the persons who have designed and trained the AI systems.The patent laws in most countries currently allow a person who designs and trains an AI system,such as DABUS,to obtain patent rights in the AI system,thus preventing others from making and using the AI system itself.However,the output designs that are generated by the AI system cannot be patented because of the lack of proper inventors.Therefore,unless those outputs are kept as trade secrets,these ideas mayonce they make their way into the public domainbe up for grabs by the public without any legal consequences.33 See 54 PAT.J.(July 2021)https:/iponline.cipc.co.za/Publications/PublishedJournals/E_Journal_July 2021 Part 2.pdf.34 See http:/www.cipc.co.za/index.php/trade-marks-patents-designs-copyright/patents/.35 See What is a Patent,CIPC,https:/www.gov.za/sites/default/files/gcis_document/201504/act-57-1978.pdf 36 See Patents Act 1990,Fed.Reg of Legis.(compiled 24 Feb.2019),https:/www.legislation.gov.au/Details/C2019C00088.37 See Thaler v Commissioner of Patents 2021 FCA 879.38 Id.39 See id.40 Stephen L.Thaler,Vast Topological Learning and Sentient AGI,8 J,A.I.&CONSCIOUSNESS 81-111(2021).6 Can Artificial Intelligence Systems Make Patented Inventions?CONCLUSION With the rapid proliferation of AI systems in todays smart devices,ranging from robots to cameras to medical devices and navigation systems(to list a few),these systems undoubtedly have,or will,come up with the“new and useful”processes,machines,or compositions of matter,or the“new and useful improvements thereof,”that constitute part of the requirements for obtaining patents in the United States and elsewhere.The difficulty remains in deciding whether such AI systems can be considered inventors,and thus whether those inventions can be protected by patents.The disruptive filings of DABUS applications have prompted several major jurisdictions to clarify that AI systems do not qualify as“inventors”under the current legal frameworks.These applications have further revealed the inadequacy of the formal patent filing procedures for accepting non-human inventorsincluding forms that require first and last names to be specified.Whether the concept of inventorship should be extended to encompass AI systems is a deserving question that requires changes in both the substantive and procedural patent laws to be undertaken by legislatures around the world.The current legal framework is similarly unclear as to how rights to the output of AI systems devised by human beings can be protected.The legislative bodies around the world may need to take a deeper look at the additional issues brought by AI systems and adapt the existing laws and regulations to account for the intricate relationship between the human beings who are behind the design and architecture of AI systems and the output of such systems.

2022-11-23 6页 5星级

波音（BOEING）：2022-2031年航空服务市场展望报告（英文版）（13页）.pdf

1Services Market Outlook 20222031 Services Market Outlook 202220311Services Market Outlook 20222031 .

2022-11-23 13页 5星级

Gartner：构建数字化未来：元宇宙（会议演讲PPT）（英文版）（26页）.pdf

Gartner WebinarsGartner delivers actionable,objective insight,guidance and tools to enable stronger .

2022-11-22 26页 5星级

麦肯锡（McKinsey）：人工智能如何加速细胞和基因疗法的研发（2022）（英文版）（10页）.pdf

Life Sciences PracticeHow AI can accelerate R&D for cell and gene therapiesCell and gene therapies s.

2022-11-21 10页 5星级

印孚瑟斯（Infosys） ：数字供应链-为未来供应链管理自动化赋能（2022）（英文版）（20页）.pdf

DIGITAL SUPPLY CHAIN POWERING THE SELF-CURATING SUPPLY CHAINS OFTHE FUTURE 2|DIGITAL SUPPLY CHAIN PO.

2022-11-17 20页 5星级

印孚瑟斯（Infosys） ：IT和商业运营-迈向去中心化的数字生态系统（2022）（英文版）（28页）.pdf

IT AND BUSINESS OPERATIONS TOWARD A DECENTRALIZED DIGITAL ECOSYSTEM2|IT AND BUSINESS OPERATIONS TOWA.

2022-11-17 28页 5星级

亚开行（ADB）：2022年亚洲发展展望更新报告：关键信息”（英文版）（20页）.pdf

SOFTENING GROWTH AMID A DARKENED GLOBAL OUTLOOKadb.org/outlookDeveloping Asias recovery progresses e.

2022-11-15 20页 5星级

MobiDev：如何有效搭建语音识别系统（2022）（英文版）（15页）.pdf

Table of ContentsVOICE RECOGNITION VS SPEECH RECOGNITIONHow do speech recognition applications work?.

2022-11-15 15页 5星级

思科（Cisco）：网络安全报告-防御关键威胁（英文版）（28页）.pdf

Solution Overview Cisco PublicDefending Against Critical Threats Analyzing Key Incident Trends Eagle.

2022-11-14 28页 5星级

思科（Cisco）：构筑安全弹性：网络安全专家的经验和建议（英文版）（26页） .pdf

Click here or press enter for the accessibility optimised versionBuilding SecurityResilienceStories .

2022-11-14 26页 5星级