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Boasting that it is on track to bring over 100 "AI-powered" improvements to Google Maps, Google has announced a series of updates that have been or are set to be released in the coming year.

The first is adding Live View, a feature that uses augmented reality cues -- arrows and accompanying directions -- to help point people in the right way and avoid the "awkward moment when you're walking the opposite direction of where you want to go".

According to Google Maps product VP Dane Glasgow, Live View relies on AI technology, known as global localisation, to scan "tens of billions" of Street View images to help understand a person's orientation, as well as the precise altitude and placement of an object inside a building, such as an airport, transit station, or shopping centre, before providing directions.

"If you're catching a plane or train, Live View can help you find the nearest elevator and escalators, your gate, platform, baggage claim, check-in counters, ticket office, restrooms, ATMs and more. And if you need to pick something up from the mall, use Live View to see what floor a store is on and how to get there so you can get in and out in a snap," Glasgow explained in a post.

For now, the indoor Live View feature is available on Android and iOS in a number of shopping centres in the US across Chicago, Long Island, Los Angeles, Newark, San Francisco, San Jose, and Seattle, with plans to expand it to a select number of airports, shopping centres, and transit stations in Tokyo and Zurich. More cities will also be added, Glasgow confirmed.

See also:Google Maps turns 15: A look back on where it all began

Glasgow added commuters will be able to view the current and forecast temperature and weather conditions, as well as the air quality in an area through Google Maps, made possible through data shared by Google partners such as The Weather Company, AirNow.gov, and the Central Pollution Board. To be available on Android and iOS, the weather layer will be made available globally, while the air quality layer will launch in Australia, the US, and India, with plans to see it expanded in other countries.

On the environment, Glasgow also noted that Google is building a new routing model using insights from the US Department of Energy's National Renewable Energy Lab to help deliver more eco-friendly route options, based on factors like road incline and traffic congestion, for commuters in the US on Android and iOS. The model will be available later this year, with plans for global expansion at an unspecified later date.

Glasgow said the move is part of the company's commitment to reduce its environmental footprint.

"Soon, Google Maps will default to the route with the lowest carbon footprint when it has approximately the same ETA as the fastest route. In cases where the eco-friendly route could significantly increase your ETA, we'll let you compare the relative CO2 impact between routes so you can choose," he said.

In further efforts to meet its sustainability commitment, the tech giant also plans to introduce in "coming months" an updated version of Maps where commuters will have a view of all routes and transportation modes available to their destination, without toggling between tabs, while also automatically prioritising a user's preferred transport mode or modes that are popular in their city.

"For example, if you bike a lot, we'll automatically show you more biking routes. And if you live in a city like New York, London, Tokyo, or Buenos Aires where taking the subway is popular, we'll rank that mode higher," Glasgow said.

Also, within Maps, Google said it is teaming up with US supermarket Fred Meyer to pilot in select stores in Portland, Oregon a feature that has been designed to make contactless grocery pickup easier, including notifying commuters what time to leave to pick up their groceries, share the arrival time with the store, and allow customers to "check-in" on the Google Maps app so their grocery orders can be brought out to their car on arrival.

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Google Maps using artificial intelligence to help point people in the right direction - ZDNet

Henry Ford did not invent the automobile. That was Karl Benz.

But Ford did perfect the assembly line for auto production. That innovation directly led to cars becoming markedly cheaper, putting them within reach of millions of Americans.

In effect, Ford democratized the automobile, and I see a direct analogy to what the University of Florida is doing for artificial intelligence AI, for short.

In July, the University of Florida announced a $100 million public-private partnership with NVIDIA the maker of graphics processing units used in computers that will catapult UFs research strength to address some of the worlds most formidable challenges, create unprecedented access to AI training and tools for under-represented communities and build momentum for transforming the future of the workforce.

At the heart of this effort is HiPerGator AI the most powerful AI supercomputer in higher education. The supercomputer, as well as related tools, training and other resources, is made possible by a donation from UF alumnus Chris Malachowsky as well as from NVIDIA, the Silicon Valley-based technology company he co-founded and a world leader in AI and accelerated computing. State support also plays a critical role, particularly as UF looks to add 100 AI-focused faculty members to the 500 new faculty recently added across the university many of whom will weave AI into their teaching and research.

UF will likely be the nations first comprehensive research institution to integrate AI across the curriculum and make it a ubiquitous part of its academic enterprise. It will offer certificates and degree programs in AI and data science, with curriculum modules for specific technical and industry-focused domains. The result? Thousands of students per year will graduate with AI skills, growing the AI-trained workforce in Florida and serving as a national model for institutions across the country. Ultimately, UFs effort will help to address the important national problem of how to train the nations 21st-century workforce at scale.

Further, due to the unparalleled capabilities of our new machine, researchers will now have the tools to solve applied problems previously out of reach. Already, researchers are eyeing how to identify at-risk students even if they are learning remotely, how to bend the medical cost curve to a sustainable level, and how to solve the problems facing Floridas coastal communities and fresh water supply.

Additionally, UF recently announced it would make its supercomputer available to the entire State University System for educational and research purposes, further bolstering research and workforce training opportunities and positioning Florida to be a national leader in a field revolutionizing the way we all work and live. Soon, we plan to offer access to the machine even more broadly, boosting the national competitiveness of the United States by partnering with educational institutions and private industry around the country.

Innovation, access, economic impact, world-changing technological advancement UFs AI initiative provides all these things and more.

If Henry Ford were alive today, I believe he would recognize the importance of whats happening at UF. And while he did not graduate from college, I believe he would be proud to see it happening at an American public university.

Joe Glover is provost and senior vice president of academic affairs at the University of Florida.

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Heres why UF is going to use artificial intelligence across its entire curriculum | Column - Tampa Bay Times

Frontex, the European Border and Coast Guard Agency, commissioned RAND Europe to carry out an Artificial intelligence (AI) research study to provide an overview of the main opportunities, challenges and requirements for the adoption of AI-based capabilities in border management.

AI offers several opportunities to the European Border and Coast Guard, including increased efficiency and improving the ability of border security agencies to adapt to a fast-paced geopolitical and security environment. However, various technological and non-technological barriers might influence how AI materializes in the performance of border security functions.

Some of the analyzed technologies included automated border control, object recognition to detect suspicious vehicles or cargo and the use of geospatial data analytics for operational awareness and threat detection.

The findings from the study have now been made public, and Frontex aims to use the data gleaned to shape the future landscape of AI-based capabilities for Integrated Border Management, including AI-related research and innovation projects.

The study identified a wide range of current and potential future uses of AI in relation to five key border security functions, namely: situation awareness and assessment; information management; communication; detection, identification and authentication; and training and exercise.

According to the report, AI is generally believed to bring at least an incremental improvement to the existing ways in which border security functions are conducted. This includes front-end capabilities that end users directly utilize, such as surveillance systems, as well as back-end capabilities that enable border security functions, like automated machine learning.

Potential barriers to AI adoption include knowledge and skills gaps, organizational and cultural issues, and a current lack of conclusive evidence from actual real-life scenarios.

Read the full report at Frontex

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Study Finds Both Opportunities and Challenges for the Use of Artificial Intelligence in Border Management Homeland Security Today - HSToday

PatentNext Summary: AI-related inventionshave experienced explosive growth. In view of this, the USPTO hasprovided guidance in the form of an example claim and an"informative" PTAB decision directed to AI-related claimsthat practitioners can use to aid in preparing robust patent claimson AI-related inventions.

Artificial Intelligence (AI) has experienced explosive growthacross various industries. From Apple's Face ID (facerecognition), Amazon's Alexa (voice recognition), to GM Cruise(autonomous vehicles), AI continues to shape the modern world.SeeArtificialIntelligence.

It comes as no surprise, therefore, that patents related toAI inventions have also experienced explosivegrowth.

Indeed, in the last quarter of 2020, the United States Patentand Trademark Office (USPTO) reported that patent filings forArtificial Intelligence (AI) related inventions more than doubledfrom 2002 to 2018.SeeOffice of the ChiefEconomist, Inventing AI: Tracking The Diffusion Of ArtificialIntelligence With Patents, IP DATA HIGHLIGHTS No. 5 (Oct.2020).

During the same period, however, the U.S. Supreme Court'sdecision inAlice Corp. v. CLS BankInternationalcast doubt on the patentability ofsoftware-related inventions, which AI sits squarelywithin.

Fortunately, since the SupremeCourt'sAlice decision, the Federal Circuitclarified (on numerous occasions) that software-related patents areindeed patent-eligible. SeeAre Software InventionsPatentable?

More recently, in 2019, the United States Patent and TrademarkOffice (USPTO) provided its own guidance on the topic of patentingAI inventions. See2019 Revised Patent Subject Matter EligibilityGuidance. Below we explore these examples.

As part of its 2019 Revised Patent Subject Matter EligibilityGuidance (the "2019 PEG"), the USPTO provided severalexample patent claims and respective analyses under thetwo-partAlicetest.SeeSubjectMatter Eligibility Examples: Abstract Ideas.

One of these examples ("Example 39") demonstrated apatent-eligible artificial intelligence invention. In particular,Example 39 provides an example AI hypothetic invention labeled"Method for Training a Neural Network for FacialDetection" and describes an invention for addressing issues ofolder facial recognition methods that suffered from the inabilityto robustly detect human faces in images where there are shifts,distortions, and variations in scale in scale and rotation of theface pattern in the image.

The example inventive method recites claim elements fortraininga neural networkacross twostages of training set data so as to minimize false positives forfacial detection. The claims are reproduced below:

collecting a set of digitalfacial images from a database;

applying one or moretransformations to each digital facial image includingmirroring, rotating, smoothing, or contrast reduction to create amodified set of digital facial images;

creating a first trainingset comprising the collected set of digital facial images, themodified set of digital facial images, and a set of digitalnon-facial images;

training the neural networkin a first stage using the first training set

creating a second trainingset for a second stage of training comprising the first trainingset and digital non-facial images that are incorrectly detected asfacial images after the first stage of training;and

training the neural networkin a second stage using the second training set.

The USPTO's analysis of Example 39 informs that the aboveclaim is patent-eligible (and not "directed to" anabstract idea) because the AI-specific claim elements do not recitea mere "abstract idea." SeeHow to Patent Software Inventions: Show an"Improvement". In particular, while some ofthe claim elements may be based on mathematical concepts, suchconcepts are not recited in the claim. Further, the claim does notrecite a mental process because the steps are not practicallyperformed in the human mind. Finally, the claim does not recite anymethod of organizing human activity, such as a fundamental economicconcept or meaning interactions between people. Because the claimsdo not fall into one of these three categories, then, according tothe USPTO, then the claim is patent-eligible.

As a further example, the Patent Trial and Appeal Board (PTAB)more recently applied the 2019 PEG (as revised) inanexparteappeal involving anartificial intelligence invention.Seeex parte Hannun (formerly Ex parteLinden), 2018-003323 (April 1,2019)(designated by the PTAB as an"Informative" decision).

InHannun, the patent-at-issuerelated to "systems and methods for improving thetranscription of speech into text." The claims includedseveral AI-related elements, including "a set of trainingsamples used to traina trained neural networkmodel" as used to interpret a string of charactersfor speech translation. Claim 11 of the patent-at-issue isillustrative and is reproduced below:

receiving an inputaudio from a user; normalizing the input audio to make a totalpower of the input audio consistent with a set of training samplesused to train a trained neural networkmodel;

generatinga jitter set of audio files from the normalized input audio bytranslating the normalized input audio by one or more timevalues;

for eachaudio file from the jitter set of audio files, which includes thenormalized input audio:

generatinga set of spectrogram frames for each audio file; inputting theaudio file along with a context of spectrogram frames into atrained neural network; obtaining predicted character probabilitiesoutputs from the trained neural network;and

decoding atranscription of the input audio using the predicted characterprobabilities outputs from the trained neural network constrainedby a language model that interprets a string of characters from thepredicted character probabilities outputs as a word orwords.

Applying the two-partAlicetest, theExaminer had rejected the claims finding them patent-ineligible asmerely abstract ideas (i.e., mathematical concepts and certainmethods of organizing human activity without significantlymore.)

The PTAB disagreed. While the PTAB generally agreed that thepatentspecificationincluded mathematicalformulas, such mathematical formulas were"notrecited in theclaims." (original emphasis).

Nor did the claims recite "organizing human activity,"at least because, according to the PTAB, the claims were directedto a specific implementation comprising technical elementsincluding AI and computer speech recognition.

Finally, and importantly, the PTAB noted the importance ofthespecificationdescribing how the claimedinvention provides animprovementto thetechnical field of speech recognition, with the PTAB specificallynoting that "the Specification describes thatusingDeepSpeech learning,i.e.,a trained neural network, along with alanguage model 'achieves higher performance than traditionalmethods on hard speech recognition tasks while also being muchsimpler.'"

For each of these reasons, the PTAB found the claims of thepatent-at-issue inHannunto bepatent-eligible.

Each of Example 39 and the PTAB's informative decisionofHannundemonstrates theimportance of properly drafting AI-related claims (and, in general,software-related claims) to follow a three-part pattern ofdescribing an improvement to the underlying computing invention,describe how the improvement overcomes problems experienced in theprior art, and recite the improvement in the claims. For moreinformation, seeHow to Patent Software Inventions: Show an"Improvement".

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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How To Patent An Artificial Intelligence (AI) Invention: Guidance From The US Patent Office (USPTO) - Intellectual Property - United States - Mondaq...

DUBLIN, March 25, 2021 /PRNewswire/ -- The "Artificial Intelligence in Military Market by Offering (Software, Hardware, Services), Technology (Machine Learning, Computer vision), Application, Installation Type, Platform, Region - Global Forecast to 2025" report has been added to ResearchAndMarkets.com's offering.

The Artificial Intelligence in military market is estimated at USD 6.3 billion in 2020 and is projected to reach USD 11.6 billion by 2025, at a CAGR of 13.1% during the forecast period.

The Artificial Intelligence in Military market includes major players such as BAE Systems Plc. (UK), Northrop Grumman Corporation (US), Raytheon Technologies Corporation (US), Lockheed Martin Corporation (US), Thales Group (US), L3Harris Technologies, Inc. (US), Rafael Advanced defense Systems (Israel), and IBM (US), among others. These players have spread their business across various countries includes North America, Europe, Asia Pacific, Middle East & Africa, and Latin America. COVID-19 has not affected the Ai in military market growth to some extent, and this varies from country to country. Industry experts believe that the pandemic has not affected the demand for Artificial Intelligence in the military market in defense applications.

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period. The space AI segment comprises CubeSat and satellites. Artificial intelligence systems for space platforms include various satellite subsystems that form the backbone of different communication systems. The integration of AI with space platforms facilitates effective communication between spacecraft and ground stations.

Software segment of the Artificial Intelligence in Military market by offering is projected to witness the highest CAGR during the forecast period

Based on offering, the Software segment is projected to witness the highest CAGR during the forecast period. Technological advances in the field of AI have resulted in the development of advanced AI software and related software development kits. AI software incorporated in computer systems is responsible for carrying out complex operations. It synthesizes the data received from hardware systems and processes it in an AI system to generate an intelligent response. The software segment is projected to witness the highest CAGR owing to the significance of AI software in strengthening the IT framework to prevent incidents of a security breach.

The North American market is projected to contribute the largest share from 2020 to 2025 in the Artificial Intelligence in Military market

The US and Canada are key countries considered for market analysis in the North American region. This region is expected to lead the market from 2020 to 2025, owing to increased investments in AI technologies by countries in this region. This market is led by the US, which is increasingly investing in AI systems to maintain its combat superiority and overcome the risk of potential threats on computer networks. The US plans to increase its spending on AI in the military to gain a competitive edge over other countries.

The North American US is recognized as one of the key manufacturers, exporters, and users of AI systems worldwide and is known to have the strongest AI capabilities. Key manufacturers of Ai systems in the US include Lockheed Martin, Northrop Grumman, L3Harris Technologies, Inc., and Raytheon. The new defense strategy of the US indicates an increase in AI spending to include advanced capabilities in existing defense systems of the US Army to counter incoming threats.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights4.1 Attractive Growth Opportunities in AI in Military Market4.2 North America AI in Military Market, by Platform4.3 Asia-Pacific AI in Military Market, by Technology4.4 AI in Military Market, by Application4.5 AI in Military Market, by Region4.6 China: AI in Military Market, by Platform

5 Market Overview5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Increased Government Spending on Defense to Improve AI Capabilities5.2.1.2 Development of Specialized AI Chips5.2.1.3 Growing Focus on Advanced C4Isr Capabilities5.2.1.4 Increasing Adoption of AI in Unmanned Vehicles5.2.1.5 Increasing Threats of Cyber Attacks5.2.2 Restraints5.2.2.1 Concerns Over Possibility of Errors in Complex Combat Situations5.2.2.2 Lack of Standards and Protocols for Use of AI in Military Applications5.2.3 Opportunities5.2.3.1 Incorporation of Quantum Computing in AI5.2.3.2 Increasing Adoption of AI in Predictive Maintenance in Military Platforms5.2.4 Challenges5.2.4.1 Absence of Backward Analysis5.2.4.2 Lack of Trained Personnel5.2.4.3 Sensitive Nature of Military Data5.3 Value Chain Analysis of AI in Military Market5.4 Trends/Disruption Impacting Customer Business5.4.1 Revenue Shift and New Revenue Pockets for AI in Military System Manufacturers5.5 Impact of COVID-19 on AI in Military Market5.6 Ranges and Scenarios5.7 Porter's Five Forces Analysis5.8 Regulatory Landscape5.9 Trade Analysis

6 Industry Trends6.1 Introduction6.2 Key Technological Trends in AI in Military Market6.2.1 Need of Quantum AI for Computation of Machine Learning Algorithms6.2.2 5G Networking for Faster Data Transfer6.2.3 Internet of Battlefield Things (Iobt)6.2.4 Blockchain6.2.5 Advanced Analytics6.2.6 Big Data Analytics6.2.7 Artificial Neural Network6.3 Use Case Analysis: AI in Military Market6.3.1 Deployment of a Pictorial Training Tool to Improve Battlefield First-AId Skills from Charles River Analytics6.3.2 C3 AI Readiness: Use of AI Predictive Maintenance in Us AIr Force6.3.3 Have Raider: Deployed to Demonstrate Manned-Unmanned Teaming6.4 Trade Analysis6.5 Impact of Megatrends6.6 Innovation & Patent Registrations

7 AI in Military Market, by Offering7.1 Introduction7.2 Hardware7.2.1 Processor7.2.1.1 Development of Specialized Chips Pave Way for Wider Application of AI in Military7.2.2 Memory7.2.2.1 High Bandwidth Parallel File Systems Increase Efficiency and Throughput of Memory Devices7.2.3 Network7.2.3.1 5G Network Improves Connection Capabilities7.3 Software7.3.1 AI Solutions7.3.1.1 Securonix (Us), IBM (Us), Darktrace (Uk): Major Companies Developing AI Solutions7.3.1.1.1 Cloud7.3.1.1.2 On-Premise7.3.2 AI Platforms7.3.2.1 Demand for Intelligent Applications and Learning Algorithms on the Rise7.4 Services7.4.1 Deployment & Integration7.4.1.1 Used to Create and Deploy Custom Text Analytics7.4.2 Upgrades & Maintenance7.4.2.1 Use of Predictive Maintenance Tools Boosts Segment Growth7.4.3 Software Support7.4.3.1 Periodic Upgradation to Improve Capabilities Drives Software Support Segment7.4.4 Others

8 AI in Military Market, by Application8.1 Introduction8.2 Warfare Platforms8.2.1 Rise of AI in Ew Platforms Boost Segment Growth8.3 Cybersecurity8.3.1 Increasing Cyber-Attacks and Need for Security Drive Segment8.4 Logistics & Transportation8.4.1 Increasing Tactical and Strategic Military Operations Fuel Segment Growth8.5 Surveillance & Situational Awareness8.5.1 Efficiency in Gathering Actionable Intelligence Drives Segment8.6 Command & Control8.6.1 Improve Ability to Gather Data for Better Decision Making8.7 Battlefield Healthcare8.7.1 Segment Driven by New Capabilities That Reduce Battlefield Causalities8.8 Simulation & Training8.8.1 Increasing Investments in Simulation & Training Sector Drive Segment Growth8.9 Threat Monitoring8.9.1 Adoption of AI in UAVs to Assist in Threat Monitoring on the Rise8.10 Information Processing8.10.1 Processing Huge Volume of Data to Gather Valuable Insights Boosts Segment Growth8.11 Others8.11.1 Need to Decrease Downtime Drives Others Segment

9 AI in Military Market, by Technology9.1 Introduction9.2 Machine Learning9.2.1 Deep Learning9.2.1.1 Deep Learning Increasingly Used in Facial Recognition9.2.2 Supervised Learning9.2.2.1 Classification and Regression: Major Segments of Supervised Learning9.2.3 Unsupervised Learning9.2.3.1 Unsupervised Learning Integral to Identifying Patterns in Critical Data9.2.4 Reinforcement Learning9.2.4.1 Reinforcement Learning Used for Autonomous Decision Making in Military Applications9.2.5 Generative Adversarial Learning9.2.5.1 Surveillance and Situational Awareness Applications Widely Use Generative Adversarial Learning9.2.6 Others9.3 Natural Language Processing9.3.1 High Demand for Programming of Computers to Process Natural Language Data9.4 Context-Aware Computing9.4.1 Used for Improvement of Rf Signals and Situational Awareness9.5 Computer Vision9.5.1 Investments in Development of High-Resolution 3D Geospatial Information Systems Boost Segment9.6 Intelligent Virtual Agent9.6.1 Demand for Virtual Identities for Recruitment, Cyber Defense, and Training9.7 Others9.7.1 Increase in Adoption of Speech Recognition and Emotional Recognition

10 AI in Military Market, by Platform10.1 Introduction10.2 AIrborne10.3 Land10.4 Naval10.5 Space

11 AI in Military Market, by Installation Type11.1 Introduction11.2 New Installation11.2.1 Growing Defense Expenditure on AI-Powered Tools and Systems Boosts New Installation Segment11.3 Upgradation11.3.1 Demand for Enhanced Military Capabilities Drives Upgradation of Hardware Components and Software Modules

12 Regional Analysis12.1 Introduction12.2 AI in Military Market: Three Global Scenarios12.3 North America12.4 Europe12.5 Asia-Pacific12.6 Middle East & Africa12.7 Latin America

13 Competitive Landscape13.1 Introduction13.2 Ranking Analysis of Key Market Players, 201913.3 Share of Key Market Players, 201913.4 Revenue Analysis of Top 5 Market Players, 201913.5 Company Evaluation Quadrant13.5.1 Star13.5.2 Emerging Leader13.5.3 Pervasive13.5.4 Participant13.5.5 AI in Military Market Competitive Leadership Mapping (SME)13.5.5.1 Progressive Companies13.5.5.2 Responsive Companies13.5.5.3 Starting Blocks13.5.5.4 Dynamic Companies13.6 Competitive Scenario13.6.1 Market Evaluation Framework13.6.2 New Product Launches and Developments13.6.3 Contracts13.6.4 Acquisitions/Partnerships/Joint Ventures/Agreements/Expansions

14 Company Profiles14.1 Introduction14.2 Key Players14.2.1 Lockheed Martin Corporation14.2.2 The Boeing Company14.2.3 General Dynamics Corporation14.2.4 Rafael Advanced Defense Systems Ltd.14.2.5 Northrop Grumman Corporation14.2.6 Thales Group14.2.7 Raytheon Technologies Corporation14.2.8 Bae Systems plc14.2.9 International Business Machines Corp. (Ibm)14.2.10 Charles River Analytics14.2.11 Caci International Inc.14.2.12 Shield AI14.2.13 Science Applications International Corp. (Saic)14.2.14 Saab Ab14.2.15 Nvidia Corporation14.2.16 Leonardo S.P.A (Leonardo)14.2.17 Soar Technologies Inc.14.2.18 L3Harris Technologies, Inc.14.2.19 Rheinmetall Ag14.2.20 Sparkcognition Inc14.2.21 Leidos Holdings Inc. (Leidos)14.2.22 Safran Sa14.2.23 Honeywell International Inc.14.2.24 Darktrace Limited14.2.25 Sz Dji Technology

15 Appendix15.1 Discussion Guide

For more information about this report visit https://www.researchandmarkets.com/r/bxvolg

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Artificial Intelligence in Military Market by Offering, Technology, Application, Installation Type, Platform and Region - Global Forecast to 2025 -...