{"id":23654,"date":"2021-08-21T14:44:00","date_gmt":"2021-08-21T14:44:00","guid":{"rendered":"https:\/\/sagaciousresearch.com\/blog\/?p=23654"},"modified":"2023-04-03T11:13:03","modified_gmt":"2023-04-03T11:13:03","slug":"ip-watchdog-drafting-ai-patents-challenges-and-solutions","status":"publish","type":"post","link":"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions","title":{"rendered":"IPWatchdog &#8211; Drafting AI Patents: Challenges and Solutions"},"content":{"rendered":"\n<p>Artificial Intelligence (AI) is the latest buzzword across all sectors. Every tech and non-tech company is vigorously filing, strategizing or planning to enter the AI patent domain. However, the journey is not as easy as it may seem. While drafting AI-based patent applications, drafters often face challenges in formulating the right strategy for writing claims and identifying the correct scope of the application. Thus, it\u2019s important to know the challenges in detail and to develop practical solutions for drafting a patent-worthy application.<\/p>\n\n\n\n<p>Before moving on to the challenges, let us first understand the key aspects of AI-based inventions or patent applications. It is imperative to identify the correct combination of these aspects so that an invention is suitably protected. Not all AI patent applications emphasize all three aspects, although they might be indirectly related. The three aspects are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Algorithm \u2013 An AI related model (or algorithm) carries out intelligent processing.<\/li>\n\n\n\n<li>Training Methodology \u2013 A training methodology and its underlying training dataset enables the \u201cintelligence\u201d in the AI related model.<\/li>\n\n\n\n<li>An application or product \u2013 Uses trained AI model for carrying out a real-world or \u201ctangible\u201d phenomena with improved efficiency.<\/li>\n<\/ul>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69db4b2f8ce44\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69db4b2f8ce44\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Challenges_Related_to_Drafting_AI_Patent_Applications\" >Challenges Related to Drafting AI Patent Applications<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Understanding_Challenges_with_the_Help_of_Case_Studies\" >Understanding Challenges with the Help of Case Studies<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Case_Study_1_Assuming_an_invention_pertains_to_the_training_of_a_neural_network_and_novelty_lies_in_the_way_it_is_trained\" >Case Study 1:&nbsp;Assuming an invention pertains to the training of a neural network and novelty lies in the way it is trained.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Case_Study_2_Lets_assume_that_the_invention_related_to_training_of_the_neural_network_and_execution_of_the_neural_network_for_a_specific_application_is_claimed\" >Case Study 2: Let\u2019s assume that the invention related to training of the neural network and execution of the neural network for a specific application is claimed.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Case_Study_3_Lets_assume_that_the_invention_is_related_to_execution_of_a_neural_network_and_a_claim_covering_proposed_execution_of_neural_network_is_framed\" >Case Study 3: Let\u2019s assume that the invention is related to execution of a neural network and a claim covering proposed execution of neural network is framed.<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Final_Thoughts\" >Final Thoughts<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/sagaciousresearch.com\/blog\/ip-watchdog-drafting-ai-patents-challenges-and-solutions\/#Having_Queries_Contact_Us_Now\" >Having Queries? Contact Us Now!<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Challenges_Related_to_Drafting_AI_Patent_Applications\"><\/span>Challenges Related to Drafting AI Patent Applications<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>While drafting, it is vital to identify which of the three aspects should be covered solely or in combination when seeking legal protection. Depending on the approach chosen, the patent drafter may face one or more of the three main challenges:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Patent Ineligibility<\/strong>: The invention or process stands the risk of being considered as abstract and patent-ineligible. This implies that it may get classified as an \u201cabstract idea\u201d by virtue of implementing a series of steps or operations that could possibly have been carried out in \u201cmind\u201d or by using \u201cpen and paper\u201d. For example, complex mathematical calculations.<\/li>\n\n\n\n<li><strong>Divided Infringement<\/strong>: Divided infringement refers to the issue wherein multiple parties are required to infringe on all the clauses mentioned in an independent claim. Thereby, it makes the invention or the process less profitable for the assignee.<\/li>\n\n\n\n<li><strong>Infringement Detectability<\/strong>: Detectability refers to the ease with which infringement of a patent by a product or service can be detected. Difficulty in detecting the infringement lowers commercialization potential of the underlying patent.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Understanding_Challenges_with_the_Help_of_Case_Studies\"><\/span>Understanding Challenges with the Help of Case Studies<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Case_Study_1_Assuming_an_invention_pertains_to_the_training_of_a_neural_network_and_novelty_lies_in_the_way_it_is_trained\"><\/span><strong>Case Study 1<\/strong>:&nbsp;Assuming an invention pertains to the training of a neural network and novelty lies in the way it is trained.<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In this scenario, from a novelty perspective, a claim covering the training procedure should be framed. However, this approach has few shortcomings that should be considered:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong><u>Claim infringement:<\/u><\/strong><\/li>\n<\/ol>\n\n\n\n<p>Conventionally, the training of the neural network is done offline in research labs, not in real-time. For example, let\u2019s assume a neural network is trained for picking up an object and is further implemented on a robot. In this case, what one can see in real-time is the robot picking up an object. Here, the task performed is the result of the invention, not the invention itself.<\/p>\n\n\n\n<p>So, one can\u2019t detect infringement because the claimed training is generally done offline. Therefore, the difficulty in detecting infringement makes it hard to file a lawsuit.<\/p>\n\n\n\n<ol class=\"wp-block-list\" start=\"2\">\n<li><strong><u>Patent-eligible subject matter:<\/u><\/strong><\/li>\n<\/ol>\n\n\n\n<p>The claim plainly covering the training procedure is not considered to be patent-eligible subject matter. Such a claim is highly prone to 35 U.S.C. \u00a7 101 rejection in the United States. The United States Patent and Trademark Office (USPTO) may assert that the claim is directed towards an abstract idea.<\/p>\n\n\n\n<p>In order to mitigate such problems, one must frame the claim in accordance with the USPTO\u2019s&nbsp;<a href=\"https:\/\/www.uspto.gov\/sites\/default\/files\/documents\/101_examples_37to42_20190107.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">example 39<\/a>. Similarly, in specifications, one must meticulously describe how the claimed training procedure solves problems related to training of the neural network. For example, it requires less training data, can be trained with different data (e.g., labelled and unlabelled), and allows the neural network to be trained faster. Lastly, describe the practical application of the trained neural network, i.e., showcase its utility.<\/p>\n\n\n\n<p>While the aforesaid solutions may solve the patent-eligible subject matter problem, the shortcoming in terms of claim infringement persists. This is the drawback of this case.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Case_Study_2_Lets_assume_that_the_invention_related_to_training_of_the_neural_network_and_execution_of_the_neural_network_for_a_specific_application_is_claimed\"><\/span>Case Study 2: Let\u2019s assume that the invention related to training of the neural network and execution of the neural network for a specific application is claimed.<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Using this approach has certain advantages. First, the utility of the neural network is directly outlined in the claim, which might make it patent-eligible subject matter. Second, when the applicant claims the execution of the neural network, the infringement detectability is improved. This is because it can be seen in real-time. However, there are certain drawbacks of this approach as well:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong><u>Inactive recitation of active steps of invention<\/u><\/strong><\/li>\n<\/ol>\n\n\n\n<p>Generally, in such a claim, the training procedure is claimed using \u201cwherein clauses\u201d. This is because the training is done offline and the execution takes place in real-time. For example, \u201cwherein the neural network is trained based on [enter training aspects].\u201d<\/p>\n\n\n\n<p>It is evident that the training process, where the novelty lies, is not claimed actively. Furthermore, claiming inactively yields the impression that there already exists a pre-trained neural network that is now being used to execute a particular workflow. As a result, the claim interpretation may incline towards the way of executing the neural network rather than the way of training the neural network.<\/p>\n\n\n\n<ol class=\"wp-block-list\" start=\"2\">\n<li><strong><u>Difficult infringement enforcement due to divided infringement<\/u><\/strong><\/li>\n<\/ol>\n\n\n\n<p>If one claims execution of the neural network, along with actively claiming the training aspect, it may lead to divided infringement. For instance, let\u2019s assume that the neural network is trained by company X, and it is integrated with a robot manufactured by company Y. This implies that the robot manufactured by company Y is receiving and processing the input with the neural network produced by company X. So, it results in divided infringement. The involvement of two entities makes the enforcement of infringement difficult and expensive.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Case_Study_3_Lets_assume_that_the_invention_is_related_to_execution_of_a_neural_network_and_a_claim_covering_proposed_execution_of_neural_network_is_framed\"><\/span>Case Study 3: Let\u2019s assume that the invention is related to execution of a neural network and a claim covering proposed execution of neural network is framed.<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Take, for example, that the invention may be related to a particular way of using a known neural network (e.g., autoencoder). It can be used for a specific application or modification to the structure of neural network. Furthermore, it can be utilized for transforming input data to a new form, such that the transformed data can be used with known neural networks for a specific application. In this case, a claim that covers proposed execution of the neural network can be framed.<\/p>\n\n\n\n<p>The purpose behind using such a claim is that it clearly outlines practical application and is less susceptible to a Section 101 rejection. Also, the claim includes aspects that are detectable in real-time, which resolves the infringement detection problem.<\/p>\n\n\n\n<p>However, this claim may also suffer from the limitation of being too narrow, as it is focused on a specific application using a specific type of neural network only.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Final_Thoughts\"><\/span>Final Thoughts<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>There is no one-size-fits-all solution when it comes to drafting claims of a patent application in the AI realm. A practitioner must weigh the pros and cons of each approach outlined above, and then choose the right one, keeping in mind their client\u2019s strategy, budget and core competencies.<\/p>\n\n\n\n<p>Realizing that every approach has certain drawbacks and benefits, one should adopt a strategy to protect the invention in light of future planned research and the business strategy of the assignee and\/or inventors. Furthermore, always find where the novelty lies and formulate a strategy to claim the invention accordingly. Lastly, no matter what kind of AI invention it is, always describe the practical application of the invention in claims and specifications. In addition to that, describe a technology-centric solution to a problem or technical improvements provided by the invention to make it patent-eligible subject matter.<\/p>\n\n\n\n<p>Originally published on &#8211; <a href=\"https:\/\/www.ipwatchdog.com\/2021\/08\/21\/drafting-ai-patents-challenges-solutions\/id=136929\/\">https:\/\/www.ipwatchdog.com\/2021\/08\/21\/drafting-ai-patents-challenges-solutions\/id=136929\/<\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Having_Queries_Contact_Us_Now\"><\/span>Having Queries? Contact Us Now!<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\nvar gform;gform||(document.addEventListener(\"gform_main_scripts_loaded\",function(){gform.scriptsLoaded=!0}),document.addEventListener(\"gform\/theme\/scripts_loaded\",function(){gform.themeScriptsLoaded=!0}),window.addEventListener(\"DOMContentLoaded\",function(){gform.domLoaded=!0}),gform={domLoaded:!1,scriptsLoaded:!1,themeScriptsLoaded:!1,isFormEditor:()=>\"function\"==typeof InitializeEditor,callIfLoaded:function(o){return!(!gform.domLoaded||!gform.scriptsLoaded||!gform.themeScriptsLoaded&&!gform.isFormEditor()||(gform.isFormEditor()&&console.warn(\"The use of gform.initializeOnLoaded() is deprecated in the form editor context and will be removed in Gravity Forms 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id='input_203_17' class='large gfield_select'    aria-required=\"true\" aria-invalid=\"false\" ><option value='' >Please Select Country<\/option><option value='United States' >United States<\/option><option value='Canada' >Canada<\/option><option value='United Kingdom' >United Kingdom<\/option><option value='China' >China<\/option><option value='India' >India<\/option><option value='North Korea' >North Korea<\/option><option value='South korea' >South korea<\/option><option value='United Arab Emirates' >United Arab Emirates<\/option><option value='Afghanistan' >Afghanistan<\/option><option value='Albania' >Albania<\/option><option value='Algeria' >Algeria<\/option><option value='Andorra' >Andorra<\/option><option value='Angola' >Angola<\/option><option value='Antigua and Barbuda' >Antigua and Barbuda<\/option><option value='Argentina' >Argentina<\/option><option value='Armenia' >Armenia<\/option><option value='Australia' >Australia<\/option><option value='Austria' 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