Cost Savings: Automate Some of the Most Time-Consuming Aspects of Patent Drafting

An effective patent application can be one of the smartest investments a company or inventor can make; however, its creation can be lengthy and time-consuming.

Good news – much of the mechanical aspects of patent drafting can now be automated! Current drafting automation tools provide highly accurate output while redirecting hours of copy-and-paste work toward substantively improving a patent application.

Automating some of the most time-consuming aspects of patent drafts can lead to significant cost savings and increased efficiency. Here are some areas where automation can make a notable difference:


Patent claims are an integral component of patent application processes, not only providing details about an invention but also serving as the basis for evaluation by patent examiners. Drafting effective claims takes experience and time – therefore this part should not be left for novices!

One way to cut costs is through automating the patent claims drafting process. This can be achieved using patent language generation tools that produce accurate, consistent output tailored to an attorney, firm, or client’s drafting style and preferences. Furthermore, such tools help streamline drafting and analysis tasks so practitioners can focus their efforts on substantive work to increase patent application quality.

Automating claim drafting can provide significant cost savings. For instance, validating and maintaining a European patent under the London Agreement over 15 years would incur total transactional costs estimated to be EUR 11 850; these costs could potentially be lower with more specialised service providers; by comparison a bundle of national patents over this same timeframe would total EUR 13 990 (excluding translation fees).

Reducing costs may also mean narrowing claims. Broader patent claims tend to be more costly because their broadness allows an examiner to cite prior art from multiple technical fields; additionally, applicants will likely be forced to file numerous office actions and responses in response to examiner rejections.

Narrower patent claims tend to be less costly to prosecute, easier for potential competitors to design around and have minimal deterrence effect for copycat products. A patent attorney should only introduce narrow claims when there is an obvious and compelling justification for doing so.

Companies and patent professionals alike can implement these strategies to both improve the quality of patent applications and save money throughout their processes. By eliminating errors and streamlining drafts, patent applicants can focus on strategic work that adds real value for clients.


Patent drafters must often produce high-quality work with limited resources and reduce fees to satisfy clients. This often leaves drafters feeling overburdened and frustrated; fortunately, many time-consuming aspects of patent drafting can be automated by using advanced software tools that automate tedious tasks such as adjusting claim numbers and dependencies, incorporating details from claims into abstract or summary sections, writing overall technology descriptions and eliminating grammatical errors while improving consistency when describing inventions.

As much as an automated tool can’t replace legal expertise, automation tools can significantly cut drafting time by eliminating repetitive and error-prone tasks that take up valuable drafters’ time and can increase quality of final applications. Furthermore, automation tools may save on redrafting costs by decreasing revision needs during USPTO prosecution processes.

An effective patent application comprises several different components, such as claims, descriptions and drawings. Each aspect requires its own format and style – which can be standardised to speed up the process. Furthermore, many administrative tasks can be automated so patent drafters can focus on creativity rather than administrative details.

Power Patent’s software, for example, can quickly and effortlessly generate a complete patent specification, diagram and abstract with minimal input from you. Furthermore, this program checks for consistency and grammatical errors in the text while automatically adapting it to match USPTO requirements – ultimately leading to an impressive professional-looking application more likely to receive approval by USPTO.

ClaimMaster’s office action response automation can also save time for patent professionals by helping them analyze and respond more quickly to office actions from the USPTO. The tool extracts pertinent arguments from patent archives and generates office action responses incorporating them – significantly decreasing draft time as well as back-and-forth with examiners.


Patent application drafters can automate the creation of claims supporting their invention with just a few clicks, saving both time and energy by focusing on substantive work rather than repetitive copy-pasting tasks.

Patent application drafters can utilize these tools to quickly generate a specification and figure list with just a click, freeing them up to focus on the details of an invention while meeting drafting standards and producing quality work. Utilizing automation tools can free up precious time that could otherwise be spent fleshing out more details of an invention, exploring alternative embodiments or telling an engaging narrative about how its technical problem-solving qualities.

Drafting a patent application for a new medical device often requires extensive explanation of its functions, materials used and differences from existing devices. Such details will help the examiner comprehend the application more readily while assuring eligibility of patenting; however, creating such descriptions takes considerable time and energy.

Utilizing automation tools can save patent professionals hours of time and allow them to focus on more substantive tasks, like analyzing applications for weaknesses. Such tools can screen for antecedent basis errors, inconsistent claims/part numbers/amendments/status indicators as well as numerous other mistakes – as well as assist them in creating high-quality applications capable of withstanding enforcement challenges.

Automation tools offer another advantage by being customizable to any attorney, firm or client’s style and preferences when drafting patent applications. This customization makes a considerable difference to the quality of work produced.

Patent professionals are revolutionizing how they perform their duties with these revolutionary tools, automating tedious, repetitive tasks so they can focus more time and attention on core elements of applications and ensure quality products, increasing IP portfolio value while decreasing litigation risks over patent rights.

Text Extract

 First, you need to extract relevant text from patent documents. This can be achieved using Optical Character Recognition techniques (OCR) for scanned documents, or Natural Language Processing (NLP), for digital texts.


 After the text has been extracted, preprocessing is performed to remove noises, formatting and non-textual components. The data used for the analysis will be clean and meaningful.

Natural Language Processing

 NLP techniques will be applied to the text after it has been pre-processed to identify its context, key sections and extract important information such as the title, abstract, claim, and description.

The heart of automation is using summarization algorithm, such as TextRank or BERT-based model, to produce concise and coherent summaries. These algorithms are used to rank sentences and phrases according to their relevance and importance in relation with the main idea of a patent.

NLP is used in a wide range of applications, including language translation, sentiment analysis and chatbots. Natural Language Processing Key Components:


  Tokenization refers to the process of breaking a text down into smaller units such as words or phrases. These tokens are the building blocks of NLP analysis.

Tagging of Parts of Speech:

The POS tag involves assigning an grammatical class (e.g. noun, verb or adjective) to every token within a sentence. This information is useful in understanding the syntactic structures of the text.

Named entity recognition (NER)

 NER is a method of identifying and classifying named entities, such as people’s names, organization names, places, dates and other items. This allows for the extraction of essential information from unstructured texts.


 Parsing is the process of analyzing a sentence’s syntactic structure to determine the relationship between words, and their roles within the sentence.

Word Embeddings

Word embeddings are dense vector representations, in which words with similar meanings appear closer together within the vector space. Word embeddings are created using popular techniques such as Word2Vec or GloVe. These techniques improve the performance of NLP models by capturing semantic relationships among words.

Sentiment Analyse

 Sentiment analyses are used to determine the emotional tone of a text and whether it’s positive, negative or neutral. This application is useful for customer feedback analysis and social media monitoring.

Machine translation

 NLP is a key component of machine translation systems such as Google Translate that automatically translate text from one language into another.

Text Summary

 You can use NLP techniques to condense long texts into short, coherent summaries.


NLP powers systems that can understand user queries and provide relevant responses. Chatbots, voice assistants such as Siri and Alexa are examples.

Natural Language Processing Challenges:

  • Ambiguity : The human language is often ambiguous. Words can have multiple meanings, depending on context. NLP models face a difficult task in resolving ambiguity.
  • Homonymy and Polysemy: The term homonymy is used to describe words that have different meanings. It is difficult to distinguish between these different meanings.
  • Words that are not in the Vocabulary: NLP Models struggle with words that are not included in their training data. It is a constant challenge to handle new or rare words.Context understanding Understanding context is important in NLP. The meaning of a sentence or word can change depending on the surrounding text. Large data requirements Many NLP models need large amounts of training data, which can be expensive to compute and make them unsuitable for smaller datasets.
  • Fairness and Bias NLP models may inherit biases from the data used for training, which can lead to biased results and perpetuate societal prejudices.

Extraction of Key Information

In addition to summarizing, automated systems are able to extract specific information such as the date of filing, names and application numbers, priority details, classifications, and inventors.


 After the summary has been generated, and the key information extracted, the summary can be presented using various formats such as bullet points or short paragraphs.

Quality assessment

 To ensure that generated summaries will be accurate and reliable, quality assessment techniques must be used. It could involve a human review or comparing the automated summaries with manually created ones.

Maintenance and Updating 

The patent database is constantly updated with new documents. The automation process must be updated and maintained to keep up with changes in patent formats and regulations.


Patent applications often involve extensive and time-consuming work. Even small errors can create substantial difficulties down the road, so highly experienced patent professionals must spend considerable time and energy writing high-quality applications for submission.

Patent drafting automation tools can assist with some of the repetitive and monotonous work associated with new application preparation. They offer attorneys and patent service providers language generation software which reduces writing application documents allowing more time for substantively improving an application.

Automating patent drafting involves several strategies, but three popular techniques include natural language processing techniques, custom templates and simple copy and pasting. These technologies can reduce hours of drafting time for every application while being customized to match an attorney, firm or client’s unique style and preferences.

Patent drafting automation tools can save money when it comes to patent application fees. Aside from saving time, these automated tools help patent professionals avoid costly errors that result from manual drafting processes – typos to errors in diagrams can all be prevented with these automated tools.

Patent application drafting automation tools can also help prevent costly rejections by USPTO examiners, which is both an inconvenience for patent applicants as well as significantly increasing costs to prepare and file an application. Therefore, investing in such an automation tool is highly advised as they offer critical quality control checks as well as error reports to assist patent applicants and help avoid rejections altogether.

Numerous market forces are compelling patent practitioners to change the way they approach patent drafting. With fees continuing to decrease and quality concerns drawing national attention, patent application drafters find their time limited and under pressure to deliver higher-quality applications. Drafting automation tools offer patent professionals a great way to save time while providing more value to clients.