The world around us is rapidly changing as a result of AI. As natural language processing (NLP) becomes more robust and useful, its applications expand. While NLP is becoming more widely used in places where we work, shop, and play, there are still questions about how to optimize its application. For successful Human-AI interaction, the decision of when to rely on AI vs. human analysis will be central to that optimization. While the two are not mutually exclusive, having clear distinctions, especially in workplaces, can help ensure that employee tasks and workflows are as beneficial as possible. AI can help innovation teams improve accuracy, efficiency, and progress at multiple stages of the innovation lifecycle. However, NLP is most effective when used to supplement rather than substitute human skills. Understanding how NLP functions can help optimize its application by informing its use.
Understanding AI and NLP
Fundamentally, artificial intelligence (AI) uses data to develop logical decision-making algorithms that interpret, synthesize, and combine data in order to imitate and automate human tasks. AI can automate tasks ranging from statistical analysis to complicated and predictive analysis that imitate human cognition at scale. In the case of NLP, large datasets obtained from commercially or publicly available sources are utilized to build computer-generated language models.
For AI, such as natural language processing (NLP), to be applied to niche and industry-specific use cases, datasets need to be both large and domain-specific. NLP primarily automates the human speech used to search databases of varying complexity, from determining the meaning of single words to understanding the ideas represented by entire phrases. AI tools such as NLP can even automate the ideation and technology validation workflow processes of the innovation lifecycle. This can improve the consistency, reliability, and standardization of search workflow results.
How are NLP Search Applications Developed?
Datasets used in AI, such as NLP, assist machines in interpreting and mimicking human queries. Previously, NLP identified key search terms within phrases to assess the meaning of a phrase. It then extracted the most semantically related ideas to those keywords in the context in which they are used. To break down a search query into its smallest meaningful parts, advanced NLP processes such as normalization, tokenization, and stemming are used. NLP technologies such as semantic search use text mining, sentiment analysis, and machine translation to produce results that are conceptually related and depict the searcher’s intent.
For those whose workflows rely on extensive database searches, NLP assists the computer in understanding the query and producing results in human terms. NLP sifts through meaningless text-based data in real time. It is much like a fishing net designed to catch only fish and not other marine life. As a result, the search workflow is faster and more accurate.
Using NLP in Search
Errors in dataset selection and processing can occur not with NLP algorithms but rather with the models on which they are based. Even well-trained NLP systems have difficulty distinguishing between synonyms, homonyms, and domain-specific language.
Recognizing these flaws requires a multidisciplinary team as well as a company culture that values common sense and innovation. At the appropriate times, employees should substitute their own workflows and decision-making. For example, searchers should be aware that jargon and alternate word meanings can decrease the actual relevance of results if they are not considered when searching. Using NLP is both an art and a science.
When entering technical domains that the searcher is unfamiliar with, using AI-generated results is an excellent starting point. Data retrieved from NLP searches can be used to gain a basic understanding of a domain, serving as a springboard for more in-depth searches and analysis later on. Understanding both the business and technology landscapes can help IP researchers make confident and informed decisions. As an illustration of Human-AI interaction, AI-enhanced workflows assist humans in developing and commercializing technology without requiring them to be experts in every relevant area.
Benefits of NLP for Innovation
You should think about where NLP can be used most effectively as an inventor who is thinking about incorporating AI into the workflows for idea validation, idea evaluation, and patent search. To develop its NLP algorithms, XLSCOUT has trained its AI using the most recent and trustworthy datasets. The outcomes are obvious. Semantic search is a very powerful tool for getting results across the innovation lifecycle, especially when used in conjunction with data visualization and document summary tools. With just a few semantic AI queries, you may get pertinent information in the areas of prior art, legality, and ownership. By moving more defendable and patentable technology further along the invention lifecycle, these insights can be leveraged to make strategic choices about what technologies to pursue that are well-informed.
NLP Application in Innovation Workflows
AI outperforms manual processes in many ways. XLSCOUT’s AI-based tools can increase success rates and keep more of the innovation lifecycle in-house. The manual execution of these workflows by outside IP consulting firms can be more error prone. Furthermore, it necessitates more office activities by engineering and support teams, whose time would be better spent on innovation-related tasks. Corporations that rely on manual processes and outside companies typically see success rates of 70% or less, but AI-enhanced innovation workflows regularly produce success rates of over 90%. Additional patented technologies, strong publications, and high-quality invention disclosures are all necessary to meet more innovation goals. However, achieving success requires more than just depending on cutting edge NLP technology. It involves the application of AI on user-friendly, human-centered platforms.
The AI steers the user away from insignificant keywords, identifies adjacent technologies, and provides a technological concept evaluation. A person would need hours to finish these tasks. The user now has more time to devote to activities where AI falls short, such as creativity, innovation, and intuition. It’s yet another example of human-AI interaction.
Workflow Comparison: Human-AI Interaction
Human-AI interaction in workflows: To highlight the successes of AI-augmented workflows, it is necessary to understand particular innovation workflows. You also need to understand when it is best to rely on humans or AI. At times, teams that expect NLP tools to replace their own decision-making may improperly use AI’s obvious benefits. XLSCOUT’s following use case can show why a balanced strategy is effective.
Using NLP for Comprehensive Patent Searches
A crucial innovation workflow task that involves human-AI interaction is evaluating the existing prior art. Using our proprietary database, users can defend their freedom to operate by making defensive publications available in the public domain. Using our AI-based tools like Novelty Checker, users can conduct thorough novelty/ prior art searches on this database. It also aids in determining the technical, legal, and economic limitations of competing technologies and assessing risks. Once all relevant prior art has been discovered, users can quickly assess critical data using visualization tools like Techscaper or Company Explorer to determine a technology’s key features and overall viability.
