AI is rapidly changing the world around us. As AI-like natural language processing (NLP) becomes more robust and useful, its benefits extend to more use cases. While NLP is being implemented more widely in the places we work, shop, and play, there are questions about how to optimize its application.

Central to that optimization will be deciding when to rely on AI vs human analyses. While the two are not exclusive, having clear delineation, especially in workplaces, can help ensure maximum benefit to employee tasks and workflows. For innovation teams, AI can facilitate efficiency, accuracy, and progress at multiple points of the innovation lifecycle in ways humans alone cannot. But NLP is most useful when deployed to enhance rather than replace human skills. Understanding how NLP works can help optimize its use by informing its application.

Understanding AI and NLP

Fundamentally, AI uses data to generate logical decision making algorithms that synthesize, interpret, and combine data to mimic and automate human tasks. Tasks that can be automated with AI range from statistical analyses to performing complex and predictive analyses that mimic human cognition at scale. In the case of NLP, large datasets acquired from publicly or commercially available sources are used to build computer-generated language models.

For AI like NLP to be applied to niche and industry-specific use cases, datasets must be both large and domain-specific. NLP essentially automates the human speech involved in searching databases with varying complexity, from identifying the meaning of single words to the concepts represented by entire phrases. Even the ideation and technology validation workflows of the innovation lifecycle can be automated with AI tools like NLP. This can make the results of search workflows more consistent, reliable, and standardized.

How NLP Search Applications are Built

Datasets like those applied to AI like NLP help machines interpret and mimic human queries. In earlier versions, NLP identified key search terms within phrases to evaluate the meaning of a phrase. It then captured the broadest range of semantically related concepts to those keywords in the context they are used. Advanced NLP applies processes like normalization, tokenization, and stemming to break down a search query into its smallest meaningful parts. NLP technologies like semantic search also apply technologies like text mining, sentiment analysis, and machine translation for results that are conceptually related and reflect searcher intent.

For those who rely on comprehensive database searches for their workflows, NLP helps the computer understand the query and produce results in human terms. Similar to how a fishing net is designed to catch only fish instead of other marine life, NLP instantly sifts through irrelevant text-based data. The result is a faster, more accurate search workflow.

Deploying NLP in Search

Dataset selection and processing can lead to errors, not with NLP algorithms but with the models they are built on. Homonyms, synonyms, colloquialisms, and domain-specific language are difficult for even well-trained NLP systems to discern.

Awareness of these shortcomings requires a multidisciplinary team and company culture that emphasizes common sense approaches to innovation. Employees should replace their own workflows and decision making at the right times. For instance, searchers must be aware that jargon and alternate word meanings can reduce the relevancy of results if they don’t take these into account when searching. Using NLP is an art as well as a science.

Using AI-generated results is an ideal starting point when entering technical domains the searcher is unfamiliar with. Data captured by NLP searches can be used to build a basic knowledge of a domain, acting as a starting point for more in-depth searches and analyses later on. For IP researchers, understanding both technology and business landscapes with full IEEE, corporate tree, and legal data can go a long way to making confident and informed decisions. Humans are supported by AI-enhanced workflows to develop and commercialize technology, without being an expert in every pertinent area.

How NLP Benefits Innovation

As an innovator considering adding AI to idea evaluation, idea validation, and patent search workflows, you’ll want to consider where NLP can best be applied. IP.com has spent years training its AI in-house with the most up-to-date and reliable datasets to produce its NLP algorithms. The results are clear. Semantic search, especially when used in conjunction with data visualization and document summary tools, is a highly effective method for achieving results across the innovation lifecycle. Capture relevant data in the domains of prior art, law, and ownership with just a handful of semantic AI searches. These insights can be used to make informed strategic decisions about what technologies to pursue, pushing more defensible and patentable technology further down the innovation lifecycle.

Applying NLP to Innovation Workflows

AI has substantial benefits over manual workflows. IP.com’s AI-powered innovation suite can improve success rates and keep more of the innovation lifecycle in-house. Outside IP consulting firms performing these workflows manually can be more error-prone and generally require more office actions by engineering and support teams whose time is better spent on innovation-specific tasks. While companies see success rates of 70% or lower when relying on manual processes and outside firms, AI-enhanced innovation workflows regularly engender success rates of over 90%. Achieving more innovation objectives means producing high-quality invention disclosures, defensible publications, and more patented technologies. But success is about more than relying on cutting-edge NLP technology. It is about the deployment of AI on platforms that are designed to be intuitive and human-centric.

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• Source: https://ip.com/blog/intuitive-technologies-that-drive-successful-human-ai-interaction/