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Life sciences industry must adopt strategies for IP protection to define, claim and safeguard their innovations.
FREMONT, CA: Artificial intelligence (AI) is a fast-growing area of the life sciences industry, with associations in drug development, biotechnology, medical diagnosis, clinical trials, precision and customized medicine, and patient monitoring. The wide availability of "big data" is likely to be responsible for increasing AI use in this area. Machine learning, deep learning, and natural language processing are examples of AI technologies that can be used to prepare large data sets to discover novel medication candidates, refine drug dosage, recognize suitable patients for drug trials, and diagnose diseases.
Life sciences organizations must take specific actions for their AI-related IP protection.
Contractually defined IP rights
Life science firms that use AI can avoid IP ownership challenges by establishing who owns AI-related IP rights in employment, licensing, or purchase agreements. Such agreements must also state who will control the data, information, or outcomes produced by AI, who will be able to use it, and how they will be utilized.
Putting AI inventions under trade secret protection
Aside from typical contractual IP safeguards, life sciences businesses should consider what kind of IP protection is appropriate for their AI-related inventions. In comparison to standard patent protection, trade secret protection can be advantageous. However, companies using this approach should be aware that trade secret protection is only maintained if the owner takes "reasonable means" to keep the information secret. .
Given the enormous number of people involved in discovery, product development, regulatory, and manufacturing operations, this demand for confidentiality may be challenging for life sciences organizations to meet. Thus, companies should take appropriate safety measures
Inventorship: Any possible inventorship dispute can be avoided by preparing patent applications so that human inventors are required. For example, patent applications can be drafted to describe human input in AI systems' design, training, and execution, and patent claims can be drafted to include human actions and processes.
Acquire a Patenting AI Playbook: Companies must follow the steps in the "AI-Patent Playbook" to seek patent protection for AI-related ideas as follows.
Enablement and Written Description Challenges: Patentees must provide enough information about their invention available to the public so that someone with ordinary competence in the art can visualize and practice the claimed invention. When AI has aided in identifying vast genera of substances or when the AI approach is stated to include extensive "black box" computer programming, meeting such standards can be difficult. Practitioners should give as much detail as feasible in describing the claimed inventions when making claims using machine learning or deep learning.
Subject matter eligibility: Patent practitioners should take action today to reduce the likelihood of future AI-related life science claims being rejected or invalidated. One strategy is to write claims that apply the AI's abstract idea or mental process in a specific or unusual way to get a practical result.
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