Uncertainty is a persistent characteristic of the modern economy.

Uncertainty is a persistent characteristic of the modern economy. Companies and their in-house counsel face daunting challenges to guide the organization from assuring compliance in the presence of a plethora of changing regulations, to managing risk in the diverse intellectual property estate that supports the value of the firm, to defending against the threat of litigation from many different participants, assuring the future viability and growth of the firm. The times have changed – gone are the days when life was simple and decisions based on static information were adequate. As part of the senior management team, in-house counsel has as much responsibility now to anticipate and manage all risks faced by the firm in a fashion that enhances shareholder value.

Recent advancements in science and technology have all but assured that the value of any firm is largely based on the intellectual property it creates, nourishes, manages, and protects. Ideas could be as valuable as technologies and trade secrets more powerful than patents. Managing a portfolio of such diverse intellectual property positions – some advertised and others not, some tangible and others not and some static and others emerging – is not an easy task. The interactions among these IP positions could be an important aspect of the IP estate management as the value and risk of individual positions cannot be disconnected from the rest of the portfolio. The perennial question for in-house counsel is how they could assure that the actions taken (or not taken) in every IP position by the company is maximizing value. This is not an internal question, for the value of an IP position largely depends on what exist in the market and what could be emerging. Keeping track of technology trends and competitor actions in a globally integrated economy with concentrated political and regime risks is not a manual task anymore. To top this off, technologies are converging, making products and applications an extremely complex bundle of protected IP that emanated from academic institutions to foreign governments. Recent trends in public domain software and know-how with a variety of licenses make it even more challenging to understand who owns what and the legal implications of actions and decisions pursued by the company.

If the external complexity was not enough, corporations constantly trying to become more efficient by cutting costs and applying technologies have put pressure on in-house counsel to better estimate the resources required to manage their portfolio of activities. Uncertainty in every aspect of activities conducted by the counsel – costs, timelines, the chance of success, and ultimate benefits – make it immensely challenging for counsel to estimate and manage what is needed. Budgetary processes are often complex in large organizations and typically focus on operating activities driven by demand seen in the previous time-period. However, the volatility in requirements and actions facing a legal counsel is likely a lot higher than other parts of the organization, making this a hard problem to solve. To make matters worse, the resources needed to execute are not just internal but come from many external sources including consultants and experts. Even internal actions, such as the timing of a patent filing, depend a lot on external factors. Changes in regulatory regimes bring discontinuous effects in resource requirements for in-house counsel and these often cannot be estimated using traditional tools and processes. This is a perfect storm – high variability in required resources for complying with changing regulations and high uncertainty in how the risk and value are changing in IP positions, making the job of in-house counsel challenging but interesting and rewarding.

It is in this regime that artificial intelligence can substantially help in-house counsel to cut through the clutter. Artificial intelligence is an emerging area that combines the latest ideas in machine- and deep-learning that operate on any available data to make predictive models that feed into market-based economic modeling to answer questions on risk and value. Recent advancements in computer technologies have provided opportunities to apply established mathematics in this area in a practical fashion. For example, models can be built to predict any event at a patent level – such as approval, infringement, maintenance, and others. These models, working from the cloud, can provide continuous predictions on every event at a patent level. These probabilistic predictions can be accumulated at the portfolio level, giving a dynamic view into how the IP estate value and risk are changing as well as what actions may be most optimal. For example, if the model’s prediction of chance of infringement on a particular patent is high, in-house counsel may proactively intervene to reduce the risk. Similarly, if the chance of approval for a filed patent is low, actions can be taken to improve the odds. More generally, these models can give guidance on the timing and design of patent applications and actions to maximize portfolio value. Models can also forecast the type and number of legal actions that are likely at the patent level, providing real time transparency into the legal risk carried by the firm within categories, locations and overall portfolio. In the modern economy, where the value of the firm is largely driven from the underlying IP positions, the risk of the IP portfolio has direct implications for shareholder value. As such, in-house counsel is a critical link in communications with markets and shareholders externally and with corporate finance internally to forecast, estimate and monitor firm performance. Instituting a systematic value and risk measurement and monitoring system for the IP portfolio can ease the burden of in-house counsel in day-to-day management and designing strategic actions to maximize firm value.

On the resource management side, models are available to predict and forecast the resources needed for anticipated actions and projects. Using historical data, machine learning algorithms can continuously predict required internal and external resources. Such predictions are useful not only for budgeting and internal management but also to understand the risk of not being able to meet certain requirements within allowed time frames. Since these models are operating from the cloud continuously, they can provide warnings if certain projects and actions are not progressing as anticipated. This is especially useful as a performance monitoring system keeping in-house counsel constantly appraised of overall time and cost risk carried by the firm. In situations where a regulatory change could impact a large number of actions by in-house counsel, the models can provide scenario expectations in different regulatory and political outcomes. This may allow in-house counsel to design and manage contingency plans in the presence of discontinuous regime changes. Since the variability in resource needs for in-house counsel is likely higher than many operating departments that have smother demand expectations, the ability to forecast resource needs better could benefit the budgetary and allocation processes in the overall firm.

In a changing and dynamic world, driven by real time information and processes, traditional techniques of monitoring and managing costs, risks and value are all but obsolete. However, recent advancements in technology, analytics and economics allow us to provide superior and robust ways to tackle this for in-house counsel in companies of any size or complexity.

Authored by:

Gill Eapen
CEO – Decision Options, LLC