MEMS (micro-electrical mechanical systems) gyroscopes, robotics, and related algorithms bring back fond memories of my technical work in the R&D realm. These devices are truly amazing and are still at the forefront of advanced technology. A recent application by the University of Michigan, with funding from DARPA (Defense Advanced Research Projects Agency) relates to robotic applications of MEMS sensors, particularly for dead-reckoning, using an advanced machine learning approach to improve the quality of low cost MEMS sensors. Claim 1 is reproduced below:

1. A method for determining rotational rate of a movable member using an array of inertial sensors, the method comprising:
defining a hidden Markov model (“HMM”), wherein hidden states of the HMM represent a discrete value measurement of the rotational rate of the movable member, and transition probability of the HHM accounts for a motion model of the movable member, and observation probability accounts for noise and bias of at least one of the inertial sensors in the array of inertial sensors;
receiving, by a processor, input from the array of inertial sensors;
determining, by the processor, the rotational rate of the movable member by solving for an output of the HMM using the input received from the array of inertial sensors; and
controlling, by the processor, motion of the movable member using the rotational rate of the movable member.

During prosecution, the examiner withdrew prior art rejections, but maintained Section 101 rejections of all the claims, forcing the University to appeal. According to the examiner, the first three of the four limitations recite an abstract idea of a mathematical concept. And as to integration of the abstract idea, the examiner alleged that the recitations of a “moveable member” and “[an] array of inertial sensors,” and “processor,” along with the step of controlling the moveable member are additional limitations that do not integrate the recited mathematical concepts into a practical application because they amount only to data gathering and insignificant extra-solution activity. Specifically, the examiner stated that the claims fail to supply an inventive concept because the additional limitations are “generically recited” and are well understood, routine, and conventional as evidenced by the prior
art of record.

However, looking back to claim 1, the PTAB zeroed in on the final “controlling” limitation.

We agree with Appellant that the “controlling” step is sufficient under Step 2A, prong 2. Specifically, we agree with Appellant that by controlling the motion of the moveable member, the claim is “effecting a transformation or reduction of a particular article to a different state or thing.” MPEP 2106.05(c). Further, we observe that the recited judicial exception here, the use of a hidden Markov model, is not monopolized in any meaningful way. That is, a myriad of other uses for hidden Markov models remain unaffected by this claim, and the claim merely precludes the use of such models for controlling a movable member based on calculated rotational rates in physical devices. As such, we view claim 1 as a case of “[a]pplying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.” MPEP § 2106.05(e). We, therefore, agree with Appellant that claim 1 integrates the recited abstract idea into a practical application, and we do not sustain its rejection.

Often in the control systems arts (such as robotics here), the addition of a controlling or action step can play a substantial role in the patent eligibility fight. Even though it may be true that the novelty and inventiveness is really in how an improve sensor estimate or other estimate is generated, and even though there may be nothing truly novel in how that data is used, it can nevertheless be important to actually describe the control action in the application when drafting. Sometimes inventors (especially university professors) might push back against such an addition, but this case illustrates exactly how such additions during drafting can save the day. Further, in some cases, there may also be downstream effects from the improved estimate that may enable simpler control system actions and so a prudent drafter will push the inventors to think outside the original box they may be placing around the invention.

So, when drafting applications where there is a risk of Section 101 rejections as to detailed signal processing or estimation algorithms, make sure to include description and examples of how the information is used in the control system to impact the real world operation.