Examiners typically have a favorite arsenal of grounds of rejection to apply to an application. In other words, not all grounds of rejection are applied evenly. Therefore it is a small wonder that the types of rejections appealed to the Board are non-uniform. Knowing which types of rejections are currently getting appealed (and seeing the results of those appeals) arms the patent prosecutor with an additional weapon against the Examiner. Here is a breakdown of the types of rejections appealed to the Board.

frequency

The big winner is obviousness. In a sample, 92.31% of the cases on appeal included an obviousness rejection. This means that almost every decision on appeal had an obviousness rejection.

Second place is anticipation. In the same sample, 28.08% had a §102 rejection. Since multiple issues can be appealed simultaneously, almost every case was appealing either a §102 or a §103 rejection.

Third place is §112(b), which includes indefiniteness and omission of essential elements. 7.05% of decisions had a §112(b) rejection.

Fourth place is §112(a), which includes written description, enablement, best mode and new matter. In the sample, 6.35% had a §112(a) rejection.

Fifth place is §101 – nonstatutory subject matter. In the sample, 3.57% had a nonstatutory subject matter rejection.

Next is obviousness-type double patenting. While technically 3.32% were on appeal, many of these were not argued by the appellant, suggesting that the appellant was in fact not appealing this particular rejection. Thus, obviousness-type double patenting may be even lower on the list in terms of issues actually being appealed.

Next is §112(d), which was included in 0.24% of the decisions.

Finally are the other §101 grounds, such as lack of utility and statutory double patenting. 0.22% decided on one of these rejections.

Data was gathered using Anticipat’s beta research database. The sample of decisions consisted of 12,358 decisions from December 26, 2012 to January 9, 2017, with the various rejections selected. Decisions that included issues that were only introduced by the Board (new rejections) were then searched for and removed from the data set to only show those decisions that were previously on appeal.

Knowing which rejections get appealed shows three things. First, the types of appealed rejections show which rejections the applicant is willing to bet significant time and money on that the Examiner is wrong. Second, the data show a correlation to the number of applied rejections in Office Actions. The greater the number of appeals, the more frequently the rejection is being applied in Office Actions. Third, if different from the Office Actions, the appealed data can show the likelihood that the area of law is in flux. As we have previously discussed, rejections are not uniformly successfully appealed. Indeed, knowing the results of the different types of rejections on appeal provides a practitioner with predictive analytics to know the likelihood of succeeding on an appeal.

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