- Sophos Ltd. / Sophos Inc. v. Open Text Inc.Institution deniedPTAB · Nov 29, 2023 · PGR2023-00031 · APJ Barbara A Parvis · PGR · Institution
The Board denied institution because Petitioner did not show that any challenged claim was more likely than not unpatentable over the asserted obviousness or indefiniteness grounds.
RulingsInstitution decision- PTAB / AIA Trials › Institution threshold (reasonable likelihood / more likely than not)Institution denied meritsInstitution denied
“review under 35 U.S.C. § 324 and 37 C.F.R. § 42.4. A post-grant review may not be instituted “unless . . . the information presented in the petition . . . , if such information is not rebutted, would demonstrate that it is more likely than not that at least 1 of the claims challenged in the petition is unpatentable.” 35 U.S.C. § 324 (2018). Having considered the arguments and evidence presented by Petitioner and Patent Owner, we determine that Petitioner has not demonstrated that it is more likely than not that at least one of the claims challenged in the patent is unpatentable. Accordingly, we do not institute a post-grant review of the challenged claims of the ’869 patent.” slip op. at 2
- Skill levelSkill level petitioner adoptedDecided for petitioner
“Pet. 10 (citing Ex. 1004 ¶ 16). Patent Owner does not dispute Petitioner’s proposal. Prelim. Resp. 33. Considering the subject matter of the ’869 patent, the background technical field, and the asserted prior art, we agree with Petitioner’s proposed qualifications for an ordinary level of skill, except we decline to adopt “at least” as that language is vague and open-ended. Otherwise, Petitioner’s proposed definition is consistent with the level of skill reflected in the Specification of the ’869 patent and the asserted prior art references. See Okajima v. Bourdeau, 261 F.3d 1350, 1355 (Fed. Cir. 2001). Accordingly, for purposes of this Decision, and based on the current record, except as noted above, we adopt Petitioner’s assessment of the level of skill for one of ordinary skill in the art.” slip op. at 13
- Obviousness (§103) › Motivation to combine / reasonable expectation of successNot reached
“We agree with Patent Owner that the claims are directed to the classification stage, as they require extracting “a plurality of static data points” from an executable file and “generating a feature vector from [that] plurality of static data points using a classifier” (emphasis added). The claims also recite that the classifier has been “trained to classify” (emphasis added). Further, the claims recite that “features of the feature vector” are selectively turned on or off, indicating that the feature vector has been generated. We also agree with Patent Owner that Petitioner has not shown that Ideses teaches or suggests a classification method in which one or more features of a feature vector generated from a plurality of static data points are selectively turned on or off based on the value of the static data point. As explained in the summary of Ideses above, Ideses describes “methods and systems for generating classifiers” (Ex. 1005 ¶ 2) in which “extracted features [may be] identified for pruning to generate the group of classifying features,” and “the selection [may be] based on identifying a classification effectiveness rank for each of the features of the training software applications” or “based on identifying resource requirements for each of the features of the training software applications.” See id. ¶¶ 98, 99. “[T]he group of classifying features [may be] selected based on the significance of the extracted features,” such that “[s]ignificant features, such as those that have a large effect on the classification outcomes, may be retained” and “[i]nsignificant features, such as those that have a small, negligible or no effect on classification outcome may be pruned.” Id. ¶¶ 101, 106. Petitioner argues that “Ideses explains that in the method depicted in Figure 1, ‘[t]he full feature list is optionally selectively pruned,’ i.e., ‘selectively turned off’ as required by [1.e],” and “[f]urther, ‘each feature of the group is selected from-the-ground-up; added to the group as opposed to removed from or retained within a larger group,’ i.e., ‘selectively turned on’ as required by [1.e].” Pet. 33 (citing Ex. 1005 ¶ 71). Petitioner also argues element 1.e is taught by Ideses’s “approaches to pruning insignificant features,” which Petitioner argues are “to selectively includ[e]/exclud[e] features based on whether a feature coefficient ‘is within a predetermined range’ as required” by element 1.e. Id. at 34 (citing, e.g., Ex. 1005 ¶¶ 107, 109–110). But all of the selection and pruning described in Ideses concerns the” slip op. at 22
- Obviousness (§103) › Graham factors (scope of art, differences, level of skill)Not shown unpatentableNot shown unpatentable
“We agree with Patent Owner that the claims are directed to the classification stage, as they require extracting “a plurality of static data points” from an executable file and “generating a feature vector from [that] plurality of static data points using a classifier” (emphasis added). The claims also recite that the classifier has been “trained to classify” (emphasis added). Further, the claims recite that “features of the feature vector” are selectively turned on or off, indicating that the feature vector has been generated. We also agree with Patent Owner that Petitioner has not shown that Ideses teaches or suggests a classification method in which one or more features of a feature vector generated from a plurality of static data points are selectively turned on or off based on the value of the static data point. As explained in the summary of Ideses above, Ideses describes “methods and systems for generating classifiers” (Ex. 1005 ¶ 2) in which “extracted features [may be] identified for pruning to generate the group of classifying features,” and “the selection [may be] based on identifying a classification effectiveness rank for each of the features of the training software applications” or “based on identifying resource requirements for each of the features of the training software applications.” See id. ¶¶ 98, 99. “[T]he group of classifying features [may be] selected based on the significance of the extracted features,” such that “[s]ignificant features, such as those that have a large effect on the classification outcomes, may be retained” and “[i]nsignificant features, such as those that have a small, negligible or no effect on classification outcome may be pruned.” Id. ¶¶ 101, 106. Petitioner argues that “Ideses explains that in the method depicted in Figure 1, ‘[t]he full feature list is optionally selectively pruned,’ i.e., ‘selectively turned off’ as required by [1.e],” and “[f]urther, ‘each feature of the group is selected from-the-ground-up; added to the group as opposed to removed from or retained within a larger group,’ i.e., ‘selectively turned on’ as required by [1.e].” Pet. 33 (citing Ex. 1005 ¶ 71). Petitioner also argues element 1.e is taught by Ideses’s “approaches to pruning insignificant features,” which Petitioner argues are “to selectively includ[e]/exclud[e] features based on whether a feature coefficient ‘is within a predetermined range’ as required” by element 1.e. Id. at 34 (citing, e.g., Ex. 1005 ¶¶ 107, 109–110). But all of the selection and pruning described in Ideses concerns the” slip op. at 22
§ 103 obviousness§ 112 disclosurecites Phillips · KSR · Graham · NautilusPatents in suitUS 11,409,869
Holdings, summaries, rulings, issues, outcomes, winners and claim constructions shown for a decision were generated by a large language model reading the opinion. They are a guide to the decision, not the decision. Confirm every one against the court's own text before relying on it. Outcomes read from the patent owner’s side. Party names are extracted from the decision and are missing on about 900 short Director denials and scans, which carry the trial number instead.