Prior art

To identify the most relevant prior art for US patent 10614344 and determine which claims they potentially anticipate under 35 U.S.C. § 102 (novelty), I will examine the patent text for cited prior art references. The patent text directly lists prior art keywords and legal status information but does not explicitly provide a list of "References Cited" with full citations and descriptions in the way a USPTO patent document typically does. However, the PTAB challenge against claims 1, 2, 4-13, 16-20, and 22-26 specifically names three prior art references.

Therefore, the most relevant prior art references, based on the PTAB's institution decision for IPR2025-01573, are:

Most Relevant Prior Art Based on PTAB IPR2025-01573

These references were cited in the PTAB's institution decision as grounds for challenging several claims of US10614344 under 35 U.S.C. § 103 (obviousness). While the PTAB's decision was for obviousness, these references would necessarily contain elements that are relevant to a novelty analysis under 35 U.S.C. § 102.

1. U.S. Patent Publication No. 2017/0286820 (Krumm)

   • Full Citation: U.S. Patent Publication No. 2017/0286820 (Krumm)
   • Publication/Filing Date: Not explicitly stated in the provided text for this specific reference, but the publication year is 2017.
   • Brief Description: Krumm is understood to contribute to the teaching of collecting human response data for training machine learning models to predict human behavior in road scenes.
   • Claims Potentially Anticipated (under 35 U.S.C. § 102): While the PTAB instituted for obviousness, the fact that Krumm is a primary reference suggests it discloses significant elements of claims 1, 2, 4-13, 16-20, and 22-26. A detailed § 102 analysis would require direct examination of the Krumm patent.

2. U.S. Patent Publication No. 2017/0053154 (Cho)

   • Full Citation: U.S. Patent Publication No. 2017/0053154 (Cho)
   • Publication/Filing Date: Not explicitly stated in the provided text for this specific reference, but the publication year is 2017.
   • Brief Description: Cho is understood to contribute to the teaching of collecting human response data for training machine learning models to predict human behavior in road scenes.
   • Claims Potentially Anticipated (under 35 U.S.C. § 102): Similar to Krumm, as a contributing reference to the obviousness finding, Cho likely discloses elements relevant to claims 1, 2, 4-13, 16-20, and 22-26. A detailed § 102 analysis would require direct examination of the Cho patent.

3. U.S. Patent No. 8,674,809 (Tiwari)

   • Full Citation: U.S. Patent No. 8,674,809 (Tiwari)
   • Publication/Filing Date: Not explicitly stated in the provided text for this specific reference, but the patent number indicates an issue year earlier than 2017.
   • Brief Description: Tiwari is understood to contribute to the teaching of collecting human response data for training machine learning models to predict human behavior in road scenes.
   • Claims Potentially Anticipated (under 35 U.S.C. § 102): As a contributing reference to the obviousness finding, Tiwari likely discloses elements relevant to claims 1, 2, 4-13, 16-20, and 22-26. A detailed § 102 analysis would require direct examination of the Tiwari patent.

Without direct access to the full text of these prior art references, a precise determination of which specific claims (or elements of claims) are potentially anticipated under 35 U.S.C. § 102 cannot be definitively made from the provided patent text alone. The PTAB's finding of obviousness under § 103 indicates that the combination of these references renders the claims unpatentable, implying that individually or in combination, they disclose many of the claimed features. For a true § 102 analysis, each claim element would need to be compared directly against the disclosure of each reference to see if every element of the claim is found, either explicitly or inherently, in a single piece of prior art.