Obviousness

The analysis of obviousness under 35 U.S.C. § 103 requires identifying combinations of prior art references that would have rendered the claimed invention obvious to a person having ordinary skill in the art (POSA), along with a motivation to combine those references.

Based on the provided patent text for US11467579, the "Prior art keywords" section lists general terms such as "neural network," "traffic," "autonomous vehicle," "values," and "probabilistic neural." However, these are keywords, not specific prior art documents that can be combined for an obviousness analysis.

The "BACKGROUND" section describes the limitations of conventional systems that predict motion using kinematics but does not cite specific prior art patents or publications in this context.

The "DETAILED DESCRIPTION" section mentions one specific prior art document: "Systems for predicting human interactions with vehicles are disclosed in U.S. patent application Ser. No. 15/830,549, filed on Dec. 4, 2017 which is incorporated herein by reference in its entirety."

Therefore, the provided patent text explicitly identifies only one specific prior art document (U.S. patent application Ser. No. 15/830,549) as a relevant reference. Since the request specifically asks to "Identify combinations of prior art references," and only one document is explicitly provided from the patent text, a direct combination of multiple distinct prior art documents, as typically required for a § 103 obviousness analysis, cannot be performed solely based on the information provided in the patent's own "Prior Art section" or directly cited within its text.

To properly analyze obviousness with combinations, at least two distinct prior art references (or one reference combined with general knowledge in the field) that together disclose all elements of the claims would be needed. Without additional explicit prior art documents, it is not possible to construct combinations of references as requested.

However, to provide some context, if U.S. patent application Ser. No. 15/830,549 were to disclose aspects closely related to the core claims of US11467579 (e.g., predicting human interactions/behavior for autonomous vehicles using machine learning), then the obviousness analysis would typically involve:

1. Identifying the teachings of U.S. patent application Ser. No. 15/830,549: A thorough review of this application would be necessary to understand what it discloses regarding predicting human behavior, the types of models used, the data sources, and the application in autonomous vehicles.
2. Comparing these teachings to the claims of US11467579: Each element of the independent claims (Claims 1, 10, and 18) of US11467579 would be mapped against the disclosures of U.S. patent application Ser. No. 15/830,549.
3. Assessing any differences: If there are differences, the analysis would then consider whether a POSA, at the time of the invention (priority date 2019-02-06), would have been motivated to modify or combine the teachings of U.S. patent application Ser. No. 15/830,549 with common general knowledge in the field to arrive at the claimed invention of US11467579. For instance, if Ser. No. 15/830,549 disclosed predicting "intent" but not explicitly "uncertainty" in a probabilistic neural network context, the analysis would ask if adding a probabilistic neural network to quantify uncertainty would have been obvious to a POSA in AI/AV development.

Given the explicit constraint to "Use the results from the Prior Art section of this page" and the absence of multiple distinct prior art references within the provided patent text, a full obviousness analysis based on combinations of references cannot be completed at this time.

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