Obviousness

To conduct an obviousness analysis under 35 U.S.C. § 103, it is necessary to identify specific prior art references that disclose elements of the patent claims and to explain the motivation for combining those references from the perspective of a person having ordinary skill in the art (PHOSITA) at the time of the invention (priority date: May 29, 2013).

Based on the provided "Prior Art section" of this patent analysis, the available information includes "Prior art keywords" (machine learning, module, results, display, data) and a "Prior art date" (2013-05-29). Additionally, "Other versions" lists US20140358825A1.

Crucially, the provided text does not contain specific prior art references (e.g., patent numbers, journal articles, or other publications) that can be combined to form an obviousness argument. The "Prior art keywords" are general terms, and US20140358825A1 is identified as a family member with the same priority date (2013-05-29), meaning it would generally not serve as prior art against US9218574B2 for commonly disclosed subject matter.

Therefore, without specific prior art references to analyze and combine, a detailed obviousness analysis as requested cannot be performed. I cannot identify combinations of non-existent references or explain motivation to combine them.

To illustrate the type of analysis that would be performed if specific references were available, consider the subject matter of Claim 1 of US9218574B2, which generally relates to a computer program product for a user interface for machine learning, enabling dynamic updating of machine learning results based on user input.

A hypothetical obviousness argument would typically require:

1. A primary reference (e.g., Reference A) disclosing a user interface for presenting machine learning results.
2. A secondary reference (e.g., Reference B) disclosing a system for receiving user input to adjust parameters in a computational or data analysis context.
3. A third reference (e.g., Reference C) disclosing techniques for dynamically updating displayed information in a user interface based on user input, potentially using pre-computation or caching to improve responsiveness.

A PHOSITA, skilled in software development for data analysis and user interfaces around the priority date, would have been motivated to combine these elements to improve the interactivity and user experience of machine learning applications. The motivation would stem from the common industry goal of making complex data analysis more accessible and responsive to users, particularly non-experts. For instance, if a user wanted to see how changing an input parameter would affect a machine learning prediction, the motivation to combine a static display of results with dynamic input and update mechanisms would be clear to provide immediate feedback, rather than requiring the user to wait for a new computation. Techniques like pre-computation or caching for frequently queried data were known methods for improving performance in data-intensive applications.

However, without actual cited prior art documents, this remains a hypothetical illustration of the methodology rather than a grounded obviousness analysis for US9218574B2.