Obviousness

Obviousness Analysis (35 U.S.C. § 103) for US12143425

Legal Standard for Obviousness

Under 35 U.S.C. § 103, a patent for a claimed invention cannot be obtained if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art (PHOSITA) to which the claimed invention pertains. Obviousness is determined by considering:

1. The scope and content of the prior art.
2. The differences between the prior art and the claims at issue.
3. The level of ordinary skill in the pertinent art.
4. Secondary considerations of non-obviousness (e.g., commercial success, long-felt but unsolved needs, failure of others).

An obviousness rejection typically relies on disclosures that qualify as prior art under 35 U.S.C. § 102. For a combination of references to render a claim obvious, there must be some teaching, suggestion, or motivation in the prior art that would have led a PHOSITA to combine the references in the way claimed, with a reasonable expectation of success.

Identifying Prior Art for Obviousness Analysis

As stated in the "Prior Art" section, I cannot directly access the "References Cited" section of US patent 12143425 from the USPTO database with my current tools. Therefore, I cannot provide specific combinations of prior art references (e.g., Patent A, Publication B) that would render the claims obvious, nor can I explain the specific motivation to combine them.

The Google Patents entry for US12143425B1 does list "Prior art keywords" as "data," "transformation," "pipeline," "computer system," and "software instructions." While these keywords broadly describe the field, they do not constitute specific prior art references that can be analyzed for obviousness.

General Discussion of Potential Obviousness Arguments (Hypothetical)

Given the patent's focus on "rapid predictive analysis of very large data sets using the distributed computational graph" and "transformation pipelines," a hypothetical obviousness argument might involve combining existing technologies.

A PHOSITA in the field of data analytics, distributed computing, and machine learning, with knowledge of handling large datasets, would likely be aware of:

• Existing data pipeline architectures (linear): The patent itself notes that "Data pipelines... have all been linear in configuration which precludes their use for analysis and conclusion or action discovery in a majority of complex situations where branching or even recurrent modification is needed." This explicitly states that linear data pipelines were known prior art.
• Distributed computational graphs: The patent's title and description heavily rely on the concept of a "distributed computational graph." Such graph-based processing for large datasets, for instance, using frameworks like Apache Spark's Resilient Distributed Datasets (RDDs) or TensorFlow's computational graphs, were known or emerging technologies around the priority date of October 28, 2015.
• Batch processing and real-time streaming analysis: The combination of batch processing for historical context and real-time streaming for current data is a common architectural pattern in big data analytics.
• Self-optimizing and adaptive systems: Concepts of system monitoring, self-correction, and machine learning for optimizing operational behavior in software systems were also established.

Hypothetical Combinations and Motivation:

If specific prior art references (e.g., Patent X describing linear data pipelines in a distributed environment, and Publication Y detailing a graph-based computational model for large-scale data processing) were available, an obviousness argument might be constructed as follows:

1. Combining Linear Pipelines with Distributed Graph Processing: A PHOSITA would likely be motivated to apply distributed computational graph principles (from Publication Y) to existing linear data pipelines (from Patent X) to improve scalability and performance when dealing with "very large data sets." The distributed computational graph offers a more flexible and robust way to manage complex data flows and transformations.
2. Introducing Non-Linearities (Branching, Cyclical) to Data Pipelines: Once a distributed computational graph model is adopted for data pipelines, the motivation to move beyond purely linear configurations (as noted in the patent's background as a limitation of prior art) would be strong. A PHOSITA would recognize the benefits of branching (e.g., for parallel processing of different analyses on the same data) or cyclical structures (e.g., for iterative algorithms, feedback loops, or recurrent modifications) to handle more "complex situations where branching or even recurrent modification is needed." This would be a logical extension of using graph theory for data flow.
3. Integrating System Sanity and Retraining: Given the complexity of "very large data sets" and distributed systems, a PHOSITA would foresee the need for robust monitoring and adaptation. The integration of a "system sanity and retrain software module" to monitor, optimize, and retrain the data filter, formalization, batch analysis, and transformation pipeline (as described in US12143425) would be an obvious step to ensure system stability and improve the accuracy and efficiency of predictive analysis. The motivation would stem from the inherent challenges of managing large, dynamic data processing environments.

Without specific prior art references, however, this remains a conceptual discussion. To perform a thorough obviousness analysis, the "References Cited" from the patent document are essential.

Conclusion on Obviousness

Due to the inability to access the "References Cited" from the USPTO database for US12143425B1, a detailed obviousness analysis under 35 U.S.C. § 103 cannot be provided at this time. Such an analysis requires concrete prior art documents to identify specific combinations and articulate the motivation for a PHOSITA to combine them.