Prior art — US Patent 5704012

Most Relevant Prior Art for US Patent 5704012

The following analysis identifies the most relevant prior art cited within US Patent 5704012, focusing on their potential to anticipate the single independent claim under 35 U.S.C. § 102. The information for each cited patent is drawn directly from the US5704012 patent document, as direct database searches for these specific older patent numbers via the search tool yielded unrelated results.

Claim 1 of US5704012 outlines a method for controlling the response of a computer system to a workload and configuration, comprising the steps of:

Gathering performance data (workload, configuration, response data for job classes).
Constructing a neural network (inputs: workload, configuration; output: system response).
Training the neural network with performance data to model the computer system.
Determining computer system response from the trained neural network's output.
Allocating resources among job classes based on the determined response and user-specified performance objectives.

1. US5325525A (Hewlett-Packard Company)

Full Citation: US5325525A, "Method of automatically controlling the allocation of resources of a parallel processor computer system by calculating a minimum execution time of a task and scheduling subtasks against resources to execute the task in the minimum time."
Publication/Filing Date: Publication Date: 1994-06-28; Priority Date: 1991-04-04.
Brief Description: This patent describes a method for automatically controlling resource allocation in a parallel processor computer system by calculating a minimum execution time for a task and scheduling subtasks to achieve that time.
Potential Anticipation (35 U.S.C. § 102): While this patent addresses resource allocation in a computer system, its mechanism for control involves calculating minimum execution times and scheduling subtasks, not the construction and training of a neural network to model system performance and then using that model for allocation based on user objectives. Thus, it does not anticipate all elements of Claim 1.

2. US5483468A (International Business Machines Corporation)

Full Citation: US5483468A, "System and method for concurrent recording and displaying of system performance data."
Publication/Filing Date: Publication Date: 1996-01-09; Priority Date: 1992-10-23.
Brief Description: This patent describes a system and method focused on concurrently recording and displaying system performance data.
Potential Anticipation (35 U.S.C. § 102): This patent discloses aspects related to "gathering performance data" (step 1 of Claim 1 of US5704012). However, it does not include the subsequent inventive steps of constructing, training, and utilizing a neural network for performance prediction and resource allocation, nor the concept of user-defined performance objectives. Therefore, it does not anticipate Claim 1.

3. US5598076A (Siemens Aktiengesellschaft)

Full Citation: US5598076A, "Process for optimizing control parameters for a system having an actual behavior depending on the control parameters."
Publication/Filing Date: Publication Date: 1997-01-28; Priority Date: 1991-12-09.
Brief Description: This patent describes a process for optimizing control parameters in a system where behavior is dependent on those parameters.
Potential Anticipation (35 U.S.C. § 102): This patent generally describes optimizing system control parameters, which broadly relates to the objective of US5704012. However, the description in US5704012 does not indicate that this process specifically involves neural networks for modeling computer system performance or for resource allocation within job classes based on user objectives. Without these specific elements, it does not anticipate Claim 1.

4. US5067107A (Hewlett-Packard Company)

Full Citation: US5067107A, "Continuous computer performance measurement tool that reduces operating system produced performance data for logging into global, process, and workload files."
Publication/Filing Date: Publication Date: 1991-11-19; Priority Date: 1988-08-05.
Brief Description: This patent describes a tool for continuous computer performance measurement, data reduction, and logging into specific files (global, process, and workload).
Potential Anticipation (35 U.S.C. § 102): Similar to US5483468A, this patent focuses on the collection and storage of computer system performance data (part of step 1 of Claim 1). It does not extend to the use of neural networks for modeling system response or for dynamically allocating resources based on user objectives. Thus, it does not anticipate Claim 1.

5. US5235673A (International Business Machines Corporation)

Full Citation: US5235673A, "Enhanced neural network shell for application programs."
Publication/Filing Date: Publication Date: 1993-08-10; Priority Date: 1991-04-18.
Brief Description: US Patent 5704012 incorporates this patent by reference for describing the operation of the IBM Neural Network Utility, which simulates neural networks. This patent pertains to providing a framework or utility for implementing neural networks in application programs.
Potential Anticipation (35 U.S.C. § 102): This patent is highly relevant as it describes the fundamental neural network technology (steps 2 and 3 of Claim 1) that US5704012 leverages. It provides the "shell" for constructing and training neural networks. However, it describes a generic utility rather than the specific application of such a neural network to model computer system performance and allocate computer resources among job classes based on user-specified performance objectives. Therefore, while foundational, it does not anticipate the complete method of Claim 1.

6. US5485545A (Mitsubishi Denki Kabushiki Kaisha)

Full Citation: US5485545A, "Control method using neural networks and a voltage/reactive-power controller for a power system using the control method."
Publication/Filing Date: Publication Date: 1996-01-16; Priority Date: 1991-06-20.
Brief Description: This patent describes a control method that employs neural networks, specifically in the context of a voltage/reactive-power controller within a power system.
Potential Anticipation (35 U.S.C. § 102): This patent is significant because it explicitly teaches the use of "neural networks" in a "control method." This demonstrates that the concept of neural network-based control was known prior to US5704012. However, the specific application is to a "power system" for electrical control, which is a different technical domain than "controlling the response of a computer system to a workload and configuration" through resource allocation among "job classes" with "performance objectives." Due to this difference in the system being controlled and the nature of the resources, it does not anticipate all elements of Claim 1.

Conclusion on Most Relevant Prior Art:

The most relevant prior art references, in terms of the underlying technology, are US5235673A (neural network shell) and US5485545A (neural network control in a different system). US5235673A provides the generic toolset for building neural networks, which are then specifically applied in US5704012. US5485545A demonstrates that using neural networks for control was known. However, none of the cited prior art documents appear to directly anticipate Claim 1 of US5704012 under 35 U.S.C. § 102 because they do not individually disclose all the elements of the claim, particularly the novel combination of using a neural network to model a computer system's performance and then specifically using that model to allocate computer system resources among job classes based on user-specified performance objectives.