Obviousness

Obviousness Analysis of US Patent 5704012 Under 35 U.S.C. § 103

This analysis considers whether the single independent claim (Claim 1) of US Patent 5704012 would have been obvious to a person having ordinary skill in the art (PHOSITA) at the time of the invention (priority date: October 8, 1993), based on the prior art identified in the "Prior Art" section.

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

1. Gathering performance data for jobs in a plurality of job classes (workload, configuration, response data for a plurality of time intervals), where jobs require different amounts of computer system resources.
2. Constructing a neural network, with inputs corresponding to the workload and configuration, and at least one output corresponding to the computer system's response.
3. Training said neural network with the gathered performance data to produce a trained neural network model of the computer system.
4. Determining the response of the computer system from the output of the trained neural network.
5. Allocating the resources in the computer system among the plurality of job classes based on the determined response and user-specified performance objectives for each job class.

Combination of Prior Art References and Rationale for Obviousness

A combination of the following prior art references would render Claim 1 of US5704012 obvious to a PHOSITA:

• US5483468A (IBM): "System and method for concurrent recording and displaying of system performance data."
• US5067107A (Hewlett-Packard Company): "Continuous computer performance measurement tool..."
• US5235673A (IBM): "Enhanced neural network shell for application programs."
• US5485545A (Mitsubishi Denki Kabushiki Kaisha): "Control method using neural networks and a voltage/reactive-power controller for a power system using the control method."
• US5325525A (Hewlett-Packard Company): "Method of automatically controlling the allocation of resources of a parallel processor computer system..."
• US5598076A (Siemens Aktiengesellschaft): "Process for optimizing control parameters for a system..."

Motivation for Combination:

The background of US5704012 itself highlights the existing problems in resource allocation, noting the difficulty of traditional heuristic and queueing theory models to adapt to constantly changing computer system configurations and workloads. It explicitly states a need for more flexible and dynamic resource allocation, and for enhanced techniques for managing system resources. A PHOSITA, faced with these known challenges, would be motivated to seek more adaptive and intelligent control solutions. Neural networks, known for their ability to learn complex, non-linear relationships and adapt to changing conditions, would be a natural choice for such an improvement.

How the Combination Renders Claim 1 Obvious:

1. Gathering performance data (Claim 1, Step 1):

   • US5483468A explicitly teaches "concurrent recording and displaying of system performance data" within a computer system. This includes collecting information regarding system performance.
   • US5067107A further details a "continuous computer performance measurement tool that reduces operating system produced performance data for logging into global, process, and workload files."
   • These references clearly establish that the gathering of comprehensive computer system performance data, including workload, configuration, and response data for various job classes, was well-known in the art. A PHOSITA would routinely collect such data to monitor and understand computer system behavior.

2. Constructing a neural network (Claim 1, Step 2):

   • US5235673A (assigned to IBM, the same assignee as US5704012) describes an "enhanced neural network shell for application programs," which provides the underlying technology for constructing neural networks. This patent teaches how to define the type and topology of a neural network, including the number of inputs, outputs, and connections.
   • US5485545A demonstrates the application of neural networks in "control method[s]" where the neural network would inherently have inputs representing system parameters to be controlled and outputs representing control actions or system responses.
   • A PHOSITA, desiring to model the complex relationships between computer system parameters (workload, configuration) and performance (response), would find it obvious to apply the general neural network construction techniques of US5235673A, designing the network with inputs corresponding to the collected computer system workload and configuration data (from US5483468A) and outputs corresponding to the system's response.

3. Training said neural network (Claim 1, Step 3):

   • US5235673A clearly teaches the training of neural networks for application programs.
   • The combination of the gathered performance data (from US5483468A) with the neural network training capability (from US5235673A) to create a model that learns the behavior of the computer system is a straightforward application of neural network technology. The motivation to create an accurate and adaptive model, as described in US5704012's background, would naturally lead a PHOSITA to train the constructed neural network with available historical performance data.

4. Determining the response of said computer system (Claim 1, Step 4):

   • Once a neural network is constructed and trained to model a system's behavior (as covered by the preceding steps), its fundamental function is to predict or "determine the response" based on new inputs.
   • US5485545A shows a neural network being used to predict system behavior (e.g., voltage/reactive-power) in a power system for control purposes.
   • Therefore, using the output of the trained neural network model of the computer system to determine its response is an inherent and obvious consequence of having such a model.

5. Allocating the resources in said computer system (Claim 1, Step 5):

   • US5325525A teaches methods for "automatically controlling the allocation of resources of a parallel processor computer system." This demonstrates that resource allocation in computer systems was a known problem and objective.
   • US5598076A describes a "process for optimizing control parameters for a system having an actual behavior depending on the control parameters." This provides a general teaching of using feedback to optimize system control.
   • US5485545A teaches using a neural network within a "control method" to effect changes in a system (a power system).
   • A PHOSITA, observing that a trained neural network can accurately predict computer system response (steps 1-4), and knowing the general art of resource allocation (US5325525A) and adaptive control using neural networks (US5485545A), would be motivated to integrate these to dynamically allocate resources. The "user-specified performance objectives" are a standard input for any control system aiming to optimize performance in a computer environment, providing the target for the resource allocation decisions. The adaptive nature of neural networks would make them a desirable component for dynamically adjusting resources to meet these objectives in the face of changing workloads, addressing the very problem articulated in the background of US5704012.

Conclusion:

Given the widespread knowledge of computer system performance monitoring and data collection (US5483468A, US5067107A), the availability of generic neural network development tools (US5235673A, from the same assignee), and the known application of neural networks for control in other complex systems (US5485545A), it would have been obvious for a PHOSITA to combine these elements. The motivation would be to develop a more adaptive and effective resource allocation mechanism for computer systems, addressing the known limitations of existing techniques, by using a neural network to model system behavior and drive resource allocation decisions based on user-defined performance objectives, thus achieving the known goal of optimized system performance. Therefore, Claim 1 of US5704012 is rendered obvious by the combination of these prior art references.