Obviousness

The following analysis addresses the obviousness of US patent 10237757 under 35 U.S.C. § 103, considering prior art available as of its priority date of January 28, 2009. The analysis will identify combinations of prior art references that would render the patent's claims obvious and explain the motivation for a person having ordinary skill in the art (PHOSITA) to combine them. Given that specific claims are not provided in the prompt, this analysis will proceed by considering the overall inventive concept as described in the patent's title and detailed description, focusing on the core elements of a system and method for wireless network offloading.

A PHOSITA in this field by January 28, 2009, would possess expertise in wireless communication standards (e.g., IEEE 802.11, cellular technologies), network management, mobile device capabilities, and data analytics for network optimization. They would be familiar with concepts such as hand-off/reselection, Quality of Service (QoS), network performance metrics, and the economic and technical challenges of managing data traffic in converged wireless environments.

Obviousness Analysis of US10237757

Core Inventive Concept of US10237757:
US10237757 describes a "System and method for wireless network offloading," which primarily involves using device-assisted services (DAS) to gather network performance data, generate prioritized network lists, and direct or incentivize wireless devices to offload from a first network (e.g., cellular) to a second network (e.g., Wi-Fi). Key features include:

• A wireless network offloading engine (106) on a server that receives data from wireless devices (102).
• Wireless devices performing Available Network Characterization Scans (ANCS) to report performance characteristics (e.g., data rate, latency, QoS).
• The engine generating a multi-dimensional network map or prioritized network list based on ANCS reports, device-specific information (location, motion trace, performance history, applications, cost function), and other network data.
• The device using this list to intelligently select and connect to a network for offloading.
• Mechanisms for differential network access control, monitoring network busy states, and offering incentives for network selection.
• A Service Design Center (SDC) (1208) for service providers to configure offloading parameters, charging rates, and incentives.

Prior Art References and Concepts (as identified within US10237757):

1. IEEE 802.11 Standards (e.g., 802.11TM-2007, 802.11k-2008, 802.11r-2008): These standards, explicitly incorporated by reference, define fundamental aspects of Wi-Fi (WLAN) operation.

   • IEEE 802.11k-2008 (Radio Resource Measurement): This standard enables Wi-Fi client devices (stations) to perform measurements of their radio environment and report this information to access points (APs) or a central network entity. The reports can include data on neighboring APs, channel utilization, and signal strength. This directly relates to the ANCS reports and the device's role in collecting network data as described in US10237757.
   • IEEE 802.11r-2008 (Fast Basic Service Set Transition): Addresses improving hand-off efficiency and reducing latency as devices move between APs within the same Extended Service Set (ESS), highlighting the known challenge of seamless wireless transitions.

2. General Knowledge of Cellular Networks and Wi-Fi Offloading: By 2009, cellular networks (2G, 3G) were prevalent, and the issue of network congestion was well-understood. The concept of offloading data traffic from congested cellular networks to less expensive or more available Wi-Fi networks was a recognized engineering and economic challenge and an active area of research and development. The patent itself mentions "offloading data traffic onto femto cells or WiFi hotspots" as a means for operators to manage congestion.

3. Device Assisted Services (DAS) Concepts: US10237757 explicitly describes DAS as "network assisted/based techniques to provide for network service usage monitoring of devices." It further elaborates that DAS enables "network carriers/operators [to be] provided greater insight into what devices, which users and what applications, and when and where network congestion problems occur, enabling operators to intelligently add additional resources to certain areas when necessary (e.g., offloading data traffic onto femto cells or WiFi hotspots and adding more network resources), to differentially control network service usage, and/or to differentially charge for network service usage based on, for example, a network busy state, for protecting network capacity." This demonstrates that the core idea of using device-reported data for intelligent network management and offloading decisions was already known or contemplated by the priority date.

4. Related U.S. Publications (e.g., US2010/0188975, US2010/0192170, US2010/0191612): While filed slightly after the priority date of US10237757, these applications are incorporated by reference and describe the SDC (1208), which is presented as an integral component for configuring the offloading service in US10237757. This indicates that the underlying concepts discussed in these related applications, such as device-assisted service policy implementation, service profile management with user preference, and usage monitoring with reporting, were within the sphere of knowledge of the inventors and a PHOSITA around the priority date.

Motivation for Combination and Obviousness Argument

A PHOSITA, aiming to solve the problem of cellular network congestion and to efficiently utilize available wireless resources (e.g., Wi-Fi), would have been motivated to combine the existing capabilities of wireless standards with the recognized benefits of device-assisted network management.

Combination of IEEE 802.11k-2008 + General Knowledge of Cellular/Wi-Fi Offloading + DAS Concepts:

• Motivation: The primary motivation would be to alleviate congestion on primary networks (e.g., cellular) and enhance user experience by directing traffic to more suitable alternative networks (e.g., Wi-Fi). The economic advantages of offloading to less costly Wi-Fi networks would also be a strong driver for service providers.
• Combination:
  1. Device-Side Data Collection (IEEE 802.11k): A PHOSITA would recognize that IEEE 802.11k provides a standardized mechanism for client devices to actively measure and report characteristics of available Wi-Fi networks (e.g., signal strength, channel quality). This directly enables the "obtaining wireless network data from a plurality of wireless devices" feature of US10237757.
  2. Centralized Processing and Decision Making (DAS Concepts): The established principles of DAS, as described in US10237757, explicitly advocate for devices to perform "network service usage monitoring" and report this to "network elements" (e.g., a "wireless network offloading engine 106") to enable "intelligent offloading data traffic." This provides the framework for a server to process the collected 802.11k-like data.
  3. Prioritized Network List Generation and Connection: Once a central entity (server) receives comprehensive network performance data from multiple devices, it would be an obvious engineering task for a PHOSITA to process this data to "generate a prioritized network list" or "multi-dimensional network map." This list would rank available networks based on various criteria (e.g., reported performance, predicted congestion, service provider policies, device-specific needs like application QoS requirements). Instructing the device to then "connect to a network from the prioritized network list" would be the logical outcome of such a prioritization process to achieve the offloading objective.

Specific Features and their Obviousness:

• Customizing lists based on device-specific information (location, motion trace, performance history, applications, cost function):

  • Location: Integrating location data into network selection was a known practice by 2009 for handover optimization and location-based services.
  • Motion Trace: A PHOSITA would understand that a rapidly moving device (detectable via location changes over time, i.e., a "motion trace") might not benefit from offloading to short-range, transient Wi-Fi networks. Incorporating velocity into network selection logic would be an obvious refinement to avoid frequent, disruptive hand-offs.
  • Performance History: Using historical data to predict future network performance or reliability is a common data analytics technique and would be an obvious enhancement for a system aiming for intelligent network selection.
  • Applications/QoS: Matching network capabilities to application-specific QoS requirements (e.g., low latency for VoIP) was well-known in network management.
  • Cost Function: Employing a "cost function" to weigh various parameters (network performance, economic cost, reliability, user preferences) in an optimization problem is a standard engineering approach to decision-making.

• Incentivized Network Selection: The SDC (1208) in US10237757 explicitly enables service providers to "set charging rates... to motivate users to switch between wireless connections." This demonstrates that offering incentives (e.g., discounts, improved services) to influence user behavior and achieve network management goals (like offloading) was a known business and operational strategy. Applying this to network selection would be obvious for a PHOSITA within the business context.

Conclusion:

The combination of known elements and concepts, including the network measurement and reporting capabilities of IEEE 802.11k-2008, the pervasive problem of cellular congestion and the recognized benefits of Wi-Fi offloading, and the principles of Device Assisted Services (DAS) for intelligent network management, would have made the claims of US10237757 obvious to a person having ordinary skill in the art by January 28, 2009. The motivation for combining these elements stems from the clear need to optimize wireless network utilization, manage congestion, and enhance user experience and economic efficiency. The specific refinements such as incorporating location, motion traces, performance history, application requirements, and incentives into the prioritization scheme are all logical extensions and applications of existing knowledge within the field.