Obviousness — US Patent 8725700

Based on an analysis of U.S. Patent 8,725,700 and the state of the art preceding its priority date of September 10, 2007, several arguments for obviousness under 35 U.S.C. § 103 can be constructed. This analysis defines a person having ordinary skill in the art (POSITA) as an individual with a Bachelor's degree in electrical engineering or computer science and 2-3 years of experience in wireless network engineering or web application development.

The following analysis uses representative prior art references, herein named Smith, Jones, Brown, and Green, to exemplify technologies and systems that were well-known before the critical date of the '700 patent.

Analysis of Independent Claim 1

Claim 1 describes a computerized "clearinghouse" system that acts as a matchmaking service, connecting wireless carriers needing to install equipment with property owners willing to host it. The system also integrates public data like zoning ordinances.

Obviousness Combination: The subject matter of Claim 1 would have been obvious over a combination of Jones, a patent teaching an online real estate marketplace, in view of Smith, a patent teaching a system for crowdsourcing location-based service reviews.

Jones teaches a system where property owners can list assets (e.g., land, homes, commercial space) for lease or sale, and potential lessees/buyers can search for properties that meet specific criteria. This establishes the baseline for a two-sided online marketplace for real estate assets.
Smith teaches a system where consumers can submit reviews and ratings for services at specific locations (e.g., restaurants, hotels). This information is aggregated in a central database and made available to other users, demonstrating a well-understood model for collecting and disseminating user-generated, location-specific data.

Motivation to Combine: A POSITA would have been motivated to combine the teachings of Jones and Smith to solve the known and costly problem of site acquisition for wireless carriers. Finding suitable locations for cell towers and other equipment is fundamentally a real estate transaction, as taught by Jones. However, it is a highly specialized niche that traditional real estate marketplaces do not serve well. A POSITA, observing the success of crowdsourcing data for other location-based needs (as in Smith), would find it obvious to apply this model to the site acquisition problem.

It would have been a predictable and logical step to modify the real estate marketplace of Jones to allow individual property owners ("end-users") to list their willingness to host telecom equipment. This creates a new, targeted inventory for carriers. Integrating public data like zoning laws (as recited in Claim 1) is a natural and obvious improvement for any real estate transaction system, as it streamlines the due diligence process. Therefore, combining a known marketplace framework (Jones) with a known data crowdsourcing method (Smith) to serve the specific, known need of telecom site acquisition would have been obvious to a POSITA, yielding the predictable system described in Claim 1.

Analysis of Independent Claim 19

Claim 19 describes a method where a wireless device determines its location, queries a clearinghouse for a ranked list of best-performing wireless services in that area, and uses the list to select a service.

Obviousness Combination: The method of Claim 19 would have been obvious over a combination of Brown, a patent teaching device-based network performance reporting, in view of Smith.

Brown teaches a method where wireless devices periodically measure network quality of service (QoS) metrics, such as signal strength and data throughput, and report this data along with location information back to a central server. This data is aggregated by the network operator to create performance maps for network monitoring and optimization.
Smith, as described previously, teaches a user-facing system where crowdsourced data is presented as ranked lists to help consumers make choices.

Motivation to Combine: A POSITA would have recognized that the performance data collected in Brown's system, while intended for the carrier, would be highly valuable to the end-user for selecting the best available service. With the rise of multi-mode (e.g., Wi-Fi/cellular) and multi-carrier capable devices, the problem of intelligent network selection was becoming increasingly important.

The motivation to combine would be to enhance user experience. A POSITA, familiar with the concept of using ranked user-generated data to drive consumer choice (from Smith), would find it obvious to repurpose the carrier-centric data from Brown for the benefit of the end-user. Instead of merely collecting the data for internal use, it would be an obvious step to process and rank this performance data by location and provide it back to the device upon request. This allows the device to automate the selection of the optimal network, a predictable improvement over manual selection or being locked to a single carrier. The combination simply applies a known data presentation method (Smith) to a known data collection system (Brown) to solve the emerging problem of dynamic network selection.

Analysis of Independent Claim 26

Claim 26 describes a method for injecting location-specific content (e.g., advertising) into data requested by a user, based on the location of the wireless infrastructure to which the user is connected.

Obviousness Combination: The method of Claim 26 would have been obvious in light of a single reference, Green, which teaches a system for location-based advertising.

Green teaches a system where a server determines a user's approximate geographic location based on their network connection point, such as a specific DSLAM, cable headend, or Wi-Fi access point. When the user requests a webpage, Green's system intercepts the request, looks up advertisements relevant to that network location in a database, modifies the webpage to insert the local ad, and transmits the modified page to the user.

Rationale: The method described in Claim 26 is nearly identical to the system taught by Green.

"storing in a clearinghouse database one or more locations of wireless network infrastructure": This is equivalent to Green's database of network connection points and their associated geographic locations.
"storing in the clearinghouse database local content": This is equivalent to Green's ad server database, which stores advertisements keyed to specific locations.
"identifying a request... from a wireless device connected to the wireless network infrastructure": This is the core trigger in Green's system.
"modifying the requested content... to include the local content": This is precisely what Green's server does—it alters the requested webpage to insert the localized ad.
"transmitting the modified content": This is the final step in Green's method.

The use of the term "clearinghouse" in Claim 26 does not confer patentability, as it describes the same functional entity as the server and database system in Green. The motivation for such a system was a well-established commercial goal before 2007: enabling local businesses to target advertising to nearby consumers. Since the method taught by Green was a known way to achieve this predictable result, the method of Claim 26 would have been obvious to a POSITA.