Prior art

The USPTO provides a Patent Public Search tool that allows users to search for patents and patent application publications. To identify the most relevant prior art for US Patent 11222349, I will access the patent information directly from the USPTO database.

Most Relevant Prior Art for US Patent 11222349

To provide a comprehensive analysis of the most relevant prior art, I would typically need to perform a detailed search within the USPTO database for US11222349 and then review the "References Cited" section of the patent. Since I do not have direct, real-time access to perform this live search and retrieve the full list of cited references with their details, I will rely on the provided patent text to identify any explicitly mentioned prior art or related applications.

The patent document itself mentions several related applications and previous works in its "PRIORITY CLAIM" and "DESCRIPTION" sections, which often serve as important prior art. Additionally, the claims themselves are judged against prior art to determine their patentability under 35 U.S.C. § 102 (novelty) and § 103 (non-obviousness).

Based on the provided patent text, here are the explicitly mentioned related applications and scientific works that would be considered relevant prior art:

1. U.S. patent application Ser. No. 15/845,203

   • Full Citation: U.S. patent application Ser. No. 15/845,203, filed Dec. 18, 2017.
   • Publication/Filing Date: December 18, 2017.
   • Brief Description: This is a continuation of this application, meaning it shares a common lineage and subject matter. It directly relates to the systems and methods for discovering neighborhood clusters based on venue check-in data.
   • Potential Anticipation (35 U.S.C. § 102): As a direct continuation, it is highly likely to anticipate elements of all claims (1-20) if its disclosure predates the priority date of any distinct invention claimed in US11222349. However, since US11222349 is a continuation, it likely benefits from the priority date of this application, meaning this application would be prior art against US11222349 only if there were subject matter in US11222349 not supported by the earlier application.

2. U.S. patent application Ser. No. 14/015,506

   • Full Citation: U.S. patent application Ser. No. 14/015,506, filed Aug. 30, 2013.
   • Publication/Filing Date: August 30, 2013.
   • Brief Description: This is a divisional of the '203 application and also claims priority to a provisional application. It covers the core invention of discovering neighborhood clusters and uses therefor.
   • Potential Anticipation (35 U.S.C. § 102): Similar to the '203 application, this forms part of the patent family. Any subject matter in US11222349 that is not supported by the '506 application's disclosure, and has an effective filing date later than the '506 application, could be anticipated by the '506 application. Given its priority date, it is a significant reference for all claims (1-20).

3. U.S. provisional application Ser. No. 61/743,263

   • Full Citation: U.S. provisional application Ser. No. 61/743,263, entitled "Utilizing social media to understand the dynamics of a city," filed Aug. 30, 2012.
   • Publication/Filing Date: August 30, 2012.
   • Brief Description: This provisional application is the earliest priority document. It would disclose the foundational concepts related to using social media data (like check-ins) to understand urban dynamics and infer neighborhood structures.
   • Potential Anticipation (35 U.S.C. § 102): As the earliest priority document, it defines the effective filing date for much of the claimed subject matter. Anything disclosed in this provisional application, and properly carried forward into US11222349, would typically establish the priority date for those claims. However, any subject matter in US11222349 that is not adequately supported by this provisional application's disclosure, and therefore relies on a later effective filing date, could potentially be anticipated by other prior art that emerged between the provisional filing date and that later effective filing date. It would likely establish prior art against any later-developed aspects of all claims (1-20).

4. D. M. Blei and P. I. Frazier, "Distance dependent Chinese restaurant processes," J. Mach. Learn. Res., 2461-2488, November 2011.

   • Full Citation: D. M. Blei and P. I. Frazier, “Distance dependent Chinese restaurant processes,” J. Mach. Learn. Res., 2461-2488, November 2011.
   • Publication/Filing Date: November 2011.
   • Brief Description: This academic paper is explicitly incorporated by reference and describes the "distance dependent Chinese restaurant process (ddCRP)," which is a core probabilistic modeling technique used in US11222349 for clustering non-exchangeable data, particularly in a spatial setting. The patent details that its Gibbs sampler "follows closely that of D. M. Blei and P. I. Frazier" for the ddCRP.
   • Potential Anticipation (35 U.S.C. § 102): This publication likely anticipates the fundamental mathematical and algorithmic aspects of using ddCRP for clustering, particularly as described in the context of "non-exchangeable data" and "customer seating arrangements in an eatery" which the patent uses as an analogy for venues. Elements of claims 1 and 13, particularly those related to the use of probabilistic models and statistical sampling (e.g., Gibbs sampling) to identify clusters based on venue categories or temporal patterns, could potentially be anticipated or rendered obvious by this reference. The method steps of the claims involving "inference" and "probabilistic distribution" could be directly affected.

5. Ghosh et al., "Spatial distance dependent Chinese restaurant processes for image segmentation," Neural Information Processing Systems, 2011.

   • Full Citation: Ghosh et al., “Spatial distance dependent Chinese restaurant processes for image segmentation,” Neural Information Processing Systems, 2011.
   • Publication/Filing Date: 2011.
   • Brief Description: This paper describes an extension of the ddCRP to hierarchical modeling, specifically for image segmentation. The patent states that its Gibbs sampler also follows the "extension of the ddCRP to hierarchical modeling by Ghosh et al." and the MATLAB implementation used in testing "used portions of the ddCRP Gibbs sampler released by Ghosh et al. . . . for 3D Mesh segmentation, which was modified and extended it to fit the hierarchical model."
   • Potential Anticipation (35 U.S.C. § 102): This reference would likely anticipate the application of hierarchical ddCRP modeling, especially in contexts involving spatial data and segmentation. While the specific application here is image segmentation, the underlying mathematical framework and algorithms for hierarchical ddCRP could be seen as anticipating the application of a similar framework to venue clustering. Elements of claims 1 and 13, particularly those related to the hierarchical ddCRP setting, sharing of neighborhood parameters across groups (cities), and the use of Gibbs sampling in such a hierarchical model, could potentially be anticipated or rendered obvious.

6. Cheng et al., "Exploring millions of footprints in location sharing services," AAAI ICWSM, 2011.

   • Full Citation: Cheng et al. (“Exploring millions of footprints in location sharing services,” AAAI ICWSM, 2011).
   • Publication/Filing Date: 2011.
   • Brief Description: This paper describes the extraction of 11 million Foursquare check-ins from check-in Tweets, which was part of the dataset used by the inventors for their experiments. This shows prior art in collecting and utilizing large-scale location-based social network data.
   • Potential Anticipation (35 U.S.C. § 102): This publication demonstrates prior art in the collection and use of "venue check-in data from multiple venue visitors for multiple venues in the geographic region" from "location-based social networking software applications." While it doesn't describe the clustering methodology, it establishes that the data source itself was known and explored in the context of "location sharing services" prior to the patent's priority date. This could potentially anticipate aspects of claims 1 and 13 related to the source and type of venue check-in data.

It is important to note that a full anticipation analysis under 35 U.S.C. § 102 requires a detailed claim-by-claim comparison of each element of the claims against the disclosures of these prior art references. The above analysis provides a high-level assessment of the potential relevance of each cited work.