US 11222349

The patent details are extracted from the provided text.
The CAFC 2026 dockets search results indicate general information about CAFC case information and PACER, but no specific dockets for patent US11222349 in 2026. I explicitly searched for "CAFC dockets US11222349 2026" and did not find relevant results. The search results mainly link to the main pages for case information and search systems (PACER, UJS Portal). One result mentioned "2026 Dockets" but it was for Oklahoma.gov, not CAFC.

Therefore, I can conclude there's no publicly available CAFC docket information for US11222349 in 2026 based on the provided search.

Here's the summary:

Summary of US Patent 11222349

• Title: Discovering neighborhood clusters and uses therefor
• Assignee: The original assignee was Carnegie Mellon University. As of June 18, 2025, the patent was assigned to the NATIONAL SCIENCE FOUNDATION via a confirmatory license.
• Inventors: Justin Cranshaw, Raz Schwartz, Jason I. Hong, Norman Sadeh-Koniecpol
• Filing Date: July 13, 2020 (Application number US16/927,671)
• Issue Date: January 11, 2022
• Abstract: The patent describes computer-based systems and methods for identifying neighborhood clusters within a geographic region. These clusters feature a diverse mix of venues and are formed using venue check-in data. The criteria for determining the mix of venues can include social similarity between venues, characteristics emblematic of specific neighborhood typologies, or patterns of temporal check-in types, or combinations thereof. The discovered neighborhood clusters are applicable for various commercial and civic purposes.

Plain-Language Overview of Independent Claims:

• Independent Claim 1 (System Claim): This claim describes a computer-based system designed to discover geographic clusters of venues. The system includes a computer database storing venue check-in data from many users across multiple venues in a region. This data can come from mobile check-in apps, point-of-sale transactions, or venue rating/review systems. The system also has one or more processors. These processors are programmed to:

  1. Generate a "check-in intensity vector" for each venue. This vector has elements corresponding to individual venue visitors, with values based on how frequently those visitors checked into that venue over a specified period.
  2. Create a "pairwise venue similarity matrix." This matrix contains similarity scores for every pair of venues. Each score is calculated based on the similarity between the check-in intensity vectors of the two venues in the pair, considering both geographical and social distance. The social distance is determined by whether common users (or groups of users) visit both venues. The similarity score can also be zero if venues are beyond a certain geographical distance or not among each other's closest neighbors.
  3. Identify two or more geographic clusters of venues within the region, utilizing the pairwise venue similarity matrix.

• Independent Claim 13 (Method Claim): This claim outlines a computer-based method for discovering geographic clusters of venues. The method involves:

  1. Storing venue check-in data from multiple venue visitors for multiple venues in a geographic region in a computer database. The check-in data sources are similar to those in Claim 1 (e.g., mobile apps, POS, rating/review systems).
  2. Using one or more processors to generate a check-in intensity vector for each venue, similar to Claim 1, reflecting visitor check-in intensity over time.
  3. Using one or more processors to generate a pairwise venue similarity matrix, similar to Claim 1, where similarity scores between venue pairs are based on both geographical and social distance (common visitors).
  4. Using one or more processors to identify two or more geographic clusters of venues in the region based on the pairwise venue similarity matrix.

• Independent Claim 18 (Computer-Readable Medium Claim): This claim covers a computer-readable medium that stores computer-executable instructions. When these instructions are run by one or more processors, they cause the processors to perform the method described in Claim 13.

Uncertainty Regarding CAFC Dockets:
As of April 26, 2026, searches for US11222349 in CAFC (U.S. Court of Appeals for the Federal Circuit) 2026 dockets did not yield any specific results indicating litigation or other case activity related to this patent. It is possible that such information exists but is not publicly indexed or easily searchable through general queries, or that no such cases have been filed or advanced to the CAFC in 2026.

As of April 26, 2026, there is no known litigation specifically involving US patent 11222349 based on the searches conducted. The search results for CAFC and PACER provide general information about accessing court records but do not show any specific dockets for this patent. Unified Patents also did not return any specific litigation cases for US11222349.

Proceedings overview

There are no AIA trial proceedings on file for US Patent 11222349. The patent is currently untested by PTAB challenges.

Strategic summary

As of the current date, US Patent 11222349 has all its claims (1-20) untested by any AIA trial proceedings. This means that a defendant facing assertion of this patent would have the full range of prior-art grounds available for an IPR or PGR petition, assuming they meet the statutory requirements for filing. There is no estoppel landscape to consider from previous PTAB challenges for this patent. The absence of PTAB activity could be a signal that the patent has not yet been asserted widely or that any prior assertions have not prompted a PTAB challenge.

Recommended next steps

No PTAB activity exists for US Patent 11222349. If you are a defendant facing assertion of this patent, consider initiating an AIA trial proceeding (e.g., IPR) if strong prior art can be identified, as the claims have not been previously challenged and thus are not "hardened" by surviving PTAB review.

Inventors

• Justin Cranshaw: Employed by Carnegie Mellon University at the time of filing.
• Raz Schwartz: Employed by Carnegie Mellon University at the time of filing.
• Jason I. Hong: Employed by Carnegie Mellon University at the time of filing.
• Norman Sadeh-Koniecpol: Employed by Carnegie Mellon University at the time of filing.

All inventors assigned their interest to Carnegie Mellon University on the filing date, indicating their employment relationship with the university at that time.

Original assignee

Carnegie Mellon University. Its primary line of business is education and academic research. It is currently an operating entity. Carnegie Mellon University does not typically ship products in the traditional commercial sense, but its research often forms the basis for licensed technologies or spin-off companies.

Assignment timeline

• 2020-07-13 (executed) / recorded 2020-10-13 — Reel 055848/0345

  • Conveyance: ASSIGNMENT OF ASSIGNORS INTEREST
  • Assignor: HONG, JASON I.; SADEH-KONIECPOL, NORMAN; CRANSHAW, JUSTIN; SCHWARTZ, RAZ
  • Assignee: CARNEGIE MELLON UNIVERSITY
  • Correspondent: ROTHWELL, FIGG, ERNST & MANBECK, P.C., 806 15TH STREET, N.W., SUITE 800, WASHINGTON, DC 20005. This correspondent recurs in this chain.
  • Context: Transfer of patent rights from the inventors to their employer, Carnegie Mellon University.

• 2025-06-18 (executed) / recorded 2025-06-23 — Reel 062085/0500

  • Conveyance: LICENSE (recorded as "LICENSE"; Google Patents specifies "CONFIRMATORY LICENSE")
  • Assignor: CARNEGIE MELLON UNIVERSITY
  • Assignee: NATIONAL SCIENCE FOUNDATION
  • Correspondent: ROTHWELL, FIGG, ERNST & MANBECK, P.C., 806 15TH STREET, N.W., SUITE 800, WASHINGTON, DC 20005. This correspondent recurs in this chain.
  • Context: Licensing agreement between Carnegie Mellon University and the National Science Foundation, likely related to government funding as noted in the patent's "GOVERNMENT INTEREST" section.

Timeline diagram

timeline
    title Ownership of US 11222349
    2020 : Inventors assign to CMU
    2022 : Issued to CMU
    2025 : CMU licenses to NSF

NPE / troll-pattern signals

1. Shell-entity transfer: not present. Both Carnegie Mellon University and the National Science Foundation are established, operating institutions.
2. Known asserter in the chain: not present. Neither Carnegie Mellon University nor the National Science Foundation are identified as known patent asserters (NPEs).
3. Repeat correspondent across the chain: present. ROTHWELL, FIGG, ERNST & MANBECK, P.C. is listed as the correspondent for the assignment recorded on 2020-10-13 (Reel 055848/0345) and the license recorded on 2025-06-23 (Reel 062085/0500).
4. Cascading transfers: not present. There are only two transfers in the chain, separated by five years, and they do not involve chained LLCs.
5. Pre-litigation transfer: unclear. No specific litigation concerning this patent has been identified in the provided information or through general searches for 2026 CAFC dockets, thus this signal cannot be assessed.
6. Bankruptcy fire-sale: not present. There is no indication of bankruptcy for Carnegie Mellon University.
7. Privateering: not present. No evidence suggests a privateering arrangement.
8. Defensive aggregator (anti-NPE): not present. The chain does not terminate at a known defensive aggregator.

Verdict

Insufficient data
The assignment chain for US11222349 shows typical transfers from inventors to a university, followed by a license to a government agency, consistent with the patent's government interest statement. While the same correspondent attorney appears for both recorded transactions, this alone is not sufficient to indicate an NPE pattern without other strong corroborating signals. There is no evidence of shell entities, known asserters, or other common NPE characteristics. [cite: Reel 055848/0345, Reel 062085/0500]
(For verification, see USPTO Patent Assignment Search: https://assignmentcenter.uspto.gov/)

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.

Obviousness Analysis of US Patent 11222349 Under 35 U.S.C. § 103

This analysis evaluates the obviousness of US Patent 11222349, "Discovering neighborhood clusters and uses therefor," under 35 U.S.C. § 103, considering prior art available before the patent's priority date of August 30, 2012. The analysis focuses on the independent claims (Claims 1, 13, and 18) and identifies combinations of prior art references that would render these claims obvious to a person having ordinary skill in the art (PHOSITA).

Independent Claims Overview

The independent claims of US Patent 11222349 center on a computer-based system and method for discovering geographic clusters of venues using venue check-in data. Key aspects include:

• Claim 1 (System): A system with a database storing venue check-in data. Processors are programmed to generate a check-in intensity vector for each venue, then a pairwise venue similarity matrix where similarity scores are based on both geographical distance and social distance (determined by common venue visitors). Finally, the system identifies geographic clusters using this matrix.
• Claim 13 (Method): A method mirroring the steps of Claim 1, involving storing data, generating intensity vectors, generating a similarity matrix combining geographical and social distance, and identifying clusters.
• Claim 18 (Computer-Readable Medium): A computer-readable medium storing instructions to perform the method of Claim 13.

The core contribution claimed is the explicit combination of both geographical and social distance in a pairwise venue similarity matrix for the purpose of identifying "neighborhood clusters."

Identified Prior Art References

The patent itself cites several relevant works that predate its priority date, indicating they were known to the inventors and thus constitute prior art:

1. Cheng et al., “Exploring millions of footprints in location sharing services,” AAAI ICWSM, 2011. This reference concerns the analysis of user check-in data from location-based social networks (LBSNs). A PHOSITA would understand that such work involved collecting, storing, and analyzing venue check-in data, and representing venues based on user activity (e.g., "bag of check-ins" or "check-in intensity vectors") to derive social relationships or similarities between venues.
2. D. M. Blei and P. I. Frazier, “Distance dependent Chinese restaurant processes,” J. Mach. Learn. Res., November 2011 (ddCRP). This paper introduced the Distance Dependent Chinese Restaurant Process, a non-parametric Bayesian method for clustering non-exchangeable data. It teaches the use of a "similarity matrix A" to specify prior assumptions about the relationships between items for clustering. The term "distance dependent" suggests the ability to incorporate various forms of distance into the clustering process.
3. Ghosh et al., “Spatial distance dependent Chinese restaurant processes for image segmentation,” Neural Information Processing Systems, 2011. This work extends the ddCRP to hierarchical modeling and applies "Spatial distance dependent Chinese restaurant processes" specifically for image segmentation. Crucially, it demonstrates the explicit incorporation of "spatial distance" within a distance-dependent clustering framework.

Obviousness Analysis

A person having ordinary skill in the art (PHOSITA) in fields such as urban computing, data mining, or location-based services, before August 30, 2012, would have possessed a strong motivation to combine elements from these prior art references to arrive at the claimed invention.

Primary Reference (e.g., Cheng et al.): A PHOSITA would be familiar with systems and methods, as exemplified by Cheng et al., for collecting and analyzing user check-in data from LBSNs. This would teach:

• Storing venue check-in data from multiple users for multiple venues in a geographic region.
• Representing each venue by a "check-in intensity vector" (or "bag of check-ins"), where elements reflect the intensity of check-ins by various users over time.
• Calculating "social similarity" between pairs of venues based on common users visiting them, often using metrics like cosine or Jaccard similarity on these intensity vectors.
• Applying general clustering techniques to group venues based on these social similarities.

Motivation to Combine with Secondary References (Blei & Frazier, Ghosh et al.):

While social similarity is valuable, a PHOSITA attempting to define "neighborhood clusters" would recognize that geographical proximity is an indispensable characteristic of a neighborhood. Clusters based solely on social similarity might group venues that are socially related but geographically distant, which would not accurately reflect conventional neighborhood structures. The patent itself highlights the need to "discover[] neighborhood clusters in a city or other geographic region, where the clusters have a mix of venues and are determined based on venue check-in data" and notes that "Almost always, the geographical proximity of venues is a factor in grouping venues into a cluster".

Therefore, a PHOSITA would be highly motivated to combine geographical information with social similarity to produce more realistic and useful "geographic clusters" or "neighborhoods." The ddCRP framework introduced by Blei and Frazier provided a flexible mechanism for clustering based on a "pairwise similarity matrix" which could incorporate various measures of relationship.

Ghosh et al. further strengthens this motivation and provides a clear technical pathway by explicitly demonstrating the application of "Spatial distance dependent Chinese restaurant processes" for clustering in a spatial context. A PHOSITA would readily understand that the principles of incorporating "spatial distance" into a similarity-based clustering framework, as shown in Ghosh et al. for image segmentation, could be analogously applied to clustering venues in a geographical region.

Combining the Elements for Obviousness:

1. Generating Check-in Intensity Vectors and Social Similarity: A PHOSITA, starting with the teachings of Cheng et al., would generate check-in intensity vectors for venues and compute social similarity scores between venue pairs.
2. Incorporating Geographical Distance into a Pairwise Similarity Matrix: Given the goal of forming "neighborhood clusters" and the existence of "distance dependent" and "spatial distance dependent" clustering methods (Blei & Frazier, Ghosh et al.), a PHOSITA would find it obvious to integrate geographical distance into the similarity matrix used for clustering. Common methods for this integration, as described in US11222349 itself, include:
   • Setting similarity to zero if venues are beyond a certain geographical distance or not among each other's 'm' closest neighbors.
   • Using a decay function of geographical distance to weight the similarity score.
     These techniques for combining distance metrics into an affinity matrix for graph-based clustering were well-known in the art before 2012.
3. Identifying Clusters: Once such a pairwise venue similarity matrix (combining both social and geographical distance) is generated, applying any known graph-based clustering algorithm (e.g., spectral clustering, k-means, hierarchical clustering, all mentioned in the patent as alternatives) to identify the geographic clusters would be a straightforward and obvious step.

Conclusion:

The independent claims of US Patent 11222349, describing a system and method for discovering geographic clusters of venues by generating a pairwise venue similarity matrix that combines both geographical and social distance derived from venue check-in data, would have been obvious to a person having ordinary skill in the art before the priority date. The motivation to combine social and geographical factors for geographically meaningful "neighborhood clusters" is inherent in the problem domain, and the technical means for doing so were readily available through the understanding and combination of prior art such as Cheng et al., Blei & Frazier, and Ghosh et al. These references collectively teach the use of venue check-in data, social similarity derivation, distance-dependent clustering, and the incorporation of spatial distance into clustering algorithms.