US 11935082

Summary of US Patent 11935082

• Title: Discovering neighborhood clusters and uses therefor
• Assignee: Carnegie Mellon University
• Inventors: Justin Cranshaw, Raz Schwartz, Jason I. Hong, Norman Sadeh-Koniecpol
• Filing Date: January 10, 2022
• Issue Date: March 19, 2024
• Abstract: The patent describes computer-based systems and methods for identifying neighborhood clusters in a geographic region. These clusters feature a specific mix of venues, determined by analyzing venue check-in data. The mix of venues can be based on social similarity between venues, characteristic neighborhood typologies, temporal check-in patterns, or a combination of these factors. The discovered neighborhood clusters have various potential commercial and civic applications.

Plain-Language Overview of Independent Claims:

US Patent 11935082 includes several independent claims, outlining different embodiments of the invention.

• Claim 1 (System-based on social and geographical similarity): This claim describes a computer system that identifies geographic clusters of venues. It works by:

  1. Storing venue check-in data from many users across multiple venues in a geographic area. This data can come from mobile check-in apps, point-of-sale transactions, venue rating systems, or review systems.
  2. Using one or more processors to create a pairwise venue similarity matrix. This matrix assigns a similarity score to every pair of venues. This score considers both the geographical distance and the social distance between the venues. The social distance is determined by whether common users (or groups of users) visit both venues.
  3. Identifying at least two geographic clusters of venues based on this similarity matrix. Each cluster consists of a mix of one or more venues.

• Claim 9 (Method-based on social and geographical similarity): This claim describes a computer-implemented method for identifying geographic clusters of venues, mirroring the functionality of Claim 1. The method involves:

  1. Storing venue check-in data.
  2. Generating a check-in intensity vector for each venue, indicating how often different users checked into that venue.
  3. Generating a pairwise venue similarity matrix, where each score reflects both geographical and social distance (based on common visitors).
  4. Identifying geographic clusters of venues based on this matrix.

• Claim 17 (System-based on sub-regions): This claim describes a computer system for identifying geographic clusters of sub-regions (e.g., census tracts, school districts) within a larger geographic area. It operates by:

  1. Storing venue check-in data for multiple venues located within these sub-regions.
  2. Using one or more processors to generate a check-in intensity vector for each sub-region. This vector measures how intensely users checked into venues within that sub-region over time.
  3. Generating a pairwise sub-region similarity matrix. Each element in this matrix is a similarity score between a pair of sub-regions, based on the similarity of their check-in intensity vectors.
  4. Identifying at least two geographic clusters of sub-regions using this matrix, where each cluster comprises a mix of one or more sub-regions.

• Claim 20 (Method-based on sub-regions): This claim describes a computer-implemented method for identifying geographic clusters of sub-regions, similar to the system of Claim 17. The steps are:

  1. Storing venue check-in data where venues are in sub-regions.
  2. Generating a check-in intensity vector for each sub-region.
  3. Generating a pairwise sub-region similarity matrix.
  4. Identifying geographic clusters of sub-regions based on this matrix.

CAFC 2026 Docket Search:
A search of CAFC 2026 dockets for the specific patent number 11935082 did not return any direct results in the provided snippets. Therefore, there is no authoritative information from this search indicating any active litigation or status updates for this patent in the U.S. Court of Appeals for the Federal Circuit dockets as of April 26, 2026.

No known litigation involving US patent 11935082 was found through the conducted searches. A direct search for patent number 11935082 on patent litigation search sites such as Unified Patents and PACER, using the provided snippets, did not return any specific case details. While PACER is a comprehensive source for federal court records, it requires an account to conduct detailed searches, and the provided search results do not include specific litigation cases for this patent.

Proceedings overview

There are no AIA trial proceedings (Inter Partes Review, Post-Grant Review, or Covered Business Method) on file for US Patent 11935082 as of the current date, based on the USPTO Open Data Portal and web searches. This means the patent has not been challenged in an AIA trial at the PTAB, offering a strong defensive posture for the patent owner, as all claims remain untested by this specific post-grant review mechanism.

Strategic summary

All claims of US Patent 11935082 remain UNTESTED by AIA trial proceedings at the PTAB. There are no claims that have been CANCELED or SUSTAINED through an IPR, PGR, or CBM. This means the patent's scope, as granted, has not been challenged and potentially narrowed through these administrative trials.

Since no AIA trials have been initiated, there is no estoppel landscape under 35 U.S.C. § 315(e)(2) for potential petitioners or their privies regarding grounds that were raised or reasonably could have been raised. This means that a defendant facing assertion of this patent would theoretically have a full range of prior art grounds available for a potential future IPR petition, should they choose to file one. The absence of PTAB activity also suggests that the patent has not yet been aggressively asserted in litigation leading to such challenges, or that any prior assertions did not result in IPR filings. There are no observable patterns of repeated petitions by the same petitioner, aggressive appeals by the patent owner, or involvement of defensive aggregators like Unified Patents, as no proceedings exist.

Recommended next steps

Since no PTAB activity exists for US Patent 11935082, the absence itself is a signal. Well-asserted patents often attract IPR challenges. If you are a defendant being asserted against, consider the following:

• Prior Art Search: Conduct a thorough prior art search to assess the patentability of the asserted claims. The lack of PTAB proceedings means that potential prior art has not been officially vetted or challenged in this forum.
• Validity Analysis: Perform a detailed validity analysis of the patent's claims against any newly discovered or existing prior art.
• PTAB Challenge Consideration: If strong prior art is found, evaluate the viability of filing an Inter Partes Review (IPR) petition. Without prior PTAB challenges, the full spectrum of prior art arguments is available.
• Monitoring: Continuously monitor for any newly filed PTAB proceedings against US11935082, as the landscape can change rapidly if a new challenger emerges.

Inventors

• Justin Cranshaw: Carnegie Mellon University
• Raz Schwartz: Carnegie Mellon University
• Jason I. Hong: Carnegie Mellon University
• Norman Sadeh-Koniecpol: Carnegie Mellon University

All inventors were affiliated with Carnegie Mellon University at the time of filing, which is the original assignee. No unusual patterns are observed.

Original assignee

The original assignee is Carnegie Mellon University.
Carnegie Mellon University is a private research university. Its primary line of business is education and academic research. As a university, it typically generates intellectual property through research and may license technologies rather than directly shipping commercial products embodying the claims. The university is currently operating.

Assignment timeline

• 2022-01-10 (executed) / recorded 2022-01-19 — Reel 062085/0793
  • Conveyance: ASSIGNMENT
  • Assignor: CARNEGIE MELLON UNIVERSITY
  • Assignee: CARNEGIE MELLON UNIVERSITY
  • Correspondent: ROTHWELL, FIGG, ERNST & MANBECK, P.C., 806 FIFTEENTH STREET, N.W., SUITE 800, WASHINGTON, DC 20005.
  • Context: Internal record, likely a re-affirmation or formal recording of the original assignment from the inventors to Carnegie Mellon University on the patent's filing date (as indicated by Google Patents data).

There are no other recorded assignments for US11935082 found in the USPTO Assignment Center.

Timeline diagram

timeline
    title Ownership of US 11935082
    2022 : Assigned to Carnegie Mellon University
    2024 : Issued to Carnegie Mellon University

NPE / troll-pattern signals

1. Shell-entity transfer — Not present. The only recorded assignment is from Carnegie Mellon University to Carnegie Mellon University (or inventors to CMU), an operating research institution, not a shell entity.
2. Known asserter in the chain — Not present. Carnegie Mellon University is not identified as a known patent asserter (NPE).
3. Repeat correspondent across the chain — Unclear. The firm ROTHWELL, FIGG, ERNST & MANBECK, P.C. appears once on reel 062085/0793. With only one record, there's no recurrence within this chain, and its presence alone does not signal an NPE pattern.
4. Cascading transfers — Not present. Only one assignment is recorded.
5. Pre-litigation transfer — Not present. No litigation has been identified for this patent.
6. Bankruptcy fire-sale — Not present. Carnegie Mellon University is an active, operating institution.
7. Privateering — Not present. There is no evidence to suggest privateering.
8. Defensive aggregator (anti-NPE) — Not present. The patent is held by Carnegie Mellon University, which is not a defensive aggregator.

Verdict

Insufficient data.
Based on the single USPTO assignment record (Reel 062085/0793, recorded 2022-01-19) showing Carnegie Mellon University as both the assignor and assignee, there are no signals indicating NPE activity or a transfer away from the original operating institution. The patent appears to be held by its original assignee.

USPTO Assignment Center Search for US11935082

To identify the most relevant prior art for US patent 11935082, I need to access the patent's full citation details and its list of cited references from the USPTO database. Since I cannot directly interact with a live USPTO search portal, I will rely on the provided patent text to extract the cited prior art references.

Based on the full patent text provided, I will extract information on cited prior art. The "Prior art keywords" section on Google Patents often lists keywords from prior art, but for specific patent citations, I need to look at the "References Cited" section of the full patent document. The provided text is a detailed description of the patent, but it doesn't explicitly list a "References Cited" section with full patent citations in a structured manner typical of a USPTO patent document. However, it does mention prior art and related research papers in the description.

Specifically, the patent description mentions the following prior art in relation to its methods:

1. "Distance dependent Chinese restaurant processes," by D. M. Blei and P. I. Frazier, J. Mach. Learn. Res., 2461-2488, November 2011. This paper is explicitly incorporated by reference and is foundational to the ddCRP modeling used in the patent for discovering neighborhood typologies.

   • Publication Date: November 2011
   • Description: This paper introduces the distance dependent Chinese restaurant process (ddCRP), which specifies a distribution over partitions of non-exchangeable data, used as a nonparametric prior over mixture components.
   • Potential Anticipation: This reference potentially anticipates claims related to the use of distance-dependent Chinese restaurant processes for clustering, particularly claims involving probabilistic models for determining neighborhood typologies based on venue category data or temporal check-in patterns. For instance, the general concept of using ddCRP for clustering and defining partitions of data could be considered.

2. "Spatial distance dependent Chinese restaurant processes for image segmentation," by Ghosh et al., Neural Information Processing Systems, 2011. This work extends the ddCRP to hierarchical modeling and is also incorporated by reference.

   • Publication Date: 2011
   • Description: This paper extends the ddCRP to hierarchical modeling, where observations in different groups are linked by sharing neighborhood parameters across cities.
   • Potential Anticipation: This reference could potentially anticipate claims relating to the hierarchical application of ddCRP, especially for segmenting data based on spatial distances, which the patent applies to clustering venues or sub-regions. Claims involving the use of hierarchical probabilistic models for determining neighborhood clusters could be affected.

3. "Exploring millions of footprints in location sharing services," by Cheng et al., AAAI ICWSM, 2011. This paper is cited as the source for approximately 11 million Foursquare check-ins used in the inventors' experiments.

   • Publication Date: 2011
   • Description: This publication describes the extraction of check-in data from various location-based social networks, specifically Twitter public feeds, which was used in the experimental validation of the claimed invention.
   • Potential Anticipation: While not directly describing a clustering methodology, this reference demonstrates the availability and use of large-scale venue check-in data from social media platforms prior to the patent's priority date. It could be relevant to the data collection and input aspects of the claims, specifically regarding the "storing venue check-in data from multiple venue visitors for multiple venues in the geographic region" element.

4. "From deformations to parts: Motion-based segmentation of 3d objects," by Ghosh et al., Advances in Neural Information Processing Systems 25, pp. 2006-2014, 2012. This reference is mentioned as providing portions of the ddCRP Gibbs sampler used in the MATLAB implementation for the patent's experiments.

   • Publication Date: 2012
   • Description: This paper describes a ddCRP Gibbs sampler for 3D Mesh segmentation, which was adapted and extended for the hierarchical model used in the patent's experiments.
   • Potential Anticipation: This reference could be relevant to the specific implementation details of the Gibbs sampling method for clustering, particularly if any claims were to focus on the particular mechanics of the sampler itself. However, the patent emphasizes its adaptation of this work for a different purpose (neighborhood clustering).

It's important to note that the patent description highlights how its methods build upon and extend these prior art techniques, particularly the ddCRP and its hierarchical extensions, by applying them to the specific problem of discovering neighborhood clusters based on venue check-in data in a novel way that considers geographical proximity, social similarity, neighborhood typologies, and temporal patterns. Therefore, while these references describe foundational techniques, the patent claims aim to distinguish the invention by its specific application and combinations of these techniques for neighborhood clustering.

To establish obviousness under 35 U.S.C. § 103 for US Patent 11935082, it is necessary to identify prior art references that, when combined, would have made the claimed invention obvious to a person having ordinary skill in the art (PHOSITA) at the time of the invention (i.e., before the priority date of August 30, 2012). The patent itself cites and incorporates several relevant academic works that predate this priority date, offering strong grounds for an obviousness analysis. A PHOSITA in this field would likely possess expertise in data mining, machine learning, geographic information systems (GIS), and social network analysis.

The independent claims of US Patent 11935082 generally describe:

• Claims 1 and 9 (Venue-based): A system/method for identifying geographic clusters of venues by storing venue check-in data, generating a pairwise venue similarity matrix based on both geographical distance and social distance (derived from common venue visitors), and then clustering venues using this matrix.
• Claims 17 and 20 (Sub-region-based): A system/method for identifying geographic clusters of sub-regions (e.g., census tracts) by storing venue check-in data for venues within those sub-regions, generating check-in intensity vectors for the sub-regions, creating a pairwise sub-region similarity matrix based on these vectors, and then clustering sub-regions.

Prior Art References for Obviousness Analysis:

1. Cheng et al., "Exploring millions of footprints in location sharing services," AAAI ICWSM, 2011 (hereinafter "Cheng"): This paper describes the collection and analysis of check-in data from location-based social networks like Foursquare, often shared via Twitter. It details methods for extracting venue names, IDs, and categories from such data.
2. Blei and Frazier, "Distance dependent Chinese restaurant processes," J. Mach. Learn. Res., November 2011 (hereinafter "Blei"): This reference introduces the Distance Dependent Chinese Restaurant Process (ddCRP), a non-parametric Bayesian method for clustering non-exchangeable data using a pairwise similarity matrix. It explicitly notes that the similarity matrix is a "flexible way to specify prior assumptions about the strength of relationships between pairs."
3. Ghosh et al., "Spatial distance dependent Chinese restaurant processes for image segmentation," Neural Information Processing Systems, 2011 (hereinafter "Ghosh 2011"): This work extends the ddCRP to hierarchical modeling and incorporates spatial information, further indicating the use of spatial data in clustering with ddCRP.
4. General knowledge in the art: Prior to 2012, clustering algorithms (e.g., spectral clustering, k-means, hierarchical clustering) and the concept of combining multiple similarity metrics (e.g., geographic and social) for data analysis were well-established.

Obviousness of Claims 1 and 9 (Venue-Based Clustering)

Claim 1 (System) and Claim 9 (Method) elements:

• A computer system/method.
• Storing venue check-in data from multiple venue visitors for multiple venues in a geographic region (from apps, POS, ratings, reviews).
• Generating a check-in intensity vector for each venue (Claim 9).
• Generating a pairwise venue similarity matrix, where each score is based on both geographical distance and social distance.
• Social distance is determined based on whether common venue visitors (or groups of visitors) visit the pair of venues.
• Identifying two or more geographic clusters of venues based on this matrix.

Combination and Motivation:

1. Data Collection and Check-in Intensity: Cheng (2011) explicitly teaches the collection of venue check-in data from location-based social services (e.g., Foursquare via Twitter), including user IDs, venue IDs, and categories. [0164-0165] This directly provides the "venue check-in data from multiple venue visitors for multiple venues" as recited in the claims. A PHOSITA would readily understand how to process this raw check-in data to quantify user activity at venues, thereby generating "check-in intensity vectors" for each venue as described in the patent.
2. Social Similarity: The patent explicitly states that "social similarity is assessed based on whether common users visit (or check-into) the venues." Given the user-venue check-in data from Cheng (2011), calculating social similarity (e.g., via cosine or Jaccard similarity of check-in intensity vectors) between pairs of venues based on common users would be a routine application of known similarity measures in data analysis. The patent itself suggests using cosine or Jaccard similarity. [0080-0081]
3. Geographical Distance: The patent discloses computing "a geographical distance d(i,j) based on, for example, the GPS coordinates (latitude and longitude) for the venues i,j." Given that location-based services inherently rely on geographical coordinates (e.g., GPS from mobile devices), obtaining such data for venues would be obvious from Cheng (2011) and general knowledge of location services.
4. Combining Social and Geographical Distance into a Similarity Matrix: Blei (2011) and Ghosh (2011) introduce the ddCRP, which uses a "similarity matrix A={a i,j}" to cluster non-exchangeable data, with Ghosh (2011) specifically extending it to incorporate spatial data. The patent's own description indicates that "Almost always, the geographical proximity of venues is a factor in grouping venues into a cluster" and that "venues are grouped based on the social similarity of the venues." A PHOSITA, wanting to discover meaningful "neighborhood clusters" (as described in the patent's abstract and the provisional application's title, "Utilizing social media to understand the dynamics of a city") from location-based social media data (Cheng 2011), would be motivated to combine these two inherently relevant factors—social interaction and geographic proximity—into a unified similarity metric. The patent itself outlines methods for combining them, such as: a(i, j) = g × s(i, j) + α where s(i, j) is social similarity and the term is zero if venues are beyond m closest neighbors, or by using a decay function of geographical distance [0084, 0086-0087]. These specific formulations would be obvious design choices for a PHOSITA implementing a combined similarity metric.
5. Identifying Clusters: The patent refers to using "spectral clustering" or "other graph-based clustering algorithms besides spectral clustering" such as "hierarchical clustering, density-based clustering, centroid-based clustering such as k-means, distribution or model based clustering such as Gaussian mixture models, graph partition clustering, social network community detection, graph layout-based clustering, and others." [0094-0096] These are all well-known clustering techniques in the art and would be obvious to apply to a similarity matrix derived as above.

Therefore, the combination of Cheng (2011) providing the necessary data, Blei (2011) and Ghosh (2011) providing the framework for distance-dependent clustering with similarity matrices, and general knowledge of combining metrics and clustering algorithms, would render Claims 1 and 9 obvious to a PHOSITA. The motivation would be to create more accurate and meaningful geographic groupings of venues by leveraging both the social and physical characteristics of urban spaces revealed by check-in data.

Obviousness of Claims 17 and 20 (Sub-region-Based Clustering)

Claim 17 (System) and Claim 20 (Method) elements:

• A computer system/method.
• Storing venue check-in data from multiple venue visitors for multiple venues in a geographic region, where each venue is located in one of multiple sub-regions.
• Generating a check-in intensity vector for each sub-region (based on check-ins to venues in that sub-region).
• Generating a pairwise sub-region similarity matrix (based on the similarity of their check-in intensity vectors).
• Identifying two or more geographic clusters of sub-regions based on this matrix.

Combination and Motivation:

1. Data Collection and Assignment to Sub-regions: Cheng (2011) teaches gathering venue check-in data. The patent itself explicitly mentions using "TIGER/Line municipal boundary Shapefiles published by the United States Census Bureau were used to assign venues to their proper local administrative unit (e.g. city or town)." This demonstrates that the practice of assigning venues to defined geographic sub-regions (like census tracts or municipal boundaries) was a known preprocessing step for location data, prior to the priority date.
2. Sub-region Check-in Intensity Vector: Once venues are assigned to sub-regions (as described in the patent and using data from Cheng 2011), aggregating the check-in data from all venues within a sub-region to form a "check-in intensity vector for each of multiple sub-regions" is a straightforward data aggregation step. The patent describes that "the elements of the check-in count vector would show the cumulative number of times that the venue visitors checked into venues in the various geographic sub-regions over a period of time." This is a direct application of the venue-level check-in intensity concept (from Claims 1/9) but aggregated to the sub-region level.
3. Pairwise Sub-region Similarity Matrix: Similar to the venue-based clustering, once check-in intensity vectors are generated for sub-regions, generating a pairwise similarity matrix between these sub-regions based on the similarity of these vectors would be an obvious extension. This leverages the same principles of social similarity (common users visiting venues within a sub-region) as applied in Claims 1 and 9. The patent states, "the elements of the pairwise similarity matrix would correspond to the similarity score between pairs of geographic sub-regions."
4. Identifying Clusters of Sub-regions: Applying known clustering algorithms (as identified for Claims 1 and 9) to this sub-region similarity matrix to "identify two or more geographic clusters of sub-regions" is a direct and obvious application of standard clustering techniques.

Motivation for Combination:
A PHOSITA, having developed or understood the methods for clustering individual venues, would be motivated to extend this analysis to larger, administratively defined geographic units (sub-regions). This is a common practice in fields like urban planning, demography, and market analysis, where insights are often needed at a broader scale than individual venues. The patent itself describes this as an "other embodiment" where "the system could be used to cluster sub-regions... such as census tracts, school districts, or some other geographic regions with defined boundaries." [0215-0216] This indicates it was a recognized and straightforward generalization of the venue-level clustering. The motivation is to provide insights at a more aggregated, policy-relevant level, such as "how a municipality allocates its resources, such as the location of fire stations, police stations, schools, polling stations, bust stops, etc."

Thus, the combination of Cheng (2011) for data, the concept of social/geographic similarity and clustering from Blei (2011) and Ghosh (2011), coupled with the routine practice of aggregating data to administrative geographic units, would render Claims 17 and 20 obvious.