DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election without traverse of Species I.A.I (claims 2 and 17) in the reply filed on Feb 19th 2026 and over the phone call on May 6th 2026 is acknowledged. The application has pending claims 1-20 (withdrawn claims 3, 6-15, and 18-20 are withdrawn from further consideration).
Information Disclosure Statement
No IDS has been filed for this application. Applicant is reminded of the duty to disclose from section 2100 of the MPEP:
37 C.F.R. 1.56; Duty to disclose information material to patentability.
A patent by its very nature is affected with a public interest. The public interest is best served, and the most effective patent examination occurs when, at the time an application is being examined, the Office is aware of and evaluates the teachings of all information material to patentability. Each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in this section.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4, 5, 16, and 17 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without significantly more.
This rejection has been made in accordance with the current USPTO subject matter eligibility framework, including MPEP §§ 2103–2106.07, the 2019 Revised Patent Subject Matter Eligibility Guidance, the October 2019 Patent Eligibility Guidance Update, the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the July 2024 AI Subject Matter Eligibility Examples, the August 4, 2025 USPTO memorandum titled “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. § 101,” and the USPTO’s guidance concerning Ex parte Desjardins, Appeal No. 2024-000567.
The claims have been evaluated under the broadest reasonable interpretation, and the claims have been considered as a whole.
Step 1: (Statutory Category)
Independent claim 1 is directed to a method and therefore falls within the statutory category of a process. Independent claim 16 is directed to a system comprising one or more memories and one or more processors and therefore falls within the statutory category of a machine. Accordingly, the analysis proceeds to Step 2A.
Step 2A, Prong One (Judicial Exception)
Independent claim 1 recites, in substance, receiving an unlabeled image; identifying one or more geohashes associated with the unlabeled image; determining whether each geohash of the one or more geohashes has been labeled; generating a coverage score for the unlabeled image based on the determination; evaluating whether the coverage score is below a predetermined threshold; and, in response to the coverage score being below the predetermined threshold, transmitting the unlabeled image to an image labeling system.
These limitations recite an abstract idea, namely collecting and analyzing information concerning geographic coverage of labeled image data, generating a score, comparing the score to a threshold, and routing data based on the result.
The claim recites information collection, information classification, data evaluation, scoring, comparison, and routing logic. The “geohash” information is used as data representing geographic area/ coverage, and the “coverage score” represents an evaluation of whether geographic regions associated with an unlabeled image are already represented in labeled training data. The claim does NOT recite an improvement to the way geohashes are generated, stored, compressed, indexed, searched, or technically processed. Rather, the geohashes are used as labels or identifiers for geographic areas in an abstract data-evaluation process.
The claim is similar in character to claims that courts have found abstract where the focus is collecting information, analyzing the information, and presenting or acting on the results of the analysis. In Electric Power Group, LLC v. Alstom S.A., the Federal Circuit recognized claims directed to monitoring and analyzing data as abstract. The present claims similarly collect image, geographic information, analyze whether corresponding geographic areas have been labeled, generate a score, and act on the result.
The claim is also consistent with the reasoning of AI Visualize, Inc. v. Nuance Communications, Inc., where the Federal Circuit looked to the character of the claims as a whole and affirmed ineligibility where the claimed advance was directed to accessing and manipulating information rather than to an improvement in computer functionality. Here, the character of claim 1 as a whole is data selection, routing based on geographic coverage scoring, not an improvement to image processing, geospatial indexing, computer memory, network operation, or machine-learning technology itself.
Independent claim 16 recites substantially the same abstract idea in system form using generic processors and memories. Merely implementing the same abstract data-evaluation and routing process on generic computer components does not avoid the judicial exception.
Accordingly, claims 1 and 16 recite an abstract idea under Step 2A, Prong One.
Step 2A, Prong Two (Integration into a Practical Application)
The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application.
The recited “unlabeled image”, “geohash”, “training data corpus”, “coverage score”, “predetermined threshold”, “image labeling system”, “one or more memories” and “one or more processors” amount to data objects, a field of use, and generic computer implementation of the abstract data-scoring and routing concept.
The claims do not recite a particular improvement to image-processing technology. They do not improve how an image is captured, encoded, decoded, segmented, classified, compressed, enhanced, transformed, or rendered. The claims also do not recite a particular improvement to geohash technology or geospatial indexing technology. The claims do not improve how geohashes are created, searched, stored, or used in a specific data structure. Rather, the geohashes are used as geographic identifiers for determining whether corresponding areas are already represented in labeled data.
The claims also do not recite a particular improvement to an artificial-intelligence model or machine-learning model. The claims do NOT train a model, update model parameters, modify model architecture, improve model inference, preserve prior model knowledge, reduce model storage, reduce model complexity, or otherwise improve how an AI model operates. At most, the claims select or route unlabeled data that may later be labeled for possible use in training. Such upstream data-management activity does not, without more, amount to a technological improvement to AI or machine-learning technology.
This analysis is consistent with the USPTO’s 2024 AI subject matter eligibility guidance and AI examples, which emphasize that AI-related claims may be eligible when they recite a specific technological improvement or otherwise integrate a judicial exception into a practical application. The present claims do not recite such a specific technological improvement. Instead, the claims use generic computer operations to perform data analysis and route an image based on the result.
This case is distinguishable from Ex parte Desjardins. In Desjardins, the claims were found to reflect an improvement in machine-learning technology itself, including training a machine-learning model on a series of tasks while preserving prior knowledge and reducing complexity, storage burdens. Here, the claims do not recite a particular training technique, model architecture, parameter-update mechanism, or data structure that improves operation of a machine-learning model. The claimed “coverage score” and threshold-based transmission merely determine whether an unlabeled image should be sent for labeling.
Nor does limiting the abstract idea to the environment of AI-training-corpus management make the claims eligible. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit rejected the argument that applying machine learning or related data-processing logic to a new field of use was sufficient for eligibility, where the claims did not recite a technical improvement in the machine-learning process itself. Similarly here, applying geographic/geohash-based scoring to select images for labeling in an AI-training-data environment is a field-of-use limitation, not an integration of the abstract idea into a practical application.
The transmitting step is also insufficient to integrate the exception into a practical application. Transmitting the unlabeled image to an image labeling system is insignificant post-solution activity following the abstract scoring and threshold comparison. Likewise, claim 4’s recitation of skipping the unlabeled image when the coverage score is above the predetermined threshold merely recites the alternative result of the abstract comparison.
Accordingly, the claims do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
Step 2B: (Significantly More/ Inventive Concept)
The additional elements, considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The claims use generic computer components to perform ordinary computer functions, including receiving data, identifying data, determining a status of data, generating a score, comparing the score to a threshold, transmitting data, and skipping data. These operations are conventional data-processing and data-routing operations performed using generic computer technology.
The ordered combination also does not provide an inventive concept. The ordered combination follows the abstract idea itself: receive an unlabeled image, identify associated geographic/geohash data, determine whether those geohashes are already represented in labeled data, generate a coverage score, compare the score to a threshold, and route or skip the image based on the comparison. This is no more than the abstract idea implemented on generic computer components.
Dependent claim 2 recites that identifying the one or more geohashes comprises determining a bounding geographic area for the unlabeled image and identifying the one or more geohashes based at least in part on the bounding geographic area. This limitation merely specifies the geographic data used in the abstract evaluation and does not add significantly more.
Dependent claim 5 recites that at least one geohash corresponds to a geographic area that is partially covered by the unlabeled image. This limitation merely describes a relationship between the image and geographic data and does not add significantly more.
Dependent claim 4 recites skipping the unlabeled image in response to the coverage score being above the predetermined threshold. This limitation merely recites the alternative result of the abstract comparison and does not add significantly more.
Claims 16 and 17 recite system counterparts using processors and memories to perform substantially the same operations as claims 1 and 2. The recitation of generic processors and memories does not transform the abstract idea into patent-eligible subject matter.
Accordingly, claims 1, 2, 4, 5, 16, and 17 are directed to a judicial exception without significantly more and are therefore rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–2, 4–5 and 16–17 are rejected under 35 U.S.C. §103 as being unpatentable over Frtunikj (Frtunikj et al, US 2022/0164602 A1, 2019) in view of Duan (Duan et al, US 2018/0113883 A1, 2018).
Regarding claim 1, with deficiencies of Frtunikj noted in square brackets [ ], Frtunikj teaches a method for managing data corpus, the method comprising:
receiving an unlabeled image;
( [0023], [0038], [Fig. 1]: Frtunikj discloses receiving “raw data logs collected" by sensors, wherein the raw/ unlabeled data logs may include 2D images, 3D images, image sequences, video, or other suitable data samples. )
identifying one or more location-based metadata [ geohashes ] associated with the unlabeled image;
( [0023-0024], [0028-0029]: Frtunikj teaches each received data log include metadata such as location information and/or map information corresponding to the location from which the data log was collected. Frtunikj further teaches using metadata associated with data logs in the training dataset, including location of collection, to identify trends and determine whether data logs collected at certain locations are missing or inadequately represented. )
determining whether each location-associated data log [ geohash ] of the one or more data logs [ geohashes ] has been labeled previously;
( [0008], [0028-0029], [0031-0033]: Frtunikj disclosesanalysis of various labels in the training dataset as well as metadata associated with the data logs, using metadata including location of collection, and identifying characteristics that are missing or inadequately represented in the training dataset, including label count by location. )
generating a coverage score for the unlabeled image based on the determination;
( [0031-0034]: Frtunikj assigns an importance score to each received data log based on existing training dataset trends or distributions. The claimed “coverage score” encompasses Frtunikj’s importance score because the score indicates whether the unlabeled image/ data log is useful for training based on whether corresponding characteristics, including location-based characteristics such as label count by location, are missing or inadequately represented in the existing labeled training dataset. )
evaluating whether the coverage score is below a predetermined threshold;
( [0034], [Fig. 1]: Frtunikj evaluates whether the importance score of a data log is greater than or equal to a threshold. Based on this comparison, decides whether to use the log for annotation/ training or to skip/ discard it. The claimed below-threshold coverage-score evaluation is an inverse scoring convention of Frtunikj’s above-threshold importance-score evaluation, because both score conventions perform the same threshold-based selection of underrepresented/ useful data. )
in response to the coverage score being below the predetermined threshold, transmitting the unlabeled image to an image labeling system;
( [0034]: Frtunikj discloses that, if the importance score satisfies the threshold condition, greater than or equal to a threshold, the system may use the data log for further annotation.)
wherein the method is performed using one or more processors.
( [0062]: Frtunikj teaches that the processing device includes a single processor or any number of processors in a set of processors. )
Frtunikj fails to represent geographic locations/ areas as geohash-grids, as noted above in square brackets [ ], where Duan teaches a known, efficient way for geohash-index technique:
identifying one or more geohashes associated with location;
( [0027-0031], [0037-0038]: Duan teaches that a target location may be associated with a geohash index and that geohash grids/ geohash trees may be used as hierarchical spatial hashing structures. Duan further teaches that geohash indexing may be initialized by calculating a minimum bounding box for a target geographic shape and forming a geohash grid by dividing the minimum bounding box into sections mapped to geohash indexes. )
determining whether each geohash of the one or more geohashes has been categorized previously;
( [0041-0044], [Figs. 5A-5C]: Duan teaches determining/categorizing each geohash-indexed section according to its relation to a corresponding geographic area, including whether the indexed section intersects the land area, is a full cover for the land area, or is a non-intersect of the land area. Duan further teaches subdividing intersect regions into sub-regions, assigning indexes to the sub-regions, and categorizing the sub-regions similarly. In this way, Duan determines, for each geohash in a set, whether it belongs to the coverage of the land area at the desired resolution. )
Regarding claim 2, Frtunikj [as modified by Duan] teaches the method of claim 1, wherein the identifying one or more geohashes associated with the unlabeled image comprises:
determining a bounding geographic area for the unlabeled image; and
( Frtunikj, in [0023]–[0024], teaches that the unlabeled data log may include image data, including 2D images, 3D images, image sequences, or video, and that each received data log may include metadata such as location information and/or map information corresponding to the location from which the data log was collected. Duan, in [0037], teaches geohash indexing by calculating a minimum bounding box (MBB) for a target geographic shape.)
identifying the one or more geohashes associated with the unlabeled image based at least in part on the bounding geographic area.
( [0037-0038]: Duan further teaches forming a geohash grid by dividing and/or separating the minimum bounding box for the geographic area into sections, and mapping the grid to a grid of geohash indexes to build an index table that relates the geohash grid segments to the geographic area. Because Frtunikj teaches that the unlabeled image/ data log is associated with location /map information, and Duan teaches identifying geohash indexes for the geographic area represented by such location/ map information, the geohashes identified by Duan’s process are associated with Frtunikj’s unlabeled image/ data log through the image/ data log’s associated location/ map information. )
Regarding claim 4, Frtunikj [as modified by Duan] teaches the method of claim 1, further comprising:
in response to the coverage score being above the predetermined threshold, skipping the unlabeled image for labeling.
( [0034], [Fig. 1]: Frtunikj teaches threshold-based skipping/non-selection of an unlabeled data log. If the importance score does not satisfy the threshold condition, the system “may not use the data log for updating/training a machine learning model” and may effectively discard it (step 110: NO → 114). )
Regarding claim 5, Frtunikj [as modified by Duan] teaches the method of claim 1, wherein the one or more geohashes include a geohash corresponding to a geographic area that is partially covered by the unlabeled image.
( Duan, in [0037] and [Fig. 5A-5C]: teaches representing geographic regions using a geohash-based grid and shows that individual geohash cells can fully cover, partially intersect, or not intersect a given geographic area (intersect vs full cover vs non-intersecting). Thus, a geohash-indexed section that intersects, but does not fully cover, the geographic area corresponds to a geographic area that is partially covered. Therefore, Frtunikj’s unlabeled image with location data modified by Duan’s geohash-indexed geographic-area mapping, teaches that the one or more geohashes include a geohash corresponding to a geographic area that is partially covered by the unlabeled image. )
Regarding claims 16–17. The rationale provided for claim 1–2 incorporated herein. In addition, the method of claims 1–2 correspond to the system of claims 16–17, and performs the steps disclosed herein. In addition, Frtunikj [as modified by Duan] further teaches internal hardware that may be included in any of the electronic components of the system, including processors, CPU, GPU, ROM, RAM, flash drives [Frtunikj: 0062; Duan: 0055]. Therefore, the claims are all rejected.
Conclusion
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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