DETAILED ACTION
Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/08/2025 and 06/11/2026 have been considered by the Examiner .
Claim Objections
Applicant is advised that should claim 7 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Examiner notes claims 1 and 8 are similar in scope with the difference between the claims coming from the updating limitation.
Claim 1 recites:
“updating the summary map by removing a subset of interest points from the summary map based on each interest point of the subset having a repeatability score below a threshold value”
Claim 8 recites:
“updating the summary map by adding interest points not in the summary map based on each additional interest point having a repeatability score above a threshold.”
Dependent claim 7, which depends from claim 1 and incorporates the limitations of claim 1, then recites the same language of claim 8. Therefore the scope of claim 7 updates the summary map by removing a subset and adding interest points based on the threshold.
Dependent claim 14, which depends from claim 8 and incorporated the limitations of claim 8, recites the same language of claim 1. Therefore the scope of claim 14 updates the summary map by removing a subset and adding interest points based on the threshold.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6-9, 13-16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Muhfellner (“Summary Maps for Lifelong Visual Localization”, provided on Applicant’s IDS dated 06/11/2026).
Regarding claim 1, Muhfellner teaches:
A computer-implemented method, comprising:
receiving first identification of a first location of a client device; (Muhlfellner page 567, This produces a set of vehicle poses T relative to some arbitrary global frame, as well as the features and odometry information associated with each retained vehicle frame)
generating a summary map of a vicinity of the location of the client device based on a plurality of interest points located in the vicinity of the first location of the client device; (Muhlfellner, page 567, The intermediate form is then transferred into the map representation… landmarks are initialized based on local image features tracked between the vehicle poses)
receiving a second identification of a second location of the client device; (Muhlfellner, page 567, When initializing the localization pipeline on a new dataset, we assume that the vehicle starts at one of the map frames)
determining a repeatability score of each interest point in the plurality of interest points based on sensor data collected by the client device at the second location; (Muhlfellner, page 569, A scoring function provides a usefulness of an individual landmark for localization…An intuitive choice for a scoring function is the number of observations for each landmark (Examiner interprets this as a repeatability score))
updating the summary map by removing a subset of interest points from the summary map based on each interest point of the subset having a repeatability score below a threshold value; (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session) and
sending the updated summary map to the client device. (Muhlfellner, page 571, While we performed the localization experiments offline, we have been running the proposed localization pipeline on the vehicle for more than one year, successfully demonstrating automated driving based on provided localization)
Regarding claim 2, Muhfellner teaches:
The computer-implemented method of claim 1, wherein determining a repeatability score of each interest point in the plurality of interest points comprises:
applying a repeatability model based on the interest point, the repeatability model trained based on a time series of images of a scene across a set time period. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 6, Muhfellner teaches:
The computer-implemented method of claim 1, wherein the repeatability score is indicative of a likelihood that a trained interest point detection model will detect the interest point based on images captured by the client device. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 7, Muhfellner teaches:
The computer-implemented method of claim 1, further comprising: updating the summary map by adding interest points not in the summary map based on each additional interest point having a repeatability score above a threshold value. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 8, Muhfellner teaches:
A computer-implemented method, comprising:
receiving first identification of a first location of a client device; (Muhlfellner page 567, This produces a set of vehicle poses T relative to some arbitrary global frame, as well as the features and odometry information associated with each retained vehicle frame)
generating a summary map of a vicinity of the location of the client device based on a plurality of interest points located in the vicinity of the location of the client device; (Muhlfellner, page 567, The intermediate form is then transferred into the map representation… landmarks are initialized based on local image features tracked between the vehicle poses)
receiving a second identification of a second location of the client device; (Muhlfellner, page 567, When initializing the localization pipeline on a new dataset, we assume that the vehicle starts at one of the map frames)
determining a repeatability score of each interest point in the plurality of interest points based on sensor data collected by the client device at the second location; (Muhlfellner, page 569, A scoring function provides a usefulness of an individual landmark for localization…An intuitive choice for a scoring function is the number of observations for each landmark (Examiner interprets this as a repeatability score))
updating the summary map by adding interest points not in the summary map based on each additional interest point having a repeatability score above a threshold value; (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session) and
sending the updated summary map to the client device. (Muhlfellner, page 571, While we performed the localization experiments offline, we have been running the proposed localization pipeline on the vehicle for more than one year, successfully demonstrating automated driving based on provided localization)
Regarding claim 9, Muhfellner teaches:
The computer-implemented method of claim 8, wherein determining a repeatability score of each interest point in the plurality of interest points comprises: applying a repeatability model based on the interest point, the repeatability model trained based on a time series of images of a scene across a set time period. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 13, Muhfellner teaches:
The computer-implemented method of claim 8, wherein the repeatability score is indicative of a likelihood that a trained interest point detection model will detect the interest point based on images captured by the client device. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 14, Muhfellner teaches:
The computer-implemented method of claim 8, further comprising: updating the summary map by removing a subset of interest points from the summary map based on each interest point of the subset having a repeatability score below a threshold value. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 15, Muhfellner teaches:
A non-transitory computer readable storage medium configured to store instructions, the instructions that, when executed by a processor, cause the processor to: (Mulhfellner, page 571, In the car we use an industrial grade computer with a desktop-architecture Intel Core i7 2600 CPU)
receive first identification of a first location of a client device; (Muhlfellner page 567, This produces a set of vehicle poses T relative to some arbitrary global frame, as well as the features and odometry information associated with each retained vehicle frame)
generate a summary map of a vicinity of the location of the client device based on a plurality of interest points located in the vicinity of the first location of the client device; (Muhlfellner, page 567, The intermediate form is then transferred into the map representation… landmarks are initialized based on local image features tracked between the vehicle poses)
receive a second identification of a second location of the client device; (Muhlfellner, page 567, When initializing the localization pipeline on a new dataset, we assume that the vehicle starts at one of the map frames)
determine a repeatability score of each interest point in the plurality of interest points based on sensor data collected by the client device at the second location; (Muhlfellner, page 569, A scoring function provides a usefulness of an individual landmark for localization…An intuitive choice for a scoring function is the number of observations for each landmark (Examiner interprets this as a repeatability score))
update the summary map by adding or removing a subset of interest points from the summary map based a repeatability score of each respective interest point; (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session) and
send the updated summary map to the client device. (Muhlfellner, page 571, While we performed the localization experiments offline, we have been running the proposed localization pipeline on the vehicle for more than one year, successfully demonstrating automated driving based on provided localization)
Regarding claim 16, Muhfellner teaches:
The non-transitory computer readable storage medium of claim 15, wherein the instruction that causes the processor to determine a repeatability score of each interest point in the plurality of interest points further causes the processor to: apply a repeatability model based on the interest point, the repeatability model trained based on a time series of images of a scene across a set time period. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Regarding claim 20, Muhfellner teaches:
The non-transitory computer readable storage medium of claim 15, wherein the repeatability score is indicative of a likelihood that a trained interest point detection model will detect the interest point based on images captured by the client device. (Muhlfellner page 569, This scoring function trades between removing intermittent landmarks, which have only been seen during a few sessions but might reappear, and landmarks that only appeared in a single session)
Allowable Subject Matter
Claims 3-5, 10-12, and 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Molly K Wilburn whose telephone number is (571)272-3589. The examiner can normally be reached Monday-Friday 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Molly Wilburn/Primary Examiner, Art Unit 2666
