Prosecution Insights
Last updated: April 19, 2026
Application No. 18/649,222

ERROR PREDICTION IN LOCATION SENSOR DATA USING MACHINE LEARNING MODEL

Final Rejection §101
Filed
Apr 29, 2024
Examiner
LIN, KATHERINE Y
Art Unit
2113
Tech Center
2100 — Computer Architecture & Software
Assignee
Here Global B V
OA Round
2 (Final)
91%
Grant Probability
Favorable
3-4
OA Rounds
2y 5m
To Grant
98%
With Interview

Examiner Intelligence

Grants 91% — above average
91%
Career Allow Rate
320 granted / 351 resolved
+36.2% vs TC avg
Moderate +7% lift
Without
With
+7.1%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
31 currently pending
Career history
382
Total Applications
across all art units

Statute-Specific Performance

§101
23.4%
-16.6% vs TC avg
§103
36.8%
-3.2% vs TC avg
§102
22.1%
-17.9% vs TC avg
§112
6.3%
-33.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 351 resolved cases

Office Action

§101
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 . 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. Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recites a limitation(s) of associating each reference point with a respective portion of the lane geometry information; calculating a first error associated with the obtained first location sensor data based on the respective portion of lane geometry information; and predicting a second error associated with at least one of the second location sensor data, the second lane, or the obtained second set of features associated with the second lane based on an output of the ML model, which is a mental process. The claim(s) recites a series of steps and, therefore, is/are a process. The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components. That is, other than reciting “memory,” “processor,” “medium,” nothing in the claim element precludes the step from practically being performed in the mind. For example, “associating” in the context of the claim(s) encompasses a user mentally associating each reference point with a respective portion of the lane geometry information, “calculating” in the context of the claim(s) encompasses the user calculating a first error associated with the obtained first location sensor data based on the respective portion of lane geometry information, “predicting” in the context of the claim(s) encompasses the user predicting a second error associated with at least one of the second location sensor data, the second lane, or the obtained second set of features associated with the second lane based on an output of the ML model. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim(s) recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, a step of “retrieving, from a map database, lane geometry information associated with a first lane within a first road link in a geographic region;” “obtaining first location sensor data for a first set of reference points, wherein the first location sensor data is obtained from at least one vehicle traveling on a road associated with the first lane;” or “obtaining a first set of features associated with each of the first set of reference points, wherein the obtained first set of features are associated with at least one of: altitude information associated with the respective reference point, or terrain information associated with the respective reference point, or a combination thereof” is recited at a high level of generality (i.e., as a general means of gathering lane geometry information, first location sensor data or a first set of features for use in the obtaining, calculating or training step, respectively), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. In addition, the claim(s) recites additional elements of “memory,” “processor,” “medium,” which are recited at a high-level of generality (i.e., as a generic “processor” performing a generic computer function of “training a machine learning (ML) model using the calculated first error and the obtained first set of features,” or “provide, as an input, the obtained second set of features to the ML model”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim(s) does not provide any indication that the recited system/medium is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that receiving or transmitting data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the step of “retrieving, from a map database, lane geometry information associated with a first lane within a first road link in a geographic region;” “obtaining first location sensor data for a first set of reference points, wherein the first location sensor data is obtained from at least one vehicle traveling on a road associated with the first lane;” or “obtaining a first set of features associated with each of the first set of reference points, wherein the obtained first set of features are associated with at least one of: altitude information associated with the respective reference point, or terrain information associated with the respective reference point, or a combination thereof” is well-understood, routine, conventional activity is supported under Berkheimer. In addition, the additional elements of using “memory,” “processor,” “medium” to perform the claimed invention amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Response to Remarks Applicant's Remarks have been fully considered but they are not persuasive. Regarding the rejections under 101, the Remarks state, “Claim 1 recites obtaining "location sensor data" and "features... associated with at least one of: altitude information... or terrain information" and using this data to train an ML model… It is not a process that can "practically be performed in the human mind"” However, the examiner respectfully disagrees. The location sensor data or the features associated with altitude info or terrain info can be obtained by viewing them on a display, reading them from a document, hearing them from another person, etc. In addition, the July 2024 Subject Matter Eligibility Example 47, claim 2 indicates training the ANN based on the input data is ineligible. The Remarks state, “Claim 1 does not merely gather data; it utilizes "altitude information" and "terrain information" to train a model that calculates error in "location sensor data." This improves the accuracy of the location data used by the vehicle.” “improving the accuracy of location sensor data by accounting for terrain and altitude.” However, the examiner respectfully disagrees. The July 2024 Subject Matter Eligibility Example 47, claim 2 indicates training the ANN, based on the input data, to detect anomalies is ineligible. The Remarks state, “Claim 17 recites: "transmit.. a control command to at least one vehicle. to control a driving mode corresponding to switching from a first driving mode to a second driving mode” However, the examiner respectfully disagrees. The term “driving mode” is vague. It could mean, for example, switching from a relaxing mode to a focused one. Conclusion THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE LIN whose telephone number is (571)431-0706. The examiner can normally be reached Monday-Friday; 8 a.m. - 5 p.m. EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at (571) 272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KATHERINE LIN/Primary Examiner, Art Unit 2113
Read full office action

Prosecution Timeline

Apr 29, 2024
Application Filed
Sep 06, 2025
Non-Final Rejection — §101
Dec 09, 2025
Response Filed
Mar 22, 2026
Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
91%
Grant Probability
98%
With Interview (+7.1%)
2y 5m
Median Time to Grant
Moderate
PTA Risk
Based on 351 resolved cases by this examiner. Grant probability derived from career allow rate.

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