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 .
Response to Arguments
Applicant's arguments filed 05/04/2026 on page 6 regarding 35 U.S.C. § 112(f) Interpretation have been fully considered they are persuasive.
Applicant's arguments filed 05/04/2026 on page 7-9 regarding 35 U.S.C. § 112(b) have been fully considered they are persuasive. Examiner withdraws the 35 U.S.C. § 112(b) rejections.
Applicant's arguments filed 05/04/2026 pages 9-12 regarding 35 U.S.C. § 101 have been fully considered but they are not persuasive.
Regarding the rejection of claims 1–15 under 35 U.S.C. § 101, applicant argues that independent claims 1, 6, and 11 are not directed to an abstract idea because they recite a specific machine learning technique for reducing location error in road-sign readings obtained from connected vehicles. Applicant particularly relies on clustering readings associated with a common road sign, matching each cluster to a tagged road-sign dataset entry, determining an aggregate location for the cluster, computing a correction vector, and training a machine learning model using road-sign metadata as input and the correction vector as a training target.
These limitations have been considered individually and as an ordered combination. The claims recite grouping and matching information, determining an aggregate location, and calculating a correction vector based on the difference between an aggregate location and a reference location. These operations constitute evaluation, comparison, and mathematical calculation and therefore recite an abstract idea.
The remaining limitations do not integrate the abstract idea into a practical application. Obtaining road-sign readings from connected vehicles merely supplies the data on which the recited analysis is performed. Training a machine learning model using selected metadata and correction vectors applies computer-based processing to the results of that analysis.
Although the claims state that the model is trained to predict a location correction, independent claims 1, 6, and 11 do not require the predicted correction to be applied in a manner that integrates the abstract idea into a practical application. Rather, the claims terminate with training a model to predict additional location related information.
Applicant’s reliance on the specification’s discussion of noisy or inaccurate vehicle generated road-sign data is acknowledged. However, the eligibility analysis is based on what the claims require. Identifying a technical problem or an intended benefit in the specification does not establish eligibility when the claims do not recite a practical technological application that produces the asserted improvement.
Applicant’s reliance on eligible claim 3 of USPTO Example 47 is also not persuasive. In Example 47, the eligible claim used the output of an artificial neural network to identify the source of malicious network traffic, drop malicious packets in real time, and block future traffic from the identified source. Thus, the claim required a concrete technological use of the ANN output.
The present independent claims do not recite a comparable technological use of the model output. They do not require the predicted correction to be applied in a manner that integrates the abstract idea into a practical application. Rather, the claims are directed to generating and training a model to produce location-correction information. Accordingly, the claims are more analogous to the claim in Example 47 that processed information using an ANN and produced output data without requiring a sufficient technological application of that output.
Applicant further argues that the claims do not merely use a generic machine learning model as a black box because they specify road-sign metadata as the model input and a cluster-derived correction vector as the training target. These limitations have been considered as part of the claims as a whole. Nevertheless, specifying the content of the training inputs and targets does not, by itself, establish an improvement to machine learning technology. The claims do not recite a particular model architecture, training mechanism, optimization technique, or other change to how the machine learning system itself operates.
Applicant also argues that the Examiner characterized the claims at an impermissibly high level of abstraction. This argument is not persuasive because the rejection identifies the specific limitations that recite grouping, matching, aggregating, comparing, and calculating information and separately evaluates the remaining claim limitations under Step 2A, Prong Two. The claims have therefore been considered as a whole rather than reduced merely to the general concept of clustering, comparing, and training.
Under Step 2B, the claims also do not recite significantly more than the identified abstract idea. The connected vehicles and road-sign data limit the claimed information processing to a particular field of use. The processor, memory, and computer-program limitations provide a generic computer implementation for performing the recited operations. Training the model using the identified inputs and targets does not add a separate inventive concept that transforms the claimed data processing operations into patent eligible subject matter.
Accordingly, Applicant’s arguments do not overcome the determination that claims 1–15 are directed to an abstract idea without significantly more. The rejection of claims 1–15 under 35 U.S.C. § 101 is maintained.
Applicant’s arguments filed 05/04/2026 on pages 12-17 regarding 35 U.S.C. § 103 with respect to claim(s) 1-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2106 (III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-15, in accordance with these steps, follows.
Step 1 Analysis:
Claims 1-5 are directed to method (processes). Claims 6-10 are directed to a device (machine). Claims 11-15 are directed to a computer program product (article of manufacture). Therefore, claims 1-15 fall into one of four statutory categories (i.e., process, machine, article of manufacture).
As to claim 1,
Step 2A Prong 1: this claim recites the following abstract ideas:
using a clustering algorithm to group together readings associated with a common road sign into clusters; (the limitation describes the act of sorting scattered observations into groups based on their similarities to identify which ones belong to the same object, which is a mental process implemented in the human mind.)
for each cluster, matching the cluster to a tagged road-sign dataset entry corresponding to the common road sign and having a correct location of the common road sign; (this limitation describes the act of comparing and matching data entries to identify correspondences, which is a mental process implemented in the human mind.)
for each cluster, determining an aggregate location for the cluster from the locations of the readings in the cluster and computing a correction vector as a difference between the aggregate location and the correct location of the matched tagged road-sign dataset entry; (this limitation describes the calculation of a correction value by comparing two locations, which is a mental process implementable using pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
obtaining an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
training a machine learning model using the road sign metadata of individual readings in the cluster as input and the correction vector of the cluster as a training target, to predict a location correction for an individual reading. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
The additional elements do not integrate the judicial exception into practical application and do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A Prong 1: this claim recites the following abstract ideas:
using a clustering algorithm to group together readings associated with a common road sign into clusters; (the limitation describes the act of sorting scattered observations into groups based on their similarities to identify which ones belong to the same object, which is a mental process implemented in the human mind.)
matching the cluster to a tagged road-sign dataset entry corresponding to the common road sign and having a correct location of the common road sign, and determining, for each cluster, an aggregate location for the cluster from the locations of the readings in the cluster and computing a correction vector as a difference between the aggregate location and the correct location of the matched tagged road-sign dataset entry; (this limitation describes the calculation of a correction value by comparing and matching data, which is a mental process implemented using pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the system to obtain an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith; (this limitation describes data collection/receiving using generic computer components, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
train a machine learning model to predict a location correction for an individual reading using the road sign metadata of individual readings in the cluster as input and the correction vector of the cluster as a training target. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
The additional elements do not integrate the judicial exception into practical application and do not amount to significantly more than the judicial exception.
As to claim 11,
Step 2A Prong 1: this claim recites the following abstract ideas:
applying a clustering algorithm to group together readings associated with a common road sign into clusters; (the limitation describes the act of sorting scattered observations into groups based on their similarities to identify which ones belong to the same object, which is a mental process implemented in the human mind.)
matching the cluster to a tagged road-sign dataset entry corresponding to the common road sign and having a correct location of the common road sign, and determining, for each cluster, an aggregate location for the cluster from the locations of the readings in the cluster and computing a correction vector as a difference between the aggregate location and the correct location of the matched tagged road-sign dataset entry; (this limitation describes the calculation of a correction value by comparing and matching data, which is a mental process implemented using pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
obtaining an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
training a machine learning model to predict a location correction for an individual reading using the road sign metadata of individual readings in the cluster as input and the correction vector of the cluster as a training target. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
The additional elements do not integrate the judicial exception into practical application and do not amount to significantly more than the judicial exception.
As to claims 2, 7, and 12
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1,
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
Running the machine learning model on the training dataset of readings. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the judicial exception.
As to claims 3, 8, and 13
Step 2A Prong 1: this claim recites the following abstract ideas:
The clustering, the matching, the determining, and the training. (These limitations describe repeatedly performing evaluation and comparison which are mental processes involving judgment and decision making.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
Until the machine learning model stops improving. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the judicial exception.
As to claims 4, 9, and 14
Step 2A Prong 1: this claim recites the following abstract ideas:
repair the entire raw dataset. (The limitation describes a form of data analysis and processing of information, which is a mental process.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
Using the trained machine learning model to. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).)
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the judicial exception.
As to claims 5, 10, and 15
Step 2A Prong 1: this claim recites the following abstract ideas:
Running the same clustering algorithm to produce the location of all signs in a raw dataset. (The limitation describes organizing and categorizing information to determine locations, which is an evaluation and judgment activity that can be performed as a mental process.)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20200192397 A1) in view of Saxena et al. (US 20230053157 A1).
As to claim 1, Zhang teaches a method of generating a machine learning model for reducing a location error of readings of road signs sensed by connected vehicle, said method comprising:
obtaining an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith (see Zhang paragraph [0059] "the sensors installed in the vehicle 301B may capture road signs along the pathway and capture the location of the road sign, type of the road sign, value of the road sign, heading at the location of the road sign, time stamp associated with the time of capture of the road sign and speed of the vehicle 301B. The captured location of the road sign, type of the road sign, value of the road sign, heading at the location of the road sign, and time stamp associated with the capture of the road sign constitute the road sign observations.")
using a clustering algorithm to group together readings associated with a common road sign into clusters (see Zhang paragraph [0057] "The system 100 may then cluster the road sign observations to generate learned road signs and map match the learned road signs to links of the road.")
(see Zhang paragraph [0062] "the system 100 may fetch ground truth data from the map database 107 … The system 100 may extract a second plurality of road sign observations from the first plurality of road sign observations based on the ground truth data.")
for each cluster, matching the cluster to a tagged road-sign dataset entry corresponding to the common road sign and having a correct location of the common road sign (see Zhang paragraph [0062] "the system 100 may fetch ground truth data from the map database 107, which may be similar to the map database 105A. The system 100 may extract a second plurality of road sign observations from the first plurality of road sign observations based on the ground truth data. The ground truth data may indicate an actual location of the road sign, an actual heading of the road sign, an actual road sign type of the road sign, and an actual road sign value.", and see Zhang paragraph [0071] "searching for one or more candidate road sign observations in the first plurality of road sign observations, with corresponding location data lying within a radius of a threshold distance from the actual location of the road sign")
for each cluster, determining an aggregate location for the cluster from the locations of the readings in the cluster and computing a correction vector as a difference between the aggregate location and the correct location of the matched tagged road-sign dataset entry; and (see Zhang paragraph [0063] "Next, the system 100 may derive a regression function and calculate a distance used to minimize the plurality of longitudinal offsets.", and see Zhang paragraph [0064] "The system 100 may thus determine positional offset of the road sign based on two factors viz. learned heading and learned location of a road sign, and the derived regression function. The learned heading and learned location may be based on the heading data and location data of a plurality of vehicles, such as the vehicle 301B. The system 100 may update the location of the road sign on a map-matched link, based on a learned location of the road sign and the determined positional offset.", and see Zhang paragraph [0077] "positional offset=a×factor+b; where a and b: the parameters in the linear regression function, factor: average of running speeds of vehicles, 85th percentile speed or the speed limit.")
training a machine learning model using the road sign metadata of individual readings in the cluster as input and the correction vector of the cluster as a training target, to predict a location correction for an individual reading.
(see Zhang paragraph [0062] "The system 100 may extract a second plurality of road sign observations from the first plurality of road sign observations based on the ground truth data. The ground truth data may indicate an actual location of the road sign, an actual heading of the road sign, an actual road sign type of the road sign, and an actual road sign value. In order to extract the second plurality of road sign observations, the system 100 may search for one or more candidate road sign observations in the first plurality of road sign observations, with corresponding location data lying within a radius of a threshold distance from the actual location of the road sign.", and see Zhang paragraph [0063] "Next, the system 100 may derive a regression function and calculate a distance used to minimize the plurality of longitudinal offsets.")
Zhang does not explicitly teaches "training a machine learning model using the road sign metadata of individual readings"
However, Saxena teaches:
training a machine learning model using the road sign metadata of individual readings in the cluster as input and the correction vector of the cluster as a training target, to predict a location correction for an individual reading. (see Saxena paragraph [0038] "The neural network trainer 121 may define the model... The model may be configured, through configuration setting or the training procedure, to output a correction value or offset to correct the GPS position as determined by the GPS receiver.", and see Saxena paragraph [0053] "The model is trained according to past ephemeris data 201 and almanac data 202 using the already calculated historical data 203 and/or offset values 204. That is known positions are paired with GNSS calculated positions to train the model. Specifically, the neural network module 120 may receive an offset value determined for a base location. The offset value may be a difference between the surveyed positions and the position calculate ed from GNSS.", and see Saxena paragraph [0063] "The neural network training module 121 determines whether to continue training the model based on the difference between the true offset and the predicted offset. At act 309, the neural network training module 121 compares the difference (i.e., compares the true offset and the predicted offset).")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhang, which discloses obtaining road sign observations from connected vehicles, clustering those observations to derive a learned sign location, comparing the learned location against ground truth data to compute a positional offset, and applying a regression function derived from road sign metadata to correct sign location errors, to replace the regression-based correction function of Zhang with a neural network machine learning model that is trained using the road sign metadata of individual readings as input features and the computed positional offset as the training target, iterating until the model stops improving, and deploying the trained model to predict location corrections for new individual readings at scale, as taught by Saxena, because Saxena explicitly teaches that its neural network approach "provide systems and techniques to correct GPS calculations without using reference stations or at least using reference stations to a lesser degree" and that the approach eliminates "the constraints of time and cost and provide a scalable solution" over fixed correction methods (see Saxena paragraph [0024] and paragraph [0026]), thereby providing a person of ordinary skill with an explicit motivation to substitute the limited linear regression correction of Zhang with the more generalizable and scalable neural network training framework of Saxena, with a reasonable expectation of success given that both references are directed to the same technical problem of correcting vehicle-sensed position errors by training on offset values computed as the difference between observed and ground truth positions, and both are assigned to the same assignee HERE Global B.V., confirming they operate in the same technical field and address the same class of location error correction problems.
As to claim 2, Zhang as modified by Saxena teaches the method according to claim 1,
further comprising running the machine learning model on the training dataset of readings. (see Saxena paragraph [0054] "Once trained, the neural network of the neural network module 120 is configured to receive new ephemeris data 201 and almanac data 202 and predict the offset values without using the known positions.", and see Saxena paragraph [0064] "When the predicted offset is not equal to, or more than the threshold amount away from the true offset, the predicted offset shown at 302 is updated and training continues (e.g., the process repeats with another location or at another time instance). Thus, the neural network training module 121 sends the corrected value for the offset to the model when the difference between the predicted offset and the true offset is greater than the threshold.")
As to claim 3, Zhang as modified by Saxena teaches the method according to claim 1,
further comprising repeating: the clustering, the matching, the determining, and the training until the machine learning model stops improving. (see Saxena paragraph [0063] "The neural network training module 121 determines whether to continue training the model based on the difference between the true offset and the predicted offset. At act 309, the neural network training module 121 compares the difference (i.e., compares the true offset and the predicted offset).", and see Saxena paragraph [0065] "When the predicted offset is equal to, or nearly equal to within the threshold, the true offset, the neural network training module 121 stops training and an updated position is transmitted. The neural network training module 121 may be deployed or otherwise ready to use for predicting locations or offsets for the GNSS system.")
As to claim 4, Zhang as modified by Saxena teaches the method according to claim 1,
further comprising using the trained machine learning model to repair the entire raw dataset.
(see Saxena paragraph [0065] "When the predicted offset is equal to, or nearly equal to within the threshold, the true offset, the neural network training module 121 stops training and an updated position is transmitted. The neural network training module 121 may be deployed or otherwise ready to use for predicting locations or offsets for the GNSS system.", and see Zhang paragraph [0064] "The system 100 may update the location of the road sign on a map-matched link, based on a learned location of the road sign and the determined positional offset.", and see Zhang paragraph [0080] "new_lon=lon+(a×factor+b)*sin(heading), new_lat=lat+(a×factor+b)*cos(heading); where new_lon and new_lat: adjusted longitude and latitude of the learned road sign, lon and lat: original longitude and latitude of the learned road sign, heading: heading of the learned road sign.")
Examiner note: Zhang discloses applying the derived correction function to update sign locations across the dataset, and Saxena discloses deploying the trained model to correct position readings at scale. The combination renders obvious applying the trained model to correct all readings in the raw dataset.
As to claim 5, Zhang as modified by Saxena teaches the method according to claim 1,
further comprising running the same clustering algorithm to produce the location of all signs in a raw dataset.(see Zhang paragraph [0057] "The system 100 may then cluster the road sign observations to generate learned road signs and map match the learned road signs to links of the road. From the learned road signs, the system 100 may generate primary speed funnels.", and see Zhang paragraph [0062] "the system 100 may extract a second plurality of road sign observations from the first plurality of road sign observations based on the ground truth data.", and see Zhang paragraph [0064] "The system 100 may update the location of the road sign on a map-matched link, based on a learned location of the road sign and the determined positional offset.")
As per claim 6, this is directed to a system or a computing device claim that corresponds to method claim 1. See the rejection for claim 1 above, which also applies to claim 6. In addition, the claim recited the following elements that Zhang teaches,
at least one processor: and (see Zhang paragraph [0029] "comprising one or more processors and/or portion(s) thereof and accompanying software and/or firmware.")
at least one memory storing instructions that. when executed by the at least one processor, cause the system to: (see Zhang paragraph [0029] "combinations of circuits and computer program product(s) comprising software and/or firmware instructions stored on one or more computer readable memories that work together to cause an apparatus to perform one or more functions described herein")
As per claim 7, this is directed to a system or a computing device claim that corresponds to method claim 2. See the rejection for claim 2 above, which also applies to claim 7.
As per claim 8, this is directed to a system or a computing device claim that corresponds to method claim 3. See the rejection for claim 3 above, which also applies to claim 8.
As per claim 9, this is directed to a system or a computing device claim that corresponds to method claim 4. See the rejection for claim 4 above, which also applies to claim 9.
As per claim 10, this is directed to a system or a computing device claim that corresponds to method claim 5. See the rejection for claim 5 above, which also applies to claim 10.
As to claim 11, this is directed to a computer-program embodiment that corresponds to method claim 1. See the rejection for claim 1 above, which also applies to claim 11.
As to claim 12, this is directed to a computer-program embodiment that corresponds to method claim 2. See the rejection for claim 2 above, which also applies to claim 12.
As to claim 13, this is directed to a computer-program embodiment that corresponds to method claim 3. See the rejection for claim 3 above, which also applies to claim 13.
As to claim 14, this is directed to a computer-program embodiment that corresponds to method claim 4. See the rejection for claim 4 above, which also applies to claim 14.
As to claim 15, this is directed to a computer-program embodiment that corresponds to method claim 5. See the rejection for claim 5 above, which also applies to claim 15.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ABDULLAH K ABOUD whose telephone number is (571)272-0025. The examiner can normally be reached Mon-Fri 8am-5pm.
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, Li B Zhen, can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDULLAH KHALED ABOUD/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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