DETAILED ACTION
This Non-Final Office Action is in response to the amendment filed 3/16/2026.
Claims 1, 6, 11-13, and 18 have been amended.
Claims 7-10 have been canceled.
Claims 1-6 and 11-20 are pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2/26/2026 and 6/2/2026 have been considered by the examiner.
Response to Arguments
Rejections under 35 U.S.C. 101
Due to the amendment filed 3/16/2026, the rejections under 35 U.S.C. 101 have been withdrawn.
Rejections under 35 U.S.C. 103
Applicant’s arguments with respect to the claims 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. Specifically, new references have been applied to teach the contended limitations.
Upon further evaluation of the amendment filed 3/16/2026, additional indefinite issues under 35 U.S.C. 112(b) have been identified and are detailed below.
Examiner’s Note
To enhance clarity, claim language is underlined throughout this Office Action.
Citations to the prior art are provided in parentheses following each claim limitation, along with any necessary supplemental explanations.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 11-14, 18, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation of a status of a previous data chunk. The term “status” is undefined and ambiguous, due to its ability to encompass anything, e.g., data corruption, missing data, priority, unprocessed, etc. One of ordinary skill in the art cannot reasonably interpret the boundaries of a “status” of a previous data chunk. Further, the term “previous data chunk” is assumed to reference the “data chunk,” such that the previous data chunk comes before the data chunk. However, one of ordinary skill in the art cannot reasonably interpret if this applies to a structural or chronological relationship. The term “previous” in claim 1 is a relative term which renders the claim indefinite, such that the boundaries of “previous” cannot be reasonably determined, given that the “previous data chunk” is not claimed to be associated with the “sequence of location probe data points.” Claims 13 and 18 are rejected under 35 U.S.C. 112(b) for similar reasons.
Claim 1 recites the limitation of a location probe data point immediately after a last location probe data point in the junction. The terms “after” and “last” can be interpreted as either temporal or spatial. While the claim describes the “sequence of location probe data points” as being received “during the travel of the vehicle,” sensor drifts or a stopped vehicle can decouple time from space. One of ordinary skill in the art cannot reasonably determine whether this limitation is intended for evaluating a chronological sequence or a spatial geometry of data points. Claims 13 and 18 are rejected under 35 U.S.C. 112(b) for similar reasons.
Claim 2 recites the limitation of a last location probe data point in the previous chunk. The term “last” can be interpreted as either temporal or spatial. The “previous chunk” is not claimed as being associated with any particular data points, and one of ordinary skill in the art cannot reasonably determine whether this limitation is intended for evaluating a chronological sequence or a spatial geometry of data points. Claims 14 and 19 are rejected under 35 U.S.C. 112(b) for similar reasons.
Claim 11 recites the limitation of the junction behavior. There is insufficient antecedent basis for this limitation in the claim. Specifically, claim 1 has been amended to recite “junction behavior classification.” One of ordinary skill cannot reasonably determine if the “junction behavior” of claim 11 is intended to be interpreted as separate and distinct from the “junction behavior classification” of claim 1 or if this is a typographical error.
Claim 12 recites the limitation of the junction behavior. There is insufficient antecedent basis for this limitation in the claim. Specifically, claim 1 has been amended to recite “junction behavior classification.” One of ordinary skill cannot reasonably determine if the “junction behavior” of claim 12 is intended to be interpreted as separate and distinct from the “junction behavior classification” of claim 1 or if this is a typographical error.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Busser (US 2019/0017832 A1), hereinafter Busser, in view of Wan et al. (“A Long Short-Term Memory-Based Approach for Detecting Turns and Generating Road Intersections from Vehicle Trajectories,” Sept. 15, 2022, MDPI), hereinafter, Wan, and Viente et al. (US 2019/0272389 A1), hereinafter Viente.
Claim 1
Busser discloses the claimed apparatus (see Figure 6, depicting apparatus 100) comprising processing circuitry and at least one memory including computer program code instructions (see ¶0056-0057, regarding apparatus 100 includes different units comprising one or more microprocessors for carrying out the method steps of method 10; ¶0040, regarding that the invention includes a computer program product that is loaded into microprocessor for carrying out the steps of the method), the computer program code instructions configured to, when executed by the processing circuity, cause the apparatus to:
receive a data chunk associated with a sequence of location probe data points via an electronic control unit of a vehicle and during travel of the vehicle along a road network (see ¶0056, regarding that apparatus 100 includes a capture unit 110 that receives captured routes; ¶0012, regarding a route, defined as spatial movement of an object from a starting point to a destination point via successive waypoints, is recorded via a navigation system belonging to a vehicle, as described in ¶0003), wherein the sequence of location probe data points correspond to a trajectory and represents respective locations of the vehicle during the travel of the vehicle along the road network (see ¶0049, with respect to the example in Figure 1, regarding that a first route R1 from starting point S1 to destination point Z1 includes dots that represent waypoints W for which position indications are available).
As specifically described in ¶0042, Figure 1 of Busser is a road map, and therefore, the route R1 may reasonably correspond to “travel of the vehicle along a road network.” Further, the vehicle navigation system of Busser may be reasonably interpreted as an “electronic control unit of a vehicle,” given its known specialized function to process GPS and sensor data for generating various forms of guidance.
Busser does not further disclose that the claimed apparatus is further caused to generate a vehicle-related feature set based on the sequence of location probe data points detected via the electronic control unit of the vehicle and input the vehicle-related feature set to a machine learning model to generate a junction behavior classification for a junction of the trajectory where two or more turn maneuvers via the road network are possible by the vehicle, wherein the machine learning model is trained based on a set of vehicle-related labels associated with junction probe data points and non-junction probe data points, so as to further identify, based on the junction behavior classification and the sequence of location probe data points obtained via the electronic control unit of the vehicle, a location probe data point immediately after a last location probe data point in the junction. However, given that Busser identifies a junction from a route of waypoints (see ¶0056, regarding the use of a segmentation point for segmenting each route into two partial routes, where the segmentation point is defined as an intersection or T-junction in ¶0054), it would be reasonable to modify the identification of a junction in Busser to be performed using a machine learning model trained using sets of labels associated with junction probe data points and non-junction probe data points, in light of Wan.
Specifically, Wan teaches the known technique of generat[ing] a vehicle-related feature set based on a trajectory T that contains a sequence of tracking points p1, p2,…, pN (similar to the sequence of location probe data points of Busser) detected via GPS trackers in vehicles (similar to the electronic control unit of the vehicle of Busser) (see page 4, sections 2.1-2.2, regarding that raw trajectories including tracking points for a vehicle, defined as trajectory T that contains a sequence of tracking points p1, p2,…, pN with associated timestamps t1, t2,…, tN, defined as captured by GPS trackers in vehicles on page 9, section 3.1; page 5, section 2.2.1, regarding that trajectory T is converted into a sequence of vectors that store attributes that describe motion characteristics of the vehicle), and input the vehicle-related feature set to a machine learning model to generate a junction behavior classification for a junction of the trajectory T (similar to the trajectory of Busser) where two or more turn maneuvers via a road (similar to the road network of Busser) are possible by the vehicle (see page 3, first paragraph, regarding that an LSTM-based model identifies the turning trajectory segments (TTSs), each of which represents a turn occurring at an intersection, by capturing a deep representation of the input feature sequence, as further described on page 4 in section 2.2; page 7, section 2.2.3, regarding that output layer of the LSTM-based model generates a classification decision for each line segment of the input trajectory T), wherein the machine learning model is trained based on a set of vehicle-related labels associated with junction probe data points and non-junction probe data points (see page 10, section 3.2, regarding that the LSTM-based model for detecting TTSs is trained using line segments (training set) manually labeled as parts of TTSs or non-TTSs).
Wan further teaches identify[ing], based on the junction behavior classification and trajectory T that contains a sequence of tracking points p1, p2,…, pN (similar to the sequence of location probe data points of Busser) detected via GPS trackers in vehicles (similar to the electronic control unit of the vehicle of Busser), a last probe data point immediately after a last location data point in the junction (see page 7, section 2.2.3, regarding the generation of a classification decision of each line segment of the input trajectory T as being part of a TTS or not being part of a TTS, where trajectory segments are associated with tracking points pi to pj, as described on page 3 in section 2, with respect to Figure 2; page 4, sections 2.1-2.2, regarding that raw trajectories including tracking points for a vehicle, defined as trajectory T that contains a sequence of tracking points p1, p2,…pN with line segment ei between two consecutive tracking points pi and pi+1, defined as captured by GPS trackers in vehicles on page 9, section 3.1). For example, with respect to Figure 1, by identifying that the line segment from tracking point pj to the subsequent point on the trajectory T is not part of a TTS, Wan clearly teaches that the “last probe data point” is identified.
Since the systems of Busser and Wan are directed to the same purpose, i.e. identifying junctions from a trajectory of location points acquired from a vehicle, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the step of identifying junctions in Busser to further generate a vehicle-related feature set based on the sequence of location probe data points detected via the electronic control unit of the vehicle, input the vehicle-related feature set to a machine learning model to generate a junction behavior classification for a junction of the trajectory where two or more turn maneuvers via the road network are possible by the vehicle, wherein the machine learning model is trained based on a set of vehicle-related labels associated with junction probe data points and non-junction probe data points, and identify, based on the junction behavior classification and the sequence of location probe data points obtained via the electronic control unit of the vehicle, a location probe data point immediately after a last location probe data point in the junction, in light of Wan, with the predictable result of detecting turns at intersections from raw trajectories using a robust method that can benefit from the movement pattern information hidden in each trajectory (last paragraph on page 2 of Wan).
Busser, as modified by Wan, discloses that the claimed apparatus is further caused to apply, based on (i) a status of a previous data chunk and (ii) a junction point that corresponds to a partial route (similar to the last probe data point immediately after the last location probe data point in the junction of Wan), a gap placement or a sub-trajectory in the sequence of location probe data points detected via the electronic control unit of the vehicle to generate at least a first subsequence of the location probe data points and a second subsequence of the location probe data points (see ¶0056-0057, regarding segmentation unit 120 of apparatus 100 uses a waypoint as the segmentation point in order to segment the route into two partial routes, where waypoints in the region of the segmentation point are removed, and the segmentation point is defined as an intersection of roads, as described in ¶0023-0024). The “gap placement” is taught by the region of removed waypoints associated with the segmentation point at an intersection, where the “previous data chunk” may be reasonably taught by any portion of waypoints before the “data chunk,” including removed waypoints in the region of the intersection when the “data chunk” is interpreted as a portion of the “sequence of location probe data points.” Due to the issues discussed in the rejection of claim 1 under 35 U.S.C. 112(b), prior art is applied under the broadest reasonable interpretation.
Busser further discloses that the claimed apparatus is caused to generate anonymized mobility location probe data for the trajectory associated with the vehicle based on the first subsequence of the location probe data points and the second subsequence of the location probe data points (see ¶0056, regarding anonymization unit 150 of apparatus 100 removes object-identifying data before further processing in the capture unit 100, which identifies segmentation points for segmentation into at least two partial routes by segmentation unit 120; ¶0023-0024, regarding that the segmentation point is an intersection or T-junction of a road, where waypoints in the region of the segmentation point are removed, so that the turning direction or selected lane cannot be accurately discerned from the start or end of a partial route). The partial routes of Busser are applied to teach the “anonymized mobility location probe data,” similar to the Applicant’s disclosure that describes “the first subsequence of the location probe data points and the second subsequence of the location probe data points correspond to anonymized mobility data for the vehicle” in at least paragraph [0038] of the specification filed 11/28/2022.
While Busser discloses that the waypoints associated with the partial routes (i.e. “anonymized mobility location probe data”) are associated with time or height indications (see ¶0049), time and height are acquired using the same GPS as the “sequence of location probe data points” (see ¶0004) and may not reasonably teach separate and distinct vehicle-related sensor data. However, it would be obvious to acquire vehicle-related sensor data in combination with the location data of Busser for augmenting similar partial routes, in light of Viente.
Specifically, Viente teaches the known technique of receiving outputs indicative of motion of a host vehicle that include position from a GPS sensor (similar to the data chunk associated with a sequence of location probe data points of Busser) (see ¶0327, with respect to step 2510 of Figure 25) and vehicle-related sensor data (see ¶0329, with respect to step 2530 of Figure 25, regarding receiving an image representative of an environment of the host vehicle along the road segment using a camera associated with the host vehicle, which is analyzed to determine a road characteristic associated with the road section, as described in ¶0330, with respect to step 2540) via processor 2315 included in navigation system 2300 (similar to the electronic control unit of a vehicle of Busser) on a road section traversed by the host vehicle (similar to the condition of during travel of the vehicle along a road network of Busser) (see ¶0324, regarding that process 2500 of Figure 25 is for collecting anonymized drive information relative to a road section traversed by a host vehicle and is performed by processor 2315 included in a navigation system 2300). Viente further teaches augment[ing] a first portion and second portion of a road section, defined as being spatially separated in ¶0342 (similar to the anonymized mobility location probe data of Busser) with the vehicle-related sensor data to generate vehicle data for the vehicle (see ¶0334, with respect to step 2550 of Figure 25, regarding the assembly of first road segment information relative to a first portion of the road section, where the first road segment information includes motion representation determined at step 2520 and a road characteristic determined at step 2540; ¶0339, with respect to step 2560 of Figure 25, regarding the assembly of second road segment information relative to a second portion of the road section, where the second road segment information includes motion representation determined at step 2520 and a road characteristic determined at step 2540), and encod[ing] the vehicle data in a database to enable anonymized localization for provision of location-based services to a plurality of vehicles (see ¶0348, with respect to step 2570 of Figure 25, regarding the first road segment information and second road segment information is transmitted to a server for assembly of an autonomous vehicle road navigation model, which is distributed to one or more vehicles for navigation along a road section, as described in ¶0404).
Since the systems of Busser and Viente are directed to the same purpose, i.e. anonymizing position data by separating the position data into portions, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the received data of Busser to further include vehicle-related sensor data, so as to further augment the anonymized mobility location probe data with the vehicle-related sensor data to generate vehicle data for the vehicle, and encode the vehicle data in a database to enable anonymized localization for provision of location-based services to a plurality of vehicles, in light of Viente, with the predictable result of ensuring sufficient information is included in the segmented road information to generate a navigational model (¶0321 of Viente) while assuring anonymity of the data (¶0348 of Viente).
Claims 2, 14, and 19
Busser, as modified by Wan, further discloses that the computer program code instructions are configured to, when executed by the processing circuity, cause the apparatus to: determine whether to apply the gap placement or the sub-trajectory in the sequence of location probe data points based on a last location probe data point in the previous data chunk (see ¶0056-0057, regarding segmentation unit 120 of apparatus 100 segments each route into at least two partial routes, where each partial route includes at least one waypoint as a segmentation point, and a data record for each partial route is stored in storage unit 130; ¶0023-0024, regarding that waypoints in the region of the segmentation point are removed, and the segmentation point is an intersection of roads). As discussed in the rejection of claim 1, the “previous data chunk” may be reasonably taught by any portion of waypoints before the “data chunk,” including removed waypoints in the region of the intersection when the “data chunk” is interpreted as a portion of the “sequence of location probe data points.” Due to the issues discussed in the rejection of claim 1 under 35 U.S.C. 112(b), prior art is applied under the broadest reasonable interpretation.
Claims 3 and 15
Busser further discloses that the computer program code instructions are configured to, when executed by the processing circuity, cause the apparatus to encode the first subsequence of the location probe data points in storage unit 130 (similar to the database of Viente) as a first modified version of the data chunk, and encode the second subsequence of the location probe data points in storage unit 130 (similar to the database of Viente) as a second modified version of the data chunk (see ¶0056-0057, regarding that data relating to each waypoint is stored in a data record for each partial route in storage unit 130, where the waypoints make up a route that has been received by the capture unit 110). The partial routes (i.e. first and second subsequences) of Busser may be reasonably interpreted as “modified versions of the data chunk,” given that the waypoints that make up the partial routes are from the overall route. A particular modification made to the “data chunk” is not claimed.
Claims 4, 16, and 20
Busser does not further disclose that the computer program code instructions are configured to, when executed by the processing circuity, cause the apparatus to: apply the gap placement in the sequence of location probe data points based on a set of anonymization parameters associated with a size for gap placements. However, the size of the gap placement of Busser may be reasonably adjusted based on a set of “anonymization parameters” associated with size, in light of Viente.
Specifically, Viente teaches a similar technique of spatially separating a first road section and a second road section by a third portion of the road section (similar to the step of apply the gap placement in the sequence of location probe data points of Busser) based on a set of anonymization parameters associated with a size for gap placements (see ¶0342-0343, regarding that third portion is used to spatially separate the first portion of the road section from the second portions, such that the third portion may be adjusted to any length, e.g., 0.5 km, 1.5 km, 2 km, etc., where the length of the third portion is determined using the means for determining the length of the first and second road segments, discussed in ¶0319 as being a function of the type or location of the road section, speed of navigation on the road section, complexity of the navigation of the road section, etc.; ¶0325-0339, with respect to Figure 5, regarding that the road information associated with the first and second portions is derived from position data collected from a GPS sensor).
Since the systems of Busser and Viente are directed to the same purpose, i.e. anonymizing position data by separating the position data into portions, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the step of apply the gap placement in the sequence of location probe data points of Busser to be based on a set of anonymization parameters associated with a size for gap placements, in light of Viente, with the predictable result of dynamically adjusting the anonymization, e.g., preferably increase the likeliness that the vehicle did not travel between the first and second section (¶0343 of Viente).
Claims 5 and 17
Because the limitation of apply the sub-trajectory in the sequence of location probe data points based on a set of anonymization parameters associated with a size for sub-trajectories further limits an optional alternative of the independent claim (i.e. “a gap placement or a sub-trajectory”) to which no prior art was applied, no prior art rejection is required for this limitation.
Claim 6
Wan further teaches determin[ing] one or more features for the vehicle-related feature set based on a combination of at least two of speed data for the vehicle (similar to the vehicle of Busser) during capture of one or more location probe data points within trajectory T that contains a sequence of tracking points p1, p2,…, pN (similar to the sequence of location probe data points of Busser) and heading data indicative of a direction of travel associated with the vehicle during capture of one or more location probe data points within the trajectory T (see page 5, section 2.2.1, regarding that the sequence of vectors stores attributes that describe the motion characteristics of the vehicle including tortuosity, turning angle, speed, and acceleration, which are further processed by the encoder, as described on page 6, section 2.2.2).
Claim 11
Busser further discloses that the computer program code instructions are configured to, when executed by the processing circuity, cause the apparatus to:
identify a first portion in the sequence of location probe data points as a potential origin location of the vehicle during a journey associated with the travel of the vehicle along the portion of the road network (see ¶0012, regarding the recorded route includes a starting point to a destination point via successive waypoints, where the route is segmented by removing waypoints in a region of a segmentation point, as described in ¶0023);
apply the gap placement in the first portion in the sequence of location probe data points (see ¶0023, regarding that waypoints in the region of the segmentation point are removed, where the segmentation point may be an intersection or T-junction).
The partial route (i.e. “first portion”) that includes the starting point (e.g., partial route R2.1 includes starting point S2 in Figure 3) may reasonably teach a “potential origin location.” The term “potential origin location” is not defined in the Applicant’s specification and may reasonably pertain to the origin of a partial route, in light of paragraph [0098] of the specification filed 11/28/2022. The term “potential” does not require the “first portion” to be an origin location.
Wan further teaches the technique of identify[ing] the junction behavior in a line segment of a trajectory T (similar to the first portion in the sequence of location probe data points of Busser) (see page 3, first paragraph, regarding that an LSTM-based model identifies the turning trajectory segments (TTSs), each of which represents a turn occurring at an intersection, by capturing a deep representation of the input feature sequence, as further described on page 4 in section 2.2; page 7, section 2.2.3, regarding that output layer of the LSTM-based model generates a classification decision for each line segment of the input trajectory T), as discussed in the rejection of claim 1. Due to the issues discussed in the rejection of claim 11 under 35 U.S.C. 112(b), prior art is applied under the broadest reasonable interpretation.
Claim 12
Busser further discloses that the computer program code instructions are configured to, when executed by the processing circuity, cause the apparatus to:
identify a last portion in the sequence of location probe data points as a potential destination location of the vehicle during a journey associated with the travel of the vehicle along the portion of the road network (see ¶0012, regarding the recorded route includes a starting point to a destination point via successive waypoints, where the route is segmented by removing waypoints in a region of a segmentation point, as described in ¶0023);
apply the gap placement in the last portion in the sequence of location probe data points (see ¶0023, regarding that waypoints in the region of the segmentation point are removed, where the segmentation point may be an intersection, T-junction, highway exit, or bus stop).
The partial route (i.e. “last portion”) that includes the destination point (e.g., partial route R2.3 includes destination point Z2 in Figure 3) may reasonably teach a “potential destination location.” The term “potential destination location” is not defined in the Applicant’s specification and may reasonably pertain to the destination of a partial route, in light of paragraph [0093] of the specification filed 11/28/2022. The term “potential” does not require the “last portion” to be a destination location.
Wan further teaches the technique of identify[ing] the junction behavior in a line segment of a trajectory T (similar to the last portion in the sequence of location probe data points of Busser) (see page 3, first paragraph, regarding that an LSTM-based model identifies the turning trajectory segments (TTSs), each of which represents a turn occurring at an intersection, by capturing a deep representation of the input feature sequence, as further described on page 4 in section 2.2; page 7, section 2.2.3, regarding that output layer of the LSTM-based model generates a classification decision for each line segment of the input trajectory T), as discussed in the rejection of claim 1. Due to the issues discussed in the rejection of claim 11 under 35 U.S.C. 112(b), prior art is applied under the broadest reasonable interpretation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Specifically, Zuberi et al. (“Detection of Road Intersections Using History Trajectory Data for Evolving Mix-Zones,” 2014, Department of Electronics & Communications, Jamia Millia Islamia, New Delhi-11025) teaches the use of mix-zones at intersections to protect location privacy of mobile users, Mao et al. (US 2019/0360819 A1) teaches removing probe data points within an intersection for map matching probe data points to road segments (see ¶0061), and Ostadzadeh et al. (US 2021/0019425 A1) teaches splitting trajectories into sub-trajectories by deliberately eliminating probe data points from the trajectory data using customized gap settings (see ¶0053-0055).
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/SARA J LEWANDROSKI/Examiner, Art Unit 3661
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