DETAILED ACTION
NOTICE OF PRE-AIA OR AIA STATUS
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
STATUS OF CLAIMS
This action is in response to the Applicant’s arguments and amendments filed on 1/26/2026. Applicant amended claims 1, 4, 11 and 12. Claims 1-15 are pending and are examined below.
RESPONSE TO REMARKS AND ARGUMENTS
In regards to the claim objections, Applicant’s amendments filed on 1/26/2026 obviate said claim objections – accordingly, the claim objections are withdrawn.
In regards to the claim rejections under § 101, Applicant’s amendments filed on 1/26/2026 obviate said claim rejections. Namely, the claims now recite a positive form of vehicle control which requires actuation of structure. Accordingly, the claim rejections under § 101 are withdrawn.
In regards to the claim rejections under §§ 102, 103, Applicant’s amendments and arguments filed on 1/26/2026 have been fully considered.
As to amended claim 1, Applicant argues Wang does not teach:
obtaining the parametric curve that approximates spatial information representing the free space boundary, the parametric curve including a set of spline samples; and
for each respective spline sample of the set of spline samples, determining a respective measurement point of the set of measurement points that corresponds to the respective spline sample. (Applicant’s emphases.)
Applicant argues that Wang’s discussion of a squared distance (SD) error term that measures a geometric distance between data points and a fitting curve, B-spline curve represented with control points Pi, and finding of control points Pi, i = 1, 2, …, m, to minimize an objection function does not read on the above claim limitations.
Applicant further argues that Wang does not disclose the amended features of:
determining an operating instruction for the host vehicle based on the measurement points; and
based on the operating instruction, one of semi-autonomously and autonomously controlling the host vehicle.
Applicant submits that none of the other cited references cure Wang’s deficiency.
As to the first argument, Examiner respectfully disagrees. Wang discloses the BRI of the claim limitations at issue. First, Wang discloses:
obtaining the parametric curve that approximates spatial information representing the free space boundary, the parametric curve including a set of spline samples (“We present a novel method that approximates unorganized data points with a B-spline curve that starts with some properly specified initial curve and converges through iterative optimization towards the target shape of data points.” Page 215. “Let Xk ∈ R², k = 1, 2, ... , n, be unorganized data points representing a target shape which is to be approximated by a closed or open planar B-spline curve
P
t
=
∑
i
=
1
m
B
i
(
t
)
P
i
, where the Bi(t) are the B-spline basis functions of a fixed order and knots, and the Pi are the control points.” Page. 216.)
Here, a specific instance of P(t) (i.e., P(1), etc.) is by definition a spline sample of a parametric B-Spline curve because such an instance produces an iterated spline out of the entire B-Spline curve. Therefore, Wang’s B-spline curve indeed includes a set of spine samples. Wang further discloses:
for each respective spline sample of the set of spline samples, determining a respective measurement point of the set of measurement points that corresponds to the respective spline sample (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 at page 221. See also FIGS. 6(a)-(f). Indeed, “for a fixed B-spline fitting curve P(t), the Euclidean distance from data point Xk to P(t) is denoted by
d
k
=
P
t
k
-
X
k
, where P(tk) is the foot point of Xk on P(t). ” Section 4 at page 222.).
Summarizing, Wang determines a measurement point (data point) corresponding to a respective spline sample (an instance of P(t), represented by foot point (P(tk)), and Wang performs this for all the spline samples. Hence, Wang discloses the BRI of the claim limitations at issue.
As to the second argument, Applicant’s arguments 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.
Accordingly, the prior art rejections under § 103 are maintained.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a vehicle assistance system configured to perform” in claim 13.
The corresponding structure described in the specification as performing the claimed function at least includes: computer-readable storage media (CRM) 510 comprising an autonomous driving system 512 – see PGPUB [0036] and FIG. 5.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
CLAIM REJECTIONS—35 U.S.C. § 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 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.
Claim(s) 1-5, 7-10 and 12-15 is/are rejected under § 103 as being unpatentable over Wang et al. (“Fitting B-spline curves to point clouds by curvature-based squared distance minimization”1; “Wang”) in view of Sithiravel et al. (US20200049511A1; “Sithiravel”).
As to claim 1, Wang discloses a computer-implemented method for associating measurement points of a free space boundary around a host vehicle to spline samples of a parametric curve representing the free space boundary, the method comprising:
obtaining a set of measurement points that define at least a subset of the free space boundary (“Data points” assumed to represent the shape of “some unknown planar curve” are obtained. See page 215. );
obtaining the parametric curve that approximates spatial information representing the free space boundary, the parametric curve including a set of spline samples (“We present a novel method that approximates unorganized data points with a B-spline curve that starts with some properly specified initial curve and converges through iterative optimization towards the target shape of data points.” Page 215. “Let Xk ∈ R², k = 1, 2, ... , n, be unorganized data points representing a target shape which is to be approximated by a closed or open planar B-spline curve
P
t
=
∑
i
=
1
m
B
i
(
t
)
P
i
, where the Bi(t) are the B-spline basis functions of a fixed order and knots, and the Pi are the control points.” Page. 216.); and
for each respective spline sample of the set of spline samples, determining a respective measurement point of the set of measurement points that corresponds to the respective spline sample (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 at page 221. See also FIGS. 6(a)-(f). Indeed, “for a fixed B-spline fitting curve P(t), the Euclidean distance from data point Xk to P(t) is denoted by
d
k
=
P
t
k
-
X
k
, where P(tk) is the foot point of Xk on P(t). ” Section 4 at page 222.).
Wang fails to explicitly disclose:
determining an operating instruction for the host vehicle based on the measurement points; and
based on the operating instruction, one of semi-autonomously and autonomously controlling the host vehicle.
Nevertheless, Sithiravel teaches:
determining an operating instruction for the host vehicle based on measurement points (“At block 1506 computing device can determine a path polynomial based on the combined output free space region 1416 and lidar data.” ¶ 60. “At block 1508 computing device output commands to controllers 112, 113, 114 to control vehicle 110 powertrain, steering and brakes to operate vehicle 110 along path polynomial.” ¶ 61. See also ¶¶ 34-39 discussing a “B-Spline” to approximating a free space map. Indeed, “The B-spline environment model is used to create an output free space region 1416 (see FIG. 14) in free space map 800.” ¶ 46. See also FIGS. 2-4 and 8-15.); and
based on the operating instruction, one of semi-autonomously and autonomously controlling the host vehicle (“At block 1508 computing device output commands to controllers 112, 113, 114 to control vehicle 110 powertrain, steering and brakes to operate vehicle 110 along path polynomial.” ¶ 61.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wang to include the feature of: determining an operating instruction for the host vehicle based on measurement points; and based on the operating instruction, one of semi-autonomously and autonomously controlling the host vehicle, as taught by Sithiravel, with a reasonable expectation of success because this feature is useful for permitting a vehicle to operate in a free space region while maintain vehicle within upper and lower limits of lateral/longitudinal accelerations and also avoiding collisions or near collisions with non-stationary objects. (See Sithiravel ¶ 60.) That is, a PHOSITA would have recognized that it would have been useful to apply Sithiravel’s vehicle control to Wang’s invention to exploit Wang’s method in a vehicle control context, thereby yielding an improved vehicle control system.
As to claim 2, Wang discloses: wherein the set of measurement points are obtained from sensor data (“Given a target curve to be approximated and the current B-spline fitting curve Pc(t) with control points Pc = {Pc,i}, a set of densely distributed points Sk, called sensor points, are first sampled on Pc(t).” Page 219.).
Wang fails to explicitly disclose: obtaining sensor data of one more sensors of a host vehicle.
Nevertheless, Sithiravel teaches: obtaining sensor data of one more sensors of a host vehicle (“Disclosed herein is a method, including determining a free space map of an environment around a vehicle by combining video sensor data and radar sensor data, determining a path polynomial by combining the free space map and lidar sensor data, and operating the vehicle with the path polynomial.” ¶ 19.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wang to include the feature of: obtaining sensor data of one more sensors of a host vehicle, as taught by Sithiravel, with a reasonable expectation of success because this feature is useful for defining a free space boundary around a host vehicle. (See at least Sithiravel, ¶ 19.) Also, it is well-known in the art that sensors provided in a host vehicle may provide sensor data.
As to claim 3, Wang discloses: wherein obtaining the parametric curve comprises: determining the parametric curve based on the sensor data of the one or more sensors (“Given a target curve to be approximated and the current B-spline fitting curve Pc(t) with control points Pc = {Pc,i}, a set of densely distributed points Sk, called sensor points, are first sampled on Pc(t).” Page 219.).
As to claim 4, Wang discloses: wherein the set of spline samples are equally distanced along the parametric curve (“In the following, we present the results of applying the three methods—PDM, TDM, and SDM—to fitting a cubic B-spline curve with uniform knots to several sets of unorganized data points.” Page 222. See also FIGS. 6(c)-(d) illustrating set of spline samples in a domain and in an equidistant manner. Note: One of ordinary skill in the art would recognize that a uniform B-spline curve is defined as a curve wherein the knots – and therefore spline samples – are equally distanced from each other.).
As to claim 5, Wang discloses: generating a perpendicular line for each spline sample of the set of spline samples, the perpendicular line passing through the respective spline sample, wherein the perpendicular line is used to determine the respective measurement point of the set of measurement points that corresponds to the respective spline sample (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 in page 221. See also FIGS. 6(a)-(f). Critically, “We measure the fitting error as defined in Equation (1), namely, orthogonal to the fitting curve.” Emphasis added; Section 3 at page 220.).
As to claim 7, Wang discloses: identifying first measurement points near the perpendicular line (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 in page 221. See also FIGS. 6(a)-(f). Critically, “We measure the fitting error as defined in Equation (1), namely, orthogonal to the fitting curve.” Emphasis added; Section 3 at page 220. Note: The calculation of the orthogonal fitting error necessarily requires an identification of a first measurement point as the error is calculated based on the respective measurement point.).
As to claim 8, Wang discloses: from among the first measurement points, identifying the respective measurement point as a measurement point among the first measurement points that is nearest the perpendicular line (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 in page 221. See also FIGS. 6(a)-(f). Critically, “We measure the fitting error as defined in Equation (1), namely, orthogonal to the fitting curve.” Emphasis added; Section 3 at page 220. Note: The calculation of the orthogonal fitting error necessarily requires an identification of a first measurement point as the error is calculated based on the respective measurement point.).
As to claim 9, Wang discloses: wherein determining the nearest measurement point is based on a Euclidean distance between each measurement point of the first measurement points and the perpendicular line (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 in page 221. See also FIGS. 6(a)-(f). Critically, “We measure the fitting error as defined in Equation (1), namely, orthogonal to the fitting curve.” Emphasis added; Section 3 at page 220. Finally, the “Euclidean distance” from data point Xk to P(t) is calculated to arrive at the error value. See page 222.).
As to claim 10, Wang discloses:
the parametric curve is defined by a plurality of control points (“Given a target curve to be approximated and the current B-spline fitting curve Pc(t) with control points Pc = {Pc,i}, a set of densely distributed points Sk, called sensor points, are first sampled on Pc(t).” Page 219.); and
associations of the respective spline samples to the corresponding measurement points is used for at least determining spline approximations (“We present a novel method that approximates unorganized data points with a B-spline curve that starts with some properly specified initial curve and converges through iterative optimization towards the target shape of data points.” Page 215.).
As to claim 12, Wang fails to explicitly disclose: determining an operating instruction for the host vehicle based on the parametric curve affecting a function of a vehicle assistance system of the host vehicle, the function comprising at least one of displaying the parametric curve on a display of the host vehicle, conducting a vehicle path planning, triggering a warning, or affecting control of the host vehicle during a parking process.
Nevertheless, Sithiravel teaches: determining an operating instruction for the host vehicle based on the parametric curve affecting a function of a vehicle assistance system of the host vehicle, the function comprising at least conducting a vehicle path planning (“At block 1506 computing device can determine a path polynomial based on the combined output free space region 1416 and lidar data.” ¶ 60. “At block 1508 computing device output commands to controllers 112, 113, 114 to control vehicle 110 powertrain, steering and brakes to operate vehicle 110 along path polynomial.” ¶ 61. See also ¶¶ 34-39 discussing a “B-Spline” to approximating a free space map. Indeed, “The B-spline environment model is used to create an output free space region 1416 (see FIG. 14) in free space map 800.” ¶ 46. See also FIGS. 2-4 and 8-15.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wang to include the feature of: determining an operating instruction for the host vehicle based on the parametric curve affecting a function of a vehicle assistance system of the host vehicle, the function comprising at least conducting a vehicle path planning, as taught by Sithiravel, with a reasonable expectation of success because this feature is useful for permitting a vehicle to operate in a free space region while maintain vehicle within upper and lower limits of lateral/longitudinal accelerations and also avoiding collisions or near collisions with non-stationary objects. (See Sithiravel ¶ 60.)
As to claim 13, Wang fails to explicitly disclose: an apparatus comprising a vehicle assistance system configured to perform the method of claim 1.
Nevertheless, Sithiravel teaches: an apparatus comprising a vehicle assistance system which may be configured to perform the method of claim 1 (“Process 1500 can be implemented by a processor of computing device 115, taking as input information from sensors 116, and executing commands and sending control signals via controllers 112, 113, 114.” ¶ 57.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wang to include the feature of: an apparatus comprising a vehicle assistance system which may be configured to perform the method of claim 1, as taught by Sithiravel, with a reasonable expectation of success because this feature is useful for implementing a method of determining a free space boundary around a host vehicle. (See at least Sithiravel, ¶ 57.)
As to claim 14, Wang fails to explicitly disclose: a vehicle comprising the apparatus according to claim 13.
Nevertheless, Sithiravel teaches: a vehicle comprising the apparatus according to claim 13 (“Vehicle 110 also includes one or more computing devices 115.” ¶ 23 and FIG. 1.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wang to include the feature of: a vehicle comprising the apparatus according to claim 13, as taught by Sithiravel, with a reasonable expectation of success because this feature is useful for implementing a method of determining a free space boundary around a host vehicle. (See at least Sithiravel, ¶ 57.)
As to claim 15, Wang fails to explicitly disclose: a non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by a computer system, cause the computer system to perform the method of claim 1.
Nevertheless, Sithiravel teaches: a non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by a computer system, cause the computer system to perform the method of claim 1 (“The computing device 115 includes a processor and a memory such as are known. Further, the memory includes one or more forms of computer-readable media.” ¶ 24.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wang to include the feature of: a non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by a computer system, cause the computer system to perform the method of claim 1, as taught by Sithiravel, with a reasonable expectation of success because this feature is useful for implementing a method of determining a free space boundary around a host vehicle. (See at least Sithiravel, ¶ 57.)
Claim(s) 6 is/are rejected under § 103 as being unpatentable over Wang in view of Sithiravel as applied to claim 1 — further in view of Usman et al. (“An Extensive Approach to Features Detection and Description for 2-D Range Data Using Active B-splines”2; “Usman” )
As to claim 6, Wang discloses: identifying the perpendicular line as passing through the respective spline sample (Disclosed is an “error term defined by a curvature-based quadratic approximant of squared distances from data points to the fitting curve. For brevity, this new error term is called the squared distance error term (SD error term), and the resulting iterative minimization scheme will be referred to as squared distance minimization (SDM). Because the SD error term faithfully measures the geometric distance between data points and the fitting curve, SDM converges fast and stably in comparison with other commonly used error terms as will be discussed shortly.” Page 215. Indeed, the SDM method includes the step of “Compute SD error terms for all data points.” See section 3.2 in page 221. See also FIGS. 6(a)-(f). Critically, “We measure the fitting error as defined in Equation (1), namely, orthogonal to the fitting curve.” Emphasis added; Section 3 at page 220.).
The combination of Wang and Sithiravel fails to explicitly disclose:
identifying neighboring spline samples immediately before and after the respective spline sample within the set of spline samples;
defining a neighbor line that passes through the neighboring spline samples; and
identifying the perpendicular line as perpendicular to the neighbor line and passing through the respective spline sample.
Nevertheless, Usman teaches:
identifying neighboring spline samples immediately before and after a respective spline sample within the set of spline samples (For an area of interest, “four control points evenly distributed along [an] axis” are determined – see pp. 2935-2936 and FIG. 2. Note: The lines between each point correspond to spline samples as they are samples of an overall spline. In FIG. 2, the respective spline sample is the middle one.);
defining a neighbor line that passes through the neighboring spline samples (For an area of interest, “four control points evenly distributed along [an] axis” are determined – see pp. 2935-2936 and FIG. 2.); and
identifying a perpendicular line as perpendicular to the neighbor line and passing through the respective spline sample (“Figure 2 demonstrates an example of the curve fitting and detection steps of the expected candidates of the interest points from a segment of the laser scan.” Page 2936 and FIG. 2. Note: FIG. 2 illustrates that the respective spline sample is moved perpendicularly towards the control point. Such requires that a the spline sample follows a perpendicular line, which would necessarily also be perpendicular to the neighbor line and pass through the respective spline sample.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang and Sithiravel to include the features of: identifying neighboring spline samples immediately before and after the respective spline sample within the set of spline samples; defining a neighbor line that passes through the neighboring spline samples; and identifying the perpendicular line as perpendicular to the neighbor line and passing through the respective spline sample, as taught by Usman, with a reasonable expectation of success because these features are useful for curve fitting a parametric curve to a set of sensor data. (See Usman, Introduction at pp. 2934-2935.)
Claim(s) 11 is/are rejected under § 103 as being unpatentable over Wang in view of Sithiravel as applied to claim 10 — further in view of Haus et al. (US20230129346A1; “Haus”).
As to claim 11, Wang discloses: determining that an approximation error has a value larger than addition threshold (The squared distance minimization (SDM) method for fitting a parametric curve to data points is performed “until a prespecified error threshold is satisfied.” Page 221.).
The combination of Wang and Sithiravel fails to explicitly disclose: wherein a control point is added to the parametric curve in response to the above determination.
Nevertheless, Haus teaches: wherein a control point is added to the parametric curve in response to an approximation error indicator having a value larger than addition threshold (“Responsive to the path smoothing module 402 determining that the robot would collide with, or come within a threshold distance of, an obstacle, the path smoothing module 402 may add an additional control point on the segment between the two control points between which the collision would occur.” ¶ 62.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang and Sithiravel to include the feature of: wherein a control point is added to the parametric curve in response to an approximation error indicator having a value larger than addition threshold, as taught by Haus, with a reasonable expectation of success because this feature is useful for “achiev[ing] an effective path through the environment with relatively minimal computational requirements.” (Haus, ¶ 12.)
CONCLUSION
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, this action is 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Mario C. Gonzalez whose telephone number is (571) 272-5633. The Examiner can normally be reached M–F, 10:00–6:00 ET.
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, Fadey S. Jabr, can be reached on (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.G./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
1 Wenping Wang, Helmut Pottmann, and Yang Liu. 2006. Fitting B-spline curves to point clouds by curvature-based squared distance minimization. ACM Trans. Graph. 25, 2 (April 2006), 214–238. https://doi.org/10.1145/1138450.1138453
2 M. Usman et al., "An Extensive Approach to Features Detection and Description for 2-D Range Data Using Active B-splines," in IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2934-2941, July 2019, doi: 10.1109/LRA.2019.2917383.
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