Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 3, 5, and 8–10 are objected to because of the following informalities. In each case the intended meaning is apparent from the context, and the claims are examined as so understood.
Claims 1, 8, 9, and 10: “the map data of the driving area” lacks antecedent basis and should read “map data of the driving area.” “which represented by” should read “which is represented by.”
Claims 1 and 10: “and the on first sensor data” should read “and on the first sensor data.”
Claims 3 and 5: “the partial parameter” should read “the two partial parameters,” consistent with amended claim 1, from which the phrase takes its antecedent.
Claim 8: “the two partial parameters estimated by the first estimation unit” (two occurrences) should read “the two partial parameters estimated in the first estimation step,” because claim 8 recites a first estimation step and no first estimation unit. “in the first estimation step of acquiring first sensor data, estimating” should read “in the first estimation step, estimating.”
Claim 9: “in the first estimation step of acquiring first sensor data, estimating” should read “in the first estimation step, estimating.”
Appropriate correction is required.
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.
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.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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:
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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.
“a first acquisition unit that acquires first sensor data …” (claims 1 and 10, and claims 3–5 and 9 through claim 1)
“a second acquisition unit that acquires second sensor data …” (claims 1 and 10, and claims 3–5 and 9 through claim 1)
“a map comparison unit that collates the first sensor data and the map data of the driving area, and that estimates the self-position …” (claims 1 and 10, and claims 3–5 and 9 through claim 1)
“a first estimation unit that estimates two partial parameters …” and “the first estimation unit estimates …” (claims 1, 5, and 10, and claims 3, 4, and 9 through claim 1)
“a second estimation unit that estimates a self-position …” (claims 1 and 10), and “the second estimation unit estimates all the six degrees-of-freedom parameters by performing optimization calculation …” (claims 3 and 4)
Claim 8 recites method limitations using the term “step” (“a step of acquiring,” “a step of collating map,” “a first estimation step of estimating,” “a second estimation step of estimating”). These limitations are not interpreted under 35 U.S.C. 112(f). First, they use the form “step of,” not “step for,” so the presumption that attaches to “step for” does not arise. Second, each limitation recites the acts that perform its function: acquiring the specified first and second sensor data, collating the first sensor data with the map data, estimating the two partial parameters based on the recited known information and first sensor data, and estimating the remaining parameters based on the expressly recited inputs. The limitations therefore do not recite a step for performing a function without reciting acts in support of it. See MPEP 2181, subsection I.
Corresponding Structure Described in the Specification
Each of the above units is a computer-implemented function. The specification discloses that the units are implemented by a processor executing a program: “The processor 11 operates in accordance with a predetermined program to exhibit functions as a first acquisition unit 110, a second acquisition unit 111, a map comparison unit 112, a first estimation unit 113, a second estimation unit 114, and an output processing unit 115.” (pub. para. [0043]) Where a computer-implemented function is claimed under 35 U.S.C. 112(f), the corresponding structure is the processor programmed with the algorithm disclosed for performing the function, except where the function can be achieved by any general-purpose computer without special programming. MPEP 2181, subsection II.B; Aristocrat Techs. Australia Pty Ltd. v. Int’l Game Tech., 521 F.3d 1328, 1333 (Fed. Cir. 2008); see In re Katz Interactive Call Processing Patent Litig., 639 F.3d 1303, 1316 (Fed. Cir. 2011). The corresponding structure for each limitation is identified as follows.
First acquisition unit. Processor 11 receiving image data from the first sensor 20 through the interface 14: “The interface 14 is an interface for transmitting and receiving various types of information to and from an external device (for example, the first sensor 20 and the second sensor 21).” (para. [0042]); “The first acquisition unit 110 acquires image data (first sensor data) capable of detecting a fixed object always present in the periphery of the autonomous moving body 1 and a non-fixed object temporarily present in the periphery of the autonomous moving body 1, from the first sensor 20.” (para. [0044]). The claimed acquisition function is no more than the generic receipt of data from a sensor, which the disclosed processor and interface perform without special programming; no special-purpose algorithm is therefore required beyond the disclosed processor and interface arrangement. See MPEP 2181, subsection II.B.
Second acquisition unit. Processor 11 receiving acceleration and angular-velocity data from the second sensor 21 through the interface 14: “The second acquisition unit 111 acquires the acceleration and the angular velocity (second sensor data) of the autonomous moving body 1 from the second sensor 21.” (para. [0045]); “the second sensor data is the acceleration and the angular velocity of the autonomous moving body 1 measured by the IMU.” (para. [0053]). As with the first acquisition unit, the function is generic receipt of data, and no special-purpose algorithm is required.
Map comparison unit. Processor 11 programmed with the algorithm of FIG. 3, steps S101–S102: “the map comparison unit 112 collates each sample data of the first sensor data and the map data D1 acquired by the first acquisition unit 110 using known pattern matching processing, and extracts sample data having a predetermined degree of match or more and having the highest degree of match as data that matches the first sensor data. The map comparison unit 112 estimates the self-position attached to the extracted sample data as the self-position of the autonomous moving body 1” (para. [0054]); see also “The map comparison unit 112 collates the first sensor data acquired by the first acquisition unit 110 with each sample data of the map data D1, and estimates the self-position attached to the sample data that matches the first sensor data as the self-position of the autonomous moving body 1.” (para. [0047]) The determination that the map comparison cannot estimate the self-position is made when no sample data meets the predetermined degree of match (paras. [0055], [0057]; FIG. 3, step S102).
First estimation unit (claim 1, including claims 3, 4, and 9). Processor 11 programmed with the algorithm of FIG. 15 (fourth embodiment), which is the embodiment corresponding to the claimed shape obtained by connecting a plurality of landmarks: “the first estimation unit 113 performs linear fitting on the point cloud data P of the extracted landmark M3, and estimates the position Xw1 of the autonomous moving body 1 on the Xw axis and the rotation angle θZw1 of the autonomous moving body 1 around the Zw axis based on the distance and the angle with respect to the straight line L and the straight line obtained from the disposition of the landmark M3 recorded in the known information D2” (para. [0124]), preceded by conversion processing and extraction of the landmarks from the first sensor data (para. [0123]; FIG. 15, steps S150–S151).
First estimation unit (claim 5). Processor 11 programmed with the algorithm of FIG. 4 (paint) or FIG. 8 (marker): image conversion, extraction of the white line by binarization, and “processing of estimating the position of the autonomous moving body 1 on the Xw axis and the rotation angle (posture) around the Zw axis based on the white line extracted from the first sensor data and on the known information D2” (para. [0062]) (paras. [0060]–[0062]); and the corresponding marker-based processing of paras. [0085]–[0090] (FIG. 8).
First estimation unit (claim 10). Processor 11 programmed with the algorithm of FIG. 11 (third embodiment): extraction of each landmark from the first sensor data, determination of a representative point of each landmark by fitting a circle to its point cloud (para. [0112]), and “the first estimation unit 113 estimates the three degrees-of-freedom parameters of the position Xw1 and the position Yw1 of the autonomous moving body 1 in the Xw axis direction and the Yw axis direction and the rotation angle θZw1 around the Zw axis” (para. [0113]) based on the representative points and the coordinates recorded in the known information D2 (paras. [0104]–[0113]).
Second estimation unit (claims 1, 9, and 10). Processor 11 programmed with the algorithm of FIG. 6: setting the first-stage parameters as relative values to the previous frame (para. [0070]), and “the second estimation unit 114 fixes the relative values of Xw1 and θZw1 and solves the following Expression (1) to obtain the relative values of the remaining four degrees-of-freedom parameters.” (para. [0071]) Expression (1) minimizes the sum of “a residual (IMU residual) between the position and posture change from the previous frame f to the latest frame f+1 and the IMU integrated value during the period” (para. [0072]) and “an image re-projection error (image residual)” (para. [0072]), after which the self-position is calculated from the relative values and the previous result (para. [0073]; FIG. 6, steps S120–S122).
Second estimation unit (claims 3 and 4). Processor 11 programmed with the algorithm of FIG. 16 (fifth embodiment): “the second estimation unit 114 performs the optimization calculation in which the constraint term of the partial parameter estimated by the first estimation unit 113 is added, and estimates the relative value with respect to the previous frame for all the six degrees-of-freedom parameters” (para. [0135]) (paras. [0131]–[0135]).
Because these claim limitations are interpreted under 35 U.S.C. 112(f), they are interpreted to cover the corresponding structure described in the specification as performing the claimed function, as identified above, and equivalents thereof. The specification discloses an algorithm for each computer-implemented function other than the acquisition functions, for which none is required; accordingly, no rejection under 35 U.S.C. 112(a) or 112(b) is made on this basis.
Application of the Prior Art Under This Interpretation
A prior-art element meets a limitation interpreted under 35 U.S.C. 112(f) when it performs the identical function specified in the claim and has the corresponding structure disclosed in the specification or an equivalent. MPEP 2182, 2183. The rejections under 35 U.S.C. 103 below are made under this interpretation, as follows.
Sano (US 2020/0019792 A1), Roumeliotis (US 2014/0333741 A1), Zhang (US 2019/0271549 A1), and Tateishi (US 2019/0137281 A1).
Acquisition units. Sano’s processing unit receives the detection results of the LRFs and cameras and of the vehicle sensor group: “Each sensor 51 to 57 is connected to the processing unit 3 and is configured to sequentially output various detection results to the processing unit 3.” (Sano [0025]) A processor receiving sensor data is the same structure that performs the identical function.
Map comparison unit. Sano’s self-position estimation unit 36, realized by a CPU executing a program (Sano [0026]), compares detected target data with stored map data and adopts the matching pose (Sano [0053]–[0054]). Sano states that the matching method is interchangeable: “A known self-position estimation method may be used as the method of estimating the position on the map.” (Sano [0054]) Zhang likewise aligns current sensor data with previously acquired map data to determine location and orientation (Zhang [0085]). Each performs the identical function (collating sensor data with map data and estimating the self-position) in substantially the same way (comparing current sensor data with prerecorded map data and adopting the pose of the best match) to obtain substantially the same result, and the interchangeability of such matching methods was known (MPEP 2183).
First estimation unit (claims 1 and 9). Tateishi’s lateral position estimation unit fits the detected row of points of a straight marking line to its mapped position by least squares and outputs the lateral position and direction (Tateishi [0058]–[0059], [0061], [0063]); Zhang computes line geometry connecting groups of landmark points and matches it with detected edges (Zhang [0119]). Each performs the identical function in substantially the same way as the disclosed linear fitting of FIG. 15, i.e., fitting a line to detected landmark points and comparing it with the recorded line to obtain an offset and an angle.
First estimation unit (claim 10). Zhang estimates x, y, and yaw by minimizing the distances between mapped landmark points and their detected counterparts (Zhang [0126], [0132]), which performs the identical function in substantially the same way as the point-correspondence estimation of FIG. 11.
Second estimation unit (claims 1, 3, 4, 9, and 10). Roumeliotis minimizes a cost function combining IMU (motion) residuals and camera (measurement) residuals by Gauss-Newton optimization (Roumeliotis [0040]–[0044], Eq. (9)), which is substantially the same way as Expression (1) of the specification (IMU residual plus image re-projection residual). The combination in which selected components are supplied as fixed values (claims 1, 9, and 10) or as a constraint term (claims 3 and 4) is set forth in the rejections under 35 U.S.C. 103.
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.
Claim 10 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 10 is internally inconsistent. It requires the first estimation unit to estimate three parameters (a position in each of the first and second axis directions, and the posture about the third axis) “as the two partial parameters,” and requires the second estimation unit to estimate “the four parameters” other than the two partial parameters, which would total seven parameters in a six-degrees-of-freedom system. A PHOSITA could not determine whether the claim requires two or three parameters to be estimated by the first estimation unit, or whether the second estimation unit estimates three or four parameters. Because the claim expressly recites both numbers, any interpretation would require disregarding one of them.
For purposes of the rejection of claim 10 under 35 U.S.C. 103 below, which is made in addition to this rejection, claim 10 is construed as requiring the first estimation unit to estimate the three components that the final wherein clause specifically identifies (the positions in the first and second axis directions and the rotation about the third axis, i.e., X, Y, and θZ), and the second estimation unit to estimate the remaining components of the six-degrees-of-freedom pose. This construction is adopted because the final wherein clause is the more specific limitation, identifying the particular components estimated, whereas “two” and “four” are carried over from the general recitations shared with claim 1. It also matches the embodiment that claim 10 tracks, in which a landmark attached to a fixed object is used to estimate “three degrees-of-freedom parameters among six degrees-of-freedom parameters” (pub. para. [0099]), namely “the position Xw1 and the position Yw1 of the autonomous moving body 1 in the Xw axis direction and the Yw axis direction and the rotation angle θZw1 around the Zw axis” (para. [0113]). Applying the prior art under this stated construction does not resolve the indefiniteness, which Applicant must correct. See MPEP 2173.06.
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.
Claims 1, 3–5, 8, and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more. Claims 1, 3–5, 8, and 9 remain rejected: the rejection maintains the mathematical-concepts ground set forth in the prior Office action. The following analysis addresses the limitations introduced by Applicant’s amendment and Applicant’s arguments regarding faster optimization and reduced drift. The Examiner does not rely on the mental-process grouping in this action. The eligibility of each claim is evaluated under the framework of Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014), and Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012), as set forth in MPEP 2106.
Claim Interpretation Relevant to Eligibility
Three features of the amended claims frame the analysis.
The claims recite data, not sensors. Claim 1 recites a first acquisition unit that “acquires first sensor data capable of detecting” fixed and non-fixed objects, and a second acquisition unit that “acquires second sensor data including an acceleration and an angular velocity.” The claims do not recite a sensor, a sensor type, a sensor location, or a sensor configuration. They recite receiving data having certain content.
Every operation after acquisition is an estimation. The map comparison unit “collates” data and “estimates” the self-position; the first estimation unit “estimates” two partial parameters; the second estimation unit “estimates” the remaining four parameters. Under the broadest reasonable interpretation in light of the specification, these estimations are optimization calculations, such as a nonlinear least squares method (pub. paras. [0007], [0071]).
The claims end with the estimate. No claim outputs, displays, or uses the estimated self-position, and no claim controls, navigates, or otherwise operates the autonomous moving body or any other machine. The autonomous moving body is recited only as the object whose position is estimated.
Step 1: Statutory Category
Claims 1 and 3–5 are directed to a device (a machine), and claim 8 is directed to a method (a process). Claim 9 recites a program that causes the device of claim 1 to execute the recited steps. The Step 1 treatment of claim 9 in the prior Office action is not revisited in this action, and claim 9 is analyzed under Step 2A and Step 2B together with claims 1 and 8.
Step 2A, Prong One: The Claims Recite a Judicial Exception
Claim 1 recites the following limitations, each of which, under its broadest reasonable interpretation, describes a mathematical concept, namely mathematical relationships and mathematical calculations (MPEP 2106.04(a)(2)(I)(A), (C)):
(a) Map comparison. “collates the first sensor data and the map data of the driving area, and … estimates the self-position of the autonomous moving body using six degrees-of-freedom parameters representing a position and a posture … in a three-dimensional coordinate system.” Collating one set of spatial data against another and computing the six numerical parameters of a rigid-body transformation that best aligns them is a mathematical calculation.
(b) Conditional selection. “in a case where the map comparison unit cannot estimate the self-position by the map comparison.” Whether calculation (a) yields an estimate is determined from the result of calculation (a). Selecting the next calculation based on the outcome of the prior calculation is part of the mathematical procedure.
(c) First-stage estimation. “estimates two partial parameters that are a part of the six degrees-of-freedom parameters, based on known information … and the on first sensor data,” specifically “a position in the first axis direction … and the posture represented by the rotation around the third axis … based on a shape obtained by connecting the plurality of landmarks extracted from the first sensor data and a shape obtained from a disposition of the landmarks recorded in the known information.” Fitting a shape through measured points and comparing it with a stored shape to compute a translational offset and a rotation angle is a geometric calculation.
(d) Second-stage estimation. “estimates a self-position … represented by the six degrees-of-freedom parameters, by estimating the four parameters … other than the two partial parameters …, based on the first sensor data, the second sensor data, and the two partial parameters.” Solving for four unknowns from measured values with two values held as known is a mathematical calculation.
A mathematical concept need not be expressed in mathematical symbols; a claim recites a mathematical calculation when it recites an act of calculating using mathematical methods, whether or not the claim uses the word “calculating.” MPEP 2106.04(a)(2)(I)(C). Limitations (a)–(d) recite nothing other than calculations of this kind. They are of the same character as the calculations held abstract in Parker v. Flook, 437 U.S. 584 (1978) (computing an updated alarm limit from measured process values), and In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367 (Fed. Cir. 2021) (Stanford I) (computing haplotype phase from measured data by a statistical model).
Claim 8 recites limitations (a)–(d) as method steps, and claim 9 recites them as steps executed by the device of claim 1. Claims 1, 8, and 9 therefore recite a judicial exception. Because the claims recite a mathematical concept, the analysis proceeds to Prong Two.
Step 2A, Prong Two: The Judicial Exception Is Not Integrated into a Practical Application
The additional elements of claims 1, 8, and 9, beyond the judicial exception, are:
(i) Acquiring first sensor data and second sensor data (the first and second acquisition units, or the steps of acquiring). These limitations obtain the input values for calculations (a)–(d). Obtaining input for a calculation is insignificant pre-solution activity. MPEP 2106.05(g); Mayo, 566 U.S. at 79 (determining metabolite levels in order to apply a correlation); In re Grams, 888 F.2d 835, 839–40 (Fed. Cir. 1989) (performing clinical tests to obtain input for an equation). The recited content of the data (data “capable of detecting” fixed and non-fixed objects; data “including an acceleration and an angular velocity”) specifies what the input is, not how it is acquired. Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (selecting information by content and source for collection and analysis is not an inventive application).
(ii) The map comparison unit, first estimation unit, and second estimation unit, and the program of claim 9. These elements are recited only by the calculations they perform, at a high level of generality, with no particular hardware, architecture, or manner of operation. They amount to no more than instructions to perform the calculations using generic computing components. MPEP 2106.05(f); Alice, 573 U.S. at 223.
(iii) The autonomous moving body, the driving area, the map data and known information, and the mark as a plurality of landmarks attached to a part of a fixed object. These limitations identify the object whose position is calculated, the setting, and the source of the stored and measured values used in the calculations. They generally link the use of the judicial exception to a particular technological environment. MPEP 2106.05(h); Flook, 437 U.S. at 588–90 (limiting a formula to a particular industrial process).
Evaluated individually and in combination, these additional elements do not integrate the judicial exception into a practical application under any of the considerations in MPEP 2106.04(d)(1):
No improvement to the functioning of a computer or to another technology (MPEP 2106.05(a)). The claims specify a particular sequence of calculations, and to that extent they specify how the processing units process the sensor and map data. But that sequence is the mathematical estimation procedure identified at Prong One. The claims do not recite any change in how data is acquired or how a sensor operates, and they do not use the estimate. A technique based on mathematics can improve a technology where the claim reflects an improvement in how that technology operates (MPEP 2106.05(a)), but these claims do not. Under the broadest reasonable interpretation, any gain in accuracy or computational efficiency results from changing which parameters are calculated and from which values, and the claims end with the calculated result. In these claims the asserted improvement is therefore in the judicial exception itself, which cannot supply the improvement. MPEP 2106.05(a); Stanford II, 991 F.3d at 1250; SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018).
No particular machine (MPEP 2106.05(b)). The recited units are defined only by their calculations, and the autonomous moving body is not used to perform them. The calculations are not tied to a particular machine in a manner that imposes a meaningful limit.
No transformation (MPEP 2106.05(c)). The claims transform data into other data. No article is transformed to a different state or thing.
No particular treatment or use, and no other meaningful limitation (MPEP 2106.04(d)(2), 2106.05(e)). The claims do not apply the calculated self-position to control, navigate, stop, or steer the autonomous moving body, or to operate any other machine. This distinguishes the claims from those found eligible in Diamond v. Diehr, 450 U.S. 175 (1981), where the calculated value was used to open a mold press in an industrial curing process.
The claims have also been considered as a whole, including the order of operations and the limitations most likely to be offered as an improvement: the fallback performed when the map comparison cannot estimate the self-position, the division of the six parameters into two and four, and the use of a shape formed by landmarks attached to a fixed object. An improvement may be provided by the additional elements in combination with the judicial exception, but not by the judicial exception alone. MPEP 2106.05(a). Each of these limitations specifies which calculation is performed, on which values, and in what order: the fallback selects a calculation based on the result of a prior calculation (limitation (b)); the two-and-four division specifies which unknowns each calculation solves (limitations (c) and (d)); and the landmark and shape limitations specify the input values and the geometric relationship used (limitation (c)). These limitations change the processing performed on the data, but only by changing the calculations. None requires a change in how data is acquired, a particular sensor configuration, a change in the processing hardware, or any use of the result. The additional elements contribute only the input data and the generic processing on which the calculations run.
Consideration of the asserted improvement under current guidance. The Examiner acknowledges that the specification identifies reduced cumulative error (drift) and reduced computational load as technological objectives of self-position estimation (pub. paras. [0007]–[0009]). Consistent with MPEP 2106.04(d)(1) and 2106.05(a), as revised by the Advance Notice of Change to the MPEP in light of Ex parte Desjardins (Dec. 5, 2025), the claims have been evaluated as a whole, and not at a high level of generality, to determine whether they themselves reflect a technological solution to those problems. Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (Appeals Review Panel Decision) (precedential). The limitations asserted to produce the improvement have been considered individually and in their particular order: (a) the map comparison, (b) the conditional invocation of a landmark-based estimation, (c) the determination of two pose components from the landmark geometry, and (d) the calculation of the remaining four components with the two supplied.
Having so considered them, the Examiner finds that the claims do not require a technological modification to the sensing system, the camera-IMU estimator, the processing hardware, the autonomous moving body, or any other physical or computational component, apart from the mathematical estimation itself. The limitations to which the asserted benefits are attributed define which pose variables are determined in the first calculation, which variables are supplied to the second calculation, and which remaining variables are optimized. Even considering the particular ordered combination and the technological context identified in the specification, the asserted improvement therefore resides in the mathematical estimation procedure identified at Prong One, rather than in an additional element, or a combination of additional elements, that applies that procedure to improve another technology. The judicial exception alone cannot provide the improvement. MPEP 2106.05(a).
Desjardins is distinguishable on this basis. There, the claims recited a particular way of training a machine learning model that protected the model’s knowledge of previous tasks while allowing it to learn new tasks, which the Appeals Review Panel found to be an improvement in how the model itself operates, i.e., in computer functionality. See MPEP 2106.05(a)(I) (as revised Dec. 5, 2025). Here, the claims do not recite any change in how the estimator, the processing units, or the sensors function; they recite which quantities are calculated from which inputs, and they end with the calculated result.
The claims are distinguishable from those held eligible in Thales Visionix Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017). There, the claims required a particular physical configuration of inertial sensors, one mounted on the tracked object and one on the moving platform, and the improvement in tracking accuracy arose from that unconventional configuration. The present claims recite no sensor at all, only the acquisition of data of a stated content, and the asserted improvement arises from the calculations performed on that data. The Federal Circuit has drawn the same distinction, contrasting the physical tracking system in Thales with a claimed advance lying in mathematical analysis. SAP America, 898 F.3d at 1167–68.
Claims 1, 8, and 9 are therefore directed to the judicial exception.
Step 2B: The Claims Do Not Recite Significantly More
The additional elements, considered individually and as an ordered combination, do not amount to significantly more than the judicial exception. Elements (ii) and (iii) remain instructions to apply the exception with generic computing components (MPEP 2106.05(f)) and field-of-use limitations (MPEP 2106.05(h)); this conclusion carries over to Step 2B. Element (i), identified as insignificant extra-solution activity at Prong Two, is re-evaluated at Step 2B to determine whether it is well-understood, routine, and conventional. MPEP 2106.05(g), 2106.05(d). It is, as supported by the following evidence (Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018); MPEP 2106.05(d)(I)(2)):
Applicant’s specification (MPEP 2106.05(d)(I)(2)(a)). The background describes, as existing technology, an autonomous moving body that “collates map data created in advance based on sensor data acquired by, for example, a laser sensor or a camera (image sensor) with current sensor data to estimate a self-position (position and posture with respect to each axis of a three-dimensional coordinate system)” (pub. para. [0003]); that “in an area where it is difficult to estimate the self-position by means of the laser sensor or the camera, a technology for estimating the self-position by substituting other sensors is considered” and “acquiring sensor data measured by each of a plurality of different sensors such as an encoder, an inertial measurement unit (IMU), a global navigation satellite system (GNSS) receiver, a camera, and a laser sensor” (para. [0004]); and that “[i]n visual inertial odometry (VIO) of the self-position estimation technology in which a camera and an IMU are combined, a self-position at which a movement amount of a feature point obtained from a camera image matches an acceleration and an angular velocity obtained from the IMU is calculated by optimization calculation (for example, a nonlinear least squares method)” (para. [0007]). Acquiring camera or laser data and acceleration and angular-velocity data for self-position estimation is thus described by Applicant as existing practice.
A publication describing the state of the art (MPEP 2106.05(d)(I)(2)(c)). Roumeliotis (US 2014/0333741 A1) describes vision-aided inertial navigation systems, which fuse camera and IMU data to track a six-degrees-of-freedom pose, as having “gained popularity within the robotics community” ([0003]).
Court-recognized conventional computer functions (MPEP 2106.05(d)(II)). Performing repetitive calculations and storing and retrieving information in memory (here, the map data and the known information) are well-understood, routine, and conventional functions of a generic computer when claimed at a high level of generality, as they are here.
As an ordered combination, the claims acquire input data, perform a map-based pose calculation, conditionally perform a landmark-based partial pose calculation, and perform a calculation of the remaining pose parameters. The sequencing belongs to the mathematical procedure identified at Prong One, and an abstract idea cannot supply the inventive concept. Alice, 573 U.S. at 217–18. What remains is the arrangement Applicant’s specification describes as existing: data from conventional sources supplied to a processor that estimates a self-position. No additional element or combination of elements amounts to significantly more than the judicial exception.
Dependent Claims
Claim 3 adds that the second estimation unit estimates all six parameters by an optimization calculation to which a constraint based on the partial parameter is added. This further defines the mathematical calculation (Prong One) and adds no additional element.
Claim 4 adds a motion constraint of the autonomous moving body to that optimization calculation. A constraint term in an optimization is a further mathematical relationship (Prong One) and adds no additional element.
Claim 5 specifies that the mark is paint or a marker and which position and posture parameters are calculated from it. This further identifies the source and content of the input values (MPEP 2106.05(h)) and the output of the calculation (Prong One).
Claims 3–5 therefore neither integrate the judicial exception into a practical application nor recite significantly more, and are rejected for the same reasons as claim 1.
For these reasons, claims 1, 3–5, 8, and 9 are rejected under 35 U.S.C. 101.
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, 3–5, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sano (US 2020/0019792 A1), in view of Roumeliotis (US 2014/0333741 A1), Zhang (US 2019/0271549 A1), and Tateishi (US 2019/0137281 A1).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sano, in view of Roumeliotis and Zhang, applying the construction stated in the rejection under 35 U.S.C. 112(b).
Regarding Claim 1,
Disclosure by Sano
Sano discloses:
A self-position estimation device
See at least: Sano: “The self-position estimation device according to the present embodiment includes a surrounding sensor group 1, a processing unit 3, a storage unit 4, and a vehicle sensor group 5.” ([0019])
Rationale: Sano expressly discloses a self-position estimation device.
that estimates a self-position
See at least: Sano: “is configured to estimate a self-position of the vehicle V.” ([0019])
Rationale: Sano expressly estimates a self-position.
in a driving area
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The road and its surroundings, in which the vehicle travels and for which map information is stored, is a driving area.
of an autonomous moving body,
See at least: Sano: “The moving object is not limited to the vehicle V as moving objects which move on land, but includes vessels, aircraft, spacecraft, and other moving objects.” ([0064]); “The microcontroller may also be used as an Electronic Control Unit (ECU) used for other control in regard of the vehicle V, e.g. automatic driving control, for example.” ([0026])
Rationale: Sano’s vehicle (moving object) is subject to automatic driving control, and is therefore an autonomous moving body.
the self-position estimation device comprising:
See at least: Sano: “The processing unit 3 includes: a target position detection unit 31, a moved amount estimation unit 32, a target position storing unit 33, a straight line extracting unit 34, a target position selection unit 35, a self-position estimation unit 36, and a target attribute estimation unit 37.” ([0026])
Rationale: Sano discloses the constituent elements of the self-position estimation device, mapped below.
a first acquisition unit
See at least: Sano: “The surrounding sensor group 1 includes a plurality of Laser Range Finders (LRFs) 101 and 102 and a plurality of cameras 201 and 202” ([0021]); “The target position detection unit 31 detects a relative position between a target present in the surroundings of the vehicle V and the vehicle V on the basis of a detection result of at least any one of the LRFs 101 and 102 and the cameras 201 and 202.” ([0027])
Rationale: The surrounding sensor group and the target position detection unit that receives its detection results constitute a first acquisition unit.
that acquires first sensor data
See at least: Sano: “on the basis of a detection result of at least any one of the LRFs 101 and 102 and the cameras 201 and 202” ([0027])
Rationale: The detection results of the LRFs and cameras are the first sensor data. Because Sano uses the detection result of the LRFs, the cameras, or both, the first sensor data encompasses both range data and image data.
capable of detecting a fixed object
See at least: Sano: “The targets which are present in the surroundings of the vehicle V include: targets indicating traveling lane boundaries present on a traveling lane in the surroundings of the vehicle V, e.g. white lines, curbs, median strips, the guardrails, and reflectors” ([0021])
Rationale: White lines, curbs, median strips, guardrails, and reflectors are stationary road features, i.e., fixed objects.
always present in a periphery of the autonomous moving body
See at least: Sano: “detects white lines 62 and 63 which are present at both sides of the vehicle V” ([0033])
Rationale: Lane boundary lines present at both sides of the vehicle are permanent features located in the periphery of the vehicle.
and a non-fixed object
See at least: Sano: “The target position detection unit 31 detects a relative position between a target present in the surroundings of the vehicle V and the vehicle V” ([0027]); “The target attribute estimation unit 37 estimates an attribute of the target” ([0029])
Rationale: Sano detects general surrounding targets and estimates their attributes for localization. In a road-environment sensing stream, a PHOSITA would understand that the detected targets necessarily include both fixed landmarks and transient non-fixed objects (e.g., vehicles or pedestrians). See also Zhang, below.
temporarily present in the periphery of the autonomous moving body;
See at least: Sano: FIG. 3, step S11: “ELIMINATE UNRELIABLE TARGET” (FIG. 3); “any targets present in general environments can not always be continuously detected with constant reliability due to aging degradation, occlusion, and other effects.” ([0048])
Rationale: Sano’s reliability-based elimination of targets, including targets affected by occlusion, addresses detections that are not map-consistent. A PHOSITA would recognize that such detections include objects temporarily present in the periphery of the vehicle.
a second acquisition unit
See at least: Sano: “The vehicle sensor group 5 includes a GPS receiver 51, an accelerator sensor 52, a steering sensor 53, a brake sensor 54, a vehicle speed sensor 55, an acceleration sensor 56, a wheel speed sensor 57, and other sensors, such as a yaw rate sensor.” ([0025])
Rationale: The vehicle sensor group is a second acquisition unit.
that acquires second sensor data
See at least: Sano: “Each sensor 51 to 57 is connected to the processing unit 3 and is configured to sequentially output various detection results to the processing unit 3.” ([0025])
Rationale: The detection results output by the vehicle sensor group are the second sensor data.
including an acceleration
See at least: Sano: “an acceleration sensor 56” ([0025])
Rationale: The acceleration sensor outputs an acceleration.
and an angular velocity
See at least: Sano: “other sensors, such as a yaw rate sensor” ([0025]); “an inertia measurement method using a gyroscope or an acceleration sensor” ([0025])
Rationale: A yaw rate sensor or gyroscope outputs an angular velocity.
of the autonomous moving body;
See at least: Sano: “can calculate the odometry indicating a moved amount of the vehicle V in a unit time, by using each detection result of the vehicle sensor group 5” ([0025])
Rationale: The acceleration and angular velocity are those of the vehicle.
a map comparison unit
See at least: Sano: “The self-position estimation unit 36 estimates a self-position which is a current position of the vehicle V by comparing the selected target position data with the map information including the position information on the target present on the road or around the road.” ([0029]); “EXECUTE MAP-MATCHING ONLY FOR TARGET HAVING HIGH RELIABILITY” (FIG. 3, step S13)
Rationale: Sano’s self-position estimation unit 36 performs map matching and corresponds to the claimed map comparison unit.
that collates the first sensor data
See at least: Sano: “the self-position estimation unit 36 compares the target position data selected by the target position selection unit 35 with the position of the target in the map information 41.” ([0053]); “an Iterative Closest Point (ICP) algorithm can be used for the comparison in Step S13.” ([0055])
Rationale: The target position data are derived from the first sensor data ([0027]); comparing them against the map is collating the first sensor data.
and the map data of the driving area,
See at least: Sano: “map information 41 including position information on targets present on a road or around the road” ([0024])
Rationale: Map information 41 is map data of the driving area.
and that estimates the self-position of the autonomous moving body
See at least: Sano: “the self-position estimation unit 36 estimates a self-position of the vehicle V by executing the above-mentioned comparison (map matching) of the position of the target.” ([0054])
Rationale: Sano estimates the self-position from the map comparison.
representing a position and a posture of the autonomous moving body
See at least: Sano: “estimates a position and an attitude angle of total three degrees of freedom composed of a position in the east-west direction of the vehicle V (X coordinate), a position in the north-south direction thereof (Y coordinate), and an azimuth angle (yaw angle θ).” ([0054])
Rationale: The X and Y coordinates are a position, and the yaw angle is a posture.
a first estimation unit
See at least: Sano: FIG. 1 (STRAIGHT LINE EXTRACTING UNIT 34; TARGET POSITION SELECTION UNIT 35); “The target position selection unit 35 measures the respective distances in a vehicle width direction from the vehicle V to the respective straight lines (N2 and N3).” ([0042])
Rationale: Sano’s straight line extracting unit and target position selection unit derive, separately from the final map matching, the lateral distance of the vehicle to lines fitted through detected points. Sano uses this for reliability evaluation; it is cited to show a separate estimation unit. The estimation of the partial parameters is taught by the combination with Tateishi (below).
based on known information,
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The pre-stored map information is the known information.
in which information including a position in the driving area of a mark
See at least: Sano: “The targets (landmark) recorded in the map information 41 includes, for example, various facilities which can be detected by the surrounding sensor group 1” ([0024])
Rationale: A landmark whose position is recorded in the map information is a mark whose position in the driving area is recorded.
that is one fixed object selected in advance from a plurality of fixed objects
See at least: Sano: “the road markings indicating stop lines, pedestrian crossings, advance notices of pedestrian crossing, section lines, and the like; structures, e.g. curbs, and the like” ([0024])
Rationale: Sano’s map records, before localization, selected fixed objects from among the many fixed road features. A PHOSITA would understand the recorded landmark as a fixed object selected in advance from a plurality of fixed objects.
is recorded,
See at least: Sano: “In the map information 41, the position information, e.g. curbs and white lines, is defined by the aggregate of linear information having two-dimensional position information on both end points.” ([0024])
Rationale: The position information of the mark is recorded in the map information.
and the on first sensor data; and
See at least: Sano: “The straight line extracting unit 34 extracts linear information from the target position data stored in the target position storing unit 33.” ([0029])
Rationale: The target position data used by the first estimation are derived from the first sensor data ([0027]).
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
using six degrees-of-freedom parameters
in a three-dimensional coordinate system,
in a case where the map comparison unit cannot estimate the self-position by the map comparison,
estimates two partial parameters
that are a part of the six degrees-of-freedom parameters,
a second estimation unit
that estimates a self-position of the autonomous moving body,
which represented by the six degrees-of-freedom parameters,
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
based on the first sensor data,
the second sensor data,
and the two partial parameters estimated by the first estimation unit,
wherein the mark is a plurality of landmarks
attached to a part of the fixed object,
the first estimation unit estimates a position in the first axis direction
extending in the horizontal direction
and the posture represented by the rotation around the third axis
extending in the vertical direction,
among the six degrees-of-freedom parameters,
based on a shape obtained by connecting the plurality of landmarks
extracted from the first sensor data
and a shape obtained from a disposition of the landmarks
recorded in the known information,
as the two partial parameters.
Disclosure by Roumeliotis
Roumeliotis discloses:
using six degrees-of-freedom parameters
See at least: Roumeliotis: “a Vision-aided Inertial Navigation System (VINS) fuses data from a camera and an Inertial Measurement Unit (IMU) to track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003])
Rationale: Roumeliotis represents the pose by six degrees-of-freedom parameters.
in a three-dimensional coordinate system,
See at least: Roumeliotis: “equipped with proprioceptive (e.g., IMU) and exteroceptive (e.g., camera) sensors, navigating in a 3D environment.” ([0039])
Rationale: The six-degrees-of-freedom pose is defined in a three-dimensional coordinate system.
that are a part of the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: In the combined system, the partial parameters are a subset of Roumeliotis’s six degrees-of-freedom pose.
a second estimation unit
See at least: Roumeliotis: “Estimator 22 of processing unit 20 process image data 14 and IMU data 18 to compute state estimates for the degrees of freedom of VINS 10” ([0031])
Rationale: Estimator 22 corresponds to the second estimation unit.
that estimates a self-position of the autonomous moving body,
See at least: Roumeliotis: “and, from the state estimates, computes position, orientation, speed, locations of observable features, a localized map, an odometry or other higher order derivative information” ([0031])
Rationale: Estimator 22 computes the position and orientation of the platform, i.e., its self-position.
which represented by the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003]); “denotes the robot pose (position and orientation) at time-step i” ([0039])
Rationale: The estimated self-position is the six degrees-of-freedom pose.
based on the first sensor data,
See at least: Roumeliotis: “Image source 12 images an environment in which VINS 10 operates so as to produce image data 14.” ([0029]); “(iii) the exteroceptive sensor measurements” ([0042])
Rationale: The camera image data (exteroceptive measurements) correspond to the first sensor data.
the second sensor data,
See at least: Roumeliotis: “IMU 14 may, for example, detect a current rate of acceleration using one or more accelerometers as VINS 10 is translated, and detect changes in rotational attributes like pitch, roll and yaw using one or more gyroscopes.” ([0030]); “(ii) the proprioceptive sensor motion information (5)” ([0042])
Rationale: The IMU acceleration and angular-rate data correspond to the second sensor data.
and the two partial parameters estimated by the first estimation unit, (mechanism)
See at least: Roumeliotis: “(i) the prior information about the initial state” ([0042]); “The estimator may compute each of the constraints as (i) an estimate of the motion from the first keyframe to the second keyframe” ([0033])
Rationale: Roumeliotis’s estimator accepts information supplied from outside its own measurements, as a prior term and as constraints on its state. This is the mechanism by which, in the combination, the partial parameters are supplied to the second estimation (see Tateishi, below).
Motivation to Combine Sano and Roumeliotis
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano and Roumeliotis before them, to implement Sano’s map-matching localization within the six-degrees-of-freedom vision/inertial estimation framework of Roumeliotis, and to use Roumeliotis’s camera/IMU estimator as the second estimation unit. Both references address localization of a moving body using exteroceptive and inertial sensors. Sano already acquires camera/LRF data together with acceleration and yaw-rate data ([0025], [0027]). Roumeliotis explains that fusing camera and IMU data exploits complementary sensing: “an IMU can accurately track dynamic motions over short time durations, while visual data can be used to estimate the pose displacement (up to scale) between consecutive views.” ([0003]) A PHOSITA would have recognized that a six-degrees-of-freedom representation, in place of Sano’s planar three-degrees-of-freedom pose, yields a complete three-dimensional pose, with predictable results.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis does not explicitly disclose the following claim limitations:
in a case where the map comparison unit cannot estimate the self-position by the map comparison,
estimates two partial parameters
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
and the two partial parameters estimated by the first estimation unit,
wherein the mark is a plurality of landmarks
attached to a part of the fixed object,
the first estimation unit estimates a position in the first axis direction
extending in the horizontal direction
and the posture represented by the rotation around the third axis
extending in the vertical direction,
among the six degrees-of-freedom parameters, (as estimated by the first estimation unit)
based on a shape obtained by connecting the plurality of landmarks
extracted from the first sensor data
and a shape obtained from a disposition of the landmarks
recorded in the known information,
as the two partial parameters.
Disclosure by Zhang
Zhang discloses:
and a non-fixed object temporarily present in the periphery of the autonomous moving body (additional support for Sano)
See at least: Zhang: “A third category of edgels include edgels corresponding to transient features. These may include shadows, moving objects (e.g., other vehicles or pedestrians), debris on a road surface” ([0104]); “if the LiDAR’s view is blocked (e.g., by surrounding vehicles)” ([0086])
Rationale: Zhang confirms that a vehicle’s sensor data captures transient, non-fixed objects temporarily present around the vehicle.
using six degrees-of-freedom parameters … in a three-dimensional coordinate system (map comparison; additional support)
See at least: Zhang: “LiDAR or other types of depth sensors can be used to capture a three-dimensional point cloud of a local area surrounding the vehicle, which can be matched with previously-acquired map data comprising points or features corresponding to different locations of the local area. By determining a transformation that best aligns the sensed point cloud with the map data, a location and orientation of the vehicle can be determined.” ([0085])
Rationale: Zhang’s map comparison aligns three-dimensional sensor data with map data to determine location and orientation, consistent with Sano [0055] (ICP comparison).
in a case where the map comparison unit cannot estimate the self-position by the map comparison,
See at least: Zhang: “other localization methods may be used to supplement or replace ICP-based localization (e.g., as a backup option in case of LiDAR failure). In addition, in some cases, ICP-based localization may fail to localize due to a variety of factors, e.g., the environment lacking enough features (e.g., highways, tunnels, bridges), or if the LiDAR’s view is blocked (e.g., by surrounding vehicles).” ([0086]); “camera-based localization techniques may be used to replace or supplement ICP-based localization techniques.” ([0087]); “camera-based localization can be utilized when LiDAR is malfunctioning or otherwise unavailable.” ([0089]); “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: Zhang teaches that the map comparison (ICP) may fail to localize, including when the view is blocked by surrounding vehicles, and that a separate landmark-based localization is then used as a backup. This teaches performing the first estimation in a case where the map comparison cannot estimate the self-position.
that are a part of the six degrees-of-freedom parameters, (additional support)
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation can be fixed, resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132])
Rationale: Zhang estimates a subset of the six pose components.
wherein the mark is a plurality of landmarks
See at least: Zhang: “For instance, a lane line stripe is typically a skinny rectangle.” ([0125]); “of the edgels projected onto the edges of a lane line segment, edgels at the endpoints of the lane line segment may be sufficient for performing localization” ([0116]); “For example, several of the edgel clusters may be aligned along a line, which may be a typical occurrence for certain types of features such as lane lines.” ([0125]); Sano: “since a detectable region of the solid line is larger than a detectable region of the dashed line” ([0045])
Rationale: The plurality of landmarks is the individual painted stripes (segments) of a lane line. Zhang describes lane line stripes and lane line segments having endpoints, and Sano describes dashed white lines, which by definition consist of separate painted segments. To the extent Zhang does not expressly state that its lane line is formed of separate stripes, it would have been obvious to use the segments of a dashed lane line, as described by Sano, as the plurality of landmarks, because Zhang teaches that lane line markings are permanent, stationary features suited to localization ([0102]) and that segment endpoints provide constraints in two directions ([0116]). Zhang’s edgels are not themselves the landmarks; they are the detected and mapped points representing the edges of the painted stripes. “FIG. 12 shows edgels grouped into different clusters 1204, where each cluster may correspond to a portion a feature captured in the image 1202 (e.g., a portion of a lane line captured in the image).” ([0115])
attached to a part of the fixed object,
See at least: Zhang: “Permanent and stationary features under this category may include lane line markings and curb falls.” ([0102]); Sano: “road surface markings, e.g. stop lines, pedestrian crossings, and speed limit markings” ([0021]); Applicant’s specification: “For example, the fixed object is a structure in which a disposition or shape of a ceiling, a floor, a shelf, a signboard, paint, or the like in the driving area does not change.” (pub. para. [0044]); “For example, the mark is paint (white line or the like) applied to the floor of the driving area or a marker (signboard or the like) installed in the driving area.” (pub. para. [0048])
Rationale: Construction. The specification includes a floor among fixed objects and describes the mark as paint applied to the floor, and dependent claim 5 recites that the mark is paint applied to the driving area. Under the broadest reasonable interpretation consistent with the specification, painted markings applied to a portion of a floor or road surface are landmarks attached to a part of a fixed object. The claim recites both “a fixed object always present in a periphery of the autonomous moving body” and a mark that is “one fixed object selected in advance”; “the fixed object” to which the landmarks are attached is read as the former (the road surface), and the mark is the lane line selected in advance, which consists of the plurality of landmarks. Finding. Lane line markings are painted on, and thus attached to, the part of the road surface along the lane boundary.
the first estimation unit estimates a position in the first axis direction
See at least: Zhang: “edgels at the endpoints of the lane line segment may be sufficient for performing localization, since they provide constraints on both dimensions (e.g., x and y directions).” ([0116])
Rationale: Zhang determines localization constraints along specific axis directions (x and y), which provides the claimed position in the first axis direction.
extending in the horizontal direction
See at least: Zhang: “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Zhang’s x and y directions are horizontal; z is identified separately as the vertical axis ([0132]).
and the posture represented by the rotation around the third axis
See at least: Zhang: “A pose of the vehicle is optimized based upon the determined correspondences by identifying a transformation that minimizes a distance between the edgels and their corresponding edge pixels.” (Abstract); “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Yaw is the posture represented by rotation around a third axis.
extending in the vertical direction,
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation” ([0132])
Rationale: Yaw is rotation about the vertical (z) axis.
among the six degrees-of-freedom parameters,
See at least: Zhang: “By reducing the search space for the transformation from 6 dimensions to 3 dimensions” ([0132]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: The position and yaw are among the six pose parameters.
based on a shape obtained by connecting the plurality of landmarks
See at least: Zhang: “In some embodiments, line geometry is computed for certain groups of edgels, such as line segments in 3D space connecting groups of edgels, as stored as part of the map.” ([0119])
Rationale: Line segments connecting groups of edgels that represent the painted stripes are a shape obtained by connecting the plurality of landmarks.
extracted from the first sensor data
See at least: Zhang: “analyzing the image frame to identify a plurality of edge pixels within the image frame” ([0008]); “the computed line geometry corresponding to multiple edgels can, during localization, be matched with corresponding edges from captured images.” ([0119])
Rationale: The edges matched to the landmark geometry are extracted from the captured images, i.e., the first sensor data.
and a shape obtained from a disposition of the landmarks
See at least: Zhang: “In some embodiments, the localization system determines structure information for an edgel cluster (e.g., the distances between edgels in a cluster) and among different edgel clusters.” ([0125])
Rationale: Structure information of edgel clusters is a shape obtained from the disposition of the landmarks.
recorded in the known information,
See at least: Zhang: “line segments in 3D space connecting groups of edgels, as stored as part of the map” ([0119]); “edgels within a threshold distance from each other may be automatically clustered on the map” ([0119]); “Landmark Map 520” (FIG. 5)
Rationale: The landmark geometry is recorded in the stored map (known information).
Motivation to Combine Sano, Roumeliotis, and Zhang
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, and Zhang before them, to modify the combined Sano/Roumeliotis system in two respects taught by Zhang. First, to perform a separate landmark-based estimation in a case where the map comparison cannot estimate the self-position, because Zhang teaches that map-comparison localization can fail when features are lacking or the view is blocked by surrounding vehicles, and that a backup localization method should then be used ([0086]–[0089]). Second, to base that estimation on the shape formed by a plurality of landmarks matched against the mapped line geometry, because Zhang teaches that “The determined structure information may be used to help guide correspondence search.” ([0125]) Each modification uses a known technique for its established purpose and yields predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416–417 (2007).
Claim limitations Not Explicitly Disclosed by the Combination of Sano, Roumeliotis, and Zhang
The combination of Sano, Roumeliotis, and Zhang does not explicitly disclose the following claim limitations:
estimates two partial parameters
as the two partial parameters.
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
and the two partial parameters estimated by the first estimation unit,
Zhang estimates a subset of three pose components (x, y, and yaw) with the other three held fixed ([0132]); it does not disclose a first estimation of exactly two parameters followed by estimation of the remaining four.
Disclosure by Tateishi
Tateishi (US 2019/0137281 A1) is relied upon only for limitations added by amendment: the “two partial parameters” and the estimation of “the four parameters … other than the two partial parameters.” Tateishi discloses:
estimates two partial parameters
See at least: Tateishi: “The lateral position estimation unit 13 estimates the lateral position of the vehicle and the direction of the vehicle, based on the relative position of the first target relative to the vehicle, recognized by the target recognition unit 12, and the position information on the first target on the map stored in the target database 5.” ([0058]); “The lateral position of the vehicle is the position of the vehicle on the map in the width direction of the traveling lane on which the vehicle travels. The direction of the vehicle is the direction of the vehicle on the map (the direction of the vehicle with respect to the traveling lane).” ([0058])
Rationale: Tateishi’s first estimation outputs exactly two pose parameters: a horizontal position (lateral position, in the lane-width direction on the map) and a heading (direction of the vehicle on the map). Tateishi is cited for the amended requirement that the first estimation output exactly two parameters; the position in the first axis direction and the posture about the third axis are mapped to Zhang above.
estimates two partial parameters (continued: how the two parameters are obtained)
See at least: Tateishi: “The lateral position estimation unit 13 estimates the lateral position of the vehicle and the direction of the vehicle by comparing the relative position of the first target relative to the vehicle with the position information on the first target on the map. The comparison is made, for example, by the least squares matching method.” ([0059]); “The first targets for lateral position estimation includes a straight marking line corresponding to a straight road section.” ([0041]); “FIG. 2A shows the case in which the target recognition unit 12 has recognized the relative positions P1 of the straight marking line La1 and the relative positions P2 of the curved marking line La2 as a row of detection points detected by the radar sensor.” ([0061])
Rationale: The two parameters are obtained by matching a line-shaped target, detected as a row of points, against its mapped position.
as the two partial parameters.
See at least: Tateishi: “the lateral position estimation unit 13 estimates the lateral position of the vehicle M and the direction of the vehicle M by comparing the relative positions P1 of the straight marking line La1, recognized by the target recognition unit 12, with the position information on the straight marking line La1 on the map stored in the target database 5.” ([0063]); “The longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle, based on the relative position of the second target relative to the vehicle M on which the lateral position of the vehicle M and the direction of the vehicle M, estimated by the lateral position estimation unit 13, have been reflected” ([0064])
Rationale: The lateral position and direction are the output of the first stage and are used by the later stage; they are therefore partial parameters of the vehicle pose, and there are two of them.
by estimating the four parameters of the six degrees of freedom
See at least: Tateishi: “The longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle” ([0064]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: Tateishi’s second stage estimates the remaining pose component. Tateishi’s pose is planar, so its remaining component is the longitudinal position only. Tateishi does not expressly disclose a six-degrees-of-freedom estimator that solves exactly four remaining parameters; that is taught by the combination, as set forth in the motivation statement below.
other than the two partial parameters estimated by the first estimation unit,
See at least: Tateishi: “means the relative position of the second target whose relationship with the position information on the second target on the map is corrected by correcting the lateral position of the vehicle M on the map and its direction, which are used as the reference, according to the estimation result of the lateral position estimation unit 13.” ([0065])
Rationale: The second stage uses the first-stage lateral position and direction as a reference and does not re-estimate them; it estimates only the other component(s).
and the two partial parameters estimated by the first estimation unit,
See at least: Tateishi: “on which the lateral position of the vehicle M and the direction of the vehicle M, estimated by the lateral position estimation unit 13, have been reflected” ([0064]); “By doing so, the longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle M while minimizing the influence of a deviation in the lateral position of the vehicle M and the direction of the vehicle M.” ([0067])
Rationale: The second stage is based on the two first-stage parameters.
in a case where the map comparison unit cannot estimate the self-position by the map comparison (operability of the first estimation)
See at least: Tateishi: “The relative position of the first target and the relative position of the second target may be recognized from the image captured by the camera of the external sensor 2, may be recognized from the object information acquired by the radar sensor of the external sensor 2, or may be recognized from both the captured image and the object information.” ([0056]); “The first target for lateral position estimation may include structures extending along the road. The structures extending along the road includes at least one of a wall, a fence, a signboard, a guardrail, and a curbstone.” ([0042]); Zhang: “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: The claim distinguishes two comparisons. The claimed map comparison collates the first sensor data with the map data of the driving area, as Sano ([0053]) and Zhang (ICP, [0085]) do. The first estimation then uses known information in which the mark’s position is recorded, which is itself map-based, after the map comparison has failed; “cannot estimate the self-position by the map comparison” therefore refers to failure of the collation of the driving-area map data, not to failure of every map-based comparison. Zhang identifies conditions under which that collation fails (lack of features; view blocked by surrounding vehicles, [0086]) and teaches using a separate landmark-based localization instead ([0086]–[0089]). Tateishi’s first stage is such a landmark-based comparison, using a single pre-selected line-shaped target recognized by camera, radar, or both. It would have been an obvious design choice to use it as the backup estimation Zhang calls for, with a reasonable expectation of success in the conditions Zhang identifies, because it relies on a pre-selected landmark rather than on the whole-scene features whose absence or occlusion caused the collation to fail.
Motivation to Combine Sano, Roumeliotis, Zhang, and Tateishi
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, Zhang, and Tateishi before them, to modify the combined system as follows.
First stage (two parameters). Configure the landmark-based first estimation to output exactly two parameters, the horizontal position perpendicular to the line-shaped landmark arrangement and the heading, as Tateishi teaches ([0058]–[0059], [0063]).
Second stage (the remaining four); the proposed modification. The following is the modification a person of ordinary skill would have made to Roumeliotis; neither Tateishi nor Roumeliotis expressly discloses a four-component solution. The two first-stage parameters enter Roumeliotis’s motion and measurement models as known values, and the Gauss-Newton minimization of its cost function is carried out over a reduced state containing only the remaining four pose components: the position along the landmark line, the vertical position, and the rotations about the two horizontal axes. Roumeliotis’s cost function “is a nonlinear least squares cost function, and a standard approach to determine its minimum is to employ Gauss-Newton iterative minimization.” ([0043]) Its Jacobians are computed with respect to the pose state ([0040]–[0041], Eqs. (3)–(6)). Treating two components as known removes the corresponding Jacobian columns. The prior, motion-model, and measurement residuals (Eq. (9)) each depend on the full pose, so after two components are fixed they remain functions of the four unknown components, and the reduced normal equations (Eq. (15)) are solved for those four.
Reason to make the modification. Tateishi states the reason: “Estimating the lateral position of the vehicle M and the longitudinal position of the vehicle M in this way makes it possible to prevent the recognition result of a target, not suitable for lateral position estimation (misrecognition or low-accuracy position recognition), from affecting the estimation of the lateral position of the vehicle” ([0073]), and the staged estimation “prevents a decrease in the estimation accuracy of the longitudinal position of the vehicle M, caused due to the influence of a deviation in the lateral position of the vehicle M and in the direction of the vehicle M” ([0074]). Zhang teaches that fixing components “effectively reduces the search space for finding a transformation by restricting certain components of the transformation to fixed values” ([0132]). Roumeliotis further teaches that, in a vision-aided inertial system, “a rotation of the sensing system around a gravity vector may be undetectable from the input of a camera of the sensing system when feature rotation is coincident with the rotation of the sensing system. Similarly, translation of the sensing system may be undetectable when observed features are identically translated.” ([0036]) The first-stage landmark estimate supplies, from an absolute mapped reference, a heading and a horizontal position, components of the kinds Roumeliotis identifies as potentially unobservable.
Reasonable expectation of success. Roumeliotis already performs nonlinear least-squares optimization over a multi-component pose state, and Zhang expressly teaches that a vehicle pose optimization can be carried out with selected components held at fixed values and only the others solved: “resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132]) Accordingly, a person of ordinary skill would have reasonably expected Roumeliotis’s estimator to remain operable when two pose components were supplied as known values and the optimization was performed over the remaining four components, which continue to be estimated from the same camera and IMU measurements from which Roumeliotis estimates the full pose. See KSR, 550 U.S. at 416–417.
Regarding Claim 3,
The combination of Sano, Roumeliotis, Zhang, and Tateishi establishes the self-position estimation device of Claim 1, which is the basis for Claim 3. Claim 3 is construed consistently with Claim 1: the second estimation unit outputs all six parameters of the self-position, with the two partial parameters imposed as constraints in the optimization and the remaining four calculated by it.
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
wherein the second estimation unit estimates all the six degrees-of-freedom parameters
by performing optimization calculation
to which a constraint
based on the partial parameter
is added.
Disclosure by Roumeliotis
Roumeliotis discloses:
wherein the second estimation unit estimates all the six degrees-of-freedom parameters
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003]); “Estimator 22 of processing unit 20 process image data 14 and IMU data 18 to compute state estimates for the degrees of freedom of VINS 10” ([0031])
Rationale: Roumeliotis’s estimator (the second estimation unit of the combined system) outputs the full six-degrees-of-freedom pose.
by performing optimization calculation
See at least: Roumeliotis: “is a nonlinear least squares cost function, and a standard approach to determine its minimum is to employ Gauss-Newton iterative minimization.” ([0043])
Rationale: Gauss-Newton minimization of a least squares cost function is an optimization calculation.
to which a constraint
See at least: Roumeliotis: “The estimator may compute each of the constraints as (i) an estimate of the motion from the first keyframe to the second keyframe” ([0033])
Rationale: Roumeliotis adds constraints to the optimization of the pose state.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis does not explicitly disclose the following claim limitations:
based on the partial parameter
is added.
Disclosure by Zhang
Zhang discloses:
based on the partial parameter
See at least: Zhang: “This effectively reduces the search space for finding a transformation by restricting certain components of the transformation to fixed values.” ([0132])
Rationale: Zhang imposes independently determined pose components as fixed-value constraints in the pose optimization. In the combined system, the two partial parameters from the first estimation unit are imposed in this way.
is added.
See at least: Zhang: “restricting certain components of the transformation to fixed values” ([0132])
Rationale: The fixed-value constraint is added to the optimization.
Motivation to Combine Sano, Roumeliotis, Zhang, and Tateishi
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, Zhang, and Tateishi before them, to have the second estimation unit of the combined system output all six parameters by an optimization calculation to which a constraint based on the partial parameters is added, because Roumeliotis teaches constrained optimization for full six-degrees-of-freedom pose estimation and Zhang teaches that restricting components to fixed values reduces complexity: “a complexity and processing burden needed to perform pose optimization can be reduced.” ([0132])
Regarding Claim 4,
The combination of Sano, Roumeliotis, Zhang, and Tateishi establishes the self-position estimation device of Claim 3, which is the basis for Claim 4.
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
wherein the second estimation unit estimates all the six degrees-of-freedom parameters
by performing optimization calculation
to which a motion constraint of the autonomous moving body
is further added.
Disclosure by Roumeliotis
Roumeliotis discloses:
wherein the second estimation unit estimates all the six degrees-of-freedom parameters
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003])
Rationale: See claim 3.
by performing optimization calculation
See at least: Roumeliotis: “a standard approach to determine its minimum is to employ Gauss-Newton iterative minimization.” ([0043])
Rationale: See claim 3.
to which a motion constraint of the autonomous moving body
See at least: Roumeliotis: “The motion model for a robot or other mobile device containing VINS 10 between time-steps i−1 and i is described by the following generic nonlinear equation” ([0040]); “(ii) the proprioceptive sensor motion information (5)” ([0042])
Rationale: The motion model of the moving platform is a motion constraint of the autonomous moving body.
is further added.
See at least: Roumeliotis: “the batch-MAP estimator utilizes all the available information to estimate the state vector (1).” ([0042])
Rationale: The motion-model term is included in the cost function (Eq. (9)) together with the other terms, i.e., it is further added.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis discloses all limitations of this claim. Zhang is cited below as additional support.
Disclosure by Zhang
Zhang discloses:
a motion constraint of the autonomous moving body (additional support)
See at least: Zhang: “due to the knowledge that the vehicle drives on the ground, the localization system may leverage the OMap to estimate a road surface normal that the vehicle is positioned on” ([0132])
Rationale: Constraining the pose optimization based on the vehicle traveling on the ground is a motion constraint of the autonomous moving body.
Motivation to Combine Sano, Roumeliotis, Zhang, and Tateishi
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, Zhang, and Tateishi before them, to further add a motion constraint of the autonomous moving body to the optimization calculation of the Claim 3 combined system, because Roumeliotis includes a motion-model term in its cost function and Zhang constrains the pose based on the vehicle traveling on the ground; adding such constraints predictably excludes physically impossible poses.
Regarding Claim 5,
The combination of Sano, Roumeliotis, Zhang, and Tateishi establishes the self-position estimation device of Claim 1, which is the basis for Claim 5. Claim 5 is construed consistently with Claim 1, the recited paint or marker constituting the plurality of landmarks.
Disclosure by Sano
Sano discloses:
wherein the mark is at least one of paint applied to the driving area
See at least: Sano: “the road markings indicating stop lines, pedestrian crossings, advance notices of pedestrian crossing, section lines, and the like” ([0024]); “road surface markings, e.g. stop lines, pedestrian crossings, and speed limit markings” ([0021])
Rationale: Road markings and white lines are paint applied to the driving area.
and a marker installed in the driving area,
See at least: Sano: “e.g. white lines, curbs, median strips, the guardrails, and reflectors; road surface markings, e.g. stop lines, pedestrian crossings, and speed limit markings; and road structures, e.g. road signs, traffic lights, and the utility-line pole.” ([0021])
Rationale: Reflectors and road signs are markers installed in the driving area.
the first estimation unit estimates a position in at least one of a first axis direction and a second axis direction
See at least: Sano: “The target position selection unit 35 measures the respective distances in a vehicle width direction from the vehicle V to the respective straight lines (N2 and N3).” ([0042]); “a position in the east-west direction of the vehicle V (X coordinate), a position in the north-south direction thereof (Y coordinate)” ([0054])
Rationale: Sano determines a position along at least one axis direction.
extending in a horizontal direction
See at least: Sano: “a position in the east-west direction of the vehicle V (X coordinate), a position in the north-south direction thereof (Y coordinate)” ([0054])
Rationale: East-west and north-south directions are horizontal.
and a posture represented by rotation around a third axis
See at least: Sano: “an azimuth angle (yaw angle θ)” ([0054])
Rationale: The azimuth (yaw) angle is a posture represented by rotation around a third axis.
extending in a vertical direction,
See at least: Sano: “an azimuth angle (yaw angle θ)” ([0054])
Rationale: Azimuth (yaw) is rotation about the vertical axis.
based on at least one of the paint and the marker extracted from the first sensor data
See at least: Sano: “the target position detection unit 31 detects white lines 62 and 63 which are present at both sides of the vehicle V, respectively on the basis of brightness information of the images captured by the cameras 201 and 202.” ([0033])
Rationale: Painted white lines are extracted from camera images (first sensor data).
and on the known information,
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41” ([0024])
Rationale: The stored map information is the known information.
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
among the six degrees-of-freedom parameters,
as the partial parameter.
Disclosure by Roumeliotis
Roumeliotis discloses:
among the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: The position and posture are among the six pose parameters of the combined system.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis does not explicitly disclose the following claim limitations:
as the partial parameter.
Disclosure by Zhang
Zhang discloses:
as the partial parameter.
See at least: Zhang: “In lane-line feature guided filtering, edgels that are very close to lane line features (e.g., within a threshold distance) are kept, while other edgels are discarded.” ([0107]); “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Zhang estimates a subset of pose components from painted lane-line landmarks, which in the combined system are the partial parameters.
Motivation to Combine Sano, Roumeliotis, Zhang, and Tateishi
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, Zhang, and Tateishi before them, to use painted road markings or installed markers as the landmarks of the Claim 1 combined system, because Zhang teaches that “Permanent and stationary features under this category may include lane line markings and curb falls.” ([0102]) and that such features are “typically suitable for performing localization” ([0102]).
Regarding Claim 8,
Claim 8 recites a method corresponding to the device of Claim 1 and is rejected for the same reasons, as mapped below. “The first estimation unit” is understood as referring to the first estimation step (see Claim Objections). The final wherein clause, “in the first estimation step of acquiring first sensor data, estimating …,” is interpreted as requiring the recited estimating to be performed in the first estimation step, using the acquired first sensor data.
Disclosure by Sano
Sano discloses:
A self-position estimation method
See at least: Sano: “The present invention relates to a self-position estimation method and a self-position estimation device.” ([0001]); FIG. 3 (steps S01–S17)
Rationale: Sano expressly discloses a self-position estimation method, shown as a sequence of steps in FIG. 3.
that estimates a self-position
See at least: Sano: “is configured to estimate a self-position of the vehicle V.” ([0019])
Rationale: Sano expressly estimates a self-position.
in a driving area
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The road and its surroundings, in which the vehicle travels and for which map information is stored, is a driving area.
of an autonomous moving body,
See at least: Sano: “The moving object is not limited to the vehicle V as moving objects which move on land, but includes vessels, aircraft, spacecraft, and other moving objects.” ([0064]); “The microcontroller may also be used as an Electronic Control Unit (ECU) used for other control in regard of the vehicle V, e.g. automatic driving control, for example.” ([0026])
Rationale: Sano’s vehicle (moving object) is subject to automatic driving control, and is therefore an autonomous moving body.
the self-position estimation method comprising:
See at least: Sano: “The processing unit 3 includes: a target position detection unit 31, a moved amount estimation unit 32, a target position storing unit 33, a straight line extracting unit 34, a target position selection unit 35, a self-position estimation unit 36, and a target attribute estimation unit 37.” ([0026])
Rationale: Sano discloses the constituent elements of the self-position estimation method, mapped below.
a step of acquiring first sensor data
See at least: Sano: “on the basis of a detection result of at least any one of the LRFs 101 and 102 and the cameras 201 and 202” ([0027])
Rationale: The detection results of the LRFs and cameras are the first sensor data. Because Sano uses the detection result of the LRFs, the cameras, or both, the first sensor data encompasses both range data and image data.
capable of detecting a fixed object
See at least: Sano: “The targets which are present in the surroundings of the vehicle V include: targets indicating traveling lane boundaries present on a traveling lane in the surroundings of the vehicle V, e.g. white lines, curbs, median strips, the guardrails, and reflectors” ([0021])
Rationale: White lines, curbs, median strips, guardrails, and reflectors are stationary road features, i.e., fixed objects.
always present in a periphery of the autonomous moving body
See at least: Sano: “detects white lines 62 and 63 which are present at both sides of the vehicle V” ([0033])
Rationale: Lane boundary lines present at both sides of the vehicle are permanent features located in the periphery of the vehicle.
and a non-fixed object
See at least: Sano: “The target position detection unit 31 detects a relative position between a target present in the surroundings of the vehicle V and the vehicle V” ([0027]); “The target attribute estimation unit 37 estimates an attribute of the target” ([0029])
Rationale: Sano detects general surrounding targets and estimates their attributes for localization. In a road-environment sensing stream, a PHOSITA would understand that the detected targets necessarily include both fixed landmarks and transient non-fixed objects (e.g., vehicles or pedestrians). See also Zhang, below.
temporarily present in the periphery of the autonomous moving body;
See at least: Sano: FIG. 3, step S11: “ELIMINATE UNRELIABLE TARGET” (FIG. 3); “any targets present in general environments can not always be continuously detected with constant reliability due to aging degradation, occlusion, and other effects.” ([0048])
Rationale: Sano’s reliability-based elimination of targets, including targets affected by occlusion, addresses detections that are not map-consistent. A PHOSITA would recognize that such detections include objects temporarily present in the periphery of the vehicle.
a step of acquiring second sensor data
See at least: Sano: “Each sensor 51 to 57 is connected to the processing unit 3 and is configured to sequentially output various detection results to the processing unit 3.” ([0025])
Rationale: The detection results output by the vehicle sensor group are the second sensor data.
including an acceleration
See at least: Sano: “an acceleration sensor 56” ([0025])
Rationale: The acceleration sensor outputs an acceleration.
and an angular velocity
See at least: Sano: “other sensors, such as a yaw rate sensor” ([0025]); “an inertia measurement method using a gyroscope or an acceleration sensor” ([0025])
Rationale: A yaw rate sensor or gyroscope outputs an angular velocity.
of the autonomous moving body;
See at least: Sano: “can calculate the odometry indicating a moved amount of the vehicle V in a unit time, by using each detection result of the vehicle sensor group 5” ([0025])
Rationale: The acceleration and angular velocity are those of the vehicle.
a step of collating map
See at least: Sano: “The self-position estimation unit 36 estimates a self-position which is a current position of the vehicle V by comparing the selected target position data with the map information including the position information on the target present on the road or around the road.” ([0029]); “EXECUTE MAP-MATCHING ONLY FOR TARGET HAVING HIGH RELIABILITY” (FIG. 3, step S13)
Rationale: Sano’s self-position estimation unit 36 performs map matching and corresponds to the claimed map comparison step.
that collates the first sensor data
See at least: Sano: “the self-position estimation unit 36 compares the target position data selected by the target position selection unit 35 with the position of the target in the map information 41.” ([0053]); “an Iterative Closest Point (ICP) algorithm can be used for the comparison in Step S13.” ([0055])
Rationale: The target position data are derived from the first sensor data ([0027]); comparing them against the map is collating the first sensor data.
and the map data of the driving area,
See at least: Sano: “map information 41 including position information on targets present on a road or around the road” ([0024])
Rationale: Map information 41 is map data of the driving area.
and that estimates the self-position of the autonomous moving body
See at least: Sano: “the self-position estimation unit 36 estimates a self-position of the vehicle V by executing the above-mentioned comparison (map matching) of the position of the target.” ([0054])
Rationale: Sano estimates the self-position from the map comparison.
representing a position and a posture of the autonomous moving body
See at least: Sano: “estimates a position and an attitude angle of total three degrees of freedom composed of a position in the east-west direction of the vehicle V (X coordinate), a position in the north-south direction thereof (Y coordinate), and an azimuth angle (yaw angle θ).” ([0054])
Rationale: The X and Y coordinates are a position, and the yaw angle is a posture.
a first estimation step
See at least: Sano: FIG. 1 (STRAIGHT LINE EXTRACTING UNIT 34; TARGET POSITION SELECTION UNIT 35); “The target position selection unit 35 measures the respective distances in a vehicle width direction from the vehicle V to the respective straight lines (N2 and N3).” ([0042])
Rationale: Sano’s straight line extracting unit and target position selection unit derive, separately from the final map matching, the lateral distance of the vehicle to lines fitted through detected points. Sano uses this for reliability evaluation; it is cited to show a separate estimation step. The estimation of the partial parameters is taught by the combination with Tateishi (below).
based on known information,
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The pre-stored map information is the known information.
in which information including a position in the driving area of a mark
See at least: Sano: “The targets (landmark) recorded in the map information 41 includes, for example, various facilities which can be detected by the surrounding sensor group 1” ([0024])
Rationale: A landmark whose position is recorded in the map information is a mark whose position in the driving area is recorded.
that is one fixed object selected in advance from a plurality of fixed objects
See at least: Sano: “the road markings indicating stop lines, pedestrian crossings, advance notices of pedestrian crossing, section lines, and the like; structures, e.g. curbs, and the like” ([0024])
Rationale: Sano’s map records, before localization, selected fixed objects from among the many fixed road features. A PHOSITA would understand the recorded landmark as a fixed object selected in advance from a plurality of fixed objects.
is recorded,
See at least: Sano: “In the map information 41, the position information, e.g. curbs and white lines, is defined by the aggregate of linear information having two-dimensional position information on both end points.” ([0024])
Rationale: The position information of the mark is recorded in the map information.
and on the first sensor data; and
See at least: Sano: “The straight line extracting unit 34 extracts linear information from the target position data stored in the target position storing unit 33.” ([0029])
Rationale: The target position data used by the first estimation are derived from the first sensor data ([0027]).
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
using six degrees-of-freedom parameters
in a three-dimensional coordinate system,
in a case where the step of collating map cannot estimate the self-position by the map comparison,
estimating two partial parameters
that are a part of the six degrees-of-freedom parameters,
a second estimation step
of estimating a self-position of the autonomous moving body,
which represented by the six degrees-of-freedom parameters,
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
based on the first sensor data,
the second sensor data,
and the two partial parameters estimated by the first estimation unit,
wherein the mark is a plurality of landmarks
attached to a part of the fixed object,
the first estimation step, estimating a position in the first axis direction
extending in the horizontal direction
and the posture represented by the rotation around the third axis
extending in the vertical direction,
among the six degrees-of-freedom parameters,
based on a shape obtained by connecting the plurality of landmarks
extracted from the first sensor data
and a shape obtained from a disposition of the landmarks
recorded in the known information,
as the two partial parameters.
Disclosure by Roumeliotis
Roumeliotis discloses:
using six degrees-of-freedom parameters
See at least: Roumeliotis: “a Vision-aided Inertial Navigation System (VINS) fuses data from a camera and an Inertial Measurement Unit (IMU) to track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003])
Rationale: Roumeliotis represents the pose by six degrees-of-freedom parameters.
in a three-dimensional coordinate system,
See at least: Roumeliotis: “equipped with proprioceptive (e.g., IMU) and exteroceptive (e.g., camera) sensors, navigating in a 3D environment.” ([0039])
Rationale: The six-degrees-of-freedom pose is defined in a three-dimensional coordinate system.
that are a part of the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: In the combined system, the partial parameters are a subset of Roumeliotis’s six degrees-of-freedom pose.
a second estimation step
See at least: Roumeliotis: “Estimator 22 of processing unit 20 process image data 14 and IMU data 18 to compute state estimates for the degrees of freedom of VINS 10” ([0031])
Rationale: Estimator 22 corresponds to the second estimation step.
of estimating a self-position of the autonomous moving body,
See at least: Roumeliotis: “and, from the state estimates, computes position, orientation, speed, locations of observable features, a localized map, an odometry or other higher order derivative information” ([0031])
Rationale: Estimator 22 computes the position and orientation of the platform, i.e., its self-position.
which represented by the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003]); “denotes the robot pose (position and orientation) at time-step i” ([0039])
Rationale: The estimated self-position is the six degrees-of-freedom pose.
based on the first sensor data,
See at least: Roumeliotis: “Image source 12 images an environment in which VINS 10 operates so as to produce image data 14.” ([0029]); “(iii) the exteroceptive sensor measurements” ([0042])
Rationale: The camera image data (exteroceptive measurements) correspond to the first sensor data.
the second sensor data,
See at least: Roumeliotis: “IMU 14 may, for example, detect a current rate of acceleration using one or more accelerometers as VINS 10 is translated, and detect changes in rotational attributes like pitch, roll and yaw using one or more gyroscopes.” ([0030]); “(ii) the proprioceptive sensor motion information (5)” ([0042])
Rationale: The IMU acceleration and angular-rate data correspond to the second sensor data.
and the two partial parameters estimated by the first estimation unit, (mechanism)
See at least: Roumeliotis: “(i) the prior information about the initial state” ([0042]); “The estimator may compute each of the constraints as (i) an estimate of the motion from the first keyframe to the second keyframe” ([0033])
Rationale: Roumeliotis’s estimator accepts information supplied from outside its own measurements, as a prior term and as constraints on its state. This is the mechanism by which, in the combination, the partial parameters are supplied to the second estimation (see Tateishi, below).
Motivation to Combine Sano and Roumeliotis
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano and Roumeliotis before them, to implement Sano’s map-matching localization within the six-degrees-of-freedom vision/inertial estimation framework of Roumeliotis, and to use Roumeliotis’s camera/IMU estimator as the second estimation step. Both references address localization of a moving body using exteroceptive and inertial sensors. Sano already acquires camera/LRF data together with acceleration and yaw-rate data ([0025], [0027]). Roumeliotis explains that fusing camera and IMU data exploits complementary sensing: “an IMU can accurately track dynamic motions over short time durations, while visual data can be used to estimate the pose displacement (up to scale) between consecutive views.” ([0003]) A PHOSITA would have recognized that a six-degrees-of-freedom representation, in place of Sano’s planar three-degrees-of-freedom pose, yields a complete three-dimensional pose, with predictable results.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis does not explicitly disclose the following claim limitations:
in a case where the step of collating map cannot estimate the self-position by the map comparison,
estimating two partial parameters
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
and the two partial parameters estimated by the first estimation unit,
wherein the mark is a plurality of landmarks
attached to a part of the fixed object,
in the first estimation step …, estimating a position in the first axis direction
extending in the horizontal direction
and the posture represented by the rotation around the third axis
extending in the vertical direction,
among the six degrees-of-freedom parameters, (as estimated by the first estimation step)
based on a shape obtained by connecting the plurality of landmarks
extracted from the first sensor data
and a shape obtained from a disposition of the landmarks
recorded in the known information,
as the two partial parameters.
Disclosure by Zhang
Zhang discloses:
and a non-fixed object temporarily present in the periphery of the autonomous moving body (additional support for Sano)
See at least: Zhang: “A third category of edgels include edgels corresponding to transient features. These may include shadows, moving objects (e.g., other vehicles or pedestrians), debris on a road surface” ([0104]); “if the LiDAR’s view is blocked (e.g., by surrounding vehicles)” ([0086])
Rationale: Zhang confirms that a vehicle’s sensor data captures transient, non-fixed objects temporarily present around the vehicle.
using six degrees-of-freedom parameters … in a three-dimensional coordinate system (map comparison; additional support)
See at least: Zhang: “LiDAR or other types of depth sensors can be used to capture a three-dimensional point cloud of a local area surrounding the vehicle, which can be matched with previously-acquired map data comprising points or features corresponding to different locations of the local area. By determining a transformation that best aligns the sensed point cloud with the map data, a location and orientation of the vehicle can be determined.” ([0085])
Rationale: Zhang’s map comparison aligns three-dimensional sensor data with map data to determine location and orientation, consistent with Sano [0055] (ICP comparison).
in a case where the step of collating map cannot estimate the self-position by the map comparison,
See at least: Zhang: “other localization methods may be used to supplement or replace ICP-based localization (e.g., as a backup option in case of LiDAR failure). In addition, in some cases, ICP-based localization may fail to localize due to a variety of factors, e.g., the environment lacking enough features (e.g., highways, tunnels, bridges), or if the LiDAR’s view is blocked (e.g., by surrounding vehicles).” ([0086]); “camera-based localization techniques may be used to replace or supplement ICP-based localization techniques.” ([0087]); “camera-based localization can be utilized when LiDAR is malfunctioning or otherwise unavailable.” ([0089]); “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: Zhang teaches that the map comparison (ICP) may fail to localize, including when the view is blocked by surrounding vehicles, and that a separate landmark-based localization is then used as a backup. This teaches performing the first estimation in a case where the map comparison cannot estimate the self-position. Because the first and second estimation steps are performed only when the recited condition is met, the broadest reasonable interpretation of the method does not require those contingent steps to be performed. See Ex parte Schulhauser, Appeal No. 2013-007847 (PTAB 2016) (precedential); MPEP 2111.04(II). The contingent steps are nonetheless mapped herein for compact prosecution.
that are a part of the six degrees-of-freedom parameters, (additional support)
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation can be fixed, resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132])
Rationale: Zhang estimates a subset of the six pose components.
wherein the mark is a plurality of landmarks
See at least: Zhang: “For instance, a lane line stripe is typically a skinny rectangle.” ([0125]); “of the edgels projected onto the edges of a lane line segment, edgels at the endpoints of the lane line segment may be sufficient for performing localization” ([0116]); “For example, several of the edgel clusters may be aligned along a line, which may be a typical occurrence for certain types of features such as lane lines.” ([0125]); Sano: “since a detectable region of the solid line is larger than a detectable region of the dashed line” ([0045])
Rationale: The plurality of landmarks is the individual painted stripes (segments) of a lane line. Zhang describes lane line stripes and lane line segments having endpoints, and Sano describes dashed white lines, which by definition consist of separate painted segments. To the extent Zhang does not expressly state that its lane line is formed of separate stripes, it would have been obvious to use the segments of a dashed lane line, as described by Sano, as the plurality of landmarks, because Zhang teaches that lane line markings are permanent, stationary features suited to localization ([0102]) and that segment endpoints provide constraints in two directions ([0116]). Zhang’s edgels are not themselves the landmarks; they are the detected and mapped points representing the edges of the painted stripes. “FIG. 12 shows edgels grouped into different clusters 1204, where each cluster may correspond to a portion a feature captured in the image 1202 (e.g., a portion of a lane line captured in the image).” ([0115])
attached to a part of the fixed object,
See at least: Zhang: “Permanent and stationary features under this category may include lane line markings and curb falls.” ([0102]); Sano: “road surface markings, e.g. stop lines, pedestrian crossings, and speed limit markings” ([0021]); Applicant’s specification: “For example, the fixed object is a structure in which a disposition or shape of a ceiling, a floor, a shelf, a signboard, paint, or the like in the driving area does not change.” (pub. para. [0044]); “For example, the mark is paint (white line or the like) applied to the floor of the driving area or a marker (signboard or the like) installed in the driving area.” (pub. para. [0048])
Rationale: Construction. The specification includes a floor among fixed objects and describes the mark as paint applied to the floor, and dependent claim 5 recites that the mark is paint applied to the driving area. Under the broadest reasonable interpretation consistent with the specification, painted markings applied to a portion of a floor or road surface are landmarks attached to a part of a fixed object. The claim recites both “a fixed object always present in a periphery of the autonomous moving body” and a mark that is “one fixed object selected in advance”; “the fixed object” to which the landmarks are attached is read as the former (the road surface), and the mark is the lane line selected in advance, which consists of the plurality of landmarks. Finding. Lane line markings are painted on, and thus attached to, the part of the road surface along the lane boundary.
in the first estimation step …, estimating a position in the first axis direction
See at least: Zhang: “edgels at the endpoints of the lane line segment may be sufficient for performing localization, since they provide constraints on both dimensions (e.g., x and y directions).” ([0116])
Rationale: Zhang determines localization constraints along specific axis directions (x and y), which provides the claimed position in the first axis direction.
extending in the horizontal direction
See at least: Zhang: “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Zhang’s x and y directions are horizontal; z is identified separately as the vertical axis ([0132]).
and the posture represented by the rotation around the third axis
See at least: Zhang: “A pose of the vehicle is optimized based upon the determined correspondences by identifying a transformation that minimizes a distance between the edgels and their corresponding edge pixels.” (Abstract); “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Yaw is the posture represented by rotation around a third axis.
extending in the vertical direction,
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation” ([0132])
Rationale: Yaw is rotation about the vertical (z) axis.
among the six degrees-of-freedom parameters,
See at least: Zhang: “By reducing the search space for the transformation from 6 dimensions to 3 dimensions” ([0132]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: The position and yaw are among the six pose parameters.
based on a shape obtained by connecting the plurality of landmarks
See at least: Zhang: “In some embodiments, line geometry is computed for certain groups of edgels, such as line segments in 3D space connecting groups of edgels, as stored as part of the map.” ([0119])
Rationale: Line segments connecting groups of edgels that represent the painted stripes are a shape obtained by connecting the plurality of landmarks.
extracted from the first sensor data
See at least: Zhang: “analyzing the image frame to identify a plurality of edge pixels within the image frame” ([0008]); “the computed line geometry corresponding to multiple edgels can, during localization, be matched with corresponding edges from captured images.” ([0119])
Rationale: The edges matched to the landmark geometry are extracted from the captured images, i.e., the first sensor data.
and a shape obtained from a disposition of the landmarks
See at least: Zhang: “In some embodiments, the localization system determines structure information for an edgel cluster (e.g., the distances between edgels in a cluster) and among different edgel clusters.” ([0125])
Rationale: Structure information of edgel clusters is a shape obtained from the disposition of the landmarks.
recorded in the known information,
See at least: Zhang: “line segments in 3D space connecting groups of edgels, as stored as part of the map” ([0119]); “edgels within a threshold distance from each other may be automatically clustered on the map” ([0119]); “Landmark Map 520” (FIG. 5)
Rationale: The landmark geometry is recorded in the stored map (known information).
Motivation to Combine Sano, Roumeliotis, and Zhang
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, and Zhang before them, to modify the combined Sano/Roumeliotis system in two respects taught by Zhang. First, to perform a separate landmark-based estimation in a case where the map comparison cannot estimate the self-position, because Zhang teaches that map-comparison localization can fail when features are lacking or the view is blocked by surrounding vehicles, and that a backup localization method should then be used ([0086]–[0089]). Second, to base that estimation on the shape formed by a plurality of landmarks matched against the mapped line geometry, because Zhang teaches that “The determined structure information may be used to help guide correspondence search.” ([0125]) Each modification uses a known technique for its established purpose and yields predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416–417 (2007).
Claim limitations Not Explicitly Disclosed by the Combination of Sano, Roumeliotis, and Zhang
The combination of Sano, Roumeliotis, and Zhang does not explicitly disclose the following claim limitations:
estimating two partial parameters
as the two partial parameters.
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
and the two partial parameters estimated by the first estimation unit,
Zhang estimates a subset of three pose components (x, y, and yaw) with the other three held fixed ([0132]); it does not disclose a first estimation of exactly two parameters followed by estimation of the remaining four.
Disclosure by Tateishi
Tateishi (US 2019/0137281 A1) is relied upon only for limitations added by amendment: the “two partial parameters” and the estimation of “the four parameters … other than the two partial parameters.” Tateishi discloses:
estimating two partial parameters
See at least: Tateishi: “The lateral position estimation unit 13 estimates the lateral position of the vehicle and the direction of the vehicle, based on the relative position of the first target relative to the vehicle, recognized by the target recognition unit 12, and the position information on the first target on the map stored in the target database 5.” ([0058]); “The lateral position of the vehicle is the position of the vehicle on the map in the width direction of the traveling lane on which the vehicle travels. The direction of the vehicle is the direction of the vehicle on the map (the direction of the vehicle with respect to the traveling lane).” ([0058])
Rationale: Tateishi’s first estimation outputs exactly two pose parameters: a horizontal position (lateral position, in the lane-width direction on the map) and a heading (direction of the vehicle on the map). Tateishi is cited for the amended requirement that the first estimation output exactly two parameters; the position in the first axis direction and the posture about the third axis are mapped to Zhang above.
estimating two partial parameters (continued: how the two parameters are obtained)
See at least: Tateishi: “The lateral position estimation unit 13 estimates the lateral position of the vehicle and the direction of the vehicle by comparing the relative position of the first target relative to the vehicle with the position information on the first target on the map. The comparison is made, for example, by the least squares matching method.” ([0059]); “The first targets for lateral position estimation includes a straight marking line corresponding to a straight road section.” ([0041]); “FIG. 2A shows the case in which the target recognition unit 12 has recognized the relative positions P1 of the straight marking line La1 and the relative positions P2 of the curved marking line La2 as a row of detection points detected by the radar sensor.” ([0061])
Rationale: The two parameters are obtained by matching a line-shaped target, detected as a row of points, against its mapped position.
as the two partial parameters.
See at least: Tateishi: “the lateral position estimation unit 13 estimates the lateral position of the vehicle M and the direction of the vehicle M by comparing the relative positions P1 of the straight marking line La1, recognized by the target recognition unit 12, with the position information on the straight marking line La1 on the map stored in the target database 5.” ([0063]); “The longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle, based on the relative position of the second target relative to the vehicle M on which the lateral position of the vehicle M and the direction of the vehicle M, estimated by the lateral position estimation unit 13, have been reflected” ([0064])
Rationale: The lateral position and direction are the output of the first stage and are used by the later stage; they are therefore partial parameters of the vehicle pose, and there are two of them.
by estimating the four parameters of the six degrees of freedom
See at least: Tateishi: “The longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle” ([0064]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: Tateishi’s second stage estimates the remaining pose component. Tateishi’s pose is planar, so its remaining component is the longitudinal position only. Tateishi does not expressly disclose a six-degrees-of-freedom estimator that solves exactly four remaining parameters; that is taught by the combination, as set forth in the motivation statement below.
other than the two partial parameters estimated by the first estimation unit,
See at least: Tateishi: “means the relative position of the second target whose relationship with the position information on the second target on the map is corrected by correcting the lateral position of the vehicle M on the map and its direction, which are used as the reference, according to the estimation result of the lateral position estimation unit 13.” ([0065])
Rationale: The second stage uses the first-stage lateral position and direction as a reference and does not re-estimate them; it estimates only the other component(s).
and the two partial parameters estimated by the first estimation unit,
See at least: Tateishi: “on which the lateral position of the vehicle M and the direction of the vehicle M, estimated by the lateral position estimation unit 13, have been reflected” ([0064]); “By doing so, the longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle M while minimizing the influence of a deviation in the lateral position of the vehicle M and the direction of the vehicle M.” ([0067])
Rationale: The second stage is based on the two first-stage parameters.
in a case where the step of collating map cannot estimate the self-position by the map comparison (operability of the first estimation)
See at least: Tateishi: “The relative position of the first target and the relative position of the second target may be recognized from the image captured by the camera of the external sensor 2, may be recognized from the object information acquired by the radar sensor of the external sensor 2, or may be recognized from both the captured image and the object information.” ([0056]); “The first target for lateral position estimation may include structures extending along the road. The structures extending along the road includes at least one of a wall, a fence, a signboard, a guardrail, and a curbstone.” ([0042]); Zhang: “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: The claim distinguishes two comparisons. The claimed map comparison collates the first sensor data with the map data of the driving area, as Sano ([0053]) and Zhang (ICP, [0085]) do. The first estimation then uses known information in which the mark’s position is recorded, which is itself map-based, after the map comparison has failed; “cannot estimate the self-position by the map comparison” therefore refers to failure of the collation of the driving-area map data, not to failure of every map-based comparison. Zhang identifies conditions under which that collation fails (lack of features; view blocked by surrounding vehicles, [0086]) and teaches using a separate landmark-based localization instead ([0086]–[0089]). Tateishi’s first stage is such a landmark-based comparison, using a single pre-selected line-shaped target recognized by camera, radar, or both. It would have been an obvious design choice to use it as the backup estimation Zhang calls for, with a reasonable expectation of success in the conditions Zhang identifies, because it relies on a pre-selected landmark rather than on the whole-scene features whose absence or occlusion caused the collation to fail.
Motivation to Combine Sano, Roumeliotis, Zhang, and Tateishi
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, Zhang, and Tateishi before them, to modify the combined system as follows.
First stage (two parameters). Configure the landmark-based first estimation to output exactly two parameters, the horizontal position perpendicular to the line-shaped landmark arrangement and the heading, as Tateishi teaches ([0058]–[0059], [0063]).
Second stage (the remaining four); the proposed modification. The following is the modification a person of ordinary skill would have made to Roumeliotis; neither Tateishi nor Roumeliotis expressly discloses a four-component solution. The two first-stage parameters enter Roumeliotis’s motion and measurement models as known values, and the Gauss-Newton minimization of its cost function is carried out over a reduced state containing only the remaining four pose components: the position along the landmark line, the vertical position, and the rotations about the two horizontal axes. Roumeliotis’s cost function “is a nonlinear least squares cost function, and a standard approach to determine its minimum is to employ Gauss-Newton iterative minimization.” ([0043]) Its Jacobians are computed with respect to the pose state ([0040]–[0041], Eqs. (3)–(6)). Treating two components as known removes the corresponding Jacobian columns. The prior, motion-model, and measurement residuals (Eq. (9)) each depend on the full pose, so after two components are fixed they remain functions of the four unknown components, and the reduced normal equations (Eq. (15)) are solved for those four.
Reason to make the modification. Tateishi states the reason: “Estimating the lateral position of the vehicle M and the longitudinal position of the vehicle M in this way makes it possible to prevent the recognition result of a target, not suitable for lateral position estimation (misrecognition or low-accuracy position recognition), from affecting the estimation of the lateral position of the vehicle” ([0073]), and the staged estimation “prevents a decrease in the estimation accuracy of the longitudinal position of the vehicle M, caused due to the influence of a deviation in the lateral position of the vehicle M and in the direction of the vehicle M” ([0074]). Zhang teaches that fixing components “effectively reduces the search space for finding a transformation by restricting certain components of the transformation to fixed values” ([0132]). Roumeliotis further teaches that, in a vision-aided inertial system, “a rotation of the sensing system around a gravity vector may be undetectable from the input of a camera of the sensing system when feature rotation is coincident with the rotation of the sensing system. Similarly, translation of the sensing system may be undetectable when observed features are identically translated.” ([0036]) The first-stage landmark estimate supplies, from an absolute mapped reference, a heading and a horizontal position, components of the kinds Roumeliotis identifies as potentially unobservable.
Reasonable expectation of success. Roumeliotis already performs nonlinear least-squares optimization over a multi-component pose state, and Zhang expressly teaches that a vehicle pose optimization can be carried out with selected components held at fixed values and only the others solved: “resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132]) Accordingly, a person of ordinary skill would have reasonably expected Roumeliotis’s estimator to remain operable when two pose components were supplied as known values and the optimization was performed over the remaining four components, which continue to be estimated from the same camera and IMU measurements from which Roumeliotis estimates the full pose. See KSR, 550 U.S. at 416–417.
Regarding Claim 9,
Claim 9 recites a program that causes the self-position estimation device of Claim 1 to execute steps corresponding to the device of Claim 1, and is rejected for the same reasons, as mapped below. “The plurality of landmarks” in claim 9 takes its meaning from claim 1, which is mapped above.
Disclosure by Sano
Sano discloses:
A program
See at least: Sano: “a plurality of information processing units (31 to 37) constituting the processing unit 3 are realized by the CPU executing a computer program preinstalled in the microcontroller.” ([0026])
Rationale: Sano discloses a computer program executed by the processing unit of the self-position estimation device.
that causes a self-position estimation device according to claim 1 to execute:
See at least: Sano: “a plurality of information processing units (31 to 37) constituting the processing unit 3 are realized by the CPU executing a computer program preinstalled in the microcontroller.” ([0026])
Rationale: The preinstalled program causes the self-position estimation device, whose units are mapped for claim 1, to execute the operations of those units, which correspond to the steps mapped below.
a step of acquiring first sensor data
See at least: Sano: “on the basis of a detection result of at least any one of the LRFs 101 and 102 and the cameras 201 and 202” ([0027])
Rationale: The detection results of the LRFs and cameras are the first sensor data. Because Sano uses the detection result of the LRFs, the cameras, or both, the first sensor data encompasses both range data and image data.
capable of detecting a fixed object
See at least: Sano: “The targets which are present in the surroundings of the vehicle V include: targets indicating traveling lane boundaries present on a traveling lane in the surroundings of the vehicle V, e.g. white lines, curbs, median strips, the guardrails, and reflectors” ([0021])
Rationale: White lines, curbs, median strips, guardrails, and reflectors are stationary road features, i.e., fixed objects.
always present in a periphery of the autonomous moving body
See at least: Sano: “detects white lines 62 and 63 which are present at both sides of the vehicle V” ([0033])
Rationale: Lane boundary lines present at both sides of the vehicle are permanent features located in the periphery of the vehicle.
and a non-fixed object
See at least: Sano: “The target position detection unit 31 detects a relative position between a target present in the surroundings of the vehicle V and the vehicle V” ([0027]); “The target attribute estimation unit 37 estimates an attribute of the target” ([0029])
Rationale: Sano detects general surrounding targets and estimates their attributes for localization. In a road-environment sensing stream, a PHOSITA would understand that the detected targets necessarily include both fixed landmarks and transient non-fixed objects (e.g., vehicles or pedestrians). See also Zhang, below.
temporarily present in the periphery of the autonomous moving body;
See at least: Sano: FIG. 3, step S11: “ELIMINATE UNRELIABLE TARGET” (FIG. 3); “any targets present in general environments can not always be continuously detected with constant reliability due to aging degradation, occlusion, and other effects.” ([0048])
Rationale: Sano’s reliability-based elimination of targets, including targets affected by occlusion, addresses detections that are not map-consistent. A PHOSITA would recognize that such detections include objects temporarily present in the periphery of the vehicle.
a step of acquiring second sensor data
See at least: Sano: “Each sensor 51 to 57 is connected to the processing unit 3 and is configured to sequentially output various detection results to the processing unit 3.” ([0025])
Rationale: The detection results output by the vehicle sensor group are the second sensor data.
including an acceleration
See at least: Sano: “an acceleration sensor 56” ([0025])
Rationale: The acceleration sensor outputs an acceleration.
and an angular velocity
See at least: Sano: “other sensors, such as a yaw rate sensor” ([0025]); “an inertia measurement method using a gyroscope or an acceleration sensor” ([0025])
Rationale: A yaw rate sensor or gyroscope outputs an angular velocity.
of the autonomous moving body;
See at least: Sano: “can calculate the odometry indicating a moved amount of the vehicle V in a unit time, by using each detection result of the vehicle sensor group 5” ([0025])
Rationale: The acceleration and angular velocity are those of the vehicle.
a step of collating map
See at least: Sano: “The self-position estimation unit 36 estimates a self-position which is a current position of the vehicle V by comparing the selected target position data with the map information including the position information on the target present on the road or around the road.” ([0029]); “EXECUTE MAP-MATCHING ONLY FOR TARGET HAVING HIGH RELIABILITY” (FIG. 3, step S13)
Rationale: Sano’s self-position estimation unit 36 performs map matching and corresponds to the claimed map comparison step.
that collates the first sensor data
See at least: Sano: “the self-position estimation unit 36 compares the target position data selected by the target position selection unit 35 with the position of the target in the map information 41.” ([0053]); “an Iterative Closest Point (ICP) algorithm can be used for the comparison in Step S13.” ([0055])
Rationale: The target position data are derived from the first sensor data ([0027]); comparing them against the map is collating the first sensor data.
and the map data of the driving area,
See at least: Sano: “map information 41 including position information on targets present on a road or around the road” ([0024])
Rationale: Map information 41 is map data of the driving area.
and that estimates the self-position of the autonomous moving body
See at least: Sano: “the self-position estimation unit 36 estimates a self-position of the vehicle V by executing the above-mentioned comparison (map matching) of the position of the target.” ([0054])
Rationale: Sano estimates the self-position from the map comparison.
representing a position and a posture of the autonomous moving body
See at least: Sano: “estimates a position and an attitude angle of total three degrees of freedom composed of a position in the east-west direction of the vehicle V (X coordinate), a position in the north-south direction thereof (Y coordinate), and an azimuth angle (yaw angle θ).” ([0054])
Rationale: The X and Y coordinates are a position, and the yaw angle is a posture.
a first estimation step
See at least: Sano: FIG. 1 (STRAIGHT LINE EXTRACTING UNIT 34; TARGET POSITION SELECTION UNIT 35); “The target position selection unit 35 measures the respective distances in a vehicle width direction from the vehicle V to the respective straight lines (N2 and N3).” ([0042])
Rationale: Sano’s straight line extracting unit and target position selection unit derive, separately from the final map matching, the lateral distance of the vehicle to lines fitted through detected points. Sano uses this for reliability evaluation; it is cited to show a separate estimation step. The estimation of the partial parameters is taught by the combination with Tateishi (below).
based on known information,
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The pre-stored map information is the known information.
in which information including a position in the driving area of a mark
See at least: Sano: “The targets (landmark) recorded in the map information 41 includes, for example, various facilities which can be detected by the surrounding sensor group 1” ([0024])
Rationale: A landmark whose position is recorded in the map information is a mark whose position in the driving area is recorded.
that is one fixed object selected in advance from a plurality of fixed objects
See at least: Sano: “the road markings indicating stop lines, pedestrian crossings, advance notices of pedestrian crossing, section lines, and the like; structures, e.g. curbs, and the like” ([0024])
Rationale: Sano’s map records, before localization, selected fixed objects from among the many fixed road features. A PHOSITA would understand the recorded landmark as a fixed object selected in advance from a plurality of fixed objects.
is recorded,
See at least: Sano: “In the map information 41, the position information, e.g. curbs and white lines, is defined by the aggregate of linear information having two-dimensional position information on both end points.” ([0024])
Rationale: The position information of the mark is recorded in the map information.
and on the first sensor data; and
See at least: Sano: “The straight line extracting unit 34 extracts linear information from the target position data stored in the target position storing unit 33.” ([0029])
Rationale: The target position data used by the first estimation are derived from the first sensor data ([0027]).
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
using six degrees-of-freedom parameters
in a three-dimensional coordinate system,
in a case where the step of collating map cannot estimate the self-position by the map comparison,
estimating two partial parameters
that are a part of the six degrees-of-freedom parameters,
a second estimation step
of estimating a self-position of the autonomous moving body,
which represented by the six degrees-of-freedom parameters,
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
based on the first sensor data,
the second sensor data,
and the two partial parameters estimated by the first estimation unit,
the first estimation step, estimating a position in the first axis direction
extending in the horizontal direction
and the posture represented by the rotation around the third axis
extending in the vertical direction,
among the six degrees-of-freedom parameters,
based on a shape obtained by connecting the plurality of landmarks
extracted from the first sensor data
and a shape obtained from a disposition of the landmarks
recorded in the known information,
as the two partial parameters.
Disclosure by Roumeliotis
Roumeliotis discloses:
using six degrees-of-freedom parameters
See at least: Roumeliotis: “a Vision-aided Inertial Navigation System (VINS) fuses data from a camera and an Inertial Measurement Unit (IMU) to track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003])
Rationale: Roumeliotis represents the pose by six degrees-of-freedom parameters.
in a three-dimensional coordinate system,
See at least: Roumeliotis: “equipped with proprioceptive (e.g., IMU) and exteroceptive (e.g., camera) sensors, navigating in a 3D environment.” ([0039])
Rationale: The six-degrees-of-freedom pose is defined in a three-dimensional coordinate system.
that are a part of the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: In the combined system, the partial parameters are a subset of Roumeliotis’s six degrees-of-freedom pose.
a second estimation step
See at least: Roumeliotis: “Estimator 22 of processing unit 20 process image data 14 and IMU data 18 to compute state estimates for the degrees of freedom of VINS 10” ([0031])
Rationale: Estimator 22 corresponds to the second estimation step.
of estimating a self-position of the autonomous moving body,
See at least: Roumeliotis: “and, from the state estimates, computes position, orientation, speed, locations of observable features, a localized map, an odometry or other higher order derivative information” ([0031])
Rationale: Estimator 22 computes the position and orientation of the platform, i.e., its self-position.
which represented by the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003]); “denotes the robot pose (position and orientation) at time-step i” ([0039])
Rationale: The estimated self-position is the six degrees-of-freedom pose.
based on the first sensor data,
See at least: Roumeliotis: “Image source 12 images an environment in which VINS 10 operates so as to produce image data 14.” ([0029]); “(iii) the exteroceptive sensor measurements” ([0042])
Rationale: The camera image data (exteroceptive measurements) correspond to the first sensor data.
the second sensor data,
See at least: Roumeliotis: “IMU 14 may, for example, detect a current rate of acceleration using one or more accelerometers as VINS 10 is translated, and detect changes in rotational attributes like pitch, roll and yaw using one or more gyroscopes.” ([0030]); “(ii) the proprioceptive sensor motion information (5)” ([0042])
Rationale: The IMU acceleration and angular-rate data correspond to the second sensor data.
and the two partial parameters estimated by the first estimation unit, (mechanism)
See at least: Roumeliotis: “(i) the prior information about the initial state” ([0042]); “The estimator may compute each of the constraints as (i) an estimate of the motion from the first keyframe to the second keyframe” ([0033])
Rationale: Roumeliotis’s estimator accepts information supplied from outside its own measurements, as a prior term and as constraints on its state. This is the mechanism by which, in the combination, the partial parameters are supplied to the second estimation (see Tateishi, below).
Motivation to Combine Sano and Roumeliotis
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano and Roumeliotis before them, to implement Sano’s map-matching localization within the six-degrees-of-freedom vision/inertial estimation framework of Roumeliotis, and to use Roumeliotis’s camera/IMU estimator as the second estimation step. Both references address localization of a moving body using exteroceptive and inertial sensors. Sano already acquires camera/LRF data together with acceleration and yaw-rate data ([0025], [0027]). Roumeliotis explains that fusing camera and IMU data exploits complementary sensing: “an IMU can accurately track dynamic motions over short time durations, while visual data can be used to estimate the pose displacement (up to scale) between consecutive views.” ([0003]) A PHOSITA would have recognized that a six-degrees-of-freedom representation, in place of Sano’s planar three-degrees-of-freedom pose, yields a complete three-dimensional pose, with predictable results.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis does not explicitly disclose the following claim limitations:
in a case where the step of collating map cannot estimate the self-position by the map comparison,
estimating two partial parameters
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
and the two partial parameters estimated by the first estimation unit,
in the first estimation step …, estimating a position in the first axis direction
extending in the horizontal direction
and the posture represented by the rotation around the third axis
extending in the vertical direction,
among the six degrees-of-freedom parameters, (as estimated by the first estimation step)
based on a shape obtained by connecting the plurality of landmarks
extracted from the first sensor data
and a shape obtained from a disposition of the landmarks
recorded in the known information,
as the two partial parameters.
Disclosure by Zhang
Zhang discloses:
and a non-fixed object temporarily present in the periphery of the autonomous moving body (additional support for Sano)
See at least: Zhang: “A third category of edgels include edgels corresponding to transient features. These may include shadows, moving objects (e.g., other vehicles or pedestrians), debris on a road surface” ([0104]); “if the LiDAR’s view is blocked (e.g., by surrounding vehicles)” ([0086])
Rationale: Zhang confirms that a vehicle’s sensor data captures transient, non-fixed objects temporarily present around the vehicle.
using six degrees-of-freedom parameters … in a three-dimensional coordinate system (map comparison; additional support)
See at least: Zhang: “LiDAR or other types of depth sensors can be used to capture a three-dimensional point cloud of a local area surrounding the vehicle, which can be matched with previously-acquired map data comprising points or features corresponding to different locations of the local area. By determining a transformation that best aligns the sensed point cloud with the map data, a location and orientation of the vehicle can be determined.” ([0085])
Rationale: Zhang’s map comparison aligns three-dimensional sensor data with map data to determine location and orientation, consistent with Sano [0055] (ICP comparison).
in a case where the step of collating map cannot estimate the self-position by the map comparison,
See at least: Zhang: “other localization methods may be used to supplement or replace ICP-based localization (e.g., as a backup option in case of LiDAR failure). In addition, in some cases, ICP-based localization may fail to localize due to a variety of factors, e.g., the environment lacking enough features (e.g., highways, tunnels, bridges), or if the LiDAR’s view is blocked (e.g., by surrounding vehicles).” ([0086]); “camera-based localization techniques may be used to replace or supplement ICP-based localization techniques.” ([0087]); “camera-based localization can be utilized when LiDAR is malfunctioning or otherwise unavailable.” ([0089]); “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: Zhang teaches that the map comparison (ICP) may fail to localize, including when the view is blocked by surrounding vehicles, and that a separate landmark-based localization is then used as a backup. This teaches performing the first estimation in a case where the map comparison cannot estimate the self-position. Because the first and second estimation steps are performed only when the recited condition is met, the broadest reasonable interpretation of the method does not require those contingent steps to be performed. See Ex parte Schulhauser, Appeal No. 2013-007847 (PTAB 2016) (precedential); MPEP 2111.04(II). The contingent steps are nonetheless mapped herein for compact prosecution.
that are a part of the six degrees-of-freedom parameters, (additional support)
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation can be fixed, resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132])
Rationale: Zhang estimates a subset of the six pose components.
in the first estimation step …, estimating a position in the first axis direction
See at least: Zhang: “edgels at the endpoints of the lane line segment may be sufficient for performing localization, since they provide constraints on both dimensions (e.g., x and y directions).” ([0116])
Rationale: Zhang determines localization constraints along specific axis directions (x and y), which provides the claimed position in the first axis direction.
extending in the horizontal direction
See at least: Zhang: “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Zhang’s x and y directions are horizontal; z is identified separately as the vertical axis ([0132]).
and the posture represented by the rotation around the third axis
See at least: Zhang: “A pose of the vehicle is optimized based upon the determined correspondences by identifying a transformation that minimizes a distance between the edgels and their corresponding edge pixels.” (Abstract); “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Yaw is the posture represented by rotation around a third axis.
extending in the vertical direction,
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation” ([0132])
Rationale: Yaw is rotation about the vertical (z) axis.
among the six degrees-of-freedom parameters,
See at least: Zhang: “By reducing the search space for the transformation from 6 dimensions to 3 dimensions” ([0132]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: The position and yaw are among the six pose parameters.
based on a shape obtained by connecting the plurality of landmarks
See at least: Zhang: “In some embodiments, line geometry is computed for certain groups of edgels, such as line segments in 3D space connecting groups of edgels, as stored as part of the map.” ([0119])
Rationale: Line segments connecting groups of edgels that represent the painted stripes are a shape obtained by connecting the plurality of landmarks.
extracted from the first sensor data
See at least: Zhang: “analyzing the image frame to identify a plurality of edge pixels within the image frame” ([0008]); “the computed line geometry corresponding to multiple edgels can, during localization, be matched with corresponding edges from captured images.” ([0119])
Rationale: The edges matched to the landmark geometry are extracted from the captured images, i.e., the first sensor data.
and a shape obtained from a disposition of the landmarks
See at least: Zhang: “In some embodiments, the localization system determines structure information for an edgel cluster (e.g., the distances between edgels in a cluster) and among different edgel clusters.” ([0125])
Rationale: Structure information of edgel clusters is a shape obtained from the disposition of the landmarks.
recorded in the known information,
See at least: Zhang: “line segments in 3D space connecting groups of edgels, as stored as part of the map” ([0119]); “edgels within a threshold distance from each other may be automatically clustered on the map” ([0119]); “Landmark Map 520” (FIG. 5)
Rationale: The landmark geometry is recorded in the stored map (known information).
Motivation to Combine Sano, Roumeliotis, and Zhang
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, and Zhang before them, to modify the combined Sano/Roumeliotis system in two respects taught by Zhang. First, to perform a separate landmark-based estimation in a case where the map comparison cannot estimate the self-position, because Zhang teaches that map-comparison localization can fail when features are lacking or the view is blocked by surrounding vehicles, and that a backup localization method should then be used ([0086]–[0089]). Second, to base that estimation on the shape formed by a plurality of landmarks matched against the mapped line geometry, because Zhang teaches that “The determined structure information may be used to help guide correspondence search.” ([0125]) Each modification uses a known technique for its established purpose and yields predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416–417 (2007).
Claim limitations Not Explicitly Disclosed by the Combination of Sano, Roumeliotis, and Zhang
The combination of Sano, Roumeliotis, and Zhang does not explicitly disclose the following claim limitations:
estimating two partial parameters
as the two partial parameters.
by estimating the four parameters of the six degrees of freedom
other than the two partial parameters estimated by the first estimation unit,
and the two partial parameters estimated by the first estimation unit,
Zhang estimates a subset of three pose components (x, y, and yaw) with the other three held fixed ([0132]); it does not disclose a first estimation of exactly two parameters followed by estimation of the remaining four.
Disclosure by Tateishi
Tateishi (US 2019/0137281 A1) is relied upon only for limitations added by amendment: the “two partial parameters” and the estimation of “the four parameters … other than the two partial parameters.” Tateishi discloses:
estimating two partial parameters
See at least: Tateishi: “The lateral position estimation unit 13 estimates the lateral position of the vehicle and the direction of the vehicle, based on the relative position of the first target relative to the vehicle, recognized by the target recognition unit 12, and the position information on the first target on the map stored in the target database 5.” ([0058]); “The lateral position of the vehicle is the position of the vehicle on the map in the width direction of the traveling lane on which the vehicle travels. The direction of the vehicle is the direction of the vehicle on the map (the direction of the vehicle with respect to the traveling lane).” ([0058])
Rationale: Tateishi’s first estimation outputs exactly two pose parameters: a horizontal position (lateral position, in the lane-width direction on the map) and a heading (direction of the vehicle on the map). Tateishi is cited for the amended requirement that the first estimation output exactly two parameters; the position in the first axis direction and the posture about the third axis are mapped to Zhang above.
estimating two partial parameters (continued: how the two parameters are obtained)
See at least: Tateishi: “The lateral position estimation unit 13 estimates the lateral position of the vehicle and the direction of the vehicle by comparing the relative position of the first target relative to the vehicle with the position information on the first target on the map. The comparison is made, for example, by the least squares matching method.” ([0059]); “The first targets for lateral position estimation includes a straight marking line corresponding to a straight road section.” ([0041]); “FIG. 2A shows the case in which the target recognition unit 12 has recognized the relative positions P1 of the straight marking line La1 and the relative positions P2 of the curved marking line La2 as a row of detection points detected by the radar sensor.” ([0061])
Rationale: The two parameters are obtained by matching a line-shaped target, detected as a row of points, against its mapped position.
as the two partial parameters.
See at least: Tateishi: “the lateral position estimation unit 13 estimates the lateral position of the vehicle M and the direction of the vehicle M by comparing the relative positions P1 of the straight marking line La1, recognized by the target recognition unit 12, with the position information on the straight marking line La1 on the map stored in the target database 5.” ([0063]); “The longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle, based on the relative position of the second target relative to the vehicle M on which the lateral position of the vehicle M and the direction of the vehicle M, estimated by the lateral position estimation unit 13, have been reflected” ([0064])
Rationale: The lateral position and direction are the output of the first stage and are used by the later stage; they are therefore partial parameters of the vehicle pose, and there are two of them.
by estimating the four parameters of the six degrees of freedom
See at least: Tateishi: “The longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle” ([0064]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: Tateishi’s second stage estimates the remaining pose component. Tateishi’s pose is planar, so its remaining component is the longitudinal position only. Tateishi does not expressly disclose a six-degrees-of-freedom estimator that solves exactly four remaining parameters; that is taught by the combination, as set forth in the motivation statement below.
other than the two partial parameters estimated by the first estimation unit,
See at least: Tateishi: “means the relative position of the second target whose relationship with the position information on the second target on the map is corrected by correcting the lateral position of the vehicle M on the map and its direction, which are used as the reference, according to the estimation result of the lateral position estimation unit 13.” ([0065])
Rationale: The second stage uses the first-stage lateral position and direction as a reference and does not re-estimate them; it estimates only the other component(s).
and the two partial parameters estimated by the first estimation unit,
See at least: Tateishi: “on which the lateral position of the vehicle M and the direction of the vehicle M, estimated by the lateral position estimation unit 13, have been reflected” ([0064]); “By doing so, the longitudinal position estimation unit 14 estimates the longitudinal position of the vehicle M while minimizing the influence of a deviation in the lateral position of the vehicle M and the direction of the vehicle M.” ([0067])
Rationale: The second stage is based on the two first-stage parameters.
in a case where the step of collating map cannot estimate the self-position by the map comparison (operability of the first estimation)
See at least: Tateishi: “The relative position of the first target and the relative position of the second target may be recognized from the image captured by the camera of the external sensor 2, may be recognized from the object information acquired by the radar sensor of the external sensor 2, or may be recognized from both the captured image and the object information.” ([0056]); “The first target for lateral position estimation may include structures extending along the road. The structures extending along the road includes at least one of a wall, a fence, a signboard, a guardrail, and a curbstone.” ([0042]); Zhang: “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: The claim distinguishes two comparisons. The claimed map comparison collates the first sensor data with the map data of the driving area, as Sano ([0053]) and Zhang (ICP, [0085]) do. The first estimation then uses known information in which the mark’s position is recorded, which is itself map-based, after the map comparison has failed; “cannot estimate the self-position by the map comparison” therefore refers to failure of the collation of the driving-area map data, not to failure of every map-based comparison. Zhang identifies conditions under which that collation fails (lack of features; view blocked by surrounding vehicles, [0086]) and teaches using a separate landmark-based localization instead ([0086]–[0089]). Tateishi’s first stage is such a landmark-based comparison, using a single pre-selected line-shaped target recognized by camera, radar, or both. It would have been an obvious design choice to use it as the backup estimation Zhang calls for, with a reasonable expectation of success in the conditions Zhang identifies, because it relies on a pre-selected landmark rather than on the whole-scene features whose absence or occlusion caused the collation to fail.
Motivation to Combine Sano, Roumeliotis, Zhang, and Tateishi
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, Zhang, and Tateishi before them, to modify the combined system as follows.
First stage (two parameters). Configure the landmark-based first estimation to output exactly two parameters, the horizontal position perpendicular to the line-shaped landmark arrangement and the heading, as Tateishi teaches ([0058]–[0059], [0063]).
Second stage (the remaining four); the proposed modification. The following is the modification a person of ordinary skill would have made to Roumeliotis; neither Tateishi nor Roumeliotis expressly discloses a four-component solution. The two first-stage parameters enter Roumeliotis’s motion and measurement models as known values, and the Gauss-Newton minimization of its cost function is carried out over a reduced state containing only the remaining four pose components: the position along the landmark line, the vertical position, and the rotations about the two horizontal axes. Roumeliotis’s cost function “is a nonlinear least squares cost function, and a standard approach to determine its minimum is to employ Gauss-Newton iterative minimization.” ([0043]) Its Jacobians are computed with respect to the pose state ([0040]–[0041], Eqs. (3)–(6)). Treating two components as known removes the corresponding Jacobian columns. The prior, motion-model, and measurement residuals (Eq. (9)) each depend on the full pose, so after two components are fixed they remain functions of the four unknown components, and the reduced normal equations (Eq. (15)) are solved for those four.
Reason to make the modification. Tateishi states the reason: “Estimating the lateral position of the vehicle M and the longitudinal position of the vehicle M in this way makes it possible to prevent the recognition result of a target, not suitable for lateral position estimation (misrecognition or low-accuracy position recognition), from affecting the estimation of the lateral position of the vehicle” ([0073]), and the staged estimation “prevents a decrease in the estimation accuracy of the longitudinal position of the vehicle M, caused due to the influence of a deviation in the lateral position of the vehicle M and in the direction of the vehicle M” ([0074]). Zhang teaches that fixing components “effectively reduces the search space for finding a transformation by restricting certain components of the transformation to fixed values” ([0132]). Roumeliotis further teaches that, in a vision-aided inertial system, “a rotation of the sensing system around a gravity vector may be undetectable from the input of a camera of the sensing system when feature rotation is coincident with the rotation of the sensing system. Similarly, translation of the sensing system may be undetectable when observed features are identically translated.” ([0036]) The first-stage landmark estimate supplies, from an absolute mapped reference, a heading and a horizontal position, components of the kinds Roumeliotis identifies as potentially unobservable.
Reasonable expectation of success. Roumeliotis already performs nonlinear least-squares optimization over a multi-component pose state, and Zhang expressly teaches that a vehicle pose optimization can be carried out with selected components held at fixed values and only the others solved: “resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132]) Accordingly, a person of ordinary skill would have reasonably expected Roumeliotis’s estimator to remain operable when two pose components were supplied as known values and the optimization was performed over the remaining four components, which continue to be estimated from the same camera and IMU measurements from which Roumeliotis estimates the full pose. See KSR, 550 U.S. at 416–417.
Additional Disclosure by Zhang and Roumeliotis
A program
See at least: Zhang: “a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps” ([0155]); Roumeliotis: “a computer readable data storage medium comprising instructions that, when executed, cause one or more processors to perform one or more of the methods” ([0093])
Rationale: Zhang and Roumeliotis each implement the localization operations as program instructions executed by a processor, consistent with Sano [0026].
Regarding Claim 10,
Claim 10 is rejected under the construction stated in the rejection under 35 U.S.C. 112(b): the first estimation unit estimates X, Y, and θZ, and the second estimation unit estimates the remaining components of the six-degrees-of-freedom pose. Claim 10 is otherwise identical to claim 1 up to its final wherein clause.
Disclosure by Sano
Sano discloses:
A self-position estimation device
See at least: Sano: “The self-position estimation device according to the present embodiment includes a surrounding sensor group 1, a processing unit 3, a storage unit 4, and a vehicle sensor group 5.” ([0019])
Rationale: Sano expressly discloses a self-position estimation device.
that estimates a self-position
See at least: Sano: “is configured to estimate a self-position of the vehicle V.” ([0019])
Rationale: Sano expressly estimates a self-position.
in a driving area
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The road and its surroundings, in which the vehicle travels and for which map information is stored, is a driving area.
of an autonomous moving body,
See at least: Sano: “The moving object is not limited to the vehicle V as moving objects which move on land, but includes vessels, aircraft, spacecraft, and other moving objects.” ([0064]); “The microcontroller may also be used as an Electronic Control Unit (ECU) used for other control in regard of the vehicle V, e.g. automatic driving control, for example.” ([0026])
Rationale: Sano’s vehicle (moving object) is subject to automatic driving control, and is therefore an autonomous moving body.
the self-position estimation device comprising:
See at least: Sano: “The processing unit 3 includes: a target position detection unit 31, a moved amount estimation unit 32, a target position storing unit 33, a straight line extracting unit 34, a target position selection unit 35, a self-position estimation unit 36, and a target attribute estimation unit 37.” ([0026])
Rationale: Sano discloses the constituent elements of the self-position estimation device, mapped below.
a first acquisition unit
See at least: Sano: “The surrounding sensor group 1 includes a plurality of Laser Range Finders (LRFs) 101 and 102 and a plurality of cameras 201 and 202” ([0021]); “The target position detection unit 31 detects a relative position between a target present in the surroundings of the vehicle V and the vehicle V on the basis of a detection result of at least any one of the LRFs 101 and 102 and the cameras 201 and 202.” ([0027])
Rationale: The surrounding sensor group and the target position detection unit that receives its detection results constitute a first acquisition unit.
that acquires first sensor data
See at least: Sano: “on the basis of a detection result of at least any one of the LRFs 101 and 102 and the cameras 201 and 202” ([0027])
Rationale: The detection results of the LRFs and cameras are the first sensor data. Because Sano uses the detection result of the LRFs, the cameras, or both, the first sensor data encompasses both range data and image data.
capable of detecting a fixed object
See at least: Sano: “The targets which are present in the surroundings of the vehicle V include: targets indicating traveling lane boundaries present on a traveling lane in the surroundings of the vehicle V, e.g. white lines, curbs, median strips, the guardrails, and reflectors” ([0021])
Rationale: White lines, curbs, median strips, guardrails, and reflectors are stationary road features, i.e., fixed objects.
always present in a periphery of the autonomous moving body
See at least: Sano: “detects white lines 62 and 63 which are present at both sides of the vehicle V” ([0033])
Rationale: Lane boundary lines present at both sides of the vehicle are permanent features located in the periphery of the vehicle.
and a non-fixed object
See at least: Sano: “The target position detection unit 31 detects a relative position between a target present in the surroundings of the vehicle V and the vehicle V” ([0027]); “The target attribute estimation unit 37 estimates an attribute of the target” ([0029])
Rationale: Sano detects general surrounding targets and estimates their attributes for localization. In a road-environment sensing stream, a PHOSITA would understand that the detected targets necessarily include both fixed landmarks and transient non-fixed objects (e.g., vehicles or pedestrians). See also Zhang, below.
temporarily present in the periphery of the autonomous moving body;
See at least: Sano: FIG. 3, step S11: “ELIMINATE UNRELIABLE TARGET” (FIG. 3); “any targets present in general environments can not always be continuously detected with constant reliability due to aging degradation, occlusion, and other effects.” ([0048])
Rationale: Sano’s reliability-based elimination of targets, including targets affected by occlusion, addresses detections that are not map-consistent. A PHOSITA would recognize that such detections include objects temporarily present in the periphery of the vehicle.
a second acquisition unit
See at least: Sano: “The vehicle sensor group 5 includes a GPS receiver 51, an accelerator sensor 52, a steering sensor 53, a brake sensor 54, a vehicle speed sensor 55, an acceleration sensor 56, a wheel speed sensor 57, and other sensors, such as a yaw rate sensor.” ([0025])
Rationale: The vehicle sensor group is a second acquisition unit.
that acquires second sensor data
See at least: Sano: “Each sensor 51 to 57 is connected to the processing unit 3 and is configured to sequentially output various detection results to the processing unit 3.” ([0025])
Rationale: The detection results output by the vehicle sensor group are the second sensor data.
including an acceleration
See at least: Sano: “an acceleration sensor 56” ([0025])
Rationale: The acceleration sensor outputs an acceleration.
and an angular velocity
See at least: Sano: “other sensors, such as a yaw rate sensor” ([0025]); “an inertia measurement method using a gyroscope or an acceleration sensor” ([0025])
Rationale: A yaw rate sensor or gyroscope outputs an angular velocity.
of the autonomous moving body;
See at least: Sano: “can calculate the odometry indicating a moved amount of the vehicle V in a unit time, by using each detection result of the vehicle sensor group 5” ([0025])
Rationale: The acceleration and angular velocity are those of the vehicle.
a map comparison unit
See at least: Sano: “The self-position estimation unit 36 estimates a self-position which is a current position of the vehicle V by comparing the selected target position data with the map information including the position information on the target present on the road or around the road.” ([0029]); “EXECUTE MAP-MATCHING ONLY FOR TARGET HAVING HIGH RELIABILITY” (FIG. 3, step S13)
Rationale: Sano’s self-position estimation unit 36 performs map matching and corresponds to the claimed map comparison unit.
that collates the first sensor data
See at least: Sano: “the self-position estimation unit 36 compares the target position data selected by the target position selection unit 35 with the position of the target in the map information 41.” ([0053]); “an Iterative Closest Point (ICP) algorithm can be used for the comparison in Step S13.” ([0055])
Rationale: The target position data are derived from the first sensor data ([0027]); comparing them against the map is collating the first sensor data.
and the map data of the driving area,
See at least: Sano: “map information 41 including position information on targets present on a road or around the road” ([0024])
Rationale: Map information 41 is map data of the driving area.
and that estimates the self-position of the autonomous moving body
See at least: Sano: “the self-position estimation unit 36 estimates a self-position of the vehicle V by executing the above-mentioned comparison (map matching) of the position of the target.” ([0054])
Rationale: Sano estimates the self-position from the map comparison.
representing a position and a posture of the autonomous moving body
See at least: Sano: “estimates a position and an attitude angle of total three degrees of freedom composed of a position in the east-west direction of the vehicle V (X coordinate), a position in the north-south direction thereof (Y coordinate), and an azimuth angle (yaw angle θ).” ([0054])
Rationale: The X and Y coordinates are a position, and the yaw angle is a posture.
a first estimation unit
See at least: Sano: FIG. 1 (STRAIGHT LINE EXTRACTING UNIT 34; TARGET POSITION SELECTION UNIT 35); “The target position selection unit 35 measures the respective distances in a vehicle width direction from the vehicle V to the respective straight lines (N2 and N3).” ([0042])
Rationale: Sano’s straight line extracting unit and target position selection unit derive, separately from the final map matching, the lateral distance of the vehicle to lines fitted through detected points. Sano uses this for reliability evaluation; it is cited to show a separate estimation unit. The estimation of the partial parameters is taught by the combination with Zhang (below).
based on known information,
See at least: Sano: “the storage unit 4 is a map information storage unit configured to store map information 41 including position information on targets present on a road or around the road.” ([0024])
Rationale: The pre-stored map information is the known information.
in which information including a position in the driving area of a mark
See at least: Sano: “The targets (landmark) recorded in the map information 41 includes, for example, various facilities which can be detected by the surrounding sensor group 1” ([0024])
Rationale: A landmark whose position is recorded in the map information is a mark whose position in the driving area is recorded.
that is one fixed object selected in advance from a plurality of fixed objects
See at least: Sano: “the road markings indicating stop lines, pedestrian crossings, advance notices of pedestrian crossing, section lines, and the like; structures, e.g. curbs, and the like” ([0024])
Rationale: Sano’s map records, before localization, selected fixed objects from among the many fixed road features. A PHOSITA would understand the recorded landmark as a fixed object selected in advance from a plurality of fixed objects.
is recorded,
See at least: Sano: “In the map information 41, the position information, e.g. curbs and white lines, is defined by the aggregate of linear information having two-dimensional position information on both end points.” ([0024])
Rationale: The position information of the mark is recorded in the map information.
and the on first sensor data; and
See at least: Sano: “The straight line extracting unit 34 extracts linear information from the target position data stored in the target position storing unit 33.” ([0029])
Rationale: The target position data used by the first estimation are derived from the first sensor data ([0027]).
Claim limitations Not Explicitly Disclosed by Sano
Sano does not explicitly disclose the following claim limitations:
using six degrees-of-freedom parameters
in a three-dimensional coordinate system,
in a case where the map comparison unit cannot estimate the self-position by the map comparison,
estimates two partial parameters [construed for purposes of this rejection as the specifically recited X, Y, and θZ components]
that are a part of the six degrees-of-freedom parameters,
a second estimation unit
that estimates a self-position of the autonomous moving body,
which represented by the six degrees-of-freedom parameters,
by estimating the four parameters of the six degrees of freedom [construed as the remaining Z, roll (θX), and pitch (θY) components]
other than the two partial parameters estimated by the first estimation unit [construed as the X, Y, and θZ components],
based on the first sensor data,
the second sensor data,
and the two partial parameters estimated by the first estimation unit [construed as the X, Y, and θZ components],
wherein the mark is a plurality of landmarks
attached to a part of the fixed object,
the first estimation unit estimates a position in each of a first axis direction and a second axis direction
extending in a horizontal direction
and a posture represented by rotation around a third axis
extending in a vertical direction,
among the six degrees-of-freedom parameters,
based on the plurality of landmarks extracted from the first sensor data
and on the known information,
as the two partial parameters [construed as the X, Y, and θZ components].
Disclosure by Roumeliotis
Roumeliotis discloses:
using six degrees-of-freedom parameters
See at least: Roumeliotis: “a Vision-aided Inertial Navigation System (VINS) fuses data from a camera and an Inertial Measurement Unit (IMU) to track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003])
Rationale: Roumeliotis represents the pose by six degrees-of-freedom parameters.
in a three-dimensional coordinate system,
See at least: Roumeliotis: “equipped with proprioceptive (e.g., IMU) and exteroceptive (e.g., camera) sensors, navigating in a 3D environment.” ([0039])
Rationale: The six-degrees-of-freedom pose is defined in a three-dimensional coordinate system.
that are a part of the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose)” ([0003])
Rationale: In the combined system, the partial parameters are a subset of Roumeliotis’s six degrees-of-freedom pose.
a second estimation unit
See at least: Roumeliotis: “Estimator 22 of processing unit 20 process image data 14 and IMU data 18 to compute state estimates for the degrees of freedom of VINS 10” ([0031])
Rationale: Estimator 22 corresponds to the second estimation unit.
that estimates a self-position of the autonomous moving body,
See at least: Roumeliotis: “and, from the state estimates, computes position, orientation, speed, locations of observable features, a localized map, an odometry or other higher order derivative information” ([0031])
Rationale: Estimator 22 computes the position and orientation of the platform, i.e., its self-position.
which represented by the six degrees-of-freedom parameters,
See at least: Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003]); “denotes the robot pose (position and orientation) at time-step i” ([0039])
Rationale: The estimated self-position is the six degrees-of-freedom pose.
based on the first sensor data,
See at least: Roumeliotis: “Image source 12 images an environment in which VINS 10 operates so as to produce image data 14.” ([0029]); “(iii) the exteroceptive sensor measurements” ([0042])
Rationale: The camera image data (exteroceptive measurements) correspond to the first sensor data.
the second sensor data,
See at least: Roumeliotis: “IMU 14 may, for example, detect a current rate of acceleration using one or more accelerometers as VINS 10 is translated, and detect changes in rotational attributes like pitch, roll and yaw using one or more gyroscopes.” ([0030]); “(ii) the proprioceptive sensor motion information (5)” ([0042])
Rationale: The IMU acceleration and angular-rate data correspond to the second sensor data.
and the two partial parameters estimated by the first estimation unit, (mechanism)
See at least: Roumeliotis: “(i) the prior information about the initial state” ([0042]); “The estimator may compute each of the constraints as (i) an estimate of the motion from the first keyframe to the second keyframe” ([0033])
Rationale: Roumeliotis’s estimator accepts information supplied from outside its own measurements, as a prior term and as constraints on its state. This is the mechanism by which, in the combination, the partial parameters are supplied to the second estimation (see Zhang, below).
Motivation to Combine Sano and Roumeliotis
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano and Roumeliotis before them, to implement Sano’s map-matching localization within the six-degrees-of-freedom vision/inertial estimation framework of Roumeliotis, and to use Roumeliotis’s camera/IMU estimator as the second estimation unit. Both references address localization of a moving body using exteroceptive and inertial sensors. Sano already acquires camera/LRF data together with acceleration and yaw-rate data ([0025], [0027]). Roumeliotis explains that fusing camera and IMU data exploits complementary sensing: “an IMU can accurately track dynamic motions over short time durations, while visual data can be used to estimate the pose displacement (up to scale) between consecutive views.” ([0003]) A PHOSITA would have recognized that a six-degrees-of-freedom representation, in place of Sano’s planar three-degrees-of-freedom pose, yields a complete three-dimensional pose, with predictable results.
Claim limitations Not Explicitly Disclosed by the Combination of Sano and Roumeliotis
The combination of Sano and Roumeliotis does not explicitly disclose the following claim limitations:
in a case where the map comparison unit cannot estimate the self-position by the map comparison,
estimates two partial parameters [construed for purposes of this rejection as the specifically recited X, Y, and θZ components]
by estimating the four parameters of the six degrees of freedom [construed as the remaining Z, roll (θX), and pitch (θY) components]
other than the two partial parameters estimated by the first estimation unit [construed as the X, Y, and θZ components],
and the two partial parameters estimated by the first estimation unit [construed as the X, Y, and θZ components],
wherein the mark is a plurality of landmarks
attached to a part of the fixed object,
the first estimation unit estimates a position in each of a first axis direction and a second axis direction
extending in a horizontal direction
and a posture represented by rotation around a third axis
extending in a vertical direction,
among the six degrees-of-freedom parameters,
based on the plurality of landmarks extracted from the first sensor data
and on the known information,
as the two partial parameters [construed as the X, Y, and θZ components].
Disclosure by Zhang
Zhang discloses:
and a non-fixed object temporarily present in the periphery of the autonomous moving body (additional support for Sano)
See at least: Zhang: “A third category of edgels include edgels corresponding to transient features. These may include shadows, moving objects (e.g., other vehicles or pedestrians), debris on a road surface” ([0104]); “if the LiDAR’s view is blocked (e.g., by surrounding vehicles)” ([0086])
Rationale: Zhang confirms that a vehicle’s sensor data captures transient, non-fixed objects temporarily present around the vehicle.
using six degrees-of-freedom parameters … in a three-dimensional coordinate system (map comparison; additional support)
See at least: Zhang: “LiDAR or other types of depth sensors can be used to capture a three-dimensional point cloud of a local area surrounding the vehicle, which can be matched with previously-acquired map data comprising points or features corresponding to different locations of the local area. By determining a transformation that best aligns the sensed point cloud with the map data, a location and orientation of the vehicle can be determined.” ([0085])
Rationale: Zhang’s map comparison aligns three-dimensional sensor data with map data to determine location and orientation, consistent with Sano [0055] (ICP comparison).
in a case where the map comparison unit cannot estimate the self-position by the map comparison,
See at least: Zhang: “other localization methods may be used to supplement or replace ICP-based localization (e.g., as a backup option in case of LiDAR failure). In addition, in some cases, ICP-based localization may fail to localize due to a variety of factors, e.g., the environment lacking enough features (e.g., highways, tunnels, bridges), or if the LiDAR’s view is blocked (e.g., by surrounding vehicles).” ([0086]); “camera-based localization techniques may be used to replace or supplement ICP-based localization techniques.” ([0087]); “camera-based localization can be utilized when LiDAR is malfunctioning or otherwise unavailable.” ([0089]); “in some embodiments, the localization process is not affected by passing vehicles, or shadows.” ([0090])
Rationale: Zhang teaches that the map comparison (ICP) may fail to localize, including when the view is blocked by surrounding vehicles, and that a separate landmark-based localization is then used as a backup. This teaches performing the first estimation in a case where the map comparison cannot estimate the self-position.
that are a part of the six degrees-of-freedom parameters, (additional support)
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation can be fixed, resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132])
Rationale: Zhang estimates a subset of the six pose components.
wherein the mark is a plurality of landmarks
See at least: Zhang: “For instance, a lane line stripe is typically a skinny rectangle.” ([0125]); “of the edgels projected onto the edges of a lane line segment, edgels at the endpoints of the lane line segment may be sufficient for performing localization” ([0116]); “For example, several of the edgel clusters may be aligned along a line, which may be a typical occurrence for certain types of features such as lane lines.” ([0125]); Sano: “since a detectable region of the solid line is larger than a detectable region of the dashed line” ([0045])
Rationale: The plurality of landmarks is the individual painted stripes (segments) of a lane line. Zhang describes lane line stripes and lane line segments having endpoints, and Sano describes dashed white lines, which by definition consist of separate painted segments. To the extent Zhang does not expressly state that its lane line is formed of separate stripes, it would have been obvious to use the segments of a dashed lane line, as described by Sano, as the plurality of landmarks, because Zhang teaches that lane line markings are permanent, stationary features suited to localization ([0102]) and that segment endpoints provide constraints in two directions ([0116]). Zhang’s edgels are not themselves the landmarks; they are the detected and mapped points representing the edges of the painted stripes. “FIG. 12 shows edgels grouped into different clusters 1204, where each cluster may correspond to a portion a feature captured in the image 1202 (e.g., a portion of a lane line captured in the image).” ([0115])
attached to a part of the fixed object,
See at least: Zhang: “Permanent and stationary features under this category may include lane line markings and curb falls.” ([0102]); Sano: “road surface markings, e.g. stop lines, pedestrian crossings, and speed limit markings” ([0021]); Applicant’s specification: “For example, the fixed object is a structure in which a disposition or shape of a ceiling, a floor, a shelf, a signboard, paint, or the like in the driving area does not change.” (pub. para. [0044]); “For example, the mark is paint (white line or the like) applied to the floor of the driving area or a marker (signboard or the like) installed in the driving area.” (pub. para. [0048])
Rationale: Construction. The specification includes a floor among fixed objects and describes the mark as paint applied to the floor, and dependent claim 5 recites that the mark is paint applied to the driving area. Under the broadest reasonable interpretation consistent with the specification, painted markings applied to a portion of a floor or road surface are landmarks attached to a part of a fixed object. The claim recites both “a fixed object always present in a periphery of the autonomous moving body” and a mark that is “one fixed object selected in advance”; “the fixed object” to which the landmarks are attached is read as the former (the road surface), and the mark is the lane line selected in advance, which consists of the plurality of landmarks. Finding. Lane line markings are painted on, and thus attached to, the part of the road surface along the lane boundary.
estimates two partial parameters [construed for purposes of this rejection as the specifically recited X, Y, and θZ components]
See at least: Zhang: “Based upon the location of the initial pose, the localization system retrieves nearby edgels from a stored OMap (e.g., created during the map creation phase discussed above), and analyzes the edgels based upon captured camera images to localize the vehicle.” ([0112]); “resulting in the localization system only needing to determine x, y, and yaw components for the transformation.” ([0132])
Rationale: Under the construction stated for claim 10, the first estimation unit estimates the X, Y, and θZ components. Zhang’s landmark (edgel) based localization estimates exactly those components, with the others held fixed.
the first estimation unit estimates a position in each of a first axis direction and a second axis direction
See at least: Zhang: “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Zhang’s x and y components are positions in a first and a second axis direction.
extending in a horizontal direction
See at least: Zhang: “the z (corresponding to a vertical axis), roll, and pitch components of the transformation” ([0132])
Rationale: Because z is the vertical axis, the x and y directions extend in the horizontal plane.
and a posture represented by rotation around a third axis
See at least: Zhang: “only needing to determine x, y, and yaw components for the transformation” ([0132])
Rationale: Yaw is a posture represented by rotation around a third axis.
extending in a vertical direction,
See at least: Zhang: “the z (corresponding to a vertical axis)” ([0132])
Rationale: Yaw is rotation about the vertical (z) axis.
among the six degrees-of-freedom parameters,
See at least: Zhang: “By reducing the search space for the transformation from 6 dimensions to 3 dimensions” ([0132])
Rationale: The x, y, and yaw components are among the six pose parameters.
based on the plurality of landmarks extracted from the first sensor data
See at least: Zhang: “loads edgels from the map located near an estimated location, detects edges from captures images (e.g., live camera feeds), and optimizes the pose of the vehicle by aligning the edgels with detected edges.” ([0089]); “analyzing the image frame to identify a plurality of edge pixels within the image frame” ([0008])
Rationale: The pose is estimated from the landmark features detected in the captured images (first sensor data).
and on the known information,
See at least: Zhang: “Once an initial pose is determined, the localization system loads 1102 a plurality of edgels from a stored map (e.g., an OMap, such as that generated as part of the map creation phase as described above).” ([0113])
Rationale: The stored map of edgels is the known information.
as the two partial parameters [construed as the X, Y, and θZ components].
See at least: Zhang: “restricting certain components of the transformation to fixed values” ([0132])
Rationale: Under the construction stated for claim 10, the X, Y, and θZ components estimated by the first estimation unit are the partial parameters; Zhang estimates them as a subset of the pose, with the remaining components treated separately.
by estimating the four parameters of the six degrees of freedom [construed as the remaining Z, roll (θX), and pitch (θY) components]
See at least: Zhang: “If the road surface normal and camera height are known, the z (corresponding to a vertical axis), roll, and pitch components of the transformation can be fixed” ([0132]); “However, in cases where the road surface is not perfectly level, depending on the OMap to compute roll and pitch based upon a determined road surface normal may not be reliable, and may introduce error into the calculation.” ([0132]); Roumeliotis: “track the six-degrees-of-freedom (d.o.f.) position and orientation (pose) of a sensing platform.” ([0003])
Rationale: Under the construction stated for claim 10, the remaining parameters are the vertical position and the rotations about the two horizontal axes. Zhang separates those components from the x, y, and yaw estimation, and itself recognizes that deriving them from the road-surface normal may be unreliable. Roumeliotis’s camera/IMU estimator estimates all pose components, including those.
other than the two partial parameters estimated by the first estimation unit [construed as the X, Y, and θZ components],
See at least: Zhang: “This effectively reduces the search space for finding a transformation by restricting certain components of the transformation to fixed values.” ([0132])
Rationale: The components estimated in one estimation are held at fixed values, and only the other components are solved.
and the two partial parameters estimated by the first estimation unit [construed as the X, Y, and θZ components],
See at least: Zhang: “restricting certain components of the transformation to fixed values” ([0132]); Roumeliotis: “(i) the prior information about the initial state” ([0042])
Rationale: The estimated components are supplied as fixed values; Roumeliotis’s estimator accepts externally supplied information as a prior term and as constraints on its state ([0033], [0042]).
Claim limitations Not Explicitly Disclosed by the Combination of Sano, Roumeliotis, and Zhang
Under the construction stated for claim 10, the combination of Sano, Roumeliotis, and Zhang discloses all limitations of claim 10. No further reference is relied upon for claim 10.
Motivation to Combine Sano, Roumeliotis, and Zhang
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Sano, Roumeliotis, and Zhang before them, to modify the combined Sano/Roumeliotis system in three respects taught by Zhang. First, to perform a separate landmark-based estimation in a case where the map comparison cannot estimate the self-position, because Zhang teaches that map-comparison localization can fail when features are lacking or the view is blocked by surrounding vehicles, and that a backup localization method should then be used ([0086]–[0089]). Second, to estimate the X, Y, and θZ components from the landmarks and the stored map, as Zhang does ([0112]–[0113], [0132]). Third, to hold those components fixed and estimate the remaining components with Roumeliotis’s camera/IMU estimator, rather than deriving them from an assumed road-surface normal, because Zhang itself teaches that fixing components reduces complexity (“a complexity and processing burden needed to perform pose optimization can be reduced” ([0132])) and that the road-normal derivation “may not be reliable, and may introduce error into the calculation” ([0132]). A person of ordinary skill would have had reason to retain the landmark-determined X, Y, and θZ components as fixed values, rather than re-optimizing all six components, because Zhang teaches that reducing the number of pose variables optimized reduces complexity and processing burden, and because those components have already been supplied by the landmark-based localization; re-estimating them would be redundant. The estimator would then be applied to the Z, roll, and pitch components, which are the components Zhang identifies as unreliable when taken from the road-surface assumption. A person of ordinary skill would have reasonably expected success, because Roumeliotis already estimates every pose component from camera and IMU data by nonlinear least-squares optimization ([0043]) and Zhang demonstrates pose optimization with selected components held fixed ([0132]). See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416–417 (2007).
Response to Arguments
Applicant’s arguments filed 06/10/2026 have been fully considered. They are addressed below.
A. Rejection under 35 U.S.C. 101
Applicant argues that the amended claims integrate the judicial exception into a practical application because estimating only four of the six parameters, with the two partial parameters determined from a fixed object, (1) permits faster and less resource-intensive optimization, and (2) suppresses the cumulative error (drift) of visual inertial odometry, citing paras. [0006], [0007], and [0056] of the specification (Remarks, section titled “Rejection under 35 U.S.C. § 101”). Applicant’s arguments have been fully considered but are not persuasive, for the following reasons.
Under the broadest reasonable interpretation, the asserted benefits arise from the recited calculations, and the claims stop at the result. The Examiner does not rely on any rule that a technique based on mathematics can never improve a technology. MPEP 2106.05(a) recognizes that it can, where the claim reflects an improvement in how a technology operates. The question is whether these claims do. Here, the asserted gain in speed follows from solving four unknowns instead of six, and the asserted reduction in drift follows from supplying two of the six values from a landmark-based calculation instead of solving for them. The amendment does change how the sensor and map data are processed: it conditionally invokes a landmark-based estimation, determines two pose components, and then calculates the remaining four. That changed processing, however, is the pose-estimation procedure itself, which is the mathematical concept identified at Prong One. The claims recite no sensor, no change in how data is acquired, and no use of the resulting estimate; they end with the calculated self-position. In these claims, the asserted improvement is therefore confined to the calculation, which the judicial exception alone cannot supply. MPEP 2106.05(a); In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245, 1250 (Fed. Cir. 2021) (Stanford II); SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018).
The particular conditional, landmark, and two-stage limitations do not change that conclusion. The fallback selects a calculation based on the result of a prior calculation. The landmark and shape limitations specify the input values and the geometric relationship used to compute two parameters. The two-and-four division specifies which unknowns the second calculation solves. Considered together and with the additional elements, these limitations define a more specific, and different, estimation procedure. The difference lies in the recited calculations; the claims do not recite any change in how a sensor acquires data, any physical process that uses the result, or any operation of the autonomous moving body. Applicant’s specification also describes, as existing technology, both estimating the self-position from other sensors when the laser sensor or camera cannot estimate it (pub. para. [0004]) and camera-IMU optimization (para. [0007]).
The cited paragraphs. The specification identifies drift in visual inertial odometry as the problem addressed (pub. paras. [0007]–[0008]); as claimed, its solution is a change to the estimation. Para. [0056] states that, when the map comparison unit has completed the estimation, the output processing unit outputs the estimated self-position to the control device “(not shown)” of the autonomous moving body. The amended claims recite neither that output nor any control. (Applicant’s citations of original claim 2 and paras. [0034], [0037], [0047], and [0048] are offered as support for the amendment and are not relied upon for eligibility.)
Ex parte Desjardins. The claims have been evaluated as a whole under MPEP 2106.04(d)(1) and 2106.05(a), as revised in light of Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential). As explained in the rejection, the fallback, the two-parameter determination, and the four-parameter calculation, considered individually and in their ordered combination, define the mathematical estimation procedure itself; the claims do not require any modification of the sensors, the estimator, the processing hardware, or the autonomous moving body apart from that procedure. Unlike the claims in Desjardins, which recited an improvement in how a machine learning model itself operates, the present claims recite which quantities are calculated from which inputs and end with the calculated result.
Thales Visionix and Diehr are distinguishable. The eligible claims in Thales Visionix Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017), required a particular physical configuration of inertial sensors, and the improvement arose from that configuration. The present claims recite no sensor configuration. See SAP America, 898 F.3d at 1167–68 (distinguishing Thales on that basis). In Diamond v. Diehr, 450 U.S. 175 (1981), the calculated result was used to operate an industrial process; here it is not used at all.
The rejection is maintained on the mathematical-concepts grouping, which was a basis of the prior rejection. The Examiner does not rely on the mental-process grouping in this action. The complete analysis appears in the rejection under 35 U.S.C. 101.
B. Rejection under 35 U.S.C. 103
Applicant argues that Sano, Roumeliotis, and Zhang fail to teach (1) the map comparison unit that collates the first sensor data with the map data of the driving area and estimates the self-position using six degrees-of-freedom parameters, and (2) the second estimation unit that estimates the four parameters other than the two partial parameters estimated by the first estimation unit (Remarks, section titled “Rejection under 35 U.S.C. § 103”).
Applicant’s arguments are directed to the combination of Sano, Roumeliotis, and Zhang as applied in the prior Office action. That combination was applied to claims reciting a single “partial parameter,” and, as articulated in the prior action, it did not expressly supply the division of the six parameters into two partial parameters and four remaining parameters that the amendment now recites. The amended limitations are addressed by a new ground of rejection that adds Tateishi, as set forth below. Applicant’s arguments have been fully considered; as applied to the rejection in this action, they are not persuasive, for the following reasons.
Limitation (1), the map comparison unit. Sano’s self-position estimation unit 36 compares target position data detected from the first sensor data with the position of targets in the map information 41 and estimates the self-position from that comparison ([0053]–[0054]), and Sano states that an ICP algorithm may be used for the comparison ([0055]). Zhang teaches ICP matching of a sensed three-dimensional point cloud against previously acquired map data to determine the location and orientation of the vehicle ([0085]). Roumeliotis supplies the six-degrees-of-freedom pose representation ([0003]). Applicant identifies this limitation as missing but does not explain which aspect of it is absent from the cited combination or why the cited passages fail to teach it.
Limitation (2), the second estimation unit. Tateishi teaches a first estimation of exactly two pose parameters (lateral position and direction) from a line-shaped target matched to the map, followed by estimation of the remaining position component with those two parameters reflected ([0058]–[0059], [0063]–[0067]). The combination with Roumeliotis’s camera/IMU estimator, in which the two parameters are supplied as known values and the remaining four are solved, is set forth in the rejection of claim 1 below, together with the reason for the modification and the reasonable expectation of success.
Specificity of the traversal. Beyond identifying the two limitations, the remarks do not explain why the cited passages fail to teach them. The Examiner has nonetheless addressed each identified limitation on its merits above. To the extent the remarks rest on the general statement that the combination fails to teach the claims, such a statement does not specifically point out how the claim language distinguishes the references. See 37 CFR 1.111(b).
Asserted advantages. Applicant argues that the claimed arrangement suppresses cumulative error and permits faster, lower-load optimization. The prior art recognizes the same advantages: Zhang teaches that holding some pose components at fixed values reduces the complexity and processing burden of pose optimization ([0132]), and Tateishi teaches that reflecting first-stage lateral position and direction improves the accuracy of the later estimate ([0073]–[0074]). The fact that Applicant has recognized another advantage that would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See MPEP 2145(II); Ex parte Obiaya, 227 USPQ 58 (Bd. Pat. App. & Inter. 1985). To the extent Applicant relies on these advantages as evidence of nonobviousness, arguments of counsel cannot take the place of evidence in the record, and no evidence of unexpected results or other objective indicia has been submitted. See MPEP 2145(I), 716.01(c). This observation concerns the asserted advantages only; Applicant’s arguments about what the references teach have been addressed on their merits above.
Standard applied. Applicant concludes that the claimed concept “cannot be easily conceived” by a person of ordinary skill. The test is whether the claimed invention as a whole would have been obvious under 35 U.S.C. 103, applying the Graham factual inquiries and the rationales recognized in KSR. The rejection below applies that test.
Claim 10. Applicant argues claim 10 together with claims 1 and 8. Claim 10 is rejected under 35 U.S.C. 112(b), and, applying the construction stated in that rejection, under 35 U.S.C. 103 over Sano, Roumeliotis, and Zhang. As to the map comparison unit, Applicant’s arguments are not persuasive for the reasons given above for claim 1. As to the second estimation unit, Zhang teaches estimating the X, Y, and yaw components with the other components held fixed ([0132]), and the combination with Roumeliotis’s estimator of the remaining components is set forth in the rejection of claim 10 below.
Conclusion
Each new ground of rejection in this action was necessitated by Applicant’s amendment, as compared with the prior Office action:
§ 103, claims 1, 8, and 9: new reference (Tateishi). Tateishi is newly cited. It is applied only to limitations added by amendment: the “two partial parameters” and the estimation of “the four parameters … other than the two partial parameters.” In the prior Office action these claims recited a single “partial parameter” and did not recite estimation of the remaining four parameters. Tateishi is not applied to any limitation that was present before the amendment, and it is not applied to claim 10.
§ 103, claims 1, 8, and 9: other newly added limitations. New citations and rationale from Sano and Zhang are applied to the map comparison unit/step and the “in a case where” condition, both added by amendment. Sano’s self-position estimation unit 36, previously relied upon for the second estimation unit, is now relied upon for the newly added map comparison unit. That change in correspondence results directly from the amendment, which added a separate map comparison unit.
§ 103, limitations unchanged by the amendment. Limitations present before the amendment are mapped on the same references and findings as in the prior Office action. The non-fixed object, the first estimation unit, the known information, and the mark selected in advance remain mapped to Sano. The position in the first axis direction, the posture about the third axis, and the shape-based comparison remain mapped to Zhang (with Roumeliotis for the orientation framework). The plurality of landmarks attached to the fixed object remains mapped to Zhang’s lane-line features, as in the prior action; this action states that finding more specifically (the painted lane-line stripes on the road surface) and cites additional passages of Zhang and Sano, both previously applied, in support of it. No new reference is applied to any limitation present before the amendment, and the basic thrust of the rejection of those limitations is unchanged. See MPEP 1207.03(a).
§ 103, claims 3–5. These claims are unchanged. Their rejections add Tateishi only because they incorporate the amended limitations of claim 1. Under MPEP 706.07(a), a dependent claim is treated as amended where its parent claim is amended.
§ 112(b), claim 10. This rejection arises from the “two partial parameters” and “four parameters” language added by amendment.
§ 101, claims 1, 3–5, 8, and 9. The rejection under 35 U.S.C. 101 is maintained on the same statutory basis and the same judicial-exception grouping (mathematical concepts) as in the prior Office action. The analysis is updated only to address the limitations added by amendment.
§ 103, claim 10. Claim 10 is rejected over Sano, Roumeliotis, and Zhang, the same references applied to claim 10 in the prior Office action. New citations and rationale are applied only to limitations added to claim 10 by amendment (the map comparison unit, the “in a case where” condition, and the estimation of the remaining parameters); limitations present before the amendment are mapped on the same basis as in the prior action. The construction applied to claim 10 is required by the amendment, which introduced the “two partial parameters” and “four parameters” language.
Claim 9. The rejection of claim 9 under 35 U.S.C. 101 is maintained on the same basis as in the prior Office action (Step 2A and Step 2B), with the analysis updated for the amended limitations. No new Step 1 ground is raised.
Objections. The claim objections concern form only and are not grounds of rejection.
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.
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/OLUWABUSAYO ADEBANJO AWORUNSE/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
