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 .
DETAILED ACTION
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/21/2024 is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 limitation(s) is/are:
Claim 11 & 12: machine learning module, perception module, comparison module, planning unit (processor [0031], FIG. 4)
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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the area" in line 6. There is insufficient antecedent basis for this limitation in the claim.
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 1-4, 6-12 are rejected under 35 USC § 101 because the claimed invention is directed to an
abstract idea without significantly more.
Claims 1-9 are directed to a method, claims 10-12 are directed to a product, which is one of the statutory categories of invention. (Step 1: YES). The examiner has identified method claim 1 as the claim that represents the claimed invention for analysis and is similar to product claims 10-12.
Regarding claim 1, the claim recites, in part, “providing a machine learning model, trained in advance…generating a temporal sequence of occupancies…using measurement data…evaluating…the sequence…predict a total occupancy…detecting deviations…determining…reliability…planning a trajectory…”. The limitations of determining and generating, when read in light of
the specification, are mental processes capable of being performed in the human mind, which have
been identified as being abstract ideas (MPEP 2106.04(a)(2)). The limitations of generating a temporal sequence using measurement data is considered an insignificant extra-solution activity for data gathering and outputting (MPEP 2106.05(g)). Additionally this measurement data are well known sensors being used in their conventional manner, and thus are not significantly more than the judicial exception (see Electric Power Group, LLC. v. Alstom, S.A., 830 F.3d, 1350 (Fed. Circ. 2016)). The additional element of a “computer” is just a generic computing device. Invocation of generic computing devices to perform or aid the abstract idea does not amount to significantly more than the judicial exception (MPEP 2106.05(f)). As per claims 9 and 12 the computer readable medium and vehicle are tools used to perform the mental process previously described.
This judicial exception is not integrated into practical application because the claim does not
include limitations that purport the improvement to the function of a computer or another technology,
apply the abstract idea by way of a particular machine, or effect a tangible transformation in state of a
particular article (MPEP 2106.05). Rather, the abstract ideas are instead merely generally linked to a
particular technical field (MPEP 2106.04(3)).
Regarding claim 2, the claim recites, in part, “reliability is determined…deviation between”. These limitations, when read in light of the specification, are considered an insignificant extra-solution activity for data gathering and outputting (MPEP 2106.05(g)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception. Determining deviations between values is considered a mathematical concept (MPEP 2106.04(a)(2)).
Regarding claim 3, the claim recites, in part, “logic opinion is determined…tuple…deviation”. The limitations of determining and generating, when read in light of the specification, are mental processes capable of being performed in the human mind, which have been identified as being abstract ideas (MPEP 2106.04(a)(2)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception. Determining deviations between values and using tuples (matrices) is considered a mathematical concept (MPEP 2106.04(a)(2)).
Regarding claim 4, the claim recites, in part, “trajectory planning”. The limitations of determining and generating, when read in light of the specification, are mental processes capable of being performed in the human mind, which have been identified as being abstract ideas (MPEP 2106.04(a)(2)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception. The avoidance or preference of specific grid cells is considered an algorithm, which is a mathematical concept.
Claim 5 integrates the abstract idea of claim 1 into practical application by affecting movement of the vehicle.
Regarding claim 6, the claim recites, in part, “input data…includes 3D tensors”. These limitations, when read in light of the specification, are considered an insignificant extra-solution activity for data gathering and outputting (MPEP 2106.05(g)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception.
Regarding claim 7, the claim recites, in part, “architecture…output data…includes”. These limitations, when read in light of the specification, are considered an insignificant extra-solution activity for data gathering and outputting (MPEP 2106.05(g)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception.
Regarding claim 8, the claim recites, in part, “architecture…input data”. These limitations, when read in light of the specification, are considered an insignificant extra-solution activity for data gathering and outputting (MPEP 2106.05(g)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception.
Regarding claim 9, the claim recites, in part, “recurrent network”. These limitations, when read in light of the specification, are considered an insignificant extra-solution activity for data gathering and outputting (MPEP 2106.05(g)). The claim recites no additional elements that are indicative of an integration into a practical application or that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1, 2, 5, 8, 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20220163966A1 Fonseca et al ("Fonseca").
As per claims 1, 10-12 Fonseca teaches the limitations of the method and products:
A method for planning a trajectory for an at least partially automated vehicle, comprising the following steps: a) providing a machine learning model, trained in advance, for determining occupancy of an occupancy grid, wherein the machine learning model is trained to predict the occupancy of the occupancy grid at a subsequent time from Fonseca at least the abstract, [0010]: “trajectory or route”, [0076]: “a machine learned model (e.g., neural network) which has been trained”, [0018]: “region indicating a potential region occupied by an object over a period of time (such as 3 seconds, 5 seconds, 10 seconds, and the like)”, FIG. 5, FIG. 6, [0077]: “alternatively, the sensor system(s) 706 can send sensor data, via the one or more networks 734, to the one or more computing device(s) at a particular frequency, after a lapse of a predetermined period of time, in near real-time”).
Fonseca does not explicitly disclose a history of a specific length of occupancies, but does teach a BRI equivalent ([0018], [0077]). One of ordinary skill in the art could, based on the teachings of Fonseca, interpret an embodiment of the invention to act on such aforementioned limitations.
As per claim 2, Fonseca teaches the invention as described above. Fonseca additionally teaches:
wherein a measure of the reliability is determined for a specific spatial area of a current occupancy depending on the deviation between the occupancy determined using the measurement data and the predicted occupancy, depending on the deviation and/or a measurement uncertainty, wherein the area includes at least one cell and/or multiple contiguous cells of the occupancy grid. (Fonseca at least [0091]: “receiving sensor data representative of a physical environment from the sensor; generating, based at least in part on the sensor data, a dilated prediction probability associated with a location of an object and an additional area exceeding the location of the object; comparing a point representing the autonomous vehicle with the dilated prediction probability; and causing, based at least in part on the comparing the point representing the vehicle with the dilated prediction probability, the autonomous vehicle to perform an action.”)
As per claim 5, Fonseca teaches the invention as described above. Fonseca additionally teaches:
wherein occupancy grids are transformed prior to processing by the machine learning model and/or prior to the comparison such that an ego movement of the vehicle is compensated. (Fonseca at least [0136]: “Another approach is to preprocess the observed OGMs, using motion information, outside of the OGM prediction system … Preprocessing to account for ego motion may be more effective. Also, the preprocessing approach (rather than determining the state transformation inside the RNN) means that the predictions are generated based on the current ego vehicle state. Such prediction may provide more flexibility for a planning algorithm to plan a proper trajectory for the vehicle.”)
As per claim 8, Fonseca teaches the invention as described above. Fonseca additionally teaches:
wherein the machine learning model has an architecture in which spatial and temporal dimensions of input data are processed separately. (Fonseca at least [0136])
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fonseca in view of EP3534297A2 Hansen et al ("Hansen", machine translation provided).
As per claim 3, Fonseca teaches the invention as described above. Fonseca does not disclose:
in step d), a subjective logic opinion is determined for determining the reliability for each cell of the occupancy grid, wherein a tuple bij, dij, uij, aij is determined for each cell ij, wherein bij represents a match between the measurement and the prediction of the occupancy of the cell ij, dij represents a deviation between the measurement and the prediction of the occupancy of the cell ij, uij represents an uncertainty of the occupancy of the cell ij, and aij describes a basic probability of the occupancy of the cell ij.
Hansen teaches the aforementioned limitation (Hansen at least: “Preferably, the represented environment is logically subdivided into a plurality of (in particular equal) tiles, wherein the map data and the reliability data are tile-resolved or resolvable or interpolatable, where a tile may be associated or assignable to a data tuple that has at least one presence probability for that tile has a reliability value and optionally also an immutability tag.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Hansen with a reasonable expectation of success. One of ordinary skill would have been motivated to combine these references in order to provide an improved position determination.
As per claim 4, Fonseca in combination with the other reference teaches the invention as described above. Fonseca additionally teaches:
in step e), a trajectory planning for the vehicle takes place such that cells and/or areas of cells of the occupancy grid: (a) with a high deviation dij and/or a high uncertainty uij are avoided and/or (b) areas with a high match bij are preferred. (Fonseca at least [0022]: “reference trajectory at the planned velocity (or series of velocities). The vehicle may then, determine a boundary of the drivable area based on the uncertainty model (e.g., the heat map) and the planned trajectory. For example, the system may perform a level set or determine a boundary of the drivable area based on a cell within the heatmap adjacent to the planned trajectory having a likelihood or probability of occupancy meets or exceeds a threshold”)
Claim(s) 6-7, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fonseca in view of US20210276598A1 Abolfathi ("Abolfathi").
As per claim 6, Fonseca teaches the invention as described above. Fonseca does not disclose:
input data for the machine learning model includes 3D tensors, wherein the 3D tensors each include occupancy grids of a specific environment at different times.
Abolfathi teaches the aforementioned limitation (Abolfathi at least [0111]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Abolfathi with a reasonable expectation of success. One of ordinary skill would have been motivated to combine these references in order to improve the accuracy of predicted OGMs (Abolfathi [0166]).
As per claim 7, Fonseca teaches the invention as described above. Fonseca does not disclose:
the machine learning model includes an encoder-decoder architecture, wherein output data for the machine learning model includes an occupancy grid of the same dimensions as the occupancy grids of the input data, but only for a specific time.
Abolfathi teaches the aforementioned limitation (Abolfathi at least [0009], [0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Abolfathi with a reasonable expectation of success. The motivation to combine these references is the same as above in claim 6.
As per claim 9, Fonseca teaches the invention as described above. Fonseca does not disclose:
the machine learning model includes a recurrent network.
Abolfathi teaches the aforementioned limitation (Abolfathi at least [0009]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Abolfathi with a reasonable expectation of success. The motivation to combine these references is the same as above in claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVER TAN whose telephone number is (703)756-4728. The examiner can normally be reached M-F 10-7.
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/O.T./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669