CTNF 19/039,884 CTNF 97070 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification 06-31 AIA The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Drawings 06-31 AIA The lengthy drawings have not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings. Claim Rejections - 35 USC § 112(b) 07-30-02 AIA 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. 07-34-01 Claims 1 – 21 and 97 – 110 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding Claims 1, 20, and 21, the claims recite the limitation “the drive information for each of the plurality of vehicles”. There is insufficient antecedent basis for this limitation in the claims. The claims previously recite “drive information captured during drives by a plurality of vehicles traversing or having traversed a road segment”. There is no requirement in the initial recitation of “drive information” that it is for, by, or similar for each of the plurality of vehicles. Related, but also separate, the claims recite the limitation “the drive information received from each of the plurality of vehicles”. There is also insufficient antecedent basis for this limitation in the claims. The claims previously recite “drive information captured during drives by a plurality of vehicles traversing or having traversed a road segment”. There is no recitation of “receiving” and furthermore that it is from each of the plurality of vehicles. Both of these limitations appear to narrow the scope further while referring back to the same limitation and thus lack antecedent basis. While the narrower scope of each limitation is considered disclosed by the prior art, in the interest of compact prosecution as the exact intended nature of the limitations is unclear in light of each adding distinctly different narrowing limitations, both limitations are interpreted as instead simply reading “the drive information”. Regarding Claims 7, 99, and 106, the claims recite the limitation “PLS analysis”. The term “PLS” is not defined in the claim. Before using an abbreviation or acronym, the full term should be provided followed by the desired shorthand reference, typically in parentheses to make clear what specifically PLS refers to. In light of the specification ([0417]), the claim has been interpreted as reading “partial least squares (PLS) analysis”. Regarding Claims 9, 10, 100, 101, 107, and 108, the claims recite the following (or equivalent) limitations: wherein a count of the one or more actual landmarks positioned along the road segment for one of the at least two landmark clusters is equal to one when a same drive identifier is not included in the distribution of the drive identifiers for the one of the at least two landmark clusters (Claim 9) and wherein a count of the one or more actual landmarks positioned along the road segment for one of the at least two landmark clusters is greater than one when a same drive identifier is included in the distribution of the drive identifiers for the one of the at least two landmark clusters (Claim 10) The claims are replete with issues of clarity which are difficult to separate and articulate due to their interrelated nature. In light of [0427] of Applicant’s Specification and in alignment with the phrasing of the claims themselves, the term “count” appears to mean the archaic definition of a reckoning, account, consideration, or estimation (Merriam-Webster Online Dictionary access 3/25/2026, definition 2(a) and 2(b)) as no actual “counting” appears disclosed, especially as the claim and disclosure appears to be that of assuming a number of landmarks based on the repetition or lack thereof of a particular piece of data. Therefore, a first potential issue appears to be that the claim merely describes a condition. The claim does not describe a step, function, structure, or similar in any of the claims. It merely states that a number of landmarks (and not the total number of landmarks for a cluster) is a particular amount under the given circumstance. This number or “count” is not used in any manner in the claims and does not otherwise appear to relate to any other limitation (i.e. the number of landmarks is irrelevant to the structure, function, or process). Additionally, as per [0427] which uses the phrase “may” this is not necessarily true but an apparent assumption , which again as noted is not claimed as occurring. Thus, the claims merely appear to describe a property considered inherent rather than a determination step, function, structure or similar based on this property. Second, the described condition occurs under a “comprising” claim and with other particularly broad phrasing. There is always some “count” (number) of landmarks in a landmark cluster. It does not need to be the total, correct, more than zero, etc. The “when” clause is furthermore a part of a “comprising” claim and thus is not presumed to be “only when” or “always when” or otherwise exclusive. Thus, the condition appears to effectively always met by any prior art reading on the claim from which it depends as the conditions are effectively non-exclusive ranges covered by broader ranges. Third, the claim uses especially broad language wherein the above is based more on assumptions than actual clear limitations. The claim recites “a count … for one of the at least two landmark clusters”. The nature of “for” is not claimed in any particularity. While it may be assumed to mean “in” or similar, the full breadth of the term includes mere association. The claim also recites “a same drive identifier” without actually qualifying what “same” means. The same as what, and same in what manner? Are two drive identifiers the same if they are of the same type or category, or only if everything about them is identical? This is not claimed and leaves the claim broad and reliant on a subjective term of “same” as the rest of the terms are not defined. While [0427] indicates that “same” means identical in every respect, the claims are not so particular and leave the breadth of the claim such that is unclear. Fourth, there is a clear issue of insufficient antecedent basis for the limitation “the distribution of the drive identifiers for the one of the at least two landmark clusters” in the claim. The independent claims originally recite “determining … a distribution of drive identifiers relative to the at least two landmark clusters”. They are not specifically for one or more of the two landmark clusters but merely a distribution relative to the at least two landmark clusters. [0427] uses the same language and does not clarify when using the same language. Finally, the statements in the latter half of [0427] conveys a different and generally clearer meaning than that of the claims and the first half of [0427]. These statements which are by contrast comprehensible appear to indicate that the claim limitations should actually read: determining there is only one actual landmark of the one or more actual landmarks positioned along the road segment in a particular landmark cluster of the at least two landmark clusters when a drive identifier of the distribution of drive identifiers only occurs once in the particular landmark cluster . and determining there is more than one actual landmark of the one or more actual landmarks positioned along the road segment in a particular landmark cluster of the at least two landmark clusters when a drive identifier of the distribution of drive identifiers only occurs more than once in the particular landmark cluster or similar. In the interest of compact prosecution, they have been interpreted as reading as such. It is still noted that without further limitation to “drive identifiers” and an “only” statement or similar before “determining” or similarly a statement of what occurs under the other half of the contingent condition (for example possibly combining the two claims) that the claims remain particularly broad. Regarding the remaining claims (Claims 2 – 6, 8, 11 – 19, 97 – 98, 102 – 105, and 109 – 110) , the claims depend from claim(s) rejected above and inherit the deficiencies of said claim(s) as described above. Therefore, the remaining claims (Claims 2 – 6, 8, 11 – 19, 97 – 98, 102 – 105, and 109 – 110) are rejected under the same logic presented above. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 – 21 and 97 – 110 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 20 will be used to illustrate the rejection with respect to all independent claims. Claim 1 recites: A system for determining one or more harvested landmarks, the system comprising: a memory comprising instructions; at least one processor programmed to: obtain drive information captured during drives by a plurality of vehicles traversing or having traversed a road segment, the drive information for each of the plurality of vehicles comprising a drive identifier and landmark detection information corresponding to one or more landmark detections; aggregate the landmark detection information included in the obtained drive information from the plurality of vehicles, and based on the aggregated landmark detection information, identifying at least two landmark clusters, each of the at least two landmark clusters being representative of a potential real world location of an actual landmark positioned along the road segment; determine, based on the drive identifier associated with the drive information received from each of the plurality of vehicles, a distribution of drive identifiers relative to the at least two landmark clusters; determine, based on the distribution of drive identifiers, a location identifier for one or more actual landmarks positioned along the road segment; store the location identifier for the one or more actual landmarks in a map; and distribute the map to one or more autonomous vehicles for use in navigating along the road segment, the navigating comprising determining at least one navigational response based on the location identifier for the one or more actual landmarks. Examiner notes that everything beyond “for use …” is an explicit and clear statement of mere intended purpose. Actual navigation is not presently claimed . 101 Analysis – Step 1: Statutory Category – Yes The claim recites a machine. The claim falls within one of the four statutory categories. MPEP 2106.03 relates. 101 Analysis – Step 2A Prong One Evaluation: Judicial Exception – Yes – Mathematical Calculations In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity. The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes”. MPEP 2106.04(a)(2)(III) relates. The claim recites limitations of analyzing and handling information. These limitations, as drafted, are the performance of mental processes but for the recitation of “a memory” and “a processor”. That is, other than reciting “a memory” and “a processor” nothing in the claim elements precludes the limitations from being performed in the human mind, with or without the aid of pen and paper. The mere nominal recitation of “a memory” and “a processor” does not take the claim limitations out of the mental processes grouping. For example, the verb “aggregate” simply means to “to collect or gather into a mass or whole” (Merriam-Webster Online Dictionary accessed 3/26/2026). Multiple pieces of information are readily capable of being “aggregated” in the human mind. Furthermore, this may readily be aided by pen and paper. Furthermore, a person may “determine” information such as a distribution of information or a location using human judgement and reasoning. Finally, the nature of “store … in a map” is not claimed with any particularity and may be retained in the human mind or written into a pen and paper map or similar. Thus, the claim recites mental processes. 101 Analysis – Step 2A Prong Two Evaluation: Practical Application – No In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” The Office submits that the foregoing underlined limitation (s) recite additional elements that do not integrate the recited judicial exception into a practical application. The claim recites additional elements or steps of the activities being functions of “at least one processor”. The use of a computing device merely describes how to generally perform the computations using a generic device, i.e. a computer and is recited at a high level of generality and is merely automating or performing the functions. Furthermore, the functions of “obtain …” and “distribute …” are insignificant extra-solution activity of mere data gathering and outputting. MPEP 2106.05(g)(3) relates. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 101 Analysis – Step 2B Evaluation: Inventive Concept – No In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05 relates. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component(s). The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer or computer component(s) cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Again, the functions of “obtain …” and “distribute …” are insignificant extra-solution activity of mere data gathering and outputting which is insufficient for both Step 2A Prong Two and Step 2B considerations. MPEP 2106.05(g)(3) relates. The specification does not provide any indication that the computing device is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp. , 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC , 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc. , 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere receipt or transmission of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the providing step is well-understood, routine, conventional activity is supported under Berkheimer. Thus, the claim is ineligible. With respect to the dependent claims, the claims merely recite additional details to the mental processes already recited or circumstantial items to the devices involved. In other words, the additional limitations merely recite details which are categorically already addressed above. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1 – 6, 8 – 21, 97 – 98, 100 – 105, and 107 – 110 are rejected under 35 U.S.C. 102( a)(1) and 102(a)(2 ) as being anticipated by Rappel-Kroyzer et al. (US 20210341303 A1) . Regarding Claim 1, Rappel-Kroyzer teaches: A method for determining one or more harvested landmarks, the method comprising: obtaining drive information captured during drives by a plurality of vehicles ( See at least [0006] “a method for aggregating informational reports received from a plurality of vehicles” ) traversing or having traversed a road segment ( See at least [0073] “system 100 may upload data and include information sufficient to uniquely identify a specific vehicle, owner/driver, and/or a portion or entirely of a route traveled by the vehicle” ) , the drive information for each of the plurality of vehicles comprising a drive identifier ( See again at least [0073] above ) and landmark detection information corresponding to one or more landmark detections ( See at least [0167] “As will be discussed in further detail below, road features (e.g., landmarks along a road segment) may be stored as small data objects that may represent a road feature in relatively few bytes, while at the same time providing sufficient information for recognizing and using such a feature for navigation” and/or 1410 of Figure 14 and associated description in at least [0220]. Examiner furthermore notes that both terms of “drive information”, “drive identifier”, “landmark”, “landmark detection information” etc. are not claimed in further detail and hold especially broad meaning under their plain and ordinary meaning ) ; aggregating the landmark detection information included in the obtained drive information from the plurality of vehicles ( See at least [0205] “Clustering or cluster analysis enables the aggregation of such informational reports into related groupings” and/or [0223] “at step 1450, process 1400 may include aggregating the informational vehicle report of the first cell with the information cluster associated with the second cell to provide an aggregated cluster” and [0200] – [0201] which disclose in detail what an information report may comprise ) , and based on the aggregated landmark detection information, identifying at least two landmark clusters, each of the at least two landmark clusters being representative of a potential real world location of an actual landmark positioned along the road segment ( See at least [0207] “For example, informational reports about a detected pothole in one location or region may be grouped together in a first cluster, while informational reports about a detected pothole in a different location or region may be grouped together in a second cluster different from the first cluster” ) ; determining, based on the drive identifier associated with the drive information received from each of the plurality of vehicles, a distribution of drive identifiers relative to the at least two landmark clusters ( The nature of this limitation is not claimed with any particularity. See again above recitations wherein informational reports clustered and/or aggregated and include a plurality of drive identifiers, thus creating a “distribution” thereof and which may relate to clusters for different landmarks (one example given being the pothole) ) ; determining, based on the distribution of drive identifiers, a location identifier for one or more actual landmarks positioned along the road segment ( The nature of this limitation is not claimed with any particularity. See at least [0223] “generating the first event report for the event based on information associated with the first cluster may include averaging the location in the environment of the host vehicle with a location associated with the second cell”. For example, the aggregation results in an averaged location which is tied to one or more identifiers and corresponds to one or more landmarks ) ; storing the location identifier for the one or more actual landmarks in a map ( The nature of “map” is not claimed with any particularity and is thus taught variously. See at least event report itself, for example in relation to 1480 of Figure 14. The English definition of “map” is merely “a representation usually on a flat surface of the whole or a part of an area” wherein the “usually on a flat surface” is not a strict requirement or even just “something that represents with a clarity suggestive of a map” (Merriam-Webster Online Dictionary accessed 3/25/2026). Additionally, and alternatively, see at least navigational report in relation to [0229] – [0232]. Finally, additionally, and alternatively, see at least sparse map as discussed with respect to other items such as the event report in [0216] “FIGS. 13A-13G illustrate example clustering operations that may be performed consistent with the disclosed embodiments … Path 1301 may represent a target trajectory in a sparse map, as described above. As shown, cluster 1310 may include three cells associated with reported events and cluster 1320 may include two cells associated with reported events. As additional informational reports are received, clusters 1310 and 1320 may be updated. For example, as shown in FIG. 13B, additional event reports may be received for locations within cells 1321 and 1322 (as indicated by the light shading in those cells) and Figure 13G ) ; and distributing the map to one or more autonomous vehicles for use ( Examiner notes that the phrasing of “for use” is a clear and explicit indication that the following is merely an intended use rather than a positively recited limitation ) in navigating along the road segment ( See at least [0203] in general, and for a particular portion “it may be both impractical and inefficient to broadcast to the fleet repetitive reports related to the same event” with respect to reports and at least [0004] “The disclosed systems may also provide for constructing and navigating with a crowdsourced sparse map” and if not clear from “crowdsourced” alone, further see [0162] “the disclosed systems and methods may distribute a sparse map” ) , the navigating comprising ( See again above with respect to “for use”. In the interest of compact prosecution, see end where still addressed ) determining at least one navigational response based on the location identifier for the one or more actual landmarks ( See at least [0204], in particular “As a result, aggregated navigational reports may enable more robust, informed decision making by navigational systems of an autonomous vehicles” and [0229] – [0232] in particular [0029] “transmitting the event report to the plurality of host vehicles as a navigational report upon which the plurality of vehicles may use in determining navigational actions” ) . In general, furthermore see the relationship between information, reports, and maps disclosed in [0195] which relates to many of the limitations “the information relating to semi-permanent or transient roadway conditions or features may be used to generate reports to one or more vehicles that may be used for navigation. These reports may be included with or may be provided separate from sparse maps delivered to one or more vehicles for navigation. In other embodiments, instead of being separate, the reports maybe in the form of a tag, a flag, or other information within the sparse maps indicating the semi-permanent or transient roadway condition”. Regarding Claim 2, Rappel-Kroyzer teaches: The method of claim 1, wherein the at least two landmark clusters are identified based on one or more regions identified in the aggregated landmark detection information ( See at least [0207] “a particular region in the real world may be subdivided into sub-regions referred to as cells” ) . Regarding Claim 3, Rappel-Kroyzer teaches: The method of claim 2, wherein the one or more regions are determined based on map information ( Appears inherent inasmuch as the term “map information” is not further disclosed. See for example [0207] “a particular region in the real world may be subdivided into sub-regions referred to as cells” or [0207] “The disclosed systems and methods may establish a cell size for each cell. In some embodiments, a cell may have a size in meters (e.g., two square meters, five square meters, ten square meters, fifty square meters, one hundred square meters, etc.) that corresponds to a real-world location having the specified size” or [0207] “As another example, portions of roadways that are associated with higher traffic congestion may have smaller cell sizes as opposed to less busy portions”, etc. ) . Regarding Claim 4, Rappel-Kroyzer teaches: The method of claim 1, wherein the at least two landmark clusters are identified based on topology information included in the aggregated landmark detection information ( The term “topology” information is not particularly defined, and furthermore “topology” often is used in the field of robotics to simply refer to 3D dimensional data in some form. See at least [0111] “The second processing device may receive images from main camera and perform vision processing to detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Additionally, the second processing device may calculate a camera displacement and, based on the displacement, calculate a disparity of pixels between successive images and create a 3D reconstruction of the scene (e.g., a structure from motion). The second processing device may send the structure from motion based 3D reconstruction to the first processing device to be combined with the stereo 3D images”, and/or [0124] “For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit 110 may then use the 3D-map to detect the road surface, as well as hazards existing above the road surface”, and/or [0148] “At step 620, processing unit 110 may execute stereo image analysis module 404 to perform stereo image analysis of the first and second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like” ) . Regarding Claim 5, Rappel-Kroyzer teaches: The method of claim 4, wherein the topology information is determined based on map information ( See again above. The terms of “map information” and “topology information” are not claimed with any particularity ) . Regarding Claim 6, Rappel-Kroyzer teaches: The method of claim 1, wherein the at least two landmark clusters are identified based on orientation information included in the aggregated landmark detection information ( The term “orientation information” is not claimed with any particularity or the nature of the basis of “based on”. See again for example [0111], [0124], and [0144] as in Claim 4. 3D information will inherently hold some form of orientation information ) . Regarding Claim 8, Rappel-Kroyzer teaches: The method of claim 1, wherein the at least two landmark clusters are identified based on one or more regions identified in the aggregated landmark detection information ( See at least [0207] “a particular region in the real world may be subdivided into sub-regions referred to as cells” ) in which an actual landmark of the one or more actual landmarks comprises features of different sizes ( The exact nature of “features” or “different sizes” is not claimed with particularity and is thus especially broad to the point that the feature is either trivial or iniherent. Furthermore, this claim appears to be purely circumstantial and non-limiting under the broadest reasonable interpretation. The landmark comprising features of different sizes does not appear to have any influence or bearing on the basis of the identification. A cell of the real world readily includes landmarks, wherein any given object may be measured in different ways such that a different “size” is established. See also at least [0185] “A sparse map may also include representations of other road-related features associated with geographic region 911. For example, a sparse map may also include representations of one or more landmarks identified in geographic region 911. Such landmarks may include a first landmark 950 associated with stop line 932, a second landmark 952 associated with stop sign 934, a third landmark associated with speed limit sign 954, and a fourth landmark 956 associated with hazard sign 938. Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current location relative to any of the shown target trajectories, such that the vehicle may adjust its heading to match a direction of the target trajectory at the determined location”. All of these examples given include object which have different sizes. For a stop sign, it has an individual side and a length between two sides which are different. For a stop line, the it is a line not a square or block ) . Regarding Claim 9, Rappel-Kroyzer teaches: The method of claim 1, wherein a count of the one or more actual landmarks positioned along the road segment for one of the at least two landmark clusters is equal to one when a same drive identifier is not included in the distribution of the drive identifiers for the one of the at least two landmark clusters ( As demonstrated above, a given cluster may comprise a number of cells which may each have any number of reports or other drive identifier information associated therewith. Furthermore, a given cell may or may not have one or more actual landmarks as reported and updated based on the incoming information. Thus, under the infinite ranges of the prior art which covers this non-exclusive range, and breadth of the claim terms the limitations appear disclosed ) . Regarding Claim 10, Rappel-Kroyzer teaches: The method of claim 1, wherein a count of the one or more actual landmarks positioned along the road segment for one of the at least two landmark clusters is greater than one when a same drive identifier is included in the distribution of the drive identifiers for the one of the at least two landmark clusters ( As demonstrated above, a given cluster may comprise a number of cells which may each have any number of reports or other drive identifier information associated therewith. Furthermore, a given cell may or may not have one or more actual landmarks as reported and updated based on the incoming information. Thus, under the infinite ranges of the prior art which covers this non-exclusive range, and breadth of the claim terms the limitations appear disclosed ) . Regarding Claim 11, Rappel-Kroyzer teaches: The method of claim 1, wherein the one or more actual landmarks include at least a traffic sign ( See at least [0185] “landmarks may include a first landmark 950 … a second landmark 952 associated with stop sign 934, a third landmark associated with speed limit sign 954, and a fourth landmark 956 associated with hazard sign 938 ) , a traffic light ( See at least “a detected traffic light” of [0154] ) , a road marking ( See at least [0185] “landmarks may include a first landmark 950 associated with stop line 932” and “lane markings” of [0154] ) , a pole ( See at least [0185] “landmarks may include … a second landmark 952 associated with stop sign 934, a third landmark associated with speed limit sign 954, and a fourth landmark 956 associated with hazard sign 938”. Examiner notes that it is common knowledge that signs and other stationary traffic indicating devices are typically mounted to poles ) , or a construction indicator ( See at least [0185] “landmarks may include … a third landmark associated with speed limit sign 954, and a fourth landmark 956 associated with hazard sign 938”. Examiner notes that it is common knowledge that speed limit signs and hazard signs are used in construction zones. See also [0195] “the information collected from vehicles traversing a roadway may include transient road features such as construction zones” ) . Regarding Claim 12, Rappel-Kroyzer teaches: The method of claim 1, wherein aggregating the landmark detection information included in the obtained drive information from the plurality of vehicles is based on one or more drivable paths of a road segment ( The nature of “based on” is not claimed with any particularity. The claim may be only “one” drivable path. Thus this limitation appears inherent to the existing limitation of “traversing or having traversed a road segment” of the independent claims. As needed alternatively also see at least “vehicle path” of [0139], “road path” of [0179], and path 1301 found in Figures 13A – 13G ) . Regarding Claim 13, Rappel-Kroyzer teaches: The method of claim 1, wherein the at least one navigational response includes at least one of steering, braking, or accelerating the one or more autonomous vehicles ( Examiner first notes that this is not a positively recited limitation. See discussion with respect to navigational response limitation in the independent claim. In the interest of compact prosecution, see at least [0029] “transmitting the event report to the plurality of host vehicles as a navigational report upon which the plurality of vehicles may use in determining navigational actions” and [0104] “For example, when vehicle 200 navigates without human intervention, system 100 may automatically control the braking, acceleration, and/or steering of vehicle 200 (e.g., by sending control signals to one or more of throttling system 220, braking system 230, and steering system 240”. See also as needed [0103] discussing the interchangeability of functions and structures, though Examiner believes it already indicated explicitly by [0029] that navigational actions include the claimed actions ) . Regarding Claim 14, Rappel-Kroyzer teaches: The method of claim 1, wherein the landmark detection information comprises one or more three-dimensional real-world coordinates corresponding to a surface of a landmark of the one or more landmarks ( See at least [0110] “create a 3D reconstruction of the environment of vehicle 200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera” ) . Regarding Claim 15, Rappel-Kroyzer teaches: The method of claim 14, wherein the one or more three-dimensional real-world coordinates determine one or more edges of the surface of the landmark ( See at least [0110] “create a 3D reconstruction of the environment of vehicle 200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera” ) . Regarding Claim 16, Rappel-Kroyzer teaches: The method of claim 1, wherein the landmark detection information comprises a type identifier identifying a type of a corresponding landmark ( See at least [0168] – [0170]. See in particular at least [0168] “When, for example, a sign or even a particular type of a sign is locally unique (e.g., when there is no other sign or no other sign of the same type) in a given area, the sparse map may use data indicating a type of a landmark (a sign or a specific type of sign), and during navigation (e.g., autonomous navigation) when a camera onboard an autonomous vehicle captures an image of the area including a sign (or of a specific type of sign), the processor may process the image, detect the sign (if indeed present in the image), classify the image as a sign (or as a specific type of sign), and correlate the location of the image with the location of the sign as stored in the sparse map” ) . Regarding Claim 17, Rappel-Kroyzer teaches: The method of claim 1, wherein the landmark detection information comprises sensor data obtained by at least one sensor of one or more of the plurality of vehicles ( See at least [0167] “This lean representation of landmarks (and other road features) may take advantage of the sensors and processors included onboard such vehicles that are configured to detect, identify, and/or classify certain road features” and [0231] “As previously discussed, the disclosed systems and methods for receiving crowd sourced road event/condition information, aggregating the received information into related event/condition clusters, and generating navigational reports based on the aggregated clusters may enable a host vehicle to make navigational decisions based on richer information than would be otherwise available solely based on the sensors of the host vehicle alone” ) . Regarding Claim 18, Rappel-Kroyzer teaches: The method of claim 17, wherein the at least one sensor includes a camera ( See at least camera 122, 124, 126, and Figure 1 ) , a radar ( See at least [0114] “one more sensors (e.g., radar” ) , or a lidar ( See at least [0114] “one more sensors (e.g., … lidar” ) . Regarding Claim 19, Rappel-Kroyzer teaches: The method of claim 17, wherein the sensor data includes at least one of images ( See at least camera 122, 124, 126, and Figure 1 ) , radar data ( See at least [0114] “one more sensors (e.g., radar” ) , or lidar data ( See at least [0114] “one more sensors (e.g., … lidar” ) . Regarding Claims 97 – 98, 100 – 105, and 107 – 110, the claims are directed to effectively the same subject matter as Claims 1 – 6 and 8 – 21 with respect to the application of prior art. The claims are therefore rejected under the same logic as Claims 1 – 6 and 8 – 21 above. These claims only effectively recite practice of the method using generic computer components, or storage of the method as instructions on and executed by generic computer components. Beyond being inherent common knowledge, see at least [0233] – [0235] as needed wherein such components are disclosed . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 7, 99, and 106 are rejected under 35 U.S.C. 103 as being unpatentable over Rappel-Kroyzer et al. further in view of Wang et al. (Wang, Qing, et al. "Object tracking via partial least squares analysis." IEEE Transactions on Image Processing 21.10 (2012): 4454-4465.) . Regarding Claim 7, Rappel-Kroyzer teaches: The method of claim 1, Rappel-Kroyzer does not explicitly teach, but in combination with Wang teaches: wherein the at least two landmark clusters are identified based on a PLS analysis of the aggregated landmark detection information ( See at least Page 1, Abstract, “In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking” Examiner furthermore notes that the nature of “based on a PLS analysis of the aggregated landmark detection information” is not claimed with any particularity ) . It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize PLS analysis in the data analysis of Rappel-Kroyzer with a reasonable expectation of success. As shown in at least Figures 6 and 17, besides its general use in object tracking, it is of particular use in vehicle/traffic related contexts as well, especially as motion is relative between two bodies (i.e. a static object will appear to move if the observer is moving). PLS analysis would therefore facilitate the identification of datapoints of interest across a data stream. Regarding Claims 99 and 106, the claims are directed to effectively the same subject matter as Claim 7 with respect to the application of prior art. The claims are therefore rejected under the same logic as Claim 7 above. These claims only effectively recite practice of the method using generic computer components, or storage of the method as instructions on and executed by generic computer components. Beyond being inherent common knowledge, see at least [0233] – [0235] of Rappel-Kroyzer as needed wherein such components are disclosed . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Akbarzadeh et al. (US 20220341750 A1) which discloses “crowdsource data generation using many vehicles and many drives” ([0029]) and identification of road features such as “a traffic sign by a first deployed neural network” ([0155]). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW C GAMMON whose telephone number is (571)272-4919. The examiner can normally be reached M - F 10:00 - 6:00. 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For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MATTHEW C GAMMON/Examiner, Art Unit 3657 /ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657 Application/Control Number: 19/039,884 Page 2 Art Unit: 3657 Application/Control Number: 19/039,884 Page 3 Art Unit: 3657 Application/Control Number: 19/039,884 Page 4 Art Unit: 3657 Application/Control Number: 19/039,884 Page 5 Art Unit: 3657 Application/Control Number: 19/039,884 Page 6 Art Unit: 3657 Application/Control Number: 19/039,884 Page 7 Art Unit: 3657 Application/Control Number: 19/039,884 Page 8 Art Unit: 3657 Application/Control Number: 19/039,884 Page 9 Art Unit: 3657 Application/Control Number: 19/039,884 Page 10 Art Unit: 3657 Application/Control Number: 19/039,884 Page 11 Art Unit: 3657 Application/Control Number: 19/039,884 Page 12 Art Unit: 3657 Application/Control Number: 19/039,884 Page 13 Art Unit: 3657 Application/Control Number: 19/039,884 Page 14 Art Unit: 3657 Application/Control Number: 19/039,884 Page 15 Art Unit: 3657 Application/Control Number: 19/039,884 Page 16 Art Unit: 3657 Application/Control Number: 19/039,884 Page 18 Art Unit: 3657 Application/Control Number: 19/039,884 Page 19 Art Unit: 3657 Application/Control Number: 19/039,884 Page 20 Art Unit: 3657 Application/Control Number: 19/039,884 Page 21 Art Unit: 3657 Application/Control Number: 19/039,884 Page 22 Art Unit: 3657 Application/Control Number: 19/039,884 Page 23 Art Unit: 3657 Application/Control Number: 19/039,884 Page 24 Art Unit: 3657 Application/Control Number: 19/039,884 Page 25 Art Unit: 3657 Application/Control Number: 19/039,884 Page 26 Art Unit: 3657 Application/Control Number: 19/039,884 Page 27 Art Unit: 3657 Application/Control Number: 19/039,884 Page 28 Art Unit: 3657 Application/Control Number: 19/039,884 Page 29 Art Unit: 3657
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