DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s amendment filed 7/2/2026 is acknowledged.
Claims 25-29 are amended.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 8/27/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Response to Amendment
Amendments filed on 7/2/2026 are entered for prosecution. Claims 10-29 remain pending in the application.
Applicant’s amendments to claims 25-29 have overcome the objection to claims 25-29 previously set forth in the Non-Final Action mailed on 4/2/2026.
Applicant was silent on the Examiner’s remarks on the 112(f) claim interpretation noted in the Non-Final Action mailed on 4/2/2026. As such claim interpretation under 112(f) is maintained.
Response to Arguments
Applicant’s response to the Non-Final Action mailed on 4/2/2026 has been fully considered. Applicant’s arguments are persuasive in part. The rejection under 35 U.S.C. §112(b) is modified as discussed below. The rejection under 35 U.S.C. §101 is maintained with clarification. The prior rejection under 35 U.S.C. §103 based upon Kuriyama in view of Petousis is withdrawn and replaced with the rejection set forth below in view of additional prior art. Because the presently applied prior-art combination differs materially from the prior rejection, this Office Action is made NON-FINAL.
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.
Claims 10-29 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 pre-AIA the applicant regards as the invention.
Regarding claims 10, 17, and 24:
Independent claims 10, 17, and 24 respectively recite: “high bandwidth”; “medium bandwidth”; and “low bandwidth”. Applicant argues that these terms are reasonably certain because the Specification provides examples in which high bandwidth may correspond to an urban environment having 5G service, medium bandwidth to a suburban environment having 4G service, and low bandwidth to a rural environment having 2G service. Applicant additionally relies upon exemplary configurations producing approximately 190, 102, and 54 clusters. Applicant’s arguments have been considered but are not persuasive as to these terms.
The Examiner acknowledges that a relative term or term of degree is not indefinite merely because it is relative. The relevant inquiry is whether the Specification provides an objective standard by which a person of ordinary skill in the art can determine the scope of the term with reasonable certainty.
The examples relied upon by Applicant, however, do not provide objective boundaries distinguishing “high”, “medium”, and “low” available bandwidth.
For example, identifying 5G, 4G, and 2G communication environment does not establish a bandwidth value, threshold, range, ratio, or other criterion defining when available bandwidth transitions from low to medium or from medium to high. Indeed, Applicant acknowledges that available bandwidth depends upon communication terminology, network loading, and operating environment.
Similarly, the exemplary 190-, 102-, and 54-cluster configurations describe particular clustering results associated with exemplary operating conditions but do not define the bandwidth thresholds that determine when each claimed clustering operation must occur.
This uncertainty is material because the claims require a different operation depending upon the bandwidth classification. Claim 10, for example, requires forming the first predetermined number of clusters when bandwidth is “high”, the smaller second predetermined number when bandwidth is “medium”, and the still smaller third predetermined number when bandwidth is “low”.
Thus, for a given available bandwidth, the claim does not provide an objective criterion for determining which of the three expressly claimed operations is required.
Accordingly, the rejection is maintained with respect to these terms.
Withdrawal of remaining §112(b) grounds
Applicant’s arguments are persuasive regarding “statistical feature”, “clustering algorithm”, and “representatives for the clusters”. The specification provides exemplary statistical features, including mean, maximum, minimum, and quantile values; identifies multiple clustering algorithms, including DSBCAN, K-means, agglomerative clustering, and mean-shift clustering; and provides exemplary techniques for selecting cluster representatives.
Accordingly, the 112(b) rejection based on separately upon those terms is withdrawn.
Regarding claims 11-16, 18-23, and 25-29:
Claims 11-16, 18-23, and 25-29 remain rejected because they are directly or indirectly dependent upon claims containing the indefinite high/medium/low bandwidth limitations.
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 10-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting additional elements sufficient to integrate the exception into a practical application or amount to significantly more than the exception.
Step 2A, Prong One
Independent claims 10, 17, and 24 recite, in their respective statutory forms, operations including:
sequencing signals into segments; determining statistical features for the segments; clustering signals according to the statistical features using a clustering algorithm; determining cluster representatives; and varying the number of clusters according to bandwidth conditions.
Applicant argues that the Office improperly oversimplified the claimed invention by characterizing it as merely collecting, analyzing, and organizing information.
The argument has been considered.
The rejection is clarified to identify principally a mathematical concept, rather than relaying upon a characterization of the entire claim as a mental process.
The claims expressly require determining statistical characteristics of data and mathematically grouping data according to those characteristics using a clustering algorithm. The number of resulting mathematical groupings is further determined according to another parameter, available bandwidth.
These operations constitute mathematical analysis and mathematical relationships and therefore recite a mathematical concept within the abstract-idea grouping.
Applicant further argues that the amount of data involved makes the operations impractical for performance in the human mind. This argument does not overcome the rejection because the Examiner no longer relies upon the proposition that the claimed process as a whole is practically performable as a mental process. Rather, the claims expressly recite mathematical concepts.
Step 2A, Prong Two
Applicant argues that the claimed mathematical operations are integrated into a practical application because adaptive clustering improves continuous transmission of vehicle signals over networks having changing bandwidth. Applicant relies particularly upon the Specification’s exemplary reduction in cluster count from approximately 190 to 102 or 54 clusters and corresponding reductions in transmitted data.
The argument has been considered but is not persuasive.
The eligibility inquiry is based upon the limitations actually recited by the claims.
Claim 10 does not require a particular technique for measuring available bandwidth, a particular communication protocol, transmitter architecture, channel-control mechanism, packet structure, physical-layer procedure, or network-layer modification.
Nor does claim 10 require the particular approximately 190/102/54 cluster configurations or the approximately 46%/70% reductions relied upon by Applicant.
Rather, the claim broadly uses available bandwidth as an input condition to determine the quantity of mathematical clusters and consequently the quantity of representatives provided for transmission.
The public application itself describes the claimed technique as using bandwidth-adaptive clustering to achieve lossy data compression, with the number of resulting clusters adapted to available bandwidth.
Accordingly, the claims use the mathematical clustering result to determine which and how much information is provided for transmission, but do not require a corresponding technological modification to the transmitter, communication network, or underlying communication protocol.
Applicant additionally argues that “providing the representatives for transmission” is integral to the invention rather than insignificant extra-solution activity.
The Examiner acknowledges the functional relationship between the number of clusters and the amount of representative information provided for transmission. Nevertheless, the claim recites transmission only at the result-oriented level of “providing the representatives for transmission”. It does not require a particular technological transmission mechanism resulting from the mathematical clustering.
The additional elements therefore do not integrate the mathematical concept into a practical application.
Step 2B
Applicant correctly observes that a finding that an additional element is well-understood, routine, and conventional must satisfy the applicable evidentiary requirements. The rejection is clarified accordingly.
The Examiner does not rely upon the mathematical operations themselves – statistical analysis, clustering, and determining the cluster quantity – as additional elements that fail to provide an inventive concept. Those operations form part of the identified judicial exception.
The remaining limitations recite the processing/transmission environment and providing the results of the mathematical analysis for transmission. Considered individually and as an ordered combination with the exception, those limitations do not impose a technological implementation beyond using the output of the recited mathematical process as the information to be transmitted.
The claim also do not incorporate the more specific implementation details relied upon by Application from the Specification.
Conclusion
Accordingly, claims 10, 17, and 24 do not recite significantly more than the judicial exception.
The dependent claims do not add limitations sufficient to alter the eligibility analysis. Accordingly, the rejection of claims 10-29 under §101 is maintained.
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 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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 10, 11, 14-18, 21-25, 28, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Kuriyama (US 2022/0042952 A1, hereinafter Kuriyama) in view of Prieditis (US 10,454,496 B1, hereinafter Prieditis) further in view of Petousis et al. (US 2018/0261020 A1, hereinafter Petousis).
Regarding claim 10:
Kuriyama teaches a method for processing signals in a process of continuous data provision, comprising:
sequencing the signals into segments (see, Kuriyama: Figs. 1, 3, and 4; and para. [0026] [0039] [0040]. Kuriyama teaches a dividing unit 10 that divides a waveform of time-series data into a plurality of partial waveforms. Kuriyama further teaches dividing continuously detected time-series waveform data into partial waveforms using predetermined division numbers and a Ramer Douglas Peucker (RDP) algorithm.”);
determining at least one statistical feature for each of the segments (see, Kuriyama: para. [0027], Kuriyama teaches that feature extraction unit 11 extracts features from each of the plurality of partial waveforms obtained by dividing the time-series data. Kuriyama expressly teaches that a feature of a partial waveform may be a static such as minimum value, maximum value, average value, or standard deviation of data constituting the wave form; Fig. 5 and para. [0040-0041], Kuriyama further describes extracting the features from the divided partial waveforms at step ST2. Accordingly, Kuriyama teaches determining at least one statistical feature for each segment.);
clustering the signals based on the determined statistical features using a clustering algorithm (see, Kuriyama: para. [0028], Kuriyama teaches that clustering unit 12 clusters partial waveforms based on the respective features extracted by feature extraction unit 11 and expressly teaches that the k-means method or K-MN method can be used for clustering.);
determining representatives for the clusters (see, Kuriyama: Fig. 6 and para. [0042-0044], Kuriyama further teaches at step ST3 clustering partial waveforms having similar shapes from a plurality of pieces of continuously detected time-series data as the same state based upon the extracted features. Thus, Kuriyama teaches clustering signals based upon determined features using a clustering algorithm.).
Kuriyama does not explicitly teach wherein providing the representatives for transmission, wherein the number of the clusters is automatically adapted to a changing available bandwidth by: forming a first predetermined number of clusters when a high bandwidth is available, thus transmitting a first predetermined number of representatives; forming a second predetermined number of clusters, less than the first predetermined number, when a medium bandwidth is available, thus transmitting a second predetermined number of representatives; and forming a third predetermined number of clusters, less than the second predetermined number, when a low bandwidth is available, thus transmitting a third predetermined number of representatives.
In the same field of endeavor, Prieditis teaches a compression system employing a clustering model having a number of clusters k (see, Preiditis: Col. 6, lines 23-26). Prieditis teaches generating a reduced/compressed vector using parameters associated with the clusters and expressly teaches that the number of cluster k can be determined based on bandwidth constraints and desired QoE (see, Prieditis: Col. 6, lines 27-28, “Note that the number of clusters k can be determined based on bandwidth constraints and desired QoE.”). Prieditis further teaches subsequently providing the resulting vector 180 to a receiver (see, Preiditis: Col. 6, lines 37-46). Accordingly, Prieditis teaches using clustering for compression, determining the cluster number based upon bandwidth constraints, and providing the resulting reduced representation to a receiver.
In the same field of endeavor, Petousis teaches processing and transmitting vehicle sensor data in response to changing communication-network conditions. Petousis teaches determining network quality and determining bandwidth allocation for communication channels (see, Petousis: para. [0058-0063]). Petousis teaches transforming vehicle sensor data using intelligent, lossless, or lossy compression and transmitting the resulting message data over available communication networks (see, Petousis: para. [0061-0064]). Petousis further teaches transmitting different amounts/sizes of data depending upon available bandwidth, including detecting availability of a high-bandwidth uplink and transmitting large, lower-priority data over the high-bandwidth uplink (see, Petousis: para. [0064]). Petousis additionally teaches updating an estimate of current network quality based upon current network performance (see, Petousis: para. [0070]). Most particularly, Petousis expressly teaches transmitting vehicle sensor data over high-bandwidth, medium-bandwidth, or low-bandwidth network channel, wherein scheduling is based upon network quality prediction and the selected network channel (see, Petousis: para. [0072]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuriyama’s feature-based clustering of signal segments in view of Prieditis such that the number of clusters is selected based upon available bandwidth constraints. Prieditis expressly teaches the desirability of determining cluster number k based upon bandwidth constraints and desired QoE. The modification would predictably permit the amount of cluster-derived information to be reduced when communication resources are constrained. It would further have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ such bandwidth dependent clustering in the variable bandwidth vehicle communication environment of Petousis because Petousis expressly teaches adapting vehicle-data handling and transmission according to available network quality/bandwidth and expressly recognizes high-, medium-, and low-bandwidth network channels.
With respect to “forming a first predetermined number of clusters when a high bandwidth is available, thus transmitting a first predetermined number of representatives; forming a second predetermined number of clusters, less than the first predetermined number, when a medium bandwidth is available, thus transmitting a second predetermined number of representatives; and forming a third predetermined number of clusters, less than the second predetermined number, when a low bandwidth is available”, Prieditis expressly teaches that cluster number k is determined based upon bandwidth constraints (see, Prieditis: Col. 6, lines 27-28), while Petousis expressly teaches high-, medium, and low-bandwidth communication conditions (see, Petousis: para. [0072]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Prieditis’s bandwidth dependent cluster number relationship using respective predetermined values of k for the discrete high-, medium-, and low-bandwidth operating states expressly taught by Petousis. Further, because the purpose of Prieditis’s clustering is compression and the number of clusters controls the size/fidelity of the reduced representation, selecting fewer clusters as available bandwidth becomes more constrained would have been a predictable optimization of the expressly disclosed bandwidth dependent cluster number parameter.
Regarding claim 11:
As discussed above, Kuriyama in view of Prieditis and Petousis teaches all limitations in claim 10.
Prieditis expressly teaches a quantitative clustering parameter k, i.e., the number of clusters, and teaches determining k based upon bandwidth constraints (see, Prieditis: Col. 6, lines 27-28, “Note that the number of clusters k can be determined based on bandwidth constraints and desired QoE.”). Petousis teaches determining bandwidth allocation for communication channels (see, Petousis: para. [0058-0063]), determining threshold data sizes based upon available bandwidth (see, Petousis: para. [0064), and categorizing available communication channels as high-, medium-, and low-bandwidth channels (see, Petousis: para. [0072]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuriyama in view of Prieditis and Petousis to quantitatively define respective predetermined values of k according to corresponding bandwidth thresholds because Prieditis expressly teaches k as a bandwidth-dependent quantitative parameter and Petousis teaches bandwidth-dependent thresholds and discrete bandwidth categories. Establishing numerical thresholds for switching among predetermined k values would have constituted predictable implementation and optimization of the expressly taught bandwidth-dependent relationship.
Regarding claim 14:
As discussed above, Kuriyama in view of Prieditis and Petousis teaches all limitations in claim 10.
Kuriyama further teaches wherein the at least one statistical feature is selected from the group consisting of a mean value, a maximum value, a minimum value, and a quantile (see, Kuriyama: para. [0027], “The feature extraction unit 11 extracts features from each of a plurality of partial waveforms obtained by dividing the time-series data by the dividing unit 10. The features of a partial waveform includes a length, a slope, or a curvature of the partial waveform. In addition, the features of a partial waveform may be a statistic such as a minimum value, a maximum value, an average value, or a standard deviation of data constituting the waveform.”).
Regarding claim 15:
As discussed above, Kuriyama in view of Prieditis and Petousis teaches all limitations in claim 10.
Kuriyama further teaches wherein the clustering employs a method selected from the group consisting of a density-based clustering method, a partitional clustering method, and a hierarchical clustering method (see, Kuriyama: para. [0028], “The clustering unit 12 clusters the partial waveforms on the basis of features of the respective partial waveforms extracted by the feature extraction unit 11. The k-mean method or the K-NN method can be used for clustering. For example, in a case where the machine tool manufactures one product in three processes from the first process to the third process, the clustering unit 12 clusters the partial waveforms corresponding to the first process into the state (1), clusters the partial waveforms corresponding to the second process into the state (2), and clusters the partial waveforms corresponding to the third process into the state (3).”).
Regarding claim 16:
As discussed above, Kuriyama in view of Prieditis and Petousis teaches all limitations in claim 10.
Prieditis expressly teaches determining cluster number k based upon bandwidth constraints (see, Prieditis: Col. 6, lines 27-28, “Note that the number of clusters k can be determined based on bandwidth constraints and desired QoE.”). Petousis teaches determining network quality, including communication bandwidth (see, Petousis: para. [0058-0060],), determining bandwidth allocation for available channels (see, Petousis: para. [0061-0063]), and generating an estimate of current network performance, which is used to update the estimation of current network quality supplied to the scheduling module (see, Petousis: para. [0070]). Petousis further teaches scheduling data according to estimated network quality and forwarding data based upon the quality/bandwidth of the available network (see, Petousis: para. [0069-0070]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuriyama in view of Prieditis and Petousis to repeatedly update Prieditis’s bandwidth-dependent cluster-number setting base upon Petousis’s updated determination of current network quality/bandwidth so that the compression/clustering setting remains appropriate to the currently available communication capacity.
Regarding claim 17:
Claim 17 is directed towards an apparatus for processing signals, comprising: a sequencing module; an analysis module; a clustering module; a selection module; and an output module (see, Kuriyama: Fig. 13B, Processor 102; para. [0082]), configured to perform the method of claim 10. Therefore, claim 17 is rejected by applying the similar rationale used to reject claim 10 above.
Regarding claim 18:
Claim 18 is directed towards the apparatus of claim 17 that is further limited to similar features to claim 11. Therefore, claim 18 is rejected by applying the similar rationale used to reject claim 11 above.
Regarding claim 21:
Claim 21 is directed towards the apparatus of claim 17 that is further limited to similar features to claim 14. Therefore, claim 21 is rejected by applying the similar rationale used to reject claim 14 above.
Regarding claim 22:
Claim 22 is directed towards the apparatus of claim 17 that is further limited to similar features to claim 15. Therefore, claim 22 is rejected by applying the similar rationale used to reject claim 15 above.
Regarding claim 23:
Claim 23 is directed towards the apparatus of claim 17 that is further limited to similar features to claim 16. Therefore, claim 23 is rejected by applying the similar rationale used to reject claim 16 above.
Regarding claim 24:
Claim 24 is directed towards a non-transitory computer-readable medium having computer-executable instructions stored thereon (see, Kuriyama: Fig. 13B, Memory 103; para. [0083]) that, when executed by a processor (see, Kuriyama: Fig. 13B, Processor 102; para. [0082]), perform the method of claim 10. Therefore, claim 24 is rejected by applying the similar rationale used to reject claim 10 above.
Regarding claim 25:
Claim 25 is directed towards the non-transitory computer-readable medium of claim 24 that is further limited to similar features to claim 11. Therefore, claim 25 is rejected by applying the similar rationale used to reject claim 11 above.
Regarding claim 28:
Claim 28 is directed towards the non-transitory computer-readable medium of claim 24 that is further limited to similar features to claim 14. Therefore, claim 28 is rejected by applying the similar rationale used to reject claim 14 above.
Regarding claim 29:
Claim 29 is directed towards the non-transitory computer-readable medium of claim 24 that is further limited to similar features to claim 15. Therefore, claim 29 is rejected by applying the similar rationale used to reject claim 15 above.
Claims 12, 13, 19, 20, 26, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Kuriyama in view of Prieditis further in view of Petousis further in view of Kim (US 2020/0242820 A1, hereinafter Kim).
Regarding claim 12:
As discussed above, Kuriyama in view of Prieditis and Petousis teaches all limitations in claim 10.
Kuriyama in view of Prieditis and Petousis does not explicitly teach wherein transforming a feature space of the determined statistical features into a space having a lower dimension prior to the clustering.
In the same field of endeavor, Kim teaches wherein transforming a feature space of the determined statistical features into a space having a lower dimension prior to the clustering (see, Kim: para. [0051], “Furthermore, the controller 20 may project the point cloud obtained by means of the 3D LiDAR sensor 10 on to a 2D circular grid map (an x-y plane) to be converted into 2D points and may cluster 2D points on the circular grid map based on a size of a reference cell. In this case, the point cloud may be data having 3D coordinate values (x, y, z), but, when the point cloud is projected onto the 2D circular grid map, it may be converted into data (2D points) having x and y values in which a z value is deleted from the 3D coordinate values (x, y, z).”; para. [0052], “Furthermore, the controller 20 may further include a storage (not shown) which stores various logics, algorithms, and programs required to project the point cloud obtained by means of the 3D LiDAR sensor 10 onto the 2D circular grid map to be converted into the 2D points and cluster the 2D points on the circular grid map based on the size of the reference cell.”; para. [0053], “Such a controller 20 may include function blocks, such as a converter 21, a representative point detector 22, and a clustering device 23, and may perform all a function of the converter 21, a function of the representative point detector 22, and a function of the clustering device 23, the functions being described below. In this case, the respective function blocks may be combined with each other to form one function block, and some function blocks may be omitted according to a manner which executes an embodiment of the present disclosure.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuriyama in view of Prieditis and Petousis in combination of the teachings of Kim in order to reduce the dimensionality of the information supplied to the clustering algorithm and thereby reduce processing complexity associated with clustering multidimensional data.
Regarding claim 13:
As discussed above, Kuriyama in view of Prieditis, Petousis, and Kim teaches all limitations in claim 12.
Kim further teaches wherein the transformation of the feature space includes applying principal component analysis to the determined statistical features or selecting at least one determined statistical feature (see, Kim: para. [0051], “Furthermore, the controller 20 may project the point cloud obtained by means of the 3D LiDAR sensor 10 on to a 2D circular grid map (an x-y plane) to be converted into 2D points and may cluster 2D points on the circular grid map based on a size of a reference cell. In this case, the point cloud may be data having 3D coordinate values (x, y, z), but, when the point cloud is projected onto the 2D circular grid map, it may be converted into data (2D points) having x and y values in which a z value is deleted from the 3D coordinate values (x, y, z).”).
Regarding claim 19:
Claim 19 is directed towards the apparatus of claim 17 that is further limited to similar features to claim 12 by a transformation module (see, Kuriyama: Fig. 13B, Processor 102; para. [0082]). Therefore, claim 19 is rejected by applying the similar rationale used to reject claim 12 above.
Regarding claim 20:
Claim 20 is directed towards the apparatus of claim 19 that is further limited to similar features to claim 13. Therefore, claim 20 is rejected by applying the similar rationale used to reject claim 13 above.
Regarding claim 26:
Claim 26 is directed towards the non-transitory computer-readable medium of claim 24 that is further limited to similar features to claim 12. Therefore, claim 26 is rejected by applying the similar rationale used to reject claim 12 above.
Regarding claim 27:
Claim 27 is directed towards the non-transitory computer-readable medium of claim 26 that is further limited to similar features to claim 13. Therefore, claim 27 is rejected by applying the similar rationale used to reject claim 13 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JI-HAE YEA whose telephone number is (571) 270-3310. The examiner can normally be reached on MON-FRI, 7am-3pm, ET.
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/JI-HAE YEA/Primary Examiner, Art Unit 2471
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