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 .
Priority
This application is a 371 of PCT/EP2022/071193 filed on 7/28/2022.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GERMANY 102021208610.1 filed on 8/6/2021.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 1/31/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 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.
Claim 17 recites “a sequencing module; a clustering module; a clustering module; and a selection module”. Claim 19 recites “a transformation module”.
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.
Regarding the limitations, the “sequencing module”; the “clustering module”; the “clustering module”; and the “selection module” recited in claim 17” and the “transformation module” recited in claim 19, it appears that the following are corresponding structures described in the specification: paragraphs [0063], “integrated circuits” or “processor” such as a GPU or a CPU.
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 Objections
Claims 25-29 are objected because of the following informalities:
In claims 25-29, it is suggested to replace “The computer-readable medium of claim …” (line 1) with “The non-transitory computer-readable medium of claim …” for clarity.
Appropriate correction is required.
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:
The terms “high bandwidth”, ‘medium bandwidth”, and “low bandwidth” are relative terms that do not provide objective boundaries for determining the scope of the claim. Additionally, terms such as “statistical feature”, “clustering algorithm”, and “representatives for the clusters” are vague and fail to clearly define the claimed subject matter.
Regarding claims 11-16, 18-23, and 25-29:
Claims 11-16, 18-23, and 25-29 are also rejected because they are directly or indirectly dependent upon the rejected claims, as set forth above.
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 significantly more.
Step 2A, Prong One (Abstract Idea)
Claims 10, 17, and 24 are directed to a method, an apparatus, and a non-transitory computer-readable medium, respectively, for processing signals comprising:
sequencing the signals into segments;
determining at least one statistical feature for each of the segments;
clustering the signals based on the determined statistical features using a clustering algorithm;
determining representatives for the clusters; and
providing the representatives for transmission, wherein the number of the clusters is automatically adapted to a changing available bandwidth.
These limitations describe collecting data, analyzing data, and organizing information, including mathematical operations such as statistical feature determination and clustering.
Such operations fall within the category of mathematical concepts and mental processes, which are recognized as abstract ideas (see MPEP § 2106.04(a)(2)).
Accordingly, claims 10, 17, and 24 recite an abstract idea.
Step 2A, Prong Two (Non Integration into a Practical Application)
The claim does not integrate the abstract idea into a practical application.
Although the claim recites “processing signals”, continuous data provision”, “transmission”, and “available bandwidth”, these elements merely:
provide a generic technological environment, and
do not impose any meaningful limit on the abstract idea.
In particular:
The claim does not recite any specific improvement to signal processing technology, networking protocols, or transmission techniques.
The step of “adapting the number of clusters based on available bandwidth” is result-oriented and lacks details regarding how bandwidth is measured, how adaptation is technically implemented, or how transmission is improved at a technical level.
The “providing the representatives for transmission” limitation merely amounts to insignificant extra-solution activity.
Thus, the claim amounts to applying the abstract idea in a generic technological concept and does not integrate the exception into a practical application (see MPEP § 2106.04(d)).
Step 2B, (Non-Inventive Concept)
The claim does not include additional elements that amount to significantly more than the abstract idea.
The additional elements, including: “continuous data provision”, “processing signals”, “transmission”, and adapting cluster number based on bandwidth conditions are well-understood, routing, and conventional activities in the field of data processing and communication systems.
Furthermore: The claim does not recite any particular machine, does not effect a transformation of an article to a different state, and does not include any unconventional arrangement of elements.
The recited clustering and adaptation based on bandwidth merely represent optimization of data presentation or transmission, which does not constitute an inventive concept.
Conclusion
Accordingly, claims 10, 17, and 24 are not patent-eligible.
Dependent claims 11-16, 18-23, and 25-29 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 11-16, 18-23, and 25-29 are not patent eligible under the same rationale as provided for in the rejection of claims 10, 17, and 24.
Therefore, claims 10-29 are not patent-eligible under 35 USC § 101.
Examiner Note / Suggestion for Amendment
The claim may be amended to overcome this rejection by including:
specific technical details regarding how bandwidth is measured or determined,
a particular improvement to signal processing or communication technology, or
a specific implementation that improves the functioning of a computer or network system.
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, 16-18, 21, 23-25, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Kuriyama (US 2022/0042952 A1, hereinafter Kuriyama) 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: Fig. 1, Dividing Unit 10; para. [0040], “Next, the feature extraction unit 11 extracts features from partial waveforms obtained by dividing the time-series data by the dividing unit 10 (step ST2).”);
determining at least one statistical feature for each of the segments (see, Kuriyama: Fig. 1, Feature Extraction Unit 11; para. [0040], “the feature extraction unit 11 extracts a slope or a curvature of a partial waveform.”);
clustering the signals based on the determined statistical features using a clustering algorithm (see, Kuriyama: Fig. 1, Clustering Unit 12; para. [0042], “the clustering unit 12 clusters the partial waveforms (step ST3). For example, the clustering unit 12 clusters partial waveforms having similar shapes among partial waveforms of a plurality of pieces of continuous time-series data as the same state on the basis of the features of the partial waveforms extracted by the feature extraction unit 11.”);
determining representatives for the clusters (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.”; para. [0043], “the clustering unit 12 clusters partial waveforms similar to the partial waveform A from a plurality of pieces of time-series data continuously detected for each data detection time and divided by the first division number on the basis of the feature of the partial waveform A extracted by the feature extraction unit 11. In addition, the clustering unit 12 clusters partial waveforms similar to the partial waveform B from a plurality of pieces of time-series data continuously detected for each data detection time and divided by the first division number on the basis of the feature of the partial waveform B extracted by the feature extraction unit 11. The clustering unit 12 clusters partial waveforms similar to the partial waveform C from a plurality of pieces of time-series data continuously detected for each data detection time and divided by the first division number on the basis of the feature of the partial waveform C extracted by the feature extraction unit 11. Further, the clustering unit 12 clusters partial waveforms similar to the partial waveform D from a plurality of pieces of time-series data continuously detected for each data detection time and divided by the first division number on the basis of the feature of the partial waveform D extracted by the feature extraction unit 11.”).
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, Petousis teaches wherein providing the representatives for transmission, wherein the number of the clusters is automatically adapted to a changing available bandwidth (see, Petousis: para. [0072], “the method can include: receiving pre-categorized vehicle sensor data, scheduling the vehicle sensor data for transmission without buffering, and transmitting the vehicle sensor data uncompressed over either a high bandwidth, medium bandwidth, or low bandwidth network channel. In this example, scheduling is performed based on network quality prediction, and the selected network (e.g., high bandwidth, medium bandwidth, low bandwidth) channel over which the vehicle sensor data is to be transmitted.”) 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 (see, Petousis: para. [0064], “transmitting message data includes determining a first threshold data size for a first available channel based on available bandwidth, and transmitting all message data under the first threshold size through the first available communication channel in decreasing priority order. The second variation also includes concurrently determining a second threshold data size for a second available channel (e.g., with higher bandwidth than the first communication channel) and transmitting data between the first and second threshold data size through the second communication channel in decreasing priority order.”).
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 combination of the teachings of Petousis in order for transmitting message data by determining a threshold data size for an available channel based on available bandwidth (see, Petousis: para. [0064]).
Regarding claim 11:
As discussed above, Kuriyama in view of Petousis teaches all limitations in claim 10.
Petousis further teaches wherein the first predetermined number of clusters, the second predetermined number of clusters, and the third predetermined number of clusters are quantitatively defined based on the bandwidth thresholds (see, Petousis: para. [0023], “The vehicle system may employ any suitable machine learning including one or more of: …, unsupervised learning (e.g., using an Apriori algorithm, k-means clustering, etc.), …, and any other suitable learning style. Each module of the plurality can implement any one or more of: …, an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), …, a decision tree learning method (e.g., classification and regression tree, …, a clustering method (e.g., k-means clustering, expectation maximization, etc.), …, a self-organizing map method, a learning vector quantization method, etc.), …, and any suitable form of machine learning algorithm. ... Further, any suitable model (e.g., machine learning, non-machine learning, etc.) can be used in classifying vehicle sensor data and/or identifying a level of importance of vehicle sensor data.”; para. [0016], “the data can be prioritized based on the current or anticipated quality of service (e.g., as measured by latency, bandwidth, etc.) of available communication channel(s).”).
Regarding claim 14:
As discussed above, Kuriyama in view of 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 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 16:
As discussed above, Kuriyama in view of Petousis teaches all limitations in claim 10.
Petousis further teaches wherein the automatic adapting of the number of clusters includes adjusting the clustering algorithm settings in real-time based on continuous monitoring of the bandwidth (see, Kuriyama: para. [0016], “The method can confer several benefits. First, the method optimizes use of the limited communication resources available to the computing system by dynamically prioritizing the data to be sent in real- or near-real time. In one variation, the data can be prioritized based on one or more requests or user queries (e.g., received from a remote computing system, wherein the remote computing system can determine the data priority using a cost function, optimization function, bidding system, contextual system, or other system) (or, based on a number of requests or a number of user queries, etc.). This enables an external system to request and receive data, previously demanded asynchronously, from the vehicle in real- or near-real time. In a second variation, the data can be prioritized based on the current or anticipated quality of service (e.g., as measured by latency, bandwidth, etc.) of available communication channel(s). This allows for critical or other high-value data to have preferential real-time transmission, precluding low-value data from consuming limited communication resources.”)
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 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 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 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.
Claims 12, 13, 19, 20, 26, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Kuriyama 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 Petousis teaches all limitations in claim 10.
Kuriyama in view of 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. [0052], “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 Petousis in combination of the teachings of Kim in order for converting the feature space into a lower dimensional space for the purpose of reducing computational load.
Regarding claim 13:
As discussed above, Kuriyama in view of 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 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 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.
Claims 15, 22, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Kuriyama in view of Petousis further in view of Huang et al. (US 2021/0192271 A1, hereinafter Huang).
Regarding claim 15:
As discussed above, Kuriyama in view of Petousis teaches all limitations in claim 10.
Kuriyama in view of Petousis does not explicitly teach 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.
In the same field of endeavor, Huang 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, Huang: para. [0076], “After obtaining the feature vector of the corresponding position of each plane pixel, according to the classification probability result output by the first decoder, the hierarchical clustering algorithm is used to classify the planar pixel and the non-planar pixel for the feature vector, and the segmentation of the planar instance is completed.”).
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 Petousis in combination of the teachings of Huang in order for converting the feature space into a lower dimensional space for the purpose of reducing computational load.
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 29:
Claim 29 is directed towards the 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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SUJOY K KUNDU can be reached on (571) 272-8586. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JI-HAE YEA/Primary Examiner, Art Unit 2471
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