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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Objections
Claims 6, 16, and 18 are objected to because of the following informalities:
Regarding claim 6, the claim states the following limitation “determine a matching performance of the at least one of the descriptor cluster centers…”. The second “of the” is redundant and should be removed.
Regarding claim 16, the claim states the following limitation “determine a position based on the at least one of the plurality of descriptor cluster centers.”. The first “the” is redundant and should be removed.
Regarding claim 18, the claim states the following limitation “…determine the at least one of the plurality of the plurality of descriptor….” The first “the” is redundant and should be removed.
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.
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.
Claims 9, 16, and 20 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.
Claim 9, states the following limitations “adjust a weight of the at least one descriptor cluster center based on the matching performance; weight the at least one descriptor cluster center based on the weight;”. Examiner is unsure what applicant means by “weight the at least one descriptor cluster center based on the weight”, when the at least one descriptor cluster center is already weighted. Does applicant mean reweighing the at least one descriptor cluster center based on the adjusted weight? Therefore, examiner is interpreting the limitation to mean weighting the at least one descriptor cluster center again. Thus, the claim will be examined as best understood by the Examiner.
Claim 16, states the following limitation “determine a position based on the at least one of the plurality of descriptor cluster centers”. Examiner is unsure what applicant means by “a position” and what position is referring to. Does applicant mean a position in the image or the position of the descriptor cluster centers? Therefore, examiner determines by broadest reasonable interpretation that “a position” is any position in association with the descriptor cluster centers. Thus, the claim will be examined as best understood by the Examiner.
Claim 20, states the following limitation “...to the display an extended reality image that includes the object.”. Examiner is unsure what applicant means by “the object” as reference to an object is not stated in any other claim. Did applicant mean to claim the extended reality image includes an object? Therefore, examiner determines by broadest reasonable interpretation that the extended reality image includes an object. Thus, the claim will be examined as best understood by the Examiner.
Claims 17-20 inherit their indefiniteness from claim 16 from which they depend.
Claims 17-20 will also be examined as best understood by the Examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6, 8, 10-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jothi et al. United States Patent Application Publication 20210019557 A1 (hereinafter Jothi).
Regarding claim 1, Jothi teaches An apparatus (Analysis System 134 Fig. 10 or Smart Home 100 Fig. 1) comprising:
a non-transitory, machine-readable storage medium (Storage Devices/Memory Para. 0163 and 0170) storing instructions;
and at least one processor (Processors 10002, Para. 0218) coupled to the non-transitory, machine- readable storage medium, the at least one processor being configured to execute the instructions to (Fig. 10):
apply a first clustering process (Any Clustering Algorithm Para. 0067, Pre-Processing Para. 0068 and 0084-0085, or Initial/Further Clustering Processes Para. 0034-0036) to a plurality of descriptors (Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) associated with a geographical location (Para. 0085, 0106, and 0109) to determine a number of descriptor clusters; The location can be used to cluster data, 0085, 0106, and 0109.
apply a second clustering process(Any Clustering Algorithm Para. 0067 or Initial/Further Clustering Processes Para. 0034-0036) to the number of descriptor clusters to determine a descriptor cluster center (Centre of Clusters or Centroids, Para. 0069-0070) for each of the number of descriptor clusters; Clustering is repeated at defined intervals, based on availability of data, or when the next batch of data is received, Para. 0071.
generate descriptor cluster data characterizing a similarity (Para. 0012-0013, 0023, 0068, and 0100-0101) between the plurality of descriptors (Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and the descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070); A distance or similarity metric is used to compare data records to cluster centers, Para. 0100-0101
and store the descriptor cluster data in a The data is stored in memory or persistent storage, Para. 0066, 0163, and 0170-0172.
While Jothi fails to explicitly teach a data repository. Jothi teaches storing large amounts of data in memory or persistent storage, Para. 0066. The data stored encompasses the results of the clustering processes (Para. 0066 and 0170-0172) and the stored data records (Para. 0034 and 0163). Jothi further teaches the data records are collections of data elements that can be in several different data structures or representations, Para. 0008. As well as the persistent storage, server, and system architecture can include other hardware/software/data known to one skilled in the art, Para. 0221-0222. Data repositories are centralized locations where data is stored, managed, and accessed and are often used to store large volumes of data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jothi’s System Architecture to incorporate a Data Repository. Since doing so would provide the benefit of storing large quantities of data that are in a variety of types. As key attributes of a data repository are data preservation and the flexibility to store a wide range of data.
Regarding claim 2, Jothi teaches the apparatus of claim 1, wherein the at least one processor(Processors 10002, Para. 0218) is further configured to execute the instructions to generate the descriptor cluster data to include a plurality of values (Distance or Similarity Metric, Para. 0068, 0074, 0100-0101, and 0131) characterizing the similarity(Para. 0012-0013, 0023, 0068, and 0100-0101) between the plurality of descriptors(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and the descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070), wherein each of the plurality of values (Distance or Similarity Metric, Para. 0068, 0074, 0100-0101, and 0131) characterizes the similarity between one of the plurality of descriptors (Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and one of the descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070).
Regarding claim 3, Jothi teaches the apparatus of claim 2, wherein each of the plurality of values (Distance or Similarity Metric, Para. 0068, 0074, 0100-0101, and 0131) identifies a probability that one of the plurality of descriptors(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) belongs to one of the descriptor cluster centers (Centre of Clusters or Centroids, Para. 0069-0070). The distance or similarity metrics include probability information, Para. 0100 and 0131.
Regarding claim 4, Jothi teaches the apparatus of claim 1, wherein the at least one processor is further configured to execute the instructions to:
receive, from a remote device (Smart Home Device Para. 0085, Client System Receiving Data from Connected Devices, Para. 0038), location data characterizing the geographic location(Para. 0085, 0106, and 0109);
and in response to receiving the location data(Para. 0085, 0106, 0109), transmit (Para. 0015, 0038, 0070-0071) the descriptor cluster data (Distance/Similarity Metric Data matching Data Records and Centre of Clusters or Centroids) to the remote device(Smart Home Device Para. 0085, Client System Receiving Data from Connected Devices, Para. 0038). Cluster Data can be transmitted to the Smart Home Control System and Client Systems, Para. 0015, 0038, 0070-0071. New batches of data received from remote devices (Smart Home Devices or Control Systems) transmit updated cluster data back to the remote devices(Smart Home Devices or Control Systems), Para. 0070-0071.
Regarding claim 5, Jothi teaches the apparatus of claim 4, wherein the at least one processor is further configured to execute the instructions to:
receive descriptor matching data (Data used to Classify New Data Records – Cluster Definitions Para. 0074 or Cluster Specification Data Para. 0013) from the remote device(Smart Home Device Para. 0085, Client System Receiving Data from Connected Devices, Para. 0038), the descriptor matching data characterizing a matching result of the descriptor cluster data (Distance/Similarity Metric Data matching Data Records and Centre of Clusters or Centroids); The cluster definitions and Cluster Specification Data are used to classify new data, Para. 0037-0038.
and adjust (Update) the descriptor cluster data(Distance/Similarity Metric Data matching Data Records and Centre of Clusters or Centroids) in the (Data used to Classify New Data Records – Cluster Definitions Para. 0074 or Cluster Specification Data Para. 0013). Cluster Definitions are updated when clustering is repeated, Para. 0071. New records are classified by assigning them to a cluster using the Distance/Similarity Metric Data, Para. 0074.
While Jothi fails to explicitly teach a data repository. Jothi teaches storing large amounts of data in memory or persistent storage, Para. 0066. The data stored encompasses the results of the clustering processes (Para. 0066 and 0170-0172) and the stored data records (Para. 0034 and 0163). Jothi further teaches the data records are collections of data elements that can be in several different data structures or representations, Para. 0008. As well as the persistent storage, server, and system architecture can include other hardware/software/data known to one skilled in the art, Para. 0221-0222. Data repositories are centralized locations where data is stored, managed, and accessed and are often used to store large volumes of data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jothi’s System Architecture to incorporate a Data Repository. Since doing so would provide the benefit of storing large quantities of data that are in a variety of types. As key attributes of a data repository are data preservation and the flexibility to store a wide range of data.
Regarding claim 6, Jothi teaches the apparatus of claim 5, wherein the descriptor matching data(Data used to Classify New Data Records – Cluster Definitions Para. 0074 or Cluster Specification Data Para. 0013) comprises a number of descriptor(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) matches for at least one of the descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070), wherein at least one processor(Processors 10002, Para. 0218) is configured to execute the instructions to:
determine a matching performance (Number of Clusters matched to Partitions, Para. 0017-0021) of the at least one of the descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070) based on the number of descriptor(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) matches;
and adjust the descriptor cluster data based on the matching performance. (Number of Clusters matched to Partitions, Para. 0017-0021) The number of cluster centers is modified based on if the partitions are less/more than the cluster count. Modifying the number of cluster centers, modifies the descriptor cluster data.
Regarding claim 8, Jothi teaches the apparatus of claim 1, wherein the at least one processor is configured to execute the instructions to:
receive from a plurality of remote devices(Smart Home Device Para. 0085, Client System Receiving Data from Connected Devices, Para. 0038) descriptor matching data(Data used to Classify New Data Records – Cluster Definitions Para. 0074 or Cluster Specification Data Para. 0013) for the geographic location(Para. 0085, 0106, and 0109), the descriptor matching data characterizing a matching result of at least one descriptor cluster center(Centre of Clusters or Centroids, Para. 0069-0070) to a number of features(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008), and a statistical measure(Distance/Similarity Metric Data matching Data Records and Centre of Clusters or Centroids) based on a number of features(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) successfully matched to the at least one descriptor cluster center(Centre of Clusters or Centroids, Para. 0069-0070);
determine a matching performance(Number of Clusters matched to Partitions, Para. 0017-0021) for the geographic location(Para. 0085, 0106, and 0109) based on the matching result of the at least one descriptor cluster center(Centre of Clusters or Centroids, Para. 0069-0070) to the number of features(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008), and the statistical measure(Distance/Similarity Metric Data matching Data Records and Centre of Clusters or Centroids);
and adjust the descriptor cluster data based on the matching performance (Number of Clusters matched to Partitions, Para. 0017-0021). The number of cluster centers is modified based on if the partitions are less/more than the cluster count. Modifying the number of cluster centers, modifies the descriptor cluster data.
Regarding claim 10, claim 10 is the method of apparatus claim 1, therefore it is rejected under the same rationale as claim 10.
Regarding claim 11, has similar limitations as of claim 2, therefore it is rejected under the same rationale as claim 2.
Regarding claim 12, has similar limitations as of claim 4, therefore it is rejected under the same rationale as claim 4.
Regarding claim 13, has similar limitations as of claim 5, therefore it is rejected under the same rationale as claim 5.
Regarding claim 15, has similar limitations as of claim 8, therefore it is rejected under the same rationale as claim 8.
Claim(s) 7, 9, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jothi et al. United States Patent Application Publication 20210019557 A1 (hereinafter Jothi) in view of NPL “Automatic Semantic Image Classification and Retrieval Based on the Weighted Feature Algorithm” by Keping Wang, Xiaojie Wang, Ke Zhang, and Yixin Zhong (hereinafter Wang).
Regarding claim 7, Jothi teaches the apparatus of claim 5, wherein the at least one processor is further configured to execute the instructions to:
(Para. 0100) the descriptor cluster centers (Centre of Clusters or Centroids, Para. 0069-0070) based on the descriptor matching data(Data used to Classify New Data Records – Cluster Definitions Para. 0074 or Cluster Specification Data Para. 0013);
apply the first clustering process(Any Clustering Algorithm Para. 0067, Pre-Processing Para. 0068 and 0084-0085, or Initial/Further Clustering Processes Para. 0034-0036) to the plurality of descriptors (Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and the (Centre of Clusters or Centroids, Para. 0069-0070) to determine a second number of descriptor clusters (Next Iteration of Clusters for New Data Records); Clustering is performed iteratively and is repeated until a termination criterion is met, Para. 0101 and 0135. The previous iteration cluster centers are used in the next iteration, Para. 0024.
apply the second clustering process(Any Clustering Algorithm Para. 0067 or Initial/Further Clustering Processes Para. 0034-0036) to the second number of descriptor clusters(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) to determine a second descriptor cluster center (Centre of Clusters or Centroids, Para. 0069-0070) for each of the second number of descriptor clusters;
and adjust the descriptor cluster data to characterize a similarity(Para. 0012-0013, 0023, 0068, and 0100-0101) between the plurality of descriptors(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and the second number of descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070).
However, Jothi fails to explicitly teach weighting descriptor cluster centers.
Jothi and Wang are analogous to the claimed invention because both of them are in the same field of clustering features using a clustering algorithm similar to K-Means.
Wang teaches weighting descriptor cluster centers (Sections: A. Weighted Feature Selection and B. Cluster Center Initialization Algorithm, Pages 88-89). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jothi’s Clustering Algorithm to incorporate Wang’s Weighting of Clusters Centers. Since doing so would provide the benefit of avoiding irrelevant features from dominating the clustering algorithm, improving performance of the classification (Wang et al, Section: II. Proposed Approach Page 88).
Regarding claim 9, Jothi teaches the apparatus of claim 8, wherein the at least one processor is configured to execute the instructions to:
adjust a (Para. 0100) of the at least one descriptor cluster center(Centre of Clusters or Centroids, Para. 0069-0070) based on the matching performance(Number of Clusters matched to Partitions, Para. 0017-0021);
(Para. 0100) the at least one descriptor cluster center(Centre of Clusters or Centroids, Para. 0069-0070) based on the
apply the first clustering process(Any Clustering Algorithm Para. 0067, Pre-Processing Para. 0068 and 0084-0085, or Initial/Further Clustering Processes Para. 0034-0036) to the plurality of descriptors(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and the descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070) to determine a second number of descriptor clusters(Next Iteration of Clusters for New Data Records), the descriptor cluster centers comprising the (Centre of Clusters or Centroids, Para. 0069-0070);
apply the second clustering process(Any Clustering Algorithm Para. 0067 or Initial/Further Clustering Processes Para. 0034-0036) to the second number of descriptor clusters(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) to determine a second descriptor cluster center for each of the second number of descriptor clusters(Centre of Clusters or Centroids, Para. 0069-0070);
and adjust the descriptor cluster data to characterize a similarity(Para. 0012-0013, 0023, 0068, and 0100-0101) between the plurality of descriptors(Data Records – Vectors, Fields, Attributes, Features, etc.., Para. 0008) and the second number of descriptor cluster centers(Centre of Clusters or Centroids, Para. 0069-0070).
However, Jothi fails to explicitly teach weighting descriptor cluster centers.
Jothi and Wang are analogous to the claimed invention because both of them are in the same field of clustering features using a clustering algorithm similar to K-Means.
Wang teaches weighting descriptor cluster centers (Sections: A. Weighted Feature Selection and B. Cluster Center Initialization Algorithm, Pages 88-89). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jothi’s Clustering Algorithm to incorporate Wang’s Weighting of Clusters Centers. Since doing so would provide the benefit of avoiding irrelevant features from dominating the clustering algorithm, improving performance of the classification (Wang et al, Section: II. Proposed Approach Page 88).
Regarding claim 14, has similar limitations as of claim 7, therefore it is rejected under the same rationale as claim 7.
Claim(s) 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over IDS Reference - Yalla et al. United States Application Publication 20190146500 A1(hereinafter Yalla).
Regarding claim 16, Yalla teaches an apparatus comprising:
a non-transitory, machine-readable storage medium (Memory 344, Para. 0060 and 0226) storing instructions;
and at least one processor (Processor 708, Para 0176-0177) coupled to the non-transitory, machine- readable storage medium (Fig. 7), the at least one processor being configured to execute the instructions to:
generate an image descriptor based on an image (Para. 0178);
receive descriptor cluster data (Cluster Data Generated by K-means Clustering, Para. 0179-182), wherein the descriptor cluster data characterizes a similarity between a plurality of descriptors (Image Descriptors, Para. 0178) and a plurality of descriptor cluster centers (Cluster Centers); K-mean clustering creates clusters of the descriptors, Para. 0180.
determine at least one of the plurality of descriptor cluster centers(Cluster Centers) based on the image descriptor (Image Descriptors, Para. 0178) and the descriptor cluster data (Data Generated by K-means Clustering, Para. 0179-182); K-mean clustering generate cluster centers, Para. 0180.
and determine a position (Position of the Words associated with Image Descriptors Para. 0182 or Position of Cluster Centers in Clusters) (Cluster Centers). Using the K-means algorithm position/orientation is retrieved faster, Para. 0182.
While Yalla fails to explicitly teach determine a position based on the at least one of the plurality of descriptor cluster centers. Yalla teaches utilizing k-means clustering on the image descriptors which classify image features as words. The k-means algorithm partitions observations into clusters and cluster centers are used to model the data, Para. 0179-0180. Corresponding positions/orientation information of the images are collected, Para. 0181. The inverted file indexing method is used to store the words associated with the images that have been clustered by the k-means algorithm, Para. 0181-0182. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yalla’s Inverted File Indexing Method to incorporate Position Information Associated with Cluster Centers. Since doing so would provide the benefit of retrieving position/orientation is faster, Para. 0182.
Regarding claim 17, Yalla teaches the apparatus of claim 16, wherein the at least one processor(Processor 708, Para 0176-0177) is further configured to execute the instructions to:
compute a distance (I-nearest Neighbor Classifier or Nearest Mean, Para. 0180) between the image descriptor (Image Descriptors, Para. 0178) and each of the plurality of descriptor cluster centers (Cluster Centers);
and determine the at least one of the plurality of descriptor cluster centers (Cluster Center) based on the distances(I-nearest Neighbor Classifier or Nearest Mean, Para. 0180). K-means clustering computes the distance between the observation and the cluster/centroid/center by finding the nearest mean, Para. 0180.
Regarding claim 18, Yalla teaches the apparatus of claim 17, wherein the descriptor cluster data(Data Generated by K-means Clustering, Para. 0179-182) comprises a plurality of values, wherein each of the plurality of values identifies a probability (Gaussian Distributions or Expectation-Maximation Algorithm, Para. 0180) that one of the plurality of descriptors(Image Descriptors, Para. 0178) belongs to one of the descriptor cluster centers (Cluster Centers), and wherein the at least one processor(Processor 708, Para 0176-0177) is further configured to execute the instructions to determine the at least one of the plurality of descriptor cluster centers (Cluster Centers) based on the plurality of values (Gaussian Distributions or Expectation-Maximation Algorithm, Para. 0180). K-means Clustering that utilizes Gaussian Distributions or Expectation-Maximation Algorithms utilizes probabilities in determining clusters.
Regarding claim 19, Yalla teaches the apparatus of claim 16 comprising at least one camera (Camera or Other Sensors, Para. 0031), wherein the at least one camera is configured to capture the image (Para. 0031 and 0033).
Regarding claim 20, Yalla teaches the apparatus of claim 16 comprising a display (Display Devices 372 Para. 0061, Vehicle Operation Display 420 Para. 0091, Auxiliary Display 424 Para. 0091, Heads-up Display 434 Para. 0091, or Power Management Display 428), wherein the at least one processor is further configured to execute the instructions to provide to the display an (Para. 0074) that includes the object (Para. 0044 and 0074).
While Yalla fails to explicitly teach an extended reality image. Yalla teaches several displays that can be configured to present a variety of information. The information can be virtual or overlayed information on a transparent displays, Para. 0091 and 0094. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yalla’s Images Displayed to incorporate Extended Reality Images Since doing so would provide the benefit of displaying information to a user while the user is still able to view the physical environment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
NPL “Weighted Clustering Ensemble: A Review” by Mimi Zhang(hereinafter Zhang) teaches an overview of various clustering algorithms they use weighted values.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIANNA R COCHRAN whose telephone number is (571)272-4671. The examiner can normally be reached Mon-Fri. 7:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at (571) 272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIANNA RENAE COCHRAN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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