Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is responsive to patent application as filed on 3/4/2024 which is a 371 of PCT/JP2022/006916 filed 02/21/2022, which claims foreign priority to Japanese Pat. App. No: 2021-150368 filed 09/15/2021.
This action is made Non-Final.
Claims 1 – 20 are pending in the case. Claims 1, 19, and 20 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/4/2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 3/4/2024 have been accepted by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 13, 14 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1, 19 and 20 recite at least “a matching integration unit that performs matching of a feature point detected from an input image, wherein the matching integration unit performs the matching of the feature point on a basis of a local feature generated on a basis of a pixel of the feature point and a pixel around the feature point and a global feature generated on a basis of entire pixels of the input image” (claims 19 and 20 recite substantially the same features). These limitations are construed as abstract ideas for being performable in the human mind and/or on paper. A human can certainly observe and match feature points of an image, especially if the image is on paper.
This judicial exception is not integrated into a practical application because the additional limitations of “processing device”, “processor” (from claim 19) and “computer” (from claim 20) are merely generic computing components on which the instructions to implement the abstract idea are applied. Additional limitations directed toward mere instructions to apply the exception to generic computing components, alone or in combination, do not integrate the judicial exception into a practical application (See MPEP§2106.05(f)).
Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology including AI/ML technology, - their collective functions merely provide conventional computer implementation. None of the additional elements "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements identified above, being directed toward mere instructions to apply the exception to generic computing components, alone or in combination, are well-understood routine and conventional, do not provide an inventive concept, and thus, do not amount to significantly more than the judicial exception. Therefore, independent claims 1, 19 and 20 are directed toward ineligible subject matter.
This judicial exception is not integrated into a practical application because claims 19 and 20 do not recite any additional limitations on top of the identified abstract idea(s), and therefore, do not integrate the judicial exception into a practical application (See MPEP§2106.05).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the as noted, there are no additional elements, and thus, do not amount to significantly more than the judicial exception. Therefore, independent claims 19 and 20 are directed toward ineligible subject matter.
Dependent claims 2, 13, 14 and 18 recite additional limitations that are also construed as additional abstract ideas (claims 2, 13, 14), mere instructions to apply the judicial exception to generic computing components (claims 18), or insignificant extra solution activity, and are, therefore, also directed toward ineligible subject matter.
The analysis of dependent claims 2, 13, 14 and 18 has resulted in the determination that these claims recite eligible subject matter.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In summary, Claim 20 recites a program...comprising various steps. The Specification provides the scope of computer readable media as including non-statutory embodiments (software) (0275-276 and 290-291).
Accordingly, Claim 20 fails to recite statutory subject matter under 35 U.S.C. 101.
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 1-20 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.
The terms “performs matching of a feature point” and “on a basis of entire pixels of the input image” in claims 1, 19 and 20 are relative terms which render the claims indefinite. The term “performs matching of a feature point” would require a comparison to feature points from a different image for a matching step to actually take place, such as by using a matching dictionary as recited in claim 9. However, the independent claims do not recite the required feature point to compare to (i.e., ground truth feature point/image) for a matching step to be possible, otherwise the claimed matching step is not a matching step at all it is just an identification step, as is understood by those with ordinary skill in the art. The term “on a basis of entire pixels of the input image” is indefinite, as it is unclear if “entire pixels” means complete pixels wherein partial pixels would not be within the scope of the claim feature, or if “entire pixels” means every pixel of the input image.
The term “learns learning” in claim 10 renders the claim indefinite. The term “learns learning” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
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 (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.
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) 1-3, 5, 8, 9, 13, 14, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boardman (USPUB 20240303833 A1 in view of Tran (USPAT 10,928,830 B1).
(EXAMINER’S INTERPRETATION: In an effort to advance prosecution, and in view of the pending 112 rejection, the Examiner interprets “performs matching of a feature point” to mean matching a feature point by comparing the feature point to other feature points using a matching dictionary. Further, “on a basis of entire pixels of the input image” is interpreted to mean every pixel of an input image).
Claim 1:
Boardman teaches An information processing device comprising: a matching integration unit that performs matching of a feature point detected from an input image, wherein the matching integration unit performs the matching of the feature point on a basis of a local feature generated on a basis of a pixel of the feature point and a global feature generated on a basis of entire pixels of the input image (0041: The Object Identifier module 179 may be configured to perform further automated operations to, for a group of acquired images that represent one or more objects, identify those one or more objects, such as to match those one or more objects to one or more previously modeled objects, and/or to use one or more trained machine learning models to identify at least a type of the object and optionally a particular instance of that type. ... For objects that move (e.g., are carried by or otherwise transported by a transporting vehicle or other transportation mechanism, such as a conveyor belt or crane), the identification of objects moving past one or more cameras (e.g., through an area being monitored) may include identifying a portion of each image (e.g., a subset of the image pixels) that corresponds to the object, and one or more other portions (e.g., with other image pixel subsets) that correspond to at least a portion of a transporting vehicle or other transportation mechanism (and optionally to background information separate from the object and transporting vehicle or other transportation mechanism, such as other objects or other visual data in front of and/or behind and/or beside the object of interest)—such object identification may in at least some embodiments include using one or more machine learning models trained for such object identification tasks (e.g., each specific to one or more types of objects), and/or may include separately modeling a size and/or shape of the transporting vehicle or other transportation mechanism (e.g., when not loaded with any objects) and using the separate model(s) to subtract from a generated model of both an object of interest and the associated transporting vehicle or other transportation mechanism to result in a model of just the object of interest or to otherwise use the separate model(s) to identify the object).
Boardman, by itself, does not seem to completely teach wherein the matching integration unit performs the matching of the feature point on a basis of a local feature generated on a basis of ... a pixel around the feature point.
The Examiner maintains that these features were previously well-known as taught by Xu.
Tran teaches wherein the matching integration unit performs the matching of the feature point on a basis of a local feature generated on a basis of ... a pixel around the feature point (Col 41 ln 19-25, Col 41 ln 54-Col 42 ln 10 and Col 45 ln 52-58: as a step in determining the position of object 814, image-analysis system 806 can determine which pixels of various images captured by cameras 802, 804 contain portions of object 814. In some embodiments, any pixel in an image can be classified as an “object” pixel or a “background” pixel depending on whether that pixel contains a portion of object 814 or not... image-analysis system 806 can quickly and accurately distinguish object pixels from background pixels by applying a brightness threshold to each pixel...Object pixels can thus be readily distinguished from background pixels based on brightness. Further, edges of the object can also be readily detected based on differences in brightness between adjacent pixels, allowing the position of the object within each image to be determined... FIG. 7B shows an exemplary process for identifying the object, while FIG. 7C-7H show in more details the object modeling process. The process checks sensors for object detection and scans the object against 3D library for matches. If a match is found, the process sets the object to the object in the library, and otherwise the process performs a best-guess of what the object is and send the object identification for subsequent 3D modeling use).
Boardman and Tran are analogous art because they are from the same problem-solving area, comparing and identifying image pixels.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Boardman and Tran before him or her, to combine the teachings of Boardman and Tran. The rationale for doing so would have been to obtain the benefit of determining the position of each object in the image.
Therefore, it would have been obvious to combine Boardman and Tran to obtain the invention as specified in the instant claim(s).
Claim 2:
Boardman teaches the matching integration unit determines whether or not the matching of the feature point is successful on a basis of a combined feature obtained by combining the local feature and the global feature (0041: The Object Identifier module 179 may be configured to perform further automated operations to, for a group of acquired images that represent one or more objects, identify those one or more objects, such as to match those one or more objects to one or more previously modeled objects, and/or to use one or more trained machine learning models to identify at least a type of the object and optionally a particular instance of that type. ... For objects that move (e.g., are carried by or otherwise transported by a transporting vehicle or other transportation mechanism, such as a conveyor belt or crane), the identification of objects moving past one or more cameras (e.g., through an area being monitored) may include identifying a portion of each image (e.g., a subset of the image pixels) that corresponds to the object, and one or more other portions (e.g., with other image pixel subsets) that correspond to at least a portion of a transporting vehicle or other transportation mechanism (and optionally to background information separate from the object and transporting vehicle or other transportation mechanism, such as other objects or other visual data in front of and/or behind and/or beside the object of interest)—such object identification may in at least some embodiments include using one or more machine learning models trained for such object identification tasks (e.g., each specific to one or more types of objects).
Claim 3:
Boardman teaches wherein the global feature includes a first global feature generated on a basis of a result of image segmentation performed on the input image (0175: the routine continues to block 608, where it determines if the object is moving past one or more cameras rather than being stationary (e.g., based at least in part on differences in feature tracking movements between an object and data in the background), and if so continues to block 609 to analyze at least the portions of the images corresponding to the object to do feature matching and/or tracking (optionally doing image segmentation to separate subsets of image pixels corresponding to the object from other image pixels), to use that information to align the images into a common coordinate system (e.g., using SfM processing), and to generate at least a partial 3D computer model or other representation of the object).
Claim 5:
Boardman teaches the global feature includes a second global feature generated on a basis of a result of depth estimation performed on the input image (0045: In addition, information such as GPS data or other location data may further be used to determine additional information about an object in some embodiments, such as to assist in determining rough scale information for the object—as one example, location data at different locations on a path or other exterior around the object may be used determine information about the width and/or length of the object, whether alone or in combination with additional data about depth or other distance values of a device or sensor to the object at particular such locations).
Claim 8:
Boardman teaches the matching integration unit learns the combined feature to perform the matching of the feature point (0041: The Object Identifier module 179 may be configured to perform further automated operations to, for a group of acquired images that represent one or more objects, identify those one or more objects, such as to match those one or more objects to one or more previously modeled objects, and/or to use one or more trained machine learning models to identify at least a type of the object and optionally a particular instance of that type. ... For objects that move (e.g., are carried by or otherwise transported by a transporting vehicle or other transportation mechanism, such as a conveyor belt or crane), the identification of objects moving past one or more cameras (e.g., through an area being monitored) may include identifying a portion of each image (e.g., a subset of the image pixels) that corresponds to the object, and one or more other portions (e.g., with other image pixel subsets) that correspond to at least a portion of a transporting vehicle or other transportation mechanism (and optionally to background information separate from the object and transporting vehicle or other transportation mechanism, such as other objects or other visual data in front of and/or behind and/or beside the object of interest)—such object identification may in at least some embodiments include using one or more machine learning models trained for such object identification tasks (e.g., each specific to one or more types of objects).
Claim 9:
Boardman teaches the matching integration unit generates a matching dictionary by accumulating the combined feature and label data in association with each other in a dictionary generation phase, and performs the matching of the feature point using the matching dictionary in a matching phase (0041: identify those one or more objects, such as to match those one or more objects to one or more previously modeled objects, and/or to use one or more trained machine learning models to identify at least a type of the object and optionally a particular instance of that type... As discussed in greater detail elsewhere herein, previously identified objects may change over time with respect to one or more attributes (e.g., shape, size, composition of materials, moisture content, temperature, etc.), and various techniques may be used to determine if an object represented by a group of acquired images corresponds to a changed object that was previously modeled or is instead a new object (e.g., an object that is newly formed after a previous acquisition of images for the same site, an object that is not newly formed but was not previously captured in acquired images, a new object moving through an area that one or more cameras monitor or otherwise have visual coverage of, etc.)—as one example one or more locations may be tracked for each existing object (e.g., GPS coordinates for a boundary of the object and/or a center point or other location within the object's footprint) and used to determine that an object being modeled overlaps at least in part with the location information for a previously identified and tracked object... such object identification may in at least some embodiments include using one or more machine learning models trained for such object identification tasks).
Claim 13:
Boardman teaches a feature point selection unit that selects a target feature point to be subject to the matching from among a plurality of the feature points detected from the input image (0041).
Claim 14:
Boardman, by itself, does not seem to completely teach the feature point selection unit selects the target feature point from among the feature points detected in a still image region determined as a stationary object in the input image.
The Examiner maintains that these features were previously well-known as taught by Tran.
Tran teaches the feature point selection unit selects the target feature point from among the feature points detected in a still image region determined as a stationary object in the input image (Col 29 ln 27-ln59: the road object recognition process performs feature extraction of the sensor reading of the road. Exemplary extractors are detailed below for road objects detected by LIDAR, sonar or radar sensors such as grass, side rail, or reflective panels, for example... The echo signal of the millimeter-wave radar contains two kinds of objects, the moving object with a certain Doppler speed and the stationary object with strong RCS energy. The stationary objects provide surrounding road information, such as road, guardrails, lamp posts and other information, which conventionally are treated as background noise and discarded. Since the target materials and the reflecting surfaces of the objects are different, the received RCS energy values are distinct. This feature can be used for distinguishing vehicles and roadside objects of interest by setting certain RCS energy thresholds. For example, road side guide posts have a unique RCS signature and it can be used to discriminate them from metal posts measurements of traffic signs).
Boardman and Tran are analogous art because they are from the same problem-solving area, comparing and identifying image pixels.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Boardman and Tran before him or her, to combine the teachings of Boardman and Tran. The rationale for doing so would have been to distinguish stationary objects that are background noise from stationary objects of importance.
Therefore, it would have been obvious to combine Boardman and Tran to obtain the invention as specified in the instant claim(s).
Claim 17:
Boardman teaches the feature point selection unit presents a result of image segmentation performed on the input image to a user, and selects the target feature point on a basis of a segmentation region designated by the user (0027: information may be presented or otherwise provided to users regarding various types of determined information, including information about generated computer models and resulting determined attribute values for one or more times. For example, one or more object model generation, attribute determination and verification modules of the IOEA system (e.g., an IOEA system Object Information Visualization and Recipient Interaction module, as discussed in greater detail below with respect to FIGS. 1 and 9 and elsewhere) may generate and provide information for display in a GUI that provides user-selectable controls and other options to allow a user to interactively request or specify types of information to display and to visually review information about one or more objects, such as determined object attribute values at one or more times, and/or information about changes in such object attribute values and the underlying objects).
Claim 18:
Boardman teaches the information processing device is mounted on a mobile object (0024 and 0030: Such described techniques further provide benefits in allowing improved automated navigation of an environment having one or more such objects by mobile devices (e.g., semi-autonomous or fully-autonomous vehicles)... images may be acquired using image acquisition capabilities of various types of devices in various embodiments, including one or more of the following: a mobile device that is carried by a human user as he or she passes around some or all of an object (e.g., a digital camera that takes individual digital photo images and/or digital video consisting of successive frames of digital images, including a camera that is carried by a human user or a body-mounted camera; a device with computing capabilities and image acquisition capabilities, such as a smart phone, a tablet computer, a pad computer, a slate computer, etc.)).
Claim 19:
Claim 19 essentially recites an information processing method performed by a processor to complete the steps of claim 1, and is therefore rejected over Boardman in view of Tran using the same rationale used above in the rejection of claim 1.
Claim 20:
Claim 20 essentially recites a program for causing a computer to function as an information processing device, comprising the steps of claim 1, and is therefore rejected over Boardman in view of Tran using the same rationale used above in the rejection of claim 1.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boardman (USPUB 20240303833 A1 in view of Tran (USPAT 10,928,830 B1) and further in view of Paz-Perez (USPAT 11,823,433 A1 hereinafter Paz).
Claim 4:
Boardman in view of Tran teaches every feature of claim 3.
Boardman, by itself, does not seem to completely teach the image segmentation includes multiclass segmentation using a neural network including an encoder and a decoder.
The Examiner maintains that these features were previously well-known as taught by Paz.
Paz teaches the image segmentation includes multiclass segmentation using a neural network including an encoder and a decoder (Col 2 ln 29-38: Semantic segmentation may be defined as a process of creating a mask, e.g., a per-pixel mask over an image, wherein pixels are assigned (or “segmented”) into a predefined set of semantic classes. Such segmentations may be binary (e.g., a given pixel may be classified as either a ‘person pixel’ or a ‘non-person pixel’), or segmentations may also be multi-class segmentations (e.g., a given pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ or ‘other’). In recent years, the most accurate semantic segmentations have been achieved using convolutional neural networks (CNNs).).
Boardman and Paz are analogous art because they are from the same problem-solving area, comparing and identifying image data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Boardman and Paz before him or her, to combine the teachings of Boardman and Paz. The rationale for doing so would have been to obtain the benefit of accurately identifying objects in an image.
Therefore, it would have been obvious to combine Boardman and Paz to obtain the invention as specified in the instant claim(s).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boardman (USPUB 20240303833 A1 in view of Tran (USPAT 10,928,830 B1) and further in view of Pillai (USPUB 20210118184 A1).
Claim 6:
Boardman in view of Tran teaches every feature of claim 5.
Boardman further teaches a neural network being used to determine the distance of objects in an image (0174).
Boardman, by itself, does not seem to completely teach the depth estimation includes processing based on pixel relative distance estimation using a neural network including an encoder and a decoder.
The Examiner maintains that these features are previously well-known as taught by Pillai.
Pillai teaches the depth estimation includes processing based on pixel relative distance estimation including an encoder and a decoder (0038: the depth model 260 can further include skip connections for providing residual information between the encoder and the decoder to facilitate memory of higher-level features between the separate components. While a particular encoder/decoder architecture is discussed, as previously noted, the depth model 260, in various approaches, may take different forms and generally functions to process the monocular images and provide depth maps that are per-pixel estimates about distances of objects/features depicted in the images).
Boardman and Pillai are analogous art because they are from the same problem-solving area, identifying image data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Boardman and Pillai before him or her, to combine the teachings of Boardman and Pillai. The rationale for doing so would have been to accurately determine the depth of objects in the image.
Therefore, it would have been obvious to combine Boardman and Pillai to obtain the invention as specified in the instant claim(s).
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boardman (USPUB 20240303833 A1 in view of Tran (USPAT 10,928,830 B1) and further in view of Han (USPUB 20080025568 A1).
Claim 15:
Boardman in view of Tran teaches every feature of claim 13.
Boardman, by itself, does not seem to completely teach the feature point selection unit selects the target feature point on a basis of an image spatial distribution histogram of the feature points detected from the input image.
The Examiner maintains that these features were previously well-known as taught by Han.
Han teaches the feature point selection unit selects the target feature point on a basis of an image spatial distribution histogram of the feature points detected from the input image (claim 1: A method for detecting at least one target object in at least one image comprising: receiving a dataset of training samples, said training samples including images of the still target object; selecting regions in the training samples potentially having the still target object; computing an extended histogram of oriented gradient (HOG) feature in an image patch of the selected region to generate an angle dimension and a distance dimension of each pixel in said image patch; and training at least one classifier based on the computed extended HOG feature to detect the still target object in said image patch).
Boardman and Han are analogous art because they are from the same problem-solving area, identifying objects in image data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Boardman and Han before him or her, to combine the teachings of Boardman and Han. The rationale for doing so would have been to accurately identify objects in an image.
Therefore, it would have been obvious to combine Boardman and Han to obtain the invention as specified in the instant claim(s).
Claim 16:
Boardman in view of Tran teaches every feature of claim 13.
Boardman, by itself, does not seem to completely teach the feature point selection unit selects the target feature point on a basis of a depth space distribution histogram of the feature points detected from the input image.
The Examiner maintains that these features were previously well-known as taught by Han.
Han teaches the feature point selection unit selects the target feature point on a basis of a depth space distribution histogram of the feature points detected from the input image (claim 1: A method for detecting at least one target object in at least one image comprising: receiving a dataset of training samples, said training samples including images of the still target object; selecting regions in the training samples potentially having the still target object; computing an extended histogram of oriented gradient (HOG) feature in an image patch of the selected region to generate an angle dimension and a distance dimension of each pixel in said image patch; and training at least one classifier based on the computed extended HOG feature to detect the still target object in said image patch).
Boardman and Han are analogous art because they are from the same problem-solving area, identifying objects in image data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Boardman and Han before him or her, to combine the teachings of Boardman and Han. The rationale for doing so would have been to accurately identify objects in an image.
Therefore, it would have been obvious to combine Boardman and Han to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 7 and 10-12 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form. Some of the most pertinent of those are:
Kang (USPUB20110158518) which discusses identifying and classifying objects in images
Sakumoto (USPAT11610430) which discusses detecting feature points from an image
Sung (USPAT11580617) which discusses generating a feature point map of an input image
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED-IBRAHIM ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached on M-Th 8-6 Fri: 7-12/OFF.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steph Hong can be reached on (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMED H ZUBERI/ Primary Examiner, Art Unit 2178
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