DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 19-38 are pending.
Claims 1-18 are canceled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 19-38 are rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of U.S. Patent No. US11100324B2 and the claims of U.S. Patent No. US12056914B2, respectively. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations in the above indicated claims of the instant application are anticipated by the respective claimed limitations in the listed claims of U.S. Patent No. US11100324B2, and by the respective claimed limitations in the listed claims of U.S. Patent No. US12056914B2, respectively. See the claim anticipation mapping below.
Instant application
Claims
Patent US11100324B2
Claims
Patent US12056914B2
Claims
19, 29
1, 5, 9
1
20, 30
1, 5, 9
1
21, 31
2, 6, 10
1, 2
22, 32
2, 6, 10
1, 2
23, 33
2, 6, 10
1, 2
24, 34
1, 5, 9
1
25, 35
1, 5, 9
1
26, 36
1, 5, 9
1
27, 37
4, 8, 12
1
28, 38
3, 7, 11
1, 3
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 19-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US20110116708A1) in view of Qutub et al (US20150071541A1).
Regarding claim 19, Zhou teaches a computer-implemented method comprising:
obtaining an image comprising a plurality of image segments;
(Zhou, "input image is received by block 100", [0019]; "Typical natural images contain multiple regions with each image region being a set of pixels grouped based on homogeneity", [0003]; obtaining an image and segmenting it into a plurality of image regions)
associating individual image segments of the plurality of image segments with information indicative of possible interpretations of the individual image segments;
(Zhou, "computes the assigning probabilities of each region belonging to each object class", [0021]; associating individual regions with probabilities of belonging to an object class, indicating possible interpretations)
generating a plurality of combinations of the plurality of image segments;
(Zhou, "generating multiple hypotheses for region label assignment", [0006]; "multiple hypotheses of region combination are explored and determined", [0028]; generating a plurality of combinations by creating multiple hypotheses of region combinations)
generating, for each combination of the plurality of combinations, a set of relationships between image segments of the combination to produce a plurality of sets of relationships; and
(Zhou, " It also encodes information such as shape (where its boundary is) and context (who are neighboring regions)", [0024]; Qutub, "determining the connectivity of the one or more objects to each other based at least in part on a graphical analysis of the one or more objects", [Abstract]; "The object location and adjacency can be translated to a connectivity graph", [0128]; Zhou teaches encoding context based on neighboring regions, while Qutub explicitly teaches generating sets of relationships in the form of connectivity graphs based on object locations and adjacency)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Qutub into the system or method of Zhou in order to explicitly model network connectivity and relationships across objects using graph-based analysis. The combination of Zhou and Qutub also teaches other enhanced capabilities.
The combination of Zhou and Qutub further teaches:
determining information indicative of a most likely interpretation of the image,
(Zhou, "Image parsing is a process to assign object labels to each region so that the most probable interpretation of the input image can be achieved.", [0025]; determining a most likely interpretation by assigning labels to achieve the most probable interpretation of the image)
wherein determining the information includes processing the possible interpretations based on the plurality of sets of relationships.
(Zhou, "optimizing the label assignment so that an overall assigning probability for all regions is maximized", [0007]; "takes into account not only regions themselves but also the contextual relationship among regions", [0023]; processing the possible interpretations by optimizing the label assignment based on the contextual relationships among the regions)
Regarding claims 20 and 30, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 19, wherein an individual combination of the plurality of combinations includes at least a pair of image segments.
(Zhou, "two nodes are connected if their corresponding regions are adjacent.", [0032]; a combination may include at least a pair of image segments connected through adjacency)
Regarding claims 21 and 31, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 19, further comprising:
generating, for a particular combination, positioning information associated with the image segments of the particular combination, wherein the set of relationships for the particular combination includes the generated positioning information.
(Qutub, "The object location and adjacency can be translated to a connectivity graph", [0128]; the relationships including positioning information via object location translated to the graph)
Regarding claims 22 and 32, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 21, wherein the positioning information includes relative positioning between the image segments of the particular combination.
(Qutub, "Adjacency can be measured by both object-object contact and distance between object centroids", [0128]; obtaining relative positioning by measuring distance between object centroids)
Regarding claims 23 and 33, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 21, wherein the positional information encodes relative positions including one or more of left/right, up/down, top/bottom, parallel/perpendicular, overlap/underlap, or proximity.
(Qutub, "distance between object centroids", [0128]; encoding proximity)
Regarding claims 24 and 34, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 19, wherein determining the information indicative of the most likely interpretation of the image is based on a surviving candidate interpretation of the image from a plurality of candidate interpretations of the image.
(Zhou, "the top best path is taken", [0036]; determining the interpretation based on a surviving candidate by selecting the top best path)
Regarding claims 25 and 35, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 24, further comprising:
generating a textual representation associated with the surviving candidate interpretation.
(Zhou, "Image parsing is a process to assign object labels to each region so that the most probable interpretation of the input image can be achieved.", [0025]; "For example, p(Si=“Same”|si=“Car”) can be learnt by counting how many times in the training set a region with label “Car” with all surrounding regions having label “Car” also.", [0038]; generating a textual representation by outputting text-based object labels, such as the string "Car")
Regarding claims 26 and 36, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 24, wherein the surviving candidate interpretation of the image is determined, at least in part, based on respective quality levels of the plurality of candidate interpretations.
(Zhou, "re-scored by another multi-class linear SVM", [0041]; evaluating candidate interpretations based on quality levels via SVM re-scoring)
Regarding claims 27 and 37, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 19, further comprising:
partitioning the image into the plurality of image segments, wherein partitioning is based on pixel mapping.
(Zhou, "pixels are grouped into super-pixels based on similarities", [0028]; partitioning based on pixel mapping by grouping pixels into super-pixels)
Regarding claims 28 and 38, the combination of Zhou and Qutub teaches its/their respective base claim(s).
The combination further teaches the method of claim 19, wherein at least a subset of combinations of the plurality of combinations is identified as sharing a common aspect, the common aspect including spatial proximity.
(Zhou, "Each region is a set of pixels grouped based on similarities such as adjacency, color and smoothness.", [0024]; combinations share a common aspect including spatial proximity via adjacency)
Regarding claim 29, Zhou teaches a computer-implemented method comprising:
obtaining an image formed from a plurality of image segments,
(Zhou, "input image is received by block 100", [0019]; "Typical natural images contain multiple regions with each image region being a set of pixels grouped based on homogeneity", [0003]; obtaining an image and segmenting it into a plurality of image regions)
wherein individual image segments are associated with information indicative of a set of image segment identigens, and
(Zhou, "computes the assigning probabilities of each region belonging to each object class.", [0021]; "Image parsing is a process to assign object labels to each region", [0025]; associating individual image segments with probabilities and object labels, which represent a set of image segment identigens)
wherein an individual image segment identigen reflects a possible interpretation of an associated image segment;
(Zhou, "semantics (what is the probability of the region belonging to each object class)", [0003]; "Image parsing is a process to assign object labels to each region so that the most probable interpretation of the input image can be achieved.", [0025]; the assigned object labels/classes reflect a possible semantic interpretation of the associated image region/segment)
generating a plurality of combinations of the plurality of image segments;
(Zhou, "generating multiple hypotheses for region label assignment", [0006]; "multiple hypotheses of region combination are explored and determined", [0028]; generating a plurality of combinations by creating multiple hypotheses of region combinations)
generating, for each combination of the plurality of combinations, a set of relationship identigens informing relationships between image segments of the combination; and
(Zhou, "It also encodes information such as shape (where its boundary is) and context (who are neighboring regions).", [0024]; Qutub, "determining, by at least one of the one or more computing devices, the connectivity of the one or more objects to each other based at least in part on a graphical analysis of the one or more objects", [Claim 1]; "The object location and adjacency can be translated to a connectivity graph", [0128]; Zhou teaches encoding context based on neighboring regions, while Qutub explicitly teaches generating sets of relationships in the form of connectivity graphs that inform the relationship between objects/segments)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Qutub into the system or method of Zhou in order to explicitly model network connectivity and relationships across objects using graph-based analysis. The combination of Zhou and Qutub also teaches other enhanced capabilities.
The combination of Zhou and Qutub further teaches:
determining information indicative of an entigen group that represents a most likely interpretation of the image, wherein determining the information includes processing the image segment identigens based on the relationship identigens.
(Zhou, "Image parsing is a process to assign object labels to each region so that the most probable interpretation of the input image can be achieved.", [0025]; "optimizing the label assignment so that an overall assigning probability for all regions is maximized", [0007]; "takes into account not only regions themselves but also the contextual relationship among regions", [0023]; processing the possible interpretations by optimizing the label assignment based on the contextual relationships among the regions to determine the most probable overall interpretation of the image)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 6/12/2026
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