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 .
Filed IDS of 12/28/2023 has been entered and considered.
Claims 1-10 are currently pending.
Please refer to the action below.
Examiner Notes
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. However, the claimed subject matter, not the specification, is the measure of the invention.
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 1-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) 1, recite(s) mental processes and software processes directed to an apparatus, and a processor.
Independent claim 1 includes limitations that recite an abstract idea. Claim 1 recites: an information processing apparatus comprising at least one processor, the at least one processor carrying out: an acquiring process of acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto; a score assigning process of assigning, to each of unit regions, a score which indicates objectness, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object; a first pseudo label assigning process of assigning a pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions; a second pseudo label assigning process of assigning a pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions; and an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background.
This judicial exception is not integrated into a practical application because the claims merely recite mental steps that can be performed by a person and/or software steps that can be performed by component or units of a software. That is, other than reciting “an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background” nothing in the claim element precludes the steps from practically being performed in the mind and/or purely by software. The additional elements of “an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background” does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence, claim 1 is not subject matter eligible.
Claim 1 recites similar limitations to those discussed above with regards to independent claims 9-10, and therefore discussion is omitted for brevity. Hence, independent claims 1, and 9-10 are not subject matter eligible.
The dependent claims 2-8 do not recite any further limitations that cause the claim(s) to be subject matter 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. Based on broadest reasonable interpretation of the claims, all of the steps recited in the independent claims 1 and currently in the dependent claims 2-8 correspond to concepts performed by at least software components which may be further performed in the human mind. Additionally, a person can mentally performed in the human mind and/or software finetuning the model parameter according to assigned pseudo labels attached to a partial region of a region having thereto an attached ground truth label or to a region having a pseudo label attached to a background region according to the calculated score. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Concepts performed in the human mind have been identified in the 2019 PEG as an exemplar in the “Mental Process” grouping of abstract ideas. For the reasons above, the claims do not amount to significantly more than an abstract idea. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept and therefore, the claims are not patent-eligible.
Furthermore, these additional generic hardware elements perform no more than their basic computer function. Generic computer‐implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding generic hardware elements are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional generic hardware elements taken into consideration individually and in combination fail to amount to significantly more than the abstract idea above.
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.
Claim(s) 1-2, 4, 6, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable and obvious over Yang et al. (NPL, cited in IDS), in view of Leng et al. (US 2022/0156585, A1).
Regarding claim 1, Yang teaches an information processing apparatus comprising at least one processor (the at least Abstract and Figs. 1-4, and section 3.1, and 4 teaches an object detection model system through weakly supervised learning further comprising said apparatus and an implied processing means conducted with use of images in each of which a rectangular bounding box region containing the image of an article has a ground-truth label attached thereto),
the at least one processor carrying out:
an acquiring process of acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto (the acquired attached training labeled images of further section 3.1, and 4, and the Abstract further comprising said acquiring training images in which a region containing an image of an object has a ground-truth label attached thereto);
a score assigning process of assigning, to each of unit regions, a score which indicates objectness, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object (section 3.1 further teaches at least calculated confidence score P and a reliable segmentation confidence value as at least said assigned score assigning process of assigning, to each of unit regions, a score which indicates an uncertainty objectness score and one of a certainty objectness score, by inputting obviously the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object);
a first pseudo label assigning process of assigning a pseudo label which indicates an object, to a partial region of the region partial region of the region, in accordance with the score assigned to each of the unit regions);
a second pseudo label assigning process of assigning a pseudo label which indicates a background, t
an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background (section 3.1 further teaches finetuning the model using the assigned pseudo labels by obviously an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background).
Yang is silent regarding the above lined-out items such as said first pseudo label assigning process of assigning a pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions; and said second pseudo label assigning process of assigning a pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions.
Leng in at least para. 0048 a score assigning process of assigning, to each of bounding boxes regions as each bounding box may obviously be divided into a plurality or subregions further indicative of the partial regions, a score which indicates objectness to a region, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object, Leng further teaches at least in para. 0048 and Figs. 2A-2B first pseudo label assigning process of assigning a pseudo label which indicates an object, to a background or foreground region indicating in a case said partial region of the region having the ground-truth label attached thereto, in accordance with the confidence score assigned to each of the unit regions and further generate at least one or more second pseudo label assigning process of assigning a pseudo label which indicates a background, to an unlabeled region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions and ultimately perform an updating process of para. 0040-0042 and Figs. 2A-2B updating at least a model parameter of the image region detection model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yang in view of Leng to include wherein first pseudo label assigning process of assigning a pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions; and said second pseudo label assigning process of assigning a pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions, as discussed above, as Yang in view of Leng are in the same field of endeavor of employing neural networks methods and systems for acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto and assigning a score to each of region which indicates background and/or foreground objectness as a region corresponding to an image of an object, Leng augments the adjusted or finetuning parameters of the object detection model of Yang with complemented pseudo label assignment processes to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions and further assigning one or more second pseudo label assigning process of assigning a pseudo label which indicates a background to a partial unlabeled region of a region not having the ground-truth label attached thereto which when combined and used with the method of Yang further suppress misdetection and mislearning a foreground region for a background region based on further in accordance with the score assigned to each of the unit regions where the system further updated the model not just for a region of a low score or a high score but updating with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background according to further known means and methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 2 (according to claim 1), Yang further teaches wherein in the first pseudo label assigning process, the at least one processor assigns the pseudo label which indicates an object, to a unit region having been assigned the score that falls within a range of scores including a highest score, the range corresponding to a predetermined proportion, in the region having the ground-truth label attached thereto (at least section 3.1 of the disclosure calculates a score Ym as an assigned pseudo label which indicates an object, to a unit region having been assigned the score that falls within a range of scores from 0 to 1 including a highest score of at least 0.7, the range corresponding to a predetermined proportion, in the region having the ground-truth label attached thereto).
Regarding claim 4 (according to claim 1), Yang further teaches wherein in the second pseudo label assigning process, the at least one processor assigns the pseudo label which indicates a background, to a unit region having been assigned the score that falls within a range of scores including a lowest score, the range corresponding to a predetermined proportion, in the region not having the ground-truth label attached thereto (at least section 3.1 of the disclosure further calculates a score Ym as an assigned pseudo label which indicates in a case a background object, which falls obviously within a range of scores including a lowest score, the range corresponding to a predetermined proportion, in the region not having the ground-truth label attached thereto).
Regarding claim 6 (according to claim 1), Yang is silent regarding wherein in the first pseudo label assigning process, the at least one processor: assigns the pseudo label which indicates an object, to the partial region of the region having the ground- truth label attached thereto, in accordance with the score assigned to each of the unit regions; and further assigns the pseudo label which indicates an object, to the partial region of the region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions (assigned bounding boxes and pseudo labels of further section 3.1 may obviously comprises in the art first pseudo label which indicates a background object, to said partial region of the bounding box region having the ground- truth label attached thereto, in accordance with the score assigned to each of the unit regions and further may obviously further assigns a foreground pseudo label which indicates an object, to said partial region of the region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions).
Regarding claim 9, Yang teaches in at least the Abstract an information processing method comprising:
at least one processor acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto (the acquired attached training labeled images of further section 3.1, and 4, and the Abstract further comprising said acquiring training images in which a region containing an image of an object has a ground-truth label attached thereto);
the at least one processor assigning, to each of unit regions, a score which indicates objectness, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object (section 3.1 further teaches at least calculated confidence score P and a reliable segmentation confidence value as at least said assigned score assigning process of assigning, to each of unit regions, a score which indicates an uncertainty objectness score and one of a certainty objectness score, by inputting obviously the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object);
the at least one processor assigning a pseudo label which indicates an object, to a partial region of the region
the at least one processor assigning a pseudo label which indicates a background, t
the at least one processor updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background (section 3.1 further teaches finetuning the model using the assigned pseudo labels by obviously an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background).
Yang is silent regarding the above lined-out items such as said the at least one processor assigning said pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with said score assigned to each of the unit regions; and said at least one processor assigning said pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions.
Leng in at least para. 0048 a score assigning process of assigning, to each of bounding boxes regions as each bounding box may obviously be divided into a plurality or subregions further indicative of the partial regions, a score which indicates objectness to a region, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object, Leng further teaches at least in para. 0048 and Figs. 2A-2B first pseudo label assigning process of assigning a pseudo label which indicates an object, to a background or foreground region indicating in a case said partial region of the region having the ground-truth label attached thereto, in accordance with the confidence score assigned to each of the unit regions and further generate at least one or more second pseudo label assigning process of assigning a pseudo label which indicates a background, to an unlabeled region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions and ultimately perform an updating process of para. 0040-0042 and Figs. 2A-2B updating at least a model parameter of the image region detection model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yang in view of Leng to include wherein said the at least one processor assigning said pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with said score assigned to each of the unit regions; and said at least one processor assigning said pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions, as discussed above, as Yang in view of Leng are in the same field of endeavor of employing neural networks methods and systems for acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto and assigning a score to each of region which indicates background and/or foreground objectness as a region corresponding to an image of an object, Leng augments the adjusted or finetuning parameters of the object detection model of Yang with complemented pseudo label assignment processes to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions and further assigning one or more second pseudo label assigning process of assigning a pseudo label which indicates a background to a partial unlabeled region of a region not having the ground-truth label attached thereto which when combined and used with the method of Yang further suppress misdetection and mislearning a foreground region for a background region based on further in accordance with the score assigned to each of the unit regions where the system further updated the model not just for a region of a low score or a high score but updating with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background according to further known means and methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 10, Yang teaches a computer-readable, non-transitory recording medium having recorded thereon a program for causing a computer to carry out:
a process of acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto (Figs. 1 and 4 further teaches at least a computer vision LineMod implementation by means of at least a known CPU comprising obviously said recording medium for acquiring at least further section 3.1, and 4, and the Abstract the acquired attached training labeled images comprising said acquiring training images in which a region containing an image of an object has a ground-truth label attached thereto);
a process of assigning, to each of unit regions, a score which indicates objectness, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object (section 3.1 further teaches at least calculated confidence score P and a reliable segmentation confidence value as at least said assigned score assigning process of assigning, to each of unit regions, a score which indicates an uncertainty objectness score and one of a certainty objectness score, by inputting obviously the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object);
a process of assigning a pseudo label which indicates an object, to a partial region of the region
a process of assigning a pseudo label which indicates a background, t
a process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background (section 3.1 further teaches finetuning the model using the assigned pseudo labels by obviously an updating process of updating a model parameter of the region detection model, with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background).
Yang is silent regarding the above lined-out items such as said process of assigning a pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions; and said process of assigning a pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions.
Leng in at least para. 0048 a score assigning process of assigning, to each of bounding boxes regions as each bounding box may obviously be divided into a plurality or subregions further indicative of the partial regions, a score which indicates objectness to a region, by inputting the training image to a region detection model for detecting, in an image, a region corresponding to an image of an object, Leng further teaches at least in para. 0048 and Figs. 2A-2B first pseudo label assigning process of assigning a pseudo label which indicates an object, to a background or foreground region indicating in a case said partial region of the region having the ground-truth label attached thereto, in accordance with the confidence score assigned to each of the unit regions and further generate at least one or more second pseudo label assigning process of assigning a pseudo label which indicates a background, to an unlabeled region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions and ultimately perform an updating process of para. 0040-0042 and Figs. 2A-2B updating at least a model parameter of the image region detection model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yang in view of Leng to include wherein said process of assigning a pseudo label which indicates an object, to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions; and said process of assigning a pseudo label which indicates a background, to a partial region of a region not having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions, as discussed above, as Yang in view of Leng are in the same field of endeavor of employing neural networks methods and systems for acquiring a training image in which a region containing an image of an object has a ground-truth label attached thereto and assigning a score to each of region which indicates background and/or foreground objectness as a region corresponding to an image of an object, Leng augments the adjusted or finetuning parameters of the object detection model of Yang with complemented pseudo label assignment processes to a partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions and further assigning one or more second pseudo label assigning process of assigning a pseudo label which indicates a background to a partial unlabeled region of a region not having the ground-truth label attached thereto which when combined and used with the method of Yang further suppress misdetection and mislearning a foreground region for a background region based on further in accordance with the score assigned to each of the unit regions where the system further updated the model not just for a region of a low score or a high score but updating with reference to a region having attached thereto the pseudo label which indicates an object or the pseudo label which indicates a background according to further known means and methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claimed Standings
Claims 3, 5, and 7-8 objected to as not being rejected by the prior arts of record and being also dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and if all outstanding rejections are overcome. The prior arts of record do not appear to teach: Claim 3 The information processing apparatus according to claim 2, wherein the training image contains a plurality of regions each being the region containing an image of an object, and in the first pseudo label assigning process, the at least one processor assigns the pseudo label which indicates an object, to the unit region having been assigned the score that falls within a range of scores including a highest score, the range corresponding to a predetermined proportion, in each of the plurality of regions each of which contains an image of an object.
Claim 5 The information processing apparatus according to claim 1, wherein the ground-truth label is a class label, in the score assigning process, the at least one processor assigns a score for each of classes, to each of the unit regions contained in the training image, and in the first pseudo label assigning process, the at least one processor assigns a pseudo label which indicates one of the classes, to the partial region of the region having the ground-truth label attached thereto, in accordance with the score assigned to each of the unit regions.
Claim 7 The information processing apparatus according to claim 6, wherein the at least one processor further carries out: an identification label assigning process of assigning, to each of the unit regions contained in the region having the ground-truth label attached thereto, an identification label which indicates an object or a background, by inputting at least a part of the training image to a second region detection model for detecting, in an image, a region corresponding to an image of an object; and a threshold determining process of determining a threshold in accordance with a score distribution of the unit regions each having attached thereto the identification label which indicates an object and a score distribution of the unit regions each having attached thereto the identification label which indicates a background, and in the first pseudo label assigning process, the at least one processor assigns the pseudo label which indicates an object, to a unit region having been assigned the score which is equal to or greater than the threshold in the training image, and in the second pseudo label assigning process, the at least one processor assigns the pseudo label which indicates a background, to a unit region having been assigned the score which is smaller than the threshold or a second threshold smaller than the threshold in the training image.
Claim 8 The information processing apparatus according to claim 7, wherein in the threshold determining process, the at least one processor uses, as the threshold, a value obtained by adding a predetermined value to an average of a center of gravity of the score distribution of the unit regions each having attached thereto the identification label which indicates an object and a center of gravity of the score distribution of the unit regions each having attached thereto the identification label which indicates a background, and uses, as the second threshold, a value obtained by subtracting the predetermined value from the average, and in the second pseudo label assigning process, the at least one processor assigns the pseudo label which indicates a background, to a unit region having been assigned the score which is smaller than the second threshold in the training image.
Conclusion
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/MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 12/10/2025