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 .
This office action is in responsive to communication(s):
Application filed on 9/15/2023 with effective filing date of 12/13/2021 based on PCT application PCT/CN2022/137442 filed on 9/15/2023 and foreign application CN202111521261.4 filed 12/13/2021.
The status of the claims is summarized as below:
Claims 1-20 are pending.
Claims 1, 13, and 20 are independent claims.
Information Disclosure Statement
The information disclosure statement(s) filed on 9/15/2023, 3/7/2024 and 5/29/2024 comply/complies with the provisions of 37 C.F.R. § 1.97, 1.98, and MPEP § 609, and therefore has/have been placed in the application file. The information referred to therein has/have been considered as to the merits.
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 13-14, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu et al. (US Pub 20180276815, hereinafter Xu).
Per claim 1, Xu teaches:
A data processing method performed in a computer device, comprising: (abstract: method for training image segmentation correction model by performing iterative model training process);
predicting, based on an initial image recognition model, initial aided annotation results of original images, the original images comprising a first original image and a second original image, and the initial aided annotation results comprising a first initial aided annotation result of the first original image; ([0020] Fig. 1 shows an initial segmentation estimate 110 (initial aided annotation result of first & second images) is provided via an annotation system 115 via conventional developed segmentation method
acquiring initial standard annotation results determined by correcting the initial aided annotation results, the initial standard annotation results comprising a first initial standard annotation result of the first original image and a second initial standard annotation result of the second original image; ([0021-0022] Fig. 1 shows users 105 can perform interactions 112 to correct the initial segmentation estimate to obtain corrections 120 (initial standard annotation result of first & second images) that are stored as ground truth);
adjusting, based on the first initial standard annotation result and the first initial aided annotation result, model parameters in the initial image recognition model so as to generate an updated image recognition model; ([0025-0028] the initial segmentation estimate 110 is combined with interaction 112, the motions 125 and the effort measurement 130 to form an annotated correction 135 (model parameters) which is presented to the a modeling computer 140, which includes a correction model 150 and segmentation model 147 that are organized as a recursive convolutional neural network; [0028-0030] after the learning step, the model is updated (updated image recognition model) to generate updated segmentation estimate 155);
predicting, based on the updated image recognition model, an updated aided annotation result of the second original image; ([0030] the correction model 147 can output an updated segmentation estimate 155 (updated aided annotation result) after the annotated correction 135 has been learned by the model);
acquiring an updated standard annotation result of the second original image by adjusting the second initial standard annotation result based on the updated aided annotation result; and ([0030] the updated segmentation estimation 155 (updated aided annotation result) is provided to the annotators 105 again to obtain correction (updated standard annotation) through the iterative process, where the initial standard annotation result was replaced/adjusted with the new updated standard annotated result);
in response to determining that the updated image recognition model satisfies a model convergence condition based on the updated aided annotation result and the updated standard annotation result, determining the updated image recognition model as a target image recognition model, the target image recognition model being for generating an annotation result of a target image. ([0033] Fig. 3: the amount of effort required to make correction from the annotators is used to adjust the training weights and detect convergence; [0009-0010] an effort measurement is used to determine when to terminate the training process, when the effort measurement is equal to the convergence value, the iterative model training process may be terminated, and the updated model becomes the target image recognition model).
Per claim 2, Xu teaches all limitations from claim 1, and further teach:
determining, in response to a model update instruction, the first original image as a sample image, the first initial standard annotation result as a sample label of the sample image, and the first initial aided annotation result as a sample prediction result of the sample image; and ([0033] Fig. 3 shows as part the annotation process – a raw image (sample image), annotated correction (sample label of the sample image), ground truth (sample prediction result));
the adjusting the model parameters in the initial image recognition model so as to generate an updated image recognition model comprising:
determining, based on the sample label and the sample prediction result, an overall loss value of the initial image recognition model; ([0022-0025] based on the annotated correction from the annotator (sample label) and the auto-segmented image (sample prediction result), annotated correction 135 (overall loss value) are determined that includes interactions 112, motions 125, and the effort measurement 130 that together forms the annotated correction 135 that are feed into the modeling computer 140);
adjusting, based on the overall loss value, the model parameters in the initial image recognition model; and ([0025-0028] the correction model is learned from the difference between the initial segmentation estimate 110 and the annotated correction 135 (model parameters adjustment));
determining, in response to the adjusted initial image recognition model satisfying a model convergence condition, the adjusted initial image recognition model as the updated image recognition model. ([0009-0010, 0033] after the effort measurement is equal to a convergence value, the iterative model training process terminates and the updated image recognition model is formed).
Per claim 13, claim 13 is an apparatus claim including a memory (Xu [0034] Fig. 4 memory 425) and a processor circuitry ([0034] computing platform with CPU), that includes limitations that are substantially the same as claim 1, and is likewise rejected.
Per claim 14, claim 14 includes limitations that are substantially the same as claim 2, and is likewise rejected.
Per claim 20, claim 20 is a medium claim including non-transitory media (Xu [0034] Fig. 4 memory 425), that includes limitations that are substantially the same as claim 1, and is likewise rejected.
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 of this title, if the differences between Frazier 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.
Claims 3-12, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, in view of Wu Jianrong et al. (CN112989087, hereinafter Wu).
Per claim 3, Xu teaches all limitations from claim 1, but does not explicitly teach:
wherein the initial aided annotation results further comprise a second initial aided annotation result of the second original image, and the method further comprises:
determining a first annotation result error between the first initial aided annotation result and the first initial standard annotation result;
determining a second annotation result error between the second initial aided annotation result and the second initial standard annotation result;
determining an average annotation result error between the first annotation result error and the second annotation result error;
determining an initial loss value of the initial image recognition model based on the average annotation result error; and
in response to the initial loss value being greater than or equal to an initial loss value threshold, adjusting, based on the first initial standard annotation result and the first initial aided annotation result, model parameters in the initial image recognition model so as to generate an updated image recognition model.
However, Wu teaches:
wherein the initial aided annotation results further comprise a second initial aided annotation result of the second original image, and the method further comprises: (page 11 line 21-66: Fig. 7 shows at least 2 different annotation results);
determining a first annotation result error between the first initial aided annotation result and the first initial standard annotation result; (page 11 line 21-66: Fig. 7 shows at least 2 standard annotation result 401h-403h, where the results are averaged and grouped to be compared to a number ratio threshold value);
determining a second annotation result error between the second initial aided annotation result and the second initial standard annotation result; (page 11 line 21-66: Fig. 7 shows at least 2 standard annotation result 401h-403h, where the results are averaged and grouped to be compared to a number ratio threshold value);
determining an average annotation result error between the first annotation result error and the second annotation result error; (page 11 line 21-66: Fig. 7 shows at least 2 standard annotation result 401h-403h, where the results are averaged and grouped to be compared to a number ratio threshold value);
determining an initial loss value of the initial image recognition model based on the average annotation result error; and (page 11 line 21-66: Fig. 7 shows at least 2 standard annotation result 401h-403h, where the results are averaged and grouped to be compared to a number ratio threshold value);
in response to the initial loss value being greater than or equal to an initial loss value threshold, adjusting, based on the first initial standard annotation result and the first initial aided annotation result, model parameters in the initial image recognition model so as to generate an updated image recognition model. ( page 12 line 43-67; Fig. 8 shows when the total loss value meets the model convergence condition (threshold), the model 50a is adjusted based on model parameters according to the total loss value to generate an updated image recognition model 50h ).
Wu and Xu are analogous art because Wu also teaches method of training models to perform image annotation. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Xu and Wu before him/her, to modify the teachings of Xu to include the teachings of Wu so that loss value threshold between initial and standard annotation can be used to determine whether to update the image recognition model and used model parameters. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide alternative ways such as determining model update based on a loss value threshold .
Per claim 4, Xu-Wu teach all the limitations of claim 3, and they further teach:
wherein the first initial aided annotation result comprises a first annotation region for a target object and a first object label for the first annotation region, the first initial standard annotation result comprises a second annotation region for the target object and a second object label for the second annotation region, and the determining the first annotation result error between the first initial aided annotation result and the first initial standard annotation result comprises:
determining an initial region error between the first annotation region and the second annotation region;
determining an initial object error between the first object label and the second object label; and
performing weighted summation on the initial region error and the initial object error to obtain the first annotation result error. (page 11 line 21-66: Fig. 7 shows at least 2 standard annotation result 401h-403h, where the difference between both the region and object label determined with sum and weight based on groupings).
Per claim 5, Xu teaches all limitations of claim 1, but Xu does not explicitly teach:
wherein the updated aided annotation result comprises an updated aided annotation region for a target object and an updated aided object label for the updated aided annotation region, the updated standard annotation result comprises an updated standard annotation region for the target object and an updated standard object label for the updated standard annotation region, and the determining the updated image recognition model as the target image recognition model comprises:
determining an updated region loss value between the updated aided annotation region and the updated standard annotation region;
determining an updated object loss value between the updated aided object label and the updated standard object label;
performing weighted summation on the updated region loss value and the updated object loss value to obtain an updated loss value of the updated image recognition model;
in response to the updated loss value being greater than or equal to an updated loss value threshold,
determining that the updated image recognition model fails to satisfy the model convergence condition, and
continuing to adjust model parameters in the updated image recognition model; and in response to the updated loss value being less than the updated loss value threshold,
determining that the updated image recognition model satisfies the model convergence condition, and
determining the updated image recognition model as the target image recognition model.
However, Wu teaches:
wherein the updated aided annotation result comprises an updated aided annotation region for a target object and an updated aided object label for the updated aided annotation region, the updated standard annotation result comprises an updated standard annotation region for the target object and an updated standard object label for the updated standard annotation region, and the determining the updated image recognition model as the target image recognition model comprises: (Fig. 8 shows updated aided result 50e/50g and updated standard annotation result 50f include both annotation region such as 503e and object label such as 501f-502f);
determining an updated region loss value between the updated aided annotation region and the updated standard annotation region; (page 12 line 43-67 Fig. 8 shows region loss between 50e and 50f is determined);
determining an updated object loss value between the updated aided object label and the updated standard object label; (page 12 line 43-67 Fig. 8 shows object label loss between 50f and 50g is determined);
performing weighted summation on the updated region loss value and the updated object loss value to obtain an updated loss value of the updated image recognition model; (page 12 line 43-67 Fig. 8 shows a total loss value is computed based on the area loss value and object loss value; also see Fig. 7 and associated paragraphs);
in response to the updated loss value being greater than or equal to an updated loss value threshold, (page 12 line 43-67, Fig. 8: determine if the total loss value meets the model convergence condition (threshold) );
determining that the updated image recognition model fails to satisfy the model convergence condition, and continuing to adjust model parameters in the updated image recognition model; and (page 12 line 43-67, Fig. 8: when the model fails to meet the model convergence condition, the model adjustment continues; also see Xu [0009-0010]);
in response to the updated loss value being less than the updated loss value threshold, determining that the updated image recognition model satisfies the model convergence condition, and determining the updated image recognition model as the target image recognition model. (page 12 line 43-67, Fig. 8: determine if the total loss value meets the model convergence condition (threshold), use the updated image model as the target image recognition model).
Wu and Xu are analogous art because Wu also teaches method of training models to perform image annotation. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Xu and Wu before him/her, to modify the teachings of Xu to include the teachings of Wu so that loss value threshold between initial and standard annotation including loss from both region and label can be used to determine whether to update the image recognition model and used model parameters. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide alternative ways such as determining model update based on a loss value that is computed based on both region and label losses, which would improve the accuracy of image annotation to include both region annotation and label annotation.
Per claim 6, Xu-Wu teach all the limitations of claim 5, and they further teach:
wherein the original images further comprise a third original image, the initial standard annotation results further comprise a third initial standard annotation result of the third original image, the initial aided annotation results further comprise a third initial aided annotation result of the third original image, and the continuing to adjust model parameters in the updated image recognition model comprises: (page 12 line 43-67, page 13 line 4-28, Fig. 8 shows different image 50i can be further fed into the model, including a third image);
determining an adjusted loss value based on the third initial standard annotation result and the third initial aided annotation result; (page 12 line 43-67, page 13 line 4-28, Fig. 8 shows a total loss value can determined based on loss values from comparison of the standard result and aided result);
performing weighted summation on the adjusted loss value and the updated loss value to obtain a target loss value; and (page 12 line 43-67, page 13 line 4-28, Fig. 8 shows evaluation of total loss value to meet a model update convergence value, where adjusted model parameters are used update the model based on the total loss value; Fig. 7 (associated paragraphs) shows a weighted summation for evaluation of the standard annotation result).
adjusting the model parameters in the updated image recognition model based on the target loss value. (page 12 line 43-67 Fig. 8: the model is updated based on model parameters derived from the loss value).
Per claim 7, Xu teaches all the limitations of claim 1, but does not explicitly teach:
wherein the acquiring the updated standard annotation result of the second original image comprises:
transmitting the updated aided annotation result to at least two annotation terminals, such that the at least two annotation terminals separately adjust the second initial standard annotation result based on the updated aided annotation result so as to obtain candidate annotation results of the second original image;
acquiring at least two candidate annotation results returned by the at least two annotation terminals, the at least two candidate annotation results separately comprising candidate annotation regions for annotating a target object in the second original image;
determining region quantities corresponding to the candidate annotation regions comprised separately in the at least two candidate annotation results;
determining initial check annotation results for the at least two candidate annotation results based on at least two region quantities; and
acquiring the updated standard annotation result based on the initial check annotation results.
However, Wu teaches:
wherein the acquiring the updated standard annotation result of the second original image comprises:
transmitting the updated aided annotation result to at least two annotation terminals, such that the at least two annotation terminals separately adjust the second initial standard annotation result based on the updated aided annotation result so as to obtain candidate annotation results of the second original image; (page 10 line 39 – page 11 line 20: Fig. 6 shows at least 2 candidates are send to the annotation terminals 104a to adjust and obtain candidate result);
acquiring at least two candidate annotation results returned by the at least two annotation terminals, the at least two candidate annotation results separately comprising candidate annotation regions for annotating a target object in the second original image; (page 10 line 39 – page 11 line 20: Fig. 6 shows at least 2 candidates are send to the annotation terminals 104a to adjust and obtain candidate result; the candidates, as shown in Fig. 4, where at least three different candidate results are returned, where candidate annotation result comprises a annotation region for a target object person; page 9 line 16 – page 10 line 38 Fig. 4);
determining region quantities corresponding to the candidate annotation regions comprised separately in the at least two candidate annotation results; (page 9 line 16 – page 10 line 38 Fig. 4 shows region quantities for each candidate result are determined in the second step);
determining initial check annotation results for the at least two candidate annotation results based on at least two region quantities; and (page 9 line 16 – page 10 line 38 Fig. 4 shows initial check annotation results for regions are determined based on quantities of regions from each candidate)
acquiring the updated standard annotation result based on the initial check annotation results. (page 10 line 39 – page 11 line 20: Fig. 6 shows the updated standard result is determined at terminal 105n after the initial check annotation results are send from 104a).
Wu and Xu are analogous art because Wu also teaches method of training models to perform image annotation. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Xu and Wu before him/her, to modify the teachings of Xu to include the teachings of Wu so the candidates results can be sent for final confirmation to acquire an updated standard annotation result. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide alternative ways for acquiring updated standard annotation after the model update such as confirmation from user terminal from a list of at least two candidates results can be used to determine initial check annotation result, which would improve accuracy of image annotation.
Per claim 8, Xu-Wu teach all the limitations of claim 7, and they further teach:
wherein the determining the initial check annotation results for the at least two candidate annotation results based on at least two region quantities comprises:
comparing the at least two region quantities, the at least two region quantities comprising a region quantity B.sub.a; a being a positive integer, and a being less than or equal to a result quantity of the at least two candidate annotation results; (Wu: page 9 line 16 – page 10 line 38, Fig. 4 shows in the middle, the region quantities for each candidate is compared);
determining, in response to a region quantity in remaining region quantities being different from the region quantity B.sub.a, the at least two candidate annotation results separately as the initial check annotation results; the remaining region quantities comprising region quantities other than the region quantity B.sub.a among the at least two region quantities; (Wu: page 9 line 16 – page 10 line 38, Fig. 4 shows when the region quantities for each candidate image are different, left branch shows each candidate result is determined separately);
acquiring, in response to the remaining region quantities being all same as the region quantity B.sub.a, candidate annotation regions separately comprised in every two candidate annotation results in the at least two candidate annotation results; and (Wu: page 9 line 16 – page 10 line 38, Fig. 4 shows in the middle, right branch, when the region quantities for each candidate are the same, compare every two candidate annotation results to obtain a coincidence degrees);
determining coincidence degrees between the candidate annotation regions separately comprised in every two candidate annotation results, and determining the initial check annotation results based on the coincidence degrees. (Wu: page 9 line 16 – page 10 line 38, Fig. 4 shows a coincidence degrees between candidates regions are determined to arrive at the initial check annotation result).
Per claim 9, Xu-Wu teach all the limitations of claim 8, and they further teach:
wherein the at least two candidate annotation results further separately comprise candidate object labels for annotating the candidate annotation regions, and the determining the initial check annotation results based on the coincidence degrees comprises: (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows object labels for each candidate region are evaluated to determine initial check annotation result based on the coincidence degrees);
determining, in response to at least one of the coincidence degrees being less than a coincidence degree threshold, the at least two candidate annotation results separately as the initial check annotation results; (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows 401h and 402h/403h have a coincidence degree less than a threshold, and are being evaluated separately in two branches);
grouping, in response to each of the coincidence degrees being equal to or greater than the coincidence degree threshold, same candidate object labels in the at least two candidate annotation results into a same object label group so as to obtain n object label groups; n being a positive integer; and (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows in the right branch, 402h and 403h have coincidence degrees being equal or greater than the threshold, and are grouped into a same object label group of 2 objects (2>0));
determining the initial check annotation results based on the n object label groups.( Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows at the bottom, the final result of initial check is based on the 2 object label groups).
Per claim 10, Xu-Wu teach all the limitations of claim 9, and they further teach:
wherein the determining the initial check annotation results based on the n object label groups comprises:
counting object label quantities of the candidate object labels separately comprised in the n object label groups, and acquiring a maximum object label quantity from the object label quantities separately corresponding to the n object label groups; (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows the total/maximum label quantities (6) are counted and used as denominator for the 2 objects group, and the total object label in the 2 object group (4) is counted to be used as nominator in the 2 object group evaluation);
determining quantity ratios between the maximum object label quantity and the object label quantities corresponding to the at least two candidate annotation results; (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows the ration between the max object label and label quantities of the 2 object group are determined);
comparing the quantity ratios with a quantity ratio threshold, and determining, in response to the quantity ratios being less than the quantity ratio threshold, the at least two candidate annotation results separately as the initial check annotation results; (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows the comparison of the ratio between the left and right branches are used to determine which one is the final result, when the quantity ratio is less than a threshold, result will be the left branch at the bottom, the at least two candidates separately as the final result);
determining, in response to the quantity ratios being equal to or greater than the quantity ratio threshold, an object label group corresponding to the maximum object label quantity as a target object label group; and (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows the comparison of the ratio between the left and right branches are used to determine which one is the final result, when the quantity ration is greater or equal than the threshold, the object label group’s result is used as the target candidate);
acquiring target candidate annotation results from candidate annotation results associated with the target object label group, and determining the target candidate annotation results as the initial check annotation results. (Wu: page 11 line 21 – page 12 line 34: Fig. 7 shows the comparison of the ratio between the left and right branches are used to determine which one is the final result).
Per claim 11, Xu-Wu teach all the limitations of claim 10, and they further teach:
wherein the acquiring the updated standard annotation result based on the initial check annotation results comprises:
transmitting, in response to the initial check annotation results are the at least two candidate annotation results, the initial check annotation results to a first check terminal, such that the first check terminal determines check annotation results to be transmitted to a second check terminal based on the at least two candidate annotation results, the second check terminal being configured to return the updated standard annotation result based on the check annotation results; and (Wu: page 10 line 39 – page 11 line 20: Fig. 6 shows that after the candidates are determined at 104a, the result is send to a second check terminal 105n, and the updated standard annotation result is determined based on confirmation from the second terminal 105n);
transmitting, in response to the initial check annotation results being the target candidate annotation results, the initial check annotation results to the second check terminal, such that the second check terminal returns the updated standard annotation result based on the target candidate annotation results. (Wu: page 10 line 39 – page 11 line 20: Fig. 6 shows initial check annotation result from 104a is send to the second check terminal 105n where the target candidate annotation is determined).
Per claim 12, Xu teaches all the limitations of claim 1, and does not explicitly teach:
wherein the predicting the initial aided annotation results of original images comprises:
acquiring the original images, the original images comprising a target object;
inputting the original images into the initial image recognition model, and acquiring image features of the original images in the initial image recognition model;
determining, based on the image features, an initial region recognition feature of the target object and an initial object recognition feature of the target object;
generating, based on the initial region recognition feature, an initial aided annotation region for the target object, and generating, based on the initial object recognition feature, an initial aided object label for the initial aided annotation region; and
determining the initial aided annotation region and the initial aided object label as the initial aided annotation results.
However, Wu teaches:
wherein the predicting the initial aided annotation results of original images comprises:
acquiring the original images, the original images comprising a target object; (page 9 line 40-63: Fig. 5 shows original image 401d comprising a target object such as building, house, pedestrian);
inputting the original images into the initial image recognition model, and acquiring image features of the original images in the initial image recognition model; (page 9 line 40-63: Fig. 5 shows the original image 401d is input into initial image recognition model 50a to perform a feature extraction, resulting image characteristic 50d);
determining, based on the image features, an initial region recognition feature of the target object and an initial object recognition feature of the target object; (page 9 line 40-63: Fig. 5 further shows 50d is further used to determine the target object (including pedestrian and house) of the predicted area information to arrive at 50e with predicted area information and predicted label for teach target object);
generating, based on the initial region recognition feature, an initial aided annotation region for the target object, and generating, based on the initial object recognition feature, an initial aided object label for the initial aided annotation region; and (page 9 line 40-63: Fig. 5 further shows 50d is further used to determine the target object (including pedestrian and house) of the predicted area information to arrive at 50e with predicted area information and predicted label for teach target object);
determining the initial aided annotation region and the initial aided object label as the initial aided annotation results. (page 9 line 40-63: Fig. 5 shows 50e which includes both target region 501e-503e, and their object label (building) as the initial aided annotation result).
Wu and Xu are analogous art because Wu also teaches method of training models to perform image annotation. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Xu and Wu before him/her, to modify the teachings of Xu to include the teachings of Wu so that annotation results would include both annotation region and object label. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide alternative ways for training image annotation model which would include both image region annotation and object label annotation.
Per claim 15-19, claims 15-19 include limitations that are substantially the same as claims 3-5, 7, 12 respectively, and are likewise rejected.
Conclusion
The examiner requests, in response to this Office action, support by shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections, See 37 CFR 1.111(c).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHOEBE X PAN whose telephone number is (571)270-7794. The examiner can normally be reached M-F 9am-6pm.
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/PHOEBE X PAN/Examiner, Art Unit 2179
/IRETE F EHICHIOYA/Supervisory Patent Examiner, Art Unit 2179
