DETAILED ACTION
Response to Arguments
Applicant has amended claims 1, 11, and 12; cancelled claim 5; with claims 1, and 3-4, 6-18 currently pending.
Applicant's arguments filed 12/4/2025 have been fully considered. Applicant’s argument with respect to 35 U.S.C. 112(b) rejections of claims 11, 12, 15, and 18 is not persuasive. The wording remains unclear despite applicant’s note of intended meaning that “Applicant notes that claims 11 and 12 refer to "an image to be classified" as the processing object during the model inference phase, but claim 1 refers to "an image to be annotated" as the sample during the model training phase. Therefore "an image to be classified" in claims 11 and 12 is different from "an image to be annotated" in claim 1. Similarly, claims 11 and 12 refer to "an image category" as the classification type of "an image to be classified," while claim 1 refers to "an image category" as the classification type of "an image to be annotated". Therefore, "an image category" in claims 11 and 12 is different from "an image category" in claim 1.” Therefore the rejection is maintained as detailed below.
Applicant’s arguments with respect to claim(s) 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
As such this action is made FINAL.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/6/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) has/have been considered by the examiner.
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.
Claim 11 recites limitations “an image”, and “an image category” in lines 1-2. It is unclear if the same image and image categories are meant from claim 1.
Claim 12 recites limitations “an image”, and “an image category” in lines 2-3. It is unclear if the same image and image categories are meant from claim 1.
Claims 15 and 18 are rejected as being dependent on rejected claim 11.
Examiner recommends writing claims 10, 13 and 16 in independent form to increase clarity and overcome the rejections.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 and 3-4, 6-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (Kumar N, Berg AC, Belhumeur PN, Nayar SK. Attribute and simile classifiers for face verification. In2009 IEEE 12th international conference on computer vision 2009 Sep 29 (pp. 365-372). IEEE.) in view of Zhu (Zhu H, Shan H, Zhang Y, Che L, Xu X, Zhang J, Shi J, Wang FY. Convolutional ordinal regression forest for image ordinal estimation. IEEE transactions on neural networks and learning systems. 2021 Feb 18;33(8):4084-95.), and Cheng (Cheng Y, Qiao X, Wang X, Yu Q. Random forest classifier for zero-shot learning based on relative attribute. IEEE transactions on neural networks and learning systems. 2017 Mar 21;29(5):1662-74.).
Regarding claim 1, Kumar discloses An image annotating method, comprising: (Kumar Abstract, binary classifiers are used to recognize presence or absence of describable aspects of appearance.) generating an image tag vector of an image to be annotated, according to a plurality of attributes for image annotating (Kumar Section 3. Our Approach – found on righthand column of p. 3; a “trait vector” is computed from each of n classifiers (see 2. Compute visual traits)) and multiple tags corresponding to each of the attributes of the plurality of attributes, wherein different tags of each of the attributes of the plurality of attributes represent different styles of each of the attributes of the plurality of attributes, the multiple tags corresponding to each of the attributes of the plurality of attributes cover all styles corresponding to the attributes; (Kumar Section 3.4 Verification Classifier - ¶2 – found on p. 5 righthand column; trait values (tags) are outputs of binary classifiers in the range of -1 to 1. The SVM classifier establishes presence or absence based on a 0 threshold. Fig. 1 shows a graphical version of a vector with trait tags. The SVM out puts establish all possible values of the traits (all styles).) identifying an image category to which the image to be annotated belongs, according to vector similarity between the image tag vector and a category tag vector of each of a plurality of image categories, wherein the category tag vector is generated according to the multiple tags corresponding to each of the attributes of the plurality of attributes; and (Kumar Section 3. Our Approach – found on righthand column of p. 3; a “trait vector” is of an individual is compared to another image to see if they have the same identity (see 2. Compute Visual Traits and 3. Perform Verification). Fig. 1 shows an example comparison using image tag vectors to identify Jennifer Garner.)
Kumar does not explicitly disclose sorting the multiple tags corresponding to each of the attributes and determining a serial number corresponding to each tag, according to tag similarity between tags, wherein the closer the serial numbers of different tags are, the greater style similarity between the different tags, the image tag vector and the category tag vector are generated according to the serial number corresponding to each tag, wherein the image tag vector comprises first serial numbers of first tags which the image to be annotated has, the first serial numbers being configured to represent sorting information of the first tags in a plurality of tags of attributes to which the first tags belong.
Zhu, however discloses sorting the multiple tags corresponding to each of the attributes and determining a serial number corresponding to each tag, according to tag similarity between tags, wherein the closer the serial numbers of different tags are, the greater style similarity between the different tags, wherein the image tag vector comprises first serial numbers of first tags which the image to be annotated has, the first serial numbers being configured to represent sorting information of the first tags in a plurality of tags of attributes to which the first tags belong (Zhu Section III. Convolutional OR Forest – A. Image Ordinal Estimation – found on p. 4086 lefthand column to righthand column; for a given attribute that can be represented as an ordinal label the tag value is determined by utilizing a classifier for each possible tag value. Fig. 1 shows how once a cross point is reached in the probability of the tag value that is the tag value that is chosen. In Section 1. Introduction ¶1 – found on p. 4084; it is made clear that the ordinal relationship can help in classification of the tag value (i.e. order matters) and that tag values are a pseudo continuum with like values next to each other (very healthy vs health or age value (see Fig. 1 (c), where the binary classifiers are ordered based on age and that informs the cross point). The cross point is the label value given (Fig. 1 (c) estimated age).)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the annotation method of Kumar with teachings of Zhu by including ordinal regression as in Zhu in order to better represent and utilize ordinal labels, such as age (Zhu Section 1. Introduction ¶1 and 3 – found on p. 4084 righthand column to 4085 lefthand column).
The combination of Kumar and Zhu do not disclose annotating an image category to which the image to be annotated belongs, the image tag vector and the category tag vector are generated according to the serial number corresponding to each tag, the category tag vector comprises second serial numbers of second tags corresponding to the each of a plurality of image categories, the second serial numbers being configured to represent sorting information of the second tags in a plurality of tags of attributes to which the second tags belong.
Cheng, however, discloses annotating an image category to which the image to be annotated belongs (Cheng Section II Proposed RF-RA – E. Zero-Shot Classifying With Trained RF-RA; the output class that matches the input images best is assigned to the input image/ testing sample.) the image tag vector and the category tag vector are generated according to the serial number corresponding to each tag, (Cheng Section I. Introduction ¶3 and 6 – found on p. 2; relational attributes are utilized in tag vectors. These are ranked according to the strength of each attribute in the class of the image (identity of the face 0 see also Fig. 2.) the category tag vector comprises second serial numbers of second tags corresponding to the each of a plurality of image categories, the second serial numbers being configured to represent sorting information of the second tags in a plurality of tags of attributes to which the second tags belong. (Cheng Section I. Introduction ¶3 and 6 – found on p. 2; relational attributes are utilized in tag vectors. These are ranked according to the strength of each attribute in the class of the image (identity of the face 0 see also Fig. 2.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the annotation method of the combination of Kumar and Zhu with teachings of Cheng by including an annotation for specific individual faces from Cheng in order to be able to group photos by the photo’s subject.
Regarding claim 3, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. They further disclose, wherein the generating an image tag vector of an image to be annotated, according to each of the attributes of the plurality of attributes for image annotating and multiple tags corresponding to each of the attributes comprises: determining one or more tags corresponding to the image to be annotated, according to feature information of the image to be annotated, to generate the image tag vector corresponding to the image to be annotated. (Kumar Section 3. Our Approach ¶1-3- found on p. 3 righthand column; features are extracted that inform the visual traits and the “trait vector” values (see 1. Extract Low-level Features and 2. Compute Visual Traits).)
Regarding claim 4, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. They further disclose, wherein the plurality of attributes for image annotating are independent of each other. (Kumar Section 3. Our Approach ¶1-3- found on p. 3 righthand column and Section 3.2 Attribute Classifiers ¶1-3 – found on p. 4 lefthand column to righthand column; each labeled attribute has it’s own individually trained classifier that determines the tag value. These classifiers have their own sets of positive and negative training samples and do not rely on each other to inform the output tag (i.e. independent).)
Regarding claim 6, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. They further disclose, wherein the plurality of attributes for image annotating are determined according to feature information of an object to be annotated, (Kumar Section 3. Our Approach ¶1-3- found on p. 3 righthand column; features are extracted that inform the visual traits and the “trait vector” values (see 1. Extract Low-level Features and 2. Compute Visual Traits).) the image category is a category of an object to be annotated in the image to be annotated. (Cheng Section II Proposed RF-RA – E. Zero-Shot Classifying With Trained RF-RA; the output class that matches the input images best is assigned to the input image/ testing sample. It can also be seen in Fig. 4 (b) that the input/ test face (object in the image) is assigned (annotated) the identity of the person in the image. Wherein, it would have been obvious to include an annotation for specific individual faces in order to group the tagged photos by who is in the photo.)
Regarding claim 7, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 6, as described above. They further disclose, wherein, the feature information is at least one of a physical feature or a facial feature of the object to be annotated. (Kumar Table 1. Attribute Classification Results – found on p. 4; a list of vector traits is seen including face and physical/facial features (for example nose shape and oval face).)
Regarding claim 8, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. They further disclose, further comprising: detecting whether an annotating result of the image to be annotated is correct, according to an image similarity between the image to be annotated and a reference image of the image category to which the image to be annotated belongs. ((Kumar Section 3. Our Approach – found on righthand column of p. 3; a “trait vector” is of an individual is compared to another image to see if they have the same identity (see 2. Compute Visual Traits and 3. Perform Verification). Fig. 1 shows an example comparison using image tag vectors to identify Jennifer Garner.))
Regarding claim 9, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 8, as described above. They further disclose, wherein the detecting whether an annotating result of the image to be annotated is correct, according to an image similarity between the image to be annotated and a reference image of the image category to which the image to be annotated belongs comprises: detecting whether the annotating result of the image to be annotated is correct, according to a similarity between an object to be annotated in the image to be annotated and a reference object in the reference image of the image category to which the image to be annotated belongs. ((Kumar Section 3. Our Approach – found on righthand column of p. 3; a “trait vector” is of an individual is compared to another image to see if they have the same identity (see 2. Compute Visual Traits and 3. Perform Verification). Fig. 1 shows an example comparison using image tag vectors to identify Jennifer Garner.) Wherein, it would have been obvious to include an annotation for specific individual faces from Cheng in order to group the tagged photos by who is in the photo.)
Regarding claim 10, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. They further disclose A machine learning model training method, comprising: training a machine learning model for image classification using the training image set annotated. . (Kumar Section 3. Our Approach ¶1-3- found on p. 3 righthand column and Section 3.2 Attribute Classifiers ¶1-3 – found on p. 4 lefthand column to righthand column; each labeled attribute has its own individually trained classifier that determines the tag value. Examples of training samples are seen in Fig. 3.)
Regarding claim 11, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 10, as described above. They further disclose an image classification method, comprising: processing an image to be classified using a machine learning model to determine an image category to which the image to be classified belongs (Kumar Section 3.4 Verification Classifier – found on p. 5 righthand column; an SVM is trained to learn if two pictures are the same individual (belong to the same classification). Wherein, it would have been obvious to include an annotation for specific individual faces from Cheng in order to group the tagged photos by who is in the photo.)
Regarding claim 12, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 10, as described above. Although Kumar does not explicitly disclose an image classification apparatus, comprising a processor, it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include a processor in the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Regarding claim 13, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. Although Kumar does not explicitly disclose an electronic device, comprising: a memory; and a processor coupled to the memory, the processor configured to, based on instructions stored in the memory, it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include a processor and memory with the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Regarding claim 14, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 10, as described above. Although Kumar does not explicitly disclose, an electronic device, comprising: a memory; and a processor coupled to the memory, the processor configured to, based on instructions stored in the memory, it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include a processor and memory with the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Regarding claim 15, the combination of Kumar, Zhu, and Cheng the claim limitations with regards to claim 11, as described above. Although Kumar does not explicitly disclose, an electronic device, comprising: a memory; and a processor coupled to the memory, the processor configured to, based on instructions stored in the memory, it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include a processor and memory with the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Regarding claim 16, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 1, as described above. Although Kumar does not explicitly disclose, A non-transitory computer-readable storage medium on which a computer program is stored it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include computer readable medium with the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Regarding claim 17, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 10, as described above. Although Kumar does not explicitly disclose, A non-transitory computer-readable storage medium on which a computer program is stored, it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include computer readable medium with the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Regarding claim 18, the combination of Kumar, Zhu, and Cheng disclose the claim limitations with regards to claim 11, as described above. Although Kumar does not explicitly disclose, A non-transitory computer-readable storage medium on which a computer program is stored, it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to include computer readable medium with the combination of Kumar, Zhu, and Cheng in order to run the code needed to implement the method.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEREDITH TAYLOR whose telephone number is (571)270-5805. The examiner can normally be reached M-Th 7:30-5. Examiner’s email is Meredith.taylor@uspto.gov.
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, Vincent Rudolph can be reached on (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MEREDITH TAYLOR/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
Prosecution Insights