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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/09/2026 has been entered.
Response to Amendment
Claims 1-3, 6, 8-10, 12-14,17 and 19 remain pending within the application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, 8-10, 12-14, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hill et al. (Pub. No.: US 2011/0320454 A1), hereafter Hill, in view of Srivastava et al. ("Bag of Tricks for Retail Product Image Classification"), hereafter Srivastava, in further view of Kasturi et al. (Pub. No.: US 2020/0118014 A1), hereafter Kasturi.
Regarding claim 1, Hill discloses:
A computer implemented method to classify an input, the method comprising (Hill, Fig. 7, element 701 and Fig. 2, step 204 classifies digital objects as input using the processor recited in Fig. 7),
receiving, by a classification platform processor from a user device via a user interface associated with a classification platform, an input comprising at least one image from a real world domain (Hill, paragraph 0037, lines 4-5 “Media items 114 are input to a classifier 116”, paragraph 0032, lines 5-9 "content is generated or otherwise obtained. Content may include any artifact including a photograph, digital image, video, graphic, etc. The content in one illustrative example includes a photo of dolphins in the ocean."),
applying, by the classification platform processor, a base model to the input … (Hill, Fig. 2, step 204 classifies digital objects as input by applying a base model),
predicting, by the classification platform processor, at least a first base concept associated with the input (Hill, Fig. 7, element 701 and Fig. 1A step 104 teaches predicting at least a first base concept associated with input 102),
querying, by the classification platform processor utilizing the at least first base concept, a mapping data structure for at least one custom concept (Hill, Fig 2 and paragraph 0040, lines 8-11 “The categorization/classification from block 204 is refined in block 212 based on the faceted ontology properties or constraints from block 210. “ teaches refining based on faceted ontology properties as querying the mapping data structure in 208 for custom concepts using the base concepts learned in 204),
receiving, by the classification platform processor, a plurality of custom concepts (Hill, paragraph [0041] and Fig 2 teaches receiving a plurality of custom concepts),
mapping, by the classification platform processor utilizing mapping data generated during the training process and stored in a mapping database, the plurality of custom concepts to the at least first base concept (Hill, Fig. 2, element 208, paragraph 0040, and paragraph 0082 teaches mapping multiple concepts to the base concept using a faceted taxonomy, which is mapping data generated during training process and stored in a database),
outputting, by the classification platform processor to a database, the custom concepts which were kept as a classification of the input (Hill, Fig. 1B teaches outputting the determined concept as classification in step 122).
Hill discloses applying a base model to the input, but does not disclose training the base model on images from a first domain different from the real world domain.
Srivastava discloses:
wherein … model was trained during a training process on images from a first domain different from the real world domain (Srivastava, page 4, Figs 6-7 and left col paragraph 1, lines 3-5 “We take in-vitro images of retail products as training data and insitu images were taken as testing data” teaches a model trained on images from a first domain different from the real world domain).
Hill and Srivastava are analogous art because they are from the same field of endeavor, machine learning and concept learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hill to include wherein … model was trained during a training process on images from a first domain different from the real world domain, based on the teachings of Srivastava. The motivation for doing so would have been “for classification to achieve better accuracy” (Srivastava, page 2, left col paragraph 1, line 4).
Hill does not disclose:
identifying, … an ignore list of concepts associated with the at least first base concept, wherein the ignore list identifies one or more custom concepts mapped to the at least first base concept that are to be ignored,
filtering, … the mapped plurality of custom concepts to remove any custom concepts identified by the ignore list and to keep the remaining custom concepts having a confidence score greater than a threshold amount.
Kasturi discloses:
identifying… an ignore list of concepts associated with the at least first base concept, wherein the ignore list identifies one or more custom concepts mapped to the at least first base concept that are to be ignored (¶[0059-0060] teaches identifying stopwords as an ignore list of custom concepts mapped to base concept that are to be ignored),
filtering…the mapped plurality of custom concepts to remove any custom concepts identified by the ignore list and to keep the remaining custom concepts having a confidence score greater than a threshold amount (¶[0060-0062] teaches removing the stopwords, i.e. custom concepts identified by the ignore list and keeping the remaining concepts having a confidence score greater than a threshold amount).
Hill, Srivastava, and Kasturi are analogous art because they are from the same field of endeavor, concept learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hill, in view of Srivastava, to include identifying, …an ignore list of concepts associated with the at least first base concept, wherein the ignore list identifies one or more custom concepts mapped to the at least first base concept that are to be ignored, and filtering, … the mapped plurality of custom concepts to remove any custom concepts identified by the ignore list and to keep the remaining custom concepts having a confidence score greater than a threshold amount, based on the teachings of Kasturi. The motivation for doing so would have been to improve accuracy and validation (¶[0100]).
Regarding claim 2, Hill, in view of Srivastava, in further view of Kasturi, discloses the computer-implemented method of claim 1…predicting at least a first base concept associated with the input. Hill further discloses:
generating a confidence score associated with the at least first base concept (Hill, Fig 1A step 104 and paragraph 0032, lines 14-15 "categories with confidence score," teaches generating confidence scores associated with the at least first base concept as demonstrated in Fig 1A step 104, such as generating “0.71” for at least "Dolphin")
Regarding claim 3, Hill, in view of Srivastava, in further view of Kasturi, discloses the computer-implemented method of claim 1 … predicting at least a first base concept associated with the input. Hill further discloses:
generating a first confidence score associated with the at least first base concept (Hill, Fig 1A step 104 teaches generating a first confidence score associated with the at least first base concept, such as generating “0.71” for at least "Dolphin")
predicting at least a second base concept associated with the input (Hill, Fig 1A step 104 teaches predicting at least a second base concept associated with the input, such as "Blue")
generating a second confidence score associated with the at least second base concept (Hill, Fig 1A step 104 teaches generating a second confidence score associated with the at least second base concept, such as generating “0.87” for at least "Blue").
Regarding claim 6, Hill, in view of Srivastava, in further view of Kasturi, discloses the computer-implemented method of claim 1. Hill further discloses:
ranking the custom concepts which were kept to output the highest ranked concept first (Hill, paragraph 0059, lines 1-3 "Each of the re-ranking factors f may serve as re-scoring factor for the classifier confidence among the different tags within the same image." Teaches ranking concepts using re-ranking factor f to classify input image for output of highest ranked concept).
Regarding claim 8, Hill, in view of Srivastava, in further view of Kasturi, discloses the computer-implemented method of claim 1. Hill further discloses:
the mapping data structure includes a plurality of base concepts including the at least first base concept (Hill, paragraph 0039, lines 1-4 "Category label refinement may be based on properties of nodes (leaf or internal, domain-specific, number of siblings, number of descendants, depth in tree, etc.); confidence scores of nodes," and paragraph 0073, lines 4-6 "Images or artifacts are categorized. Each category represents a unique semantic idea and is represented as a node, called a category node." teaches using the image categories as the base concepts in the mapping data structure, which includes the categories for refinement, i.e., the first base concept)
Regarding claim 9, Hill, in view of Srivastava, in further view of Kasturi, discloses the computer-implemented method of claim 8. Hill further discloses:
the mapping data structure includes, for each of the plurality of base concepts, information identifying one or more corresponding custom concepts (Hill, paragraph 0073, lines 6-8 "Each category node is optionally linked to one or more children nodes that reflect semantic decomposition of the parent category." teaches children nodes as information identifying one or more corresponding concepts).
Regarding claim 10, Hill, in view of Srivastava, in further view of Kasturi, discloses the computer-implemented method of claim 9. Hill further discloses:
the mapping data structure further includes, for the one or more corresponding custom concepts, a confidence score indicating a confidence in the relationship between the one or more corresponding custom concepts and the associated base concept (Hill, Fig 2 and paragraph 0052, lines 6-8 "We can augment the set of tags by propagating tag confidence scores bottom-up in the taxonomy" teaches confidence scores indicating a confidence in the relationship between base and custom concepts in the taxonomy mapping structure of 208 in Fig. 2).
Claim 12 is substantially similar to claim 1, but for the recitation of a mapping module operably connected to the classification platform processing unit (Hill, Fig. 7, element 708 and ¶[0083] teaches the refinement module as a mapping module operably connected to the classification platform processing unit), and thus claim 12 is rejected on the same basis as claim 1.
Regarding Claim 13, Hill, in view of Srivastava, in further view of Kasturi, discloses the classification platform of claim 12. Hill further discloses:
the input is one of an image and a video (Hill, paragraph 0032, lines 6-8 "Content may include any artifact including a photograph, digital image, video, graphic, etc.").
Claims 14 is substantially similar to claim 3, and thus is rejected on the same basis as claim 3.
Claims 17 is substantially similar to claim 6, and thus is rejected on the same basis as claim 6.
Regarding claim 19, Hill, in view of Srivastava, in further view of Kasturi, discloses the classification platform of claim 12. Srivastava further discloses:
the input is from a different domain than a set of inputs used to train … model (Srivastava, page 4, Figs 6-7 and left col paragraph 1, lines 3-5 “We take in-vitro images of retail products as training data and insitu images were taken as testing data” teaches a test insitu image as input, which is from a different domain than a set of in vitro inputs used to train the model).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hill to include the input is from a different domain than a set of inputs used to train … model, based on the teachings of Srivastava. The motivation for doing so would have been “for classification to achieve better accuracy” (Srivastava, page 2, left col paragraph 1, line 4).
Response to Arguments
Applicant's arguments filed 06/09/2026 have been fully considered with regards to the 35 U.S.C. 102/103 rejection, but they are not persuasive.
Applicant’s arguments with respect to claim(s) 1 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
- U.S. Pub No. 20220114345 A1: Belem et al. teaches concept mapping and machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUMAIRA ZAHIN MAUNI whose telephone number is (703)756-5654. The examiner can normally be reached Monday - Friday, 9 am - 5 pm (ET).
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, MATT ELL can be reached at (571) 270-3264. 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.
/H.Z.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
Prosecution Insights