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 .
DETAILED ACTION
Status of Claims
Claim(s) 2-21 have been examined.
Claim(s) 1 have been canceled.
Response to Amendment
The amendment to the claims filed on 3/4/2026 does not comply with the requirements of 37 CFR 1.121(c) because claims 1-20 are identified as canceled, yet the newly presented claims reuse the canceled claim numbers 2-20 and additional present new claim 21. Under 37 CFR 1.121(c), claims added by amendment must be numbered consecutively beginning with the number next following the highest-numbered claim previously presented (i.e., claim 20). Applicant is required to renumber the newly added claims consecutively (for example, as claims 21-40) and to conform to the claim dependencies accordingly.
For purposes of examination on the merits and compact prosecution, and consistent with the rejection under 35 U.S.C. 112(b) below, the independent method claim labeled “21” is treated as the indented claim from which dependent claims 2-7 depend.
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.
Claims 2-7 are rejected under 35 U.S.C. 112(b) as being indefinite. Each of claims 2-7 recites dependency, directly or indirectly, from claim 1. Claim 1 has been canceled. A claim that depends from a canceled claim fails to particularly point out and distinctly claim the subject matter regarded as the invention because the scope of the claim cannot be ascertained. Correction of the dependencies as set forth in the objection above is required.
Claims 3, 4, 10, 11, 17, and 18 are rejected under 35 U.S.C. 112(b) as being indefinite for lack of antecedent basis. Claims 2, 9, and 16 each introduce “a product list”. Claims 3-4, 10-11, and 17-18 subsequently recite “the candidate product list”. There is insufficient antecedent basis for “the candidate product list” in the claims.
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 2-21 are rejected under 35 U.S.C. 101 because the claims recite a judicial exception which is not integrated into a practical application and the claims lack an inventive concept.
Step 1 is the first inquiry into eligibility analysis and asks whether the claims are directed to a statutory category. In this instance, the answer must be in the affirmative because they recite a method, medium, and system.
Step 2A prong 1 is the next step in the eligibility analyses and asks whether the claimed invention recites a judicial exception. In this instance, the claims recite the following limitations which comprise the abstract idea:
receiving an input query image;
analyzing the input query image to identify input query image visual text content;
determining a visual similarity measure between a candidate product image visual text content and the input query image visual text content based on image signatures associated with the candidate product image and the input query image;
recommending a candidate product based on the visual similarity measure;
causing presentation of the recommended candidate product on a graphical user interface of a client device.
This is an abstract idea because it is a certain method of organizing human activity because it involves commercial or legal interactions such as marketing and/or sales activities and behaviors.
Step 2A prong 2 is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements such as:
a machine learning model
a processor (claim 15)
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
In addition, the recitations of the additional limitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, claims 2-5 are directed to the abstract idea itself. As for claims 6-7, these claims do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
a machine learning model
a processor (claim 15)
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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.
Claim(s) 21, 2-6, 8-13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gokturk (US 2008/0082426) in view of Zomet (US 2014/0152847).
Referring to Claim 21, Gokturk teaches a method comprising:
receiving an input query image (see Gokturk ¶¶0189,0201; a search input is received from a user, wherein the search input may comprise an unprocessed image input 1249 that is subsequently subjected to image analysis; see also Gokturk ¶0286);
analyzing the input query image using a machine learning model to identify input query image visual content (see Gokturk ¶0065, teaching that “a classifier can be built, such as nearest neighbor classifier, support vector machines, neural networks, or naïve bayes classification,” wherein “a training set of items with corresponding categories is obtained,” the classifier is built using image content information, and “a new input item is classified using the learnt classifier”; see also Gokturk ¶¶0084,0228);
determining a visual similarity measure between a candidate product image visual content and the input query image visual content based on image signatures associated with the candidate product image and the input query image (see Gokturk ¶¶0223-0224, teaching that similarity between images is quantified using distance measurements, the output being a numerical “feature distance” measuring dissimilarity between two images; see also Gokturk ¶¶0062-0064,0197-0198, teaching that a signature comprising a vector representation of the extracted features is generated for each image, and Gokturk ¶¶0290-0291, teaching that the visual signature of the query image is referenced against a database of image signatures to determine the search result);
recommending a candidate product based on the visual similarity measure (see Gokturk ¶¶0160-0162, teaching that the system searches for the top-N most similar images and that potential images are ranked and returned; see also Gokturk ¶0191);
causing presentation of the recommended candidate product on a graphical user interface of a client device (see Gokturk ¶¶0204-0205, teaching that a search result is returned to the user and presented in panels containing images and associated data; also see Gokturk ¶0189, teaching a web page component downloaded onto a terminal of a user).
Gokturk teaches wherein the visual content may be “colors, patterns, shapes, or other physical characteristics” of the image (see Goktuk ¶0122), but does not expressly teach wherein the visual content also includes text content. However, Zomet teaches wherein visual content includes text (see Zomet ¶0024, teaching that “visual feature functionality or algorithms may also extract text, barcodes, or other coded information from images. This information may be compared against data from the image-product database 180 to identify products or categories of products within the image. The text extracted [from] the image may also include product names, model numbers, manufacturer name, or any other text to use in searching the image product database.”) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these references because the results would be predictable. Specifically, the prior art of Gokturk would continue to teach the visual content in image which it analyzed and used in the determination of the visual similarity measure, except that now that visual content would include text according to the teachings of Zomet. This is a predictable result of the combination.
Referring to Claim 2, the combination teaches the method of claim 1, wherein the recommending a candidate product further comprises ranking the candidate product in a product list based on the visual similarity measure (see Gokturk ¶¶0161-0162, wherein potential images are assigned a ranking and sorted based on similarity scores).
Referring to Claim 3, the combination teaches the method of claim 2, wherein the candidate product has a highest ranking in the candidate product list (see Gokturk ¶0160, teaches “top N most similar,” and ¶0162 teaches wherein the output is the images sorted in descending order of votes such that the most similar image is ranked highest).
Referring to Claim 4, the combination teaches the method of claim 3, wherein the causing presentation of the recommended candidate product comprises presenting the candidate product at a top position of the candidate product list (see Gokturk ¶¶0162,0164, wherein the images are sorted in descending order of their similarity scores, and Gokturk ¶0204 teaches wherein the sorted results are displayed to the user; a result sorted in descending order and displayed necessarily presents the highest-ranked candidate at a top position).
Referring to Claim 5, the combination teaches the method of claim 1, wherein the candidate product image is associated with an electronic marketplace (see Gokturk ¶0235 and Fig. 15A, illustrating a search result on merchandise items; see also Gokturk ¶¶0258-0260, describing an e-commerce system in which content items include images of merchandise and products for sale, and Gokturk ¶0047).
Referring to Claim 6, the combination teaches the method of claim 1, wherein the machine learning model comprises a neural network (see Gokturk ¶0065, expressly teaching that the classifier may be a neural network).
Referring to Claims 8-13 and 15-20, these claims are similar to claims 21 and 2-6 and are therefore rejected under the same reasons and rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gokturk (US 2008/0082426) in view of Zomet (US 2014/0152847) in further view of BURGE (US 2018/010742).
Referring to Claim 7, the combination teaches the method of claim 6, wherein the machine learning model comprises a neural network (see Gokturk ¶0065), but does not expressly teach further comprising retraining the neural network. However, BURGE teaches retraining a machine learning model using a new image of a new product provided to an electronic marketplace (see BURGE ¶0073) wherein the machine learning model is a deep neural network (see BURGE Abstract, teaching that an image may be processed with a deep neural network to produce a k-dimensional feature vector). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these references because the results would be predictable. Specifically, the machine learning model would continue to comprise a neutral network according to Gokturk except that now the neural network would further be retrained according to the teachings of BURGE. This is a predictable result of the combination.
Remarks
Additional prior art relevant to the application but not relied upon include:
JO (US 2016/0364788) which also teaches extracting textual information from images.
Dhua (US 9,830,631) which teaches image recognition systems for determining content contained within an image.
Reference U (see PTO-892) which teaches using analysis of mobile product image searching.
ConclusionAny inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW E ZIMMERMAN whose telephone number is (571)270-5278. The examiner can normally be reached 8-4pm M-T, 8-12pm W.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW E ZIMMERMAN/Primary Examiner, Art Unit 3688
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