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 .
Information Disclosure Statement
The information disclosure statement (IDS) filed on 11/26/2024 was considered and placed on the file of record by the examiner.
Election/Restrictions
Applicant’s election with traverse of and species group I claims 1-10 in the reply filed on 12/22/2025 is acknowledged.
Group II claims 11-18 and group III claims 19-20 are non-elected.
The traversal is on the grounds that claims do not differ significantly. This is not found persuasive because, Restriction for examination purposes as indicated is proper because all these inventions listed in this action are independent or distinct for the reasons given and there would be a serious search and examination burden if restriction were not required because one or more of the following reasons apply: (a) the inventions have acquired a separate status in the art in view of their different classification; (b) the inventions have acquired a separate status in the art due to their recognized divergent subject matter;(c) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries); (d) the prior art applicable to one invention would not likely be applicable to another invention.
Group II claims 11-18 state distinct claim subject matter “training a base machine learning model (MLM) to generate N-dimensional feature vectors for item images of items per price lookup (PLU) code for each item; training a principal component analysis (PCA) MLM to generate fewer vectors per PLU code as reference vectors, each reference vector having fewer dimensions that the N-dimensional feature vectors; training a similarity MLM to generate a similarity score between a given item and a given reference item based on a given PLU code, corresponding reference vectors for the given PLU code, and a given reduced dimensionality feature vector for a given image of the given item; receiving a current item image for a current item and a current PLU code associated with the current item; obtaining a current N-dimensional feature vector from the base MLM using the current item image and the current PL U code; obtaining current reference vectors linked to the current PLU code from a cache table; obtaining a current reduced dimensionality feature vector from the PCA MLM based on the current PLU code and the current N-dimensional feature vector.”
Group III claims 19-20 state distinct claim subject matter “receiving an item image for an item placed on a scale of a terminal during a transaction; receiving an item code entered for the item at the terminal; obtaining an N-dimensional feature vector based on the item image and the item code; obtaining a reduced dimensionality feature vector for the N-dimensional vector based on the N-dimensional vector and the item code; obtaining reference vectors linked to the item code, each reference vector having a same number of dimensions as the reduced dimensionality feature vector.”
The requirement is still deemed proper and is therefore made FINAL.
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 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The following claim 3 language is vague and indefinite because it is not clear if the override based on a similar result or the override is to change the result to similar:
“3. The method of claim 2 further comprising: receiving an override for the transaction indicating the item is similar to the reference item.”
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yuan et al. (US 9,424,461).
Regarding claim 1, Yuan teaches a method, comprising:
obtaining a feature vector having N-dimensions based on an image of an item associated with a transaction (see figure 5, Yuan discusses obtaining feature vectors of products purchased);
reducing the N-dimensions to fewer dimensions to generate a reduced dimensionality feature vector for the image (see figure 5, Yuan discusses decreasing the size of the feature vectors);
obtaining reference vectors linked to an item code provided for the item during the transaction (see figure 5, Yuan discusses database of product feature vectors associated with product identification codes);
determining based on the reference vectors, the reduced dimensionality feature vector, and the item code whether the item is similar or not similar to a reference item linked to the reference vectors (see figure 5, Yuan discusses calculating similarity between reduced vector with database of product feature vectors).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (US 9,424,461) in view of Brakob et al. (US 2023/0005342).
Regarding claim 2, Yuan does not expressly disclose further comprising: interrupting the transaction for an audit of the item when the determining indicates the item is dissimilar to the reference item. However, Brakob teaches further comprising: interrupting the transaction for an audit of the item when the determining indicates the item is dissimilar to the reference item (see para. 0019, Brakob discusses stopping a transaction performed at the checkout lane based on the determination that the unknown product does not match the product associated with the scanned product identifier).
Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Brakob to derive at the invention of claim 2. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Yuan in this manner in order to improve product vector similarity calculation for product purchases by interrupting the transaction of the item when the item is dissimilar to the reference item in order to prevent fraudulent purchases. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Yuan, while the teaching of Brakob continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of preventing illegal transactions by interrupting a transaction when an item is dissimilar to a reference item. The Yuan and Brakob systems perform product vector calculation, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 3, 4, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (US 9,424,461) in view of Verma et al. (US 11,367,116).
Regarding claim 3, Yuan does not expressly disclose further comprising: receiving an override for the transaction indicating the item is similar to the reference item.
However, Verma teaches further comprising: receiving an override for the transaction indicating the item is similar to the reference item (see col. 16 lines 1-40, Verma discusses user interface that may manually override matching result and updating the reference entries).
Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Verma to derive at the invention of claim 3. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Yuan in this manner in order to improve product vector similarity calculation for product purchases by overriding comparison results to prevent incorrect purchases. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Yuan, while the teaching of Verma continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of preventing illegal transactions by overriding a transaction when an item comparison is incorrectly performed. The Yuan and Verma systems perform product vector calculation, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 4, Verma teaches wherein receiving the override further includes adding or updating a reference storage back to include the reduced dimensionality feature vector as a new reference vector linked to the item code (see col. 16 lines 25-40, Verma discusses matching engine may identify a combination of items as reviewed in computer-accessible matching database. For instance, the matching engine 536 may mark (e.g., flag or un-flag) the item as reviewed, send it to the end of the queue of items for review, remove it from a list or set of entries).
The same motivation of claim 3 is applied to claim 4. Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Verma to derive at the invention of claim 4. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
Regarding claim 10, Yuan does not expressly disclose wherein determining further includes providing the item code, the reference vectors, the reduced dimensionality feature vector, and threshold score linked to the item code to a similarity machine learning model (MLM) as input and receiving a decision as to whether the item is similar or dissimilar to the reference item as output from the similarity MLM.
However, Verma teaches wherein determining further includes providing the item code, the reference vectors, the reduced dimensionality feature vector, and threshold score linked to the item code to a similarity machine learning model (MLM) as input and receiving a decision as to whether the item is similar or dissimilar to the reference item as output from the similarity MLM (see col. 9 lines 30-37, Verma discusses machine learning model that compares items, calculates a match scores, and uses a similarity threshold level).
Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Verma to derive at the invention of claim 10. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Yuan in this manner in order to improve product vector similarity calculation for product purchases by overriding comparison results to prevent incorrect purchases. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Yuan, while the teaching of Verma continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of preventing illegal transactions by overriding a transaction when an item comparison is incorrectly performed. The Yuan and Verma systems perform product vector calculation, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (US 9,424,461) in view of Du et al. (US 10,043,109).
Regarding claim 5, Yuan does not expressly disclose wherein obtaining the feature vector further includes providing the image and the item code to a base machine learning model (MLM) as input and receiving the feature vector as output from the base MLM.
However, Du teaches wherein obtaining the feature vector further includes providing the image and the item code to a base machine learning model (MLM) as input and receiving the feature vector as output from the base MLM (see col. 12 lines 15-41, Du discusses inputting feature vector and object labels to a machine learning model).
Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Du to derive at the invention of claim 5. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Yuan in this manner in order to improve product vector similarity calculation by implementing a machine learning model able to properly calculate similarities between image vectors. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Yuan, while the teaching of Du continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of perform image comparison using a machine learning model to properly analyze image data. The Yuan and Du systems perform product vector calculation, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 6, Yuan teaches wherein reducing further includes processing a principal component analysis on the N-dimensions of the feature vector to generate the fewer dimensions in the reduced dimensionality feature vector (see col. 9 lines 29-34, Yuan discusses calculating similarity between reduced vector with database of product feature vectors).
The same motivation of claim 5 is applied to claim 6. Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Du to derive at the invention of claim 6. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
Regarding claim 7, Du teaches wherein reducing further includes providing the feature vector and the item code to a principal component analysis (PCA) MLM as input and receiving the reduced dimensionality feature vector as output from the PCA MLM (see col. 12 lines 15-41, Du discusses dimensions of these object feature vectors can be reduced by applying at least one of Principal Component Analysis PCA).
The same motivation of claim 5 is applied to claim 7. Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Du to derive at the invention of claim 7. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
Regarding claim 8, Yuan does not expressly disclose wherein determining further includes providing the item code, the reference vectors, and the reduced dimensionality feature vector to a similarity machine learning model (MLM) as input and receiving a similarity score indicating a degree to which the item is similar to the reference item.
However, Du teaches wherein determining further includes providing the item code, the reference vectors, and the reduced dimensionality feature vector to a similarity machine learning model (MLM) as input and receiving a similarity score indicating a degree to which the item is similar to the reference item (see col. 6 line 65- col. 7 line 3, col. 15 lines 12- 27, Du discusses calculating similarity score indicating the similarity distance between items and determining the item with highest similarity score).
Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan with Du to derive at the invention of claim 8. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Yuan in this manner in order to improve product vector similarity calculation by implementing a machine learning model able to properly calculate similarities between image vectors. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Yuan, while the teaching of Du continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of perform image comparison using a machine learning model to properly analyze image data. The Yuan and Du systems perform product vector calculation, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (US 9,424,461) in view of Du et al. (US 10,043,109) in view of Verma et al. (US 11,367,116).
Regarding claim 9, Yuan and Du do not expressly disclose further comprising, comparing the similarity score to a threshold score to determined whether the item is similar or not similar to the reference item. However, Verma teaches further comprising, comparing the similarity score to a threshold score to determined whether the item is similar or not similar to the reference item (see col. 12 lines 8-32, Verma discusses comparing a similarity score to a threshold to determine whether items match).
Motivation to combine may be gleaned from the prior art considered. 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 invention of Yuan and Du with Verma to derive at the invention of claim 9. The result would have been expected, routine, and predictable in order to perform product vector similarity calculation.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Yuan and Du in this manner in order to improve product vector similarity calculation for product purchases by overriding comparison results to prevent incorrect purchases. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Yuan and Du, while the teaching of Verma continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of preventing illegal transactions by overriding a transaction when an item comparison is incorrectly performed. The Yuan, Du, and Verma systems perform product vector calculation, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Welch et al. (US 5,883,968) discusses set of N reference vectors correlated in the database with the identification code ascertained from the item, and transaction has been verified, i.e., that the identification code affixed to the item to be purchased is the correct code for that item.
Hu et al. (US 2019/0318405) discusses visual words of the query image can be compared with visual words of index images of an image index database by comparing DNN vectors of index images with a DNN vector of the query image.
Woolf (US 2023/0044463) discusses a product data locator that runs cosine similarity algorithm on the target image embedding vector with embedding vectors of the product database.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNY A CESE whose telephone number is (571) 270-1896. The examiner can normally be reached on Monday – Friday, 9am – 4pm.
If attempts to reach the primary examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Kenny A Cese/
Primary Examiner, Art Unit 2663