DETAILED ACTION
The following is a Non-Final, First Office Action on the Merits in response to communications filed March 10, 2025. Claims 1–12 are currently pending.
Claim Objections
Claims 1–3, 5–7, and 9–11 are objected to because of the following informalities:
Claims 1, 5, and 9 recite “receiving … metadata of a retailer product, wherein the metadata comprises”. However, claims 1, 5, and 9 subsequently recite, for example, “metadata of a plurality of competitor products”. In view of the above, Examiner recommends amending the claims to recite “receiving … metadata of a retailer product, wherein the metadata of the retailer product comprises” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 1, 5, and 9 recite “the product attributes” in the element for “standardizing”. However, claims 1, 5, and 9 previously recite “a plurality of product attributes” in the element for “translating”. In view of the above, Examiner recommends amending the claims to recite “segregating the plurality of product attributes” in the element for “standardizing” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 1, 5, and 9 recite “the plurality of competitor product” in the element reciting “arranging”. However, the claims previously recite “a plurality of competitor products”. In view of the above, Examiner recommends amending the claims to recite “the plurality of competitor products
Claims 1, 5, and 9 recite “the retailer-competitor product pairs” in the element reciting “calculating … semantic similarity of the retailer-competitor product pairs”. However, the claims previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending the claims to recite “calculating … semantic similarity of the plurality of retailer-competitor product pairs” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 1, 5, and 9 recite “a pre-trained ML model, wherein the model combines” in the element reciting “computing”. Examiner recommends amending the claims to recite “a pre-trained ML model, wherein the pre-trained ML model combines” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 1, 5, and 9 further recite “each of the plurality of retailer-competitor product pair” in the element reciting “computing”. However, the claims previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending the claims to recite “each of the plurality of retailer-competitor product pairs” in the element reciting “computing” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 1, 5, and 9 recite “each of the of the plurality of competitor product attributes” in the element for “assigning”. Examiner recommends amending the claims to recite “each
Claims 1, 5, and 9 further recite “the retailer-competitor pairs” in the element for “assigning”. However, the claims previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending the claims to recite “assigning … to obtain combined weighted scores of the plurality of retailer-competitor product pairs” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 1, 5, and 9 similarly recite “the retailer-competitor pairs” in the element for “normalizing”. However, the claims previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending the claims to recite “normalizing … the combined weighted scores of the plurality of retailer-competitor product pairs” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Finally, claims 1, 5, and 9 recite “the retailer-competitor product pairs” and “the retailer-competitor product pair” in the element for “identifying”. As noted above, the claims previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending the claims to recite “the plurality of retailer-competitor product pairs” and “the plurality of retailer-competitor product pairs
Claims 2, 6, and 10 recite “the retailer-competitor product pairs”. However, claims 1, 5, and 9, from which claims 2, 6, and 10 depend, previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending claims 2, 6, and 10 to recite “the plurality of retailer-competitor product pairs” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 2, 6, and 10 further recite “a back-end dictionary” and “the dictionary”. Examiner recommends amending the claims to recite “the back-end dictionary” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 3, 7, and 11 recite “the retailer-competitor product pairs”. However, claims 1, 5, and 9, from which claims 3, 7, and 11 depend, previously recite “a plurality of retailer-competitor product pairs”. In view of the above, Examiner recommends amending claims 3, 7, and 11 to recite “the plurality of retailer-competitor product pairs” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 1–12 are 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.
Claims 1, 5, and 9 recite “metadata of the retailer product and the competitor products” in the element reciting “translating”. Although, claims 1, 5, and 9 previously recite “metadata of a retailer product” and “metadata of a plurality of competitor products”, there is insufficient antecedent basis for “metadata of the retailer product and the competitor products” in the claims.
For purposes of examination, claims 1, 5, and 9 are interpreted as reciting “translating … the metadata of the retailer product and the metadata of the plurality of competitor products”.
Claims 1, 5, and 9 recite “the translated metadata of the retailer product and the plurality of competitor products” in the element for “standardizing”. Although the claims previously recite “translated metadata comprising a plurality of product attributes” in the element for “translating”, there is insufficient antecedent basis for “the translated metadata of the retailer product and the plurality of competitor products” in the claims.
Claims 1, 5, and 9 further recite “the standardized metadata” in the element for “arranging”. There is insufficient antecedent basis for this limitation in the claims.
For purposes of examination, claims 1, 5, and 9 are interpreted as reciting “standardizing … the translated metadata to obtain standardized metadata by segregating the plurality of product attributes”.
Claims 1, 5, and 9 recite “filtering … using binary classification … wherein binary classification segregates”. Examiner submits that the second recitation of “binary classification” renders the scope of the claims indefinite because it is unclear whether Applicant intends for the second recitation to reference the first recitation or intends to introduce a second, different “binary classification”.
For purposes of examination, claims 1, 5, and 9 are interpreted as reciting “filtering … using binary classification … wherein the binary classification segregates”.
Claims 1, 5, and 9 recite “the filtered dataset” in the element reciting “calculating … a lexical similarity”. Although the claims previously recite “filtering … to obtain filtered data”, there is insufficient antecedent basis for “the filtered dataset” in the claims.
Claims 1, 5, and 9 similarly recite “the filtered dataset” in the element reciting “calculating … semantic similarity”. Although the claims previously recite “filtering … to obtain filtered data”, there is insufficient antecedent basis for “the filtered dataset” in the claims.
For purposes of examination, the claims are interpreted as reciting “filtering … the plurality of retailer-competitor product pairs using the binary classification to obtain a filtered dataset”.
Claims 1, 5, and 9 recite “the lexical similarity score” and “the semantic similarity score” in the element reciting “computing”. Although the claims previously recite “calculating … a lexical similarity … to obtain TFIDF score” and “calculating … semantic similarity … to obtain cosine similarity score”, there is insufficient antecedent basis for “the lexical similarity score” and “the semantic similarity score” in the claims.
For purposes of examination, claims 1, 5, and 9 are interpreted as reciting “wherein the pre-trained ML model combines the TFIDF cosine similarity score”.
Claims 1, 5, and 9 recite “the plurality of competitor product attributes” in the element reciting “assigning”. Although the claims previously recite “receiving … metadata of a plurality of competitor products from the domain with product attributes comparable to the plurality of retailer product attributes”, there is insufficient antecedent basis for “the plurality of competitor product attributes” in the claims.
For purposes of examination, the claims are interpreted as reciting “receiving, via the one or more hardware processors, metadata of a plurality of competitor products from the domain with a plurality of competitor product attributes that are comparable to the plurality of retailer product attributes”.
Finally, claims 1, 5, and 9 recite “the normalized scores” in the element for “identifying”. There is insufficient antecedent basis for this limitation in the claims.
For purposes of examination, the claims are interpreted as reciting “normalizing, via the one or more hardware processors, the combined weighted scores of the plurality of retailer-competitor product pairs to obtain normalized scores”.
In view of the above, claims 1, 5, and 9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2–4, 6–8, and 10–12, which depend from claims 1, 5, and 9, inherit the deficiencies described above. As a result, claims 2–4, 6–8, and 10–12 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2, 6, and 10 recite “the lexical similarity calculation” and “the feature importance score”. Although claims 1, 5, and 9, from which claims 2, 6, and 10 depend, previously recite “calculating … a lexical similarity” and “computing … feature importance scores”, there is insufficient antecedent basis for “the lexical similarity calculation” and “the feature importance score” in the claims.
For purposes of examination, the claims are interpreted as reciting “wherein calculating the lexical similarity back-end dictionary to process the feature importance scores instructions to calculate the lexical similarity calculation receives input from the back-end dictionary to process the feature importance score” (claim 6).
Claims 3, 7, and 11 recite “the pairs” in the elements reciting “the positive dataset” and “the negative dataset”. There is insufficient antecedent basis for “the pairs” in each limitation.
Claims 3, 7, and 11 further recite “metadata of the retailer product and the competitor product” in the elements reciting “the positive dataset” and “the negative dataset”. However, claims 1, 5, and 9, from which claims 3, 7, and 11 depend, previously recite “metadata of a retailer product”, “metadata of a plurality of competitor products”, and “metadata of the retailer product and the competitor products”. As a result, the elements of claims 3, 7, and 11 are indefinite because it is unclear whether Applicant intends for the recitations of claims 3, 7, and 11 to reference the previous recitations or intends to introduce distinct elements.
Finally, claims 3, 7, and 11 recite “binary classification” in the “wherein” clause. However, claims 1, 5, and 9, from which claims 3, 7, and 11 depend, previously recite “binary classification”. As a result, the elements of claims 3, 7, and 11 are indefinite because it is unclear whether Applicant intends for the recitation of claims 3, 7, and 11 to reference the previous recitation or intends to introduce distinct elements.
For purposes of examination, claims 3, 7, and 11 are interpreted as reciting “(a) the positive dataset when a retailer-competitor product pair derives word similarity in the metadata of the retailer product and the metadata of the plurality of competitor products, and (b) the negative dataset when the retailer-competitor product pair does not derive word similarity in the metadata of the retailer product and the metadata of the plurality of competitor products; and wherein the filtered dataset obtained through the binary classification is processed by discarding the negative dataset.”
In view of the above, Examiner respectfully requests that Applicant thoroughly review the claims for compliance with the requirements set forth under 35 U.S.C. 112(b).
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 1–12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–12 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements for “translating metadata of the retailer product and the competitor products to obtain translated metadata comprising a plurality of product attributes”; “standardizing the translated metadata of the retailer product and the plurality of competitor products by segregating the product attributes into (a) a product name, (b) a product category, and (c) a product description”; “arranging the standardized metadata by mapping each of the retailer product with each of the plurality of competitor product to create a plurality of retailer-competitor product pairs”; “filtering the plurality of retailer-competitor product pairs to obtain filtered data [that] segregates the plurality of retailer-competitor product pairs as (a) a positive dataset and (b) a negative dataset”; “calculating a lexical similarity of each of the plurality of retailer-competitor product pairs of the filtered dataset to obtain TFIDF score”; “calculating semantic similarity of the retailer-competitor product pairs of the filtered dataset to obtain cosine similarity score”; “computing feature importance scores for each of the plurality of retailer-competitor product pairs [that] combines the lexical similarity score and the semantic similarity score associated with each of the plurality of retailer-competitor product pair to obtain weighted scores of each of the plurality of attributes associated with the plurality of retailer-competitor pairs”; “assigning the weighted scores to each of the plurality of retailer product attributes and each of the of the plurality of competitor product attributes to obtain combined weighted scores of the retailer-competitor product pairs”; “normalizing the combined weighted scores of the retailer-competitor product pairs”; and “identifying a plurality of alternative products for the retailer product by ranking the retailer-competitor product pairs based on the normalized scores of each of the retailer-competitor product pair.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for commercial sales activities or behaviors because the elements describe a process for identifying alternative retailer products by evaluating retailer products and competitor products. The limitations further recite mental processes because the elements embody observations or evaluations that can be practically performed in the human mind or by a human using pen and paper. Finally, the “calculating” elements recite mathematical concepts because the elements recite mathematical calculations. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 5 and 9 include substantially similar limitations to those included with respect to claim 1. As a result, claims 5 and 9 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–4, 6–8, and 10–12 further describe the process for identifying alternative retailer products by evaluating retailer products and competitor products and further recite certain methods of organizing human activity, mental processes, and/or mathematical concepts for the same reasons as stated above. As a result, claims 2–4, 6–8, and 10–12 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include one or more hardware processors, binary classification, a pre-trained machine learning model, and steps for “receiving” metadata. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea; the classification and machine learning elements do no more than generally link the use of the recited abstract idea to a particular technological environment; and the steps for “receiving” amount to no more than insignificant extrasolution activities to the recited abstract idea. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 5 and 9 include substantially similar limitations to those included with respect to claim 1. Although claim 5 further includes a memory and one or more communication interfaces and claim 9 further includes machine-readable information storage mediums, the additional elements, when considered in view of the claim as a whole, do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea. As a result, claims 5 and 9 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2, 6, and 10 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a back-end dictionary for storing. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 2, 6, and 10 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 3–4, 7–8, and 11–12 do not include any additional elements beyond those included with respect to the claims from which claims 3–4, 7–8, and 11–12 depend. As a result, claims 3–4, 7–8, and 11–12 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include one or more hardware processors, binary classification, a pre-trained machine learning model, and steps for “receiving” metadata. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea; the classification and machine learning elements do no more than generally link the use of the recited abstract idea to a particular technological environment; and the steps for “receiving” embody well-understood, routine, and conventional computer functions in view of MPEP 2106.05(d)(II), which identifies receiving data as a conventional computer function. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claims 5 and 9 include substantially similar limitations to those included with respect to claim 1. Although claim 5 further includes a memory and one or more communication interfaces and claim 9 further includes machine-readable information storage mediums, the additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 5 and 9 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2, 6, and 10 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a back-end dictionary for storing. The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 2, 6, and 10 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 3–4, 7–8, and 11–12 do not include any additional elements beyond those included with respect to the claims from which claims 3–4, 7–8, and 11–12 depend. As a result, claims 3–4, 7–8, and 11–12 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–12 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Manchanda et al. (U.S. 2024/0070742) discloses a system directed to evaluating associations between product data entries using semantic similarity metrics;
Narlikar (U.S. 2023/0147670) discloses a system directed to identifying replacement items using TF-IDF values;
Banerjee et al. (U.S. 2023/0093756) discloses a system directed to generating item recommendations using weighted semantic similarity scores;
Miller et al. (U.S. 2022/0253871) discloses a system directed to analyzing product information using lexical and semantic vectors;
Sinha et al. (U.S. 2016/0117737) discloses a system directed to mapping products based on attribute similarities;
Dykstra et al. (U.S. 9,286,391) discloses a system directed to clustering items by analyzing keywords using semantic and TF-IDF analysis;
Surya et al. (U.S. 2014/0136549) discloses a system directed to matching source and competitor products using similarity scores;
Hunt et al. (U.S. 2008/0294583) discloses a system directed to matching source and competitor products using similarity analysis and weighted attributes; and
Ristoski et al. (Ristoski, Petar, et al. "A machine learning approach for product matching and categorization: Use case: Enriching product ads with semantic structured data." Semantic web 9.5 (2018): 707-728.) discloses a system directed to matching products using a mixed measure of cosine similarity and TF-IDF weights.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM S BROCKINGTON III whose telephone number is (571)270-3400. The examiner can normally be reached M-F, 8am-5pm, EST.
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/WILLIAM S BROCKINGTON III/ Primary Examiner, Art Unit 3623
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