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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/12/2025 has been entered.
Remarks
Claims 1-9 and 11-15 are pending.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “A method, performed by at least one processor, for improving the ranking and displaying of product determining categories for based on a search query, the method comprising: training a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results; obtaining distribution information on each of a plurality of categories of one or more words included in the search query; calculating features of the one or more words based on the distribution information on each of the plurality of categories; calculating, by the trained classification model, information on at least one category related to the search query based on the features of the one or more words and the search query; calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query; and providing a product search result based on the product category related scores”.
The limitations of “A method, performed by
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using “at least one processor” to perform the claimed steps. The “at least one processor” in these steps is recited at a high-level of generality (i.e., as “at least one processor” performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional element of “training a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results” and “by the trained classification model” that are mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). The claim also recites the additional elements of “obtaining distribution information on each of a plurality of categories of one or more words included in the search query” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “at least one processor” to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional element of “training a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results” and “by the trained classification model” that are mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, wherein words in each of the plurality of categories in plural pieces of product data” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim also recites the additional elements of “the obtaining of the distribution information comprises: obtaining probability information that is calculated based on an appearance frequency of the one or more words in each of the plurality of categories in plural pieces of product data” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, wherein the calculating of the features of the one or more words comprises: calculating an importance of the one or more words based on the distribution information on each of the plurality of categories and features of the search query; and calculating the features of the one or more words by applying the importance to the distribution information on each of the plurality of categories as a weight”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 3, wherein the calculating of the importance of the one or more words comprises: calculating the feature of each of the one or more words based on the distribution information on each of the plurality of categories; calculating, by a language model, the features of the search query; and calculating, by an attention model, the importance of the one or more words based on the features of each of the one or more words and the features of the search query”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, wherein the calculating of the features of the one or more words comprises: selecting a predetermined number of words from the one or more words based on a number of categories in which each of the one or more words appears in plural pieces of product data; calculating an importance of each of the selected words based on the distribution information on each of the plurality of categories of the selected words and the features of the search query; and calculating features of the selected words by applying the importance of each of the selected words to the distribution information on each of the plurality of categories as a weight”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, wherein the calculating of the features of the one or more words comprises: calculating an average value of category appearance frequencies related to each of the one or more words based on an appearance frequency of the one or more words in each of the plurality of categories in plural pieces of product data; and obtaining the features of the one or more words based on the calculated average value”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, wherein the obtaining of the distribution information on each of a plurality of categories of the one or more words included in the search query comprises: calculating the distribution information on each of the plurality of categories of each of the one or more words or each of the words in which two or more of the one or more words are combined, and wherein the calculating of the features of the one or more words comprises calculating the features of the one or more words based on the distribution information on each of the plurality of categories of each of the one or more words or each of the words in which two or more of the one or more words are combined”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, further comprising: adjusting ranking information of a product search result for the search query based on a probability for the calculated at least one category”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “A method, performed by at least one processor, for training a classification model for a search query, the method comprising: training the classification model for executing a category classification for the search query based on first training data including a category probability based on product names; and training the classification model based on second training data including a category probability based on a user's selection of the product search result for the search query, wherein the training of the classification model for executing a category classification for the search query comprises: obtaining distribution information on each of a plurality of categories of one or more words included in the search query; calculating features of the one or more words based on distribution information on each of the plurality of categories; calculating, by the classification model, a probability for at least one category related to the search query based on the features of the one or more words and the search query; and calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query”.
The limitations of “A method, performed by
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using “at least one processor” to perform the claimed steps. The “at least one processor” in these steps is recited at a high-level of generality (i.e., as “at least one processor” performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional element of “training the classification model for executing a category classification for the search query based on first training data including a category probability based on product names; and training the classification model based on second training data including a category probability based on a user's selection of the product search result for the search query” and “by the classification model” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). The claim also recites the additional elements of “obtaining distribution information on each of a plurality of categories of one or more words included in the search query” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “at least one processor” to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional element of “training the classification model for executing a category classification for the search query based on first training data including a category probability based on product names; and training the classification model based on second training data including a category probability based on a user's selection of the product search result for the search query” and “by the classification model” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). The claim also recites the additional elements of “obtaining distribution information on each of a plurality of categories of one or more words included in the search query” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 9, wherein the first training data is generated based on a probability of each of a plurality of product names being included in product meta information related to a plurality of categories”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 9, wherein the second training data is generated based on a category probability of a product selected by a user among search results for a plurality of queries”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “An information processing system comprising: a memory; and at least one processor connected to the memory and configured to execute at least one computer readable program included in the memory, wherein the at least one program includes instructions to: train a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results; obtain distribution information on each of a plurality of categories of one or more words included in a search query, calculate features of the one or more words based on the distribution information on each of the plurality of categories, and calculate, by the trained classification model, information on at least one category related to the search query based on the features of the one or more words and the search query, and calculate a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query: and provide a product search result based on the product category related scores”.
The limitations of “claim precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2).
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using “an information processing system comprising: a memory; and at least one processor” to perform the claimed steps. The “information processing system comprising: a memory; and at least one processor” in these steps is recited at a high-level of generality (i.e., as “an information processing system comprising: a memory; and at least one processor” performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional element of “train a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results” and “by the trained classification model” that are mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). The claim also recites the additional elements of “obtain distribution information on each of a plurality of categories of one or more words included in a search query” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “an information processing system comprising: a memory; and at least one processor” to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional element of “train a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results” and “by the trained classification model” that are mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). The claim also recites the additional elements of “obtain distribution information on each of a plurality of categories of one or more words included in a search query” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method as claimed in claim 1, wherein the product search result includes a plurality of product categories that are displayed in an order based on the product category related scores”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “
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.
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.
Claims 1-2, 7-8 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (‘Li’ hereinafter) (Li et al., "A feature-free search query classification approach using semantic distance." Expert Systems with Applications 39.12 (2012): 10739-10748) in view of Kota et al. (‘Kota’ hereinafter) (Publication Number 20210286851) and further in view of Wang et al. (‘Wang’ hereinafter) (Wang, Haiming, and Kenny Wong. "Personalized search: An interactive and iterative approach." 2014 IEEE World Congress on Services. IEEE, 2014) and further in view of Benyamin et al. (‘Benyamin’ hereinafter) (Patent Number 8380697).
As per claim 1, Li teaches
A method,
obtaining distribution information on each of a plurality of categories of one or more words included in the search query; (probability of joint distribution where statistics show probabilities of query “apple” in classified topics, sections 2.3.2.3 - 2.3.3)
calculating features of the one or more words based on the distribution information on each of the plurality of categories; (section 2.3.3., where f() functions show calculations based on distribution probabilities of words in different categories; see also sections 2.3.2.3 – 2.3.3)
Li does not explicitly indicate “training a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results”
However, Wang discloses “training a classification model using training data based on information on whether a user selects one or more product search results corresponding to one or more search queries or based on product information associated with the one or more product search results” (train classifiers using items from a user search that the user has viewed so far, page 2 & figure 1.1; page 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li and Wang because using the steps claimed would have given those skilled in the art the tools to improve the invention by an interactive and iterative approach to infer a user’s intentions implicitly, and adapt to changing user requirements by gathering relevance feedback from the user (see Wang, abstract). This gives the user the advantage of having better trained classification models to better infer a user’s intentions during search sessions.
Neither Li nor Wang explicitly indicate “performed by at least one processor” or “calculating, by the trained classification model, information on at least one category related to the search query based on the features of the one or more words and the search query”.
However, Kota discloses “performed by at least one processor” (paragraph [0114]), “calculating, by the trained classification model, information on at least one category related to the search query based on the features of the one or more words and the search query;” (model trained to generate probability values for a set of classification labels for search terms, paragraphs [0026],[0042]; note that Wang teaches a trained classifier as shown previously).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Wang and Kota because using the steps claimed would have given those skilled in the art the tools to improve the invention by providing users with help formulating searches based on their previous activity related to previously searched keywords and the type of data they are looking for (see Kota, paragraphs [0002], [0019]-[0021]). This gives the user the advantage of faster access to desired results.
Neither Li, Wang nor Kota explicitly indicates “calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query; and providing a product search result based on the product category related scores”.
However, Benyamin discloses “calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query; and providing a product search result based on the product category related scores” (query scored for each category and messages with highest score in this category and that contain some of the matched terms are returned as results, column 6, lines 3-12; see also query score calculations that match keywords of category to terms in the search query, column 6, lines 50-58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Wang, Kota and Benyamin because using the steps claimed would have given those skilled in the art the tools to improve the invention by being able to more efficiently search the huge volume of short messages that flow through social media networks (see Benyamin, background). This gives the user the advantage of finding relevant messages more quickly.
As per claim 2, Li teaches
the obtaining of the distribution information comprises: obtaining probability information that is calculated based on an appearance frequency of the one or more words in each of the plurality of categories in plural pieces of sections 2.3.2.3 - 2.3.3; Li does not expressly show “product [data]”. However, these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The training steps would be performed the same regardless of the data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). See MPEP 2106.05(g). Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to train using data having any type of content because such data does not functionally relate to the steps claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention).
As per claim 7, Li teaches
the obtaining of the distribution information on each of a plurality of categories of the one or more words included in the search query comprises: calculating the distribution information on each of the plurality of categories of each of the one or more words or each of the words in which two or more of the one or more words are combined, and wherein the calculating of the features of the one or more words comprises calculating the features of the one or more words based on the distribution information on each of the plurality of categories of each of the one or more words or each of the words in which two or more of the one or more words are combined. (section 2.3.3., where f() functions show calculations based on distribution probabilities of words in different categories; see also sections 2.3.2.3 – 2.3.3)
As per claim 8, Li teaches
adjusting ranking information of a product search result for the search query based on a probability for the calculated at least one category. (ranking strategies, sections 2.3 – 2.3.3)
As per claim 13,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 1 and is similarly rejected.
As per claim 14,
Neither Li, Wang nor Kota explicitly indicates “the product search result includes a plurality of product categories that are displayed in an order based on the product category related scores”.
However, Benyamin discloses “the product search result includes a plurality of product categories that are displayed in an order based on the product category related scores” (column 5, lines 20-32).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Wang, Kota and Benyamin because using the steps claimed would have given those skilled in the art the tools to improve the invention by being able to more efficiently search the huge volume of short messages that flow through social media networks (see Benyamin, background). This gives the user the advantage of finding relevant messages more quickly.
As per claim 15,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 14 and is similarly rejected.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (‘Li’ hereinafter) (Li et al., "A feature-free search query classification approach using semantic distance." Expert Systems with Applications 39.12 (2012): 10739-10748) in view of Kota et al. (‘Kota’ hereinafter) (Publication Number 20210286851) and further in view of Wang et al. (‘Wang’ hereinafter) (Wang, Haiming, and Kenny Wong. "Personalized search: An interactive and iterative approach." 2014 IEEE World Congress on Services. IEEE, 2014) and further in view of Benyamin et al. (‘Benyamin’ hereinafter) (Patent Number 8380697) and further in view of SARANATHAN et al. (‘SARANATHAN’ hereinafter) (Publication Number 20250086389).
As per claim 3,
Neither Li, Kota, Wang nor Benyamin explicitly indicates “the calculating of the features of the one or more words comprises: calculating an importance of the one or more words based on the distribution information on each of the plurality of categories and features of the search query; and calculating the features of the one or more words by applying the importance to the distribution information on each of the plurality of categories as a weight.”
However, SARANATHAN discloses “the calculating of the features of the one or more words comprises: calculating an importance of the one or more words based on the distribution information on each of the plurality of categories and features of the search query; and calculating the features of the one or more words by applying the importance to the distribution information on each of the plurality of categories as a weight” (paragraph [0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Kota, Wang, Benyamin and SARANATHAN because using the steps claimed would have given those skilled in the art the tools to improve the invention by improving the accuracy of natural language processing models by generating textual features by determining a topic probability score and a confidence score to be used as input to a classification model (see SARANATHAN, paragraphs [0028]-[0029]). This gives the user the advantage of more appropriate classifications for features of text queries.
As per claim 4, Li teaches
the calculating of the importance of the one or more words comprises: calculating the feature of each of the one or more words based on the distribution information on each of the plurality of categories; calculating, by a language model, the features of the search query; (section 2.3.3., where f() functions show calculations based on distribution probabilities of words in different categories; see also sections 2.3.2.3 – 2.3.3)
Neither Li, Kota, Wang nor Benyamin explicitly indicate “and calculating, by an attention model, the importance of the one or more words based on the features of each of the one or more words and the features of the search query”
However, SARANATHAN discloses “and calculating, by an attention model, the importance of the one or more words based on the features of each of the one or more words and the features of the search query” (paragraph [0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Kota, Wang, Benyamin and SARANATHAN because using the steps claimed would have given those skilled in the art the tools to improve the invention by improving the accuracy of natural language processing models by generating textual features by determining a topic probability score and a confidence score to be used as input to a classification model (see SARANATHAN, paragraphs [0028]-[0029]). This gives the user the advantage of more appropriate classifications for features of text queries.
As per claim 5,
Neither Li, Kota, Wang nor Benyamin explicitly indicate “the calculating of the features of the one or more words comprises: selecting a predetermined number of words from the one or more words based on a number of categories in which each of the one or more words appears in plural pieces of product data; calculating an importance of each of the selected words based on the distribution information on each of the plurality of categories of the selected words and the features of the search query; and calculating features of the selected words by applying the importance of each of the selected words to the distribution information on each of the plurality of categories as a weight.”
However, SARANATHAN discloses “the calculating of the features of the one or more words comprises: selecting a predetermined number of words from the one or more words based on a number of categories in which each of the one or more words appears in plural pieces of product data; calculating an importance of each of the selected words based on the distribution information on each of the plurality of categories of the selected words and the features of the search query; and calculating features of the selected words by applying the importance of each of the selected words to the distribution information on each of the plurality of categories as a weight” (paragraph [0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Kota, Wang, Benyamin and SARANATHAN because using the steps claimed would have given those skilled in the art the tools to improve the invention by improving the accuracy of natural language processing models by generating textual features by determining a topic probability score and a confidence score to be used as input to a classification model (see SARANATHAN, paragraphs [0028]-[0029]). This gives the user the advantage of more appropriate classifications for features of text queries.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (‘Li’ hereinafter) (Li et al., "A feature-free search query classification approach using semantic distance." Expert Systems with Applications 39.12 (2012): 10739-10748) in view of Kota et al. (‘Kota’ hereinafter) (Publication Number 20210286851) and further in view of Wang et al. (‘Wang’ hereinafter) (Wang, Haiming, and Kenny Wong. "Personalized search: An interactive and iterative approach." 2014 IEEE World Congress on Services. IEEE, 2014) and further in view of Benyamin et al. (‘Benyamin’ hereinafter) (Patent Number 8380697) and further in view of Lee et al. (‘Lee’ hereinafter) (Publication Number 20170178206).
As per claim 6,
Neither Li, Kota, Wang nor Benyamin explicitly indicate “the calculating of the features of the one or more words comprises: calculating an average value of category appearance frequencies related to each of the one or more words based on an appearance frequency of the one or more words in each of the plurality of categories in plural pieces of product data; and obtaining the features of the one or more words based on the calculated average value.”
However, Lee discloses “the calculating of the features of the one or more words comprises: calculating an average value of category appearance frequencies related to each of the one or more words based on an appearance frequency of the one or more words in each of the plurality of categories in plural pieces of product data; and obtaining the features of the one or more words based on the calculated average value” (paragraphs [0016],[0080]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Li, Kota, Wang, Benyamin and Lee because using the steps claimed would have given those skilled in the art the tools to improve the invention by allowing for the classification of a product using objective numeric values (see Lee, paragraphs [0001]-[0007]). This gives the user the advantage of not having subjective motivational or social characteristics of products effect their classification.
Claims 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kota et al. (‘Kota’ hereinafter) (Publication Number 20210286851) in view of Wang et al. (‘Wang’ hereinafter) (Wang, Haiming, and Kenny Wong. "Personalized search: An interactive and iterative approach." 2014 IEEE World Congress on Services. IEEE, 2014) and further in view of Li et al. (‘Li’ hereinafter) (Li et al., "A feature-free search query classification approach using semantic distance." Expert Systems with Applications 39.12 (2012): 10739-10748) and further in view of Benyamin et al. (‘Benyamin’ hereinafter) (Patent Number 8380697).
As per claim 9, Kota teaches
A method, performed by at least one processor, for training a classification model for a search query, the method comprising: (see abstract and background; paragraph [0114])
training the classification model for executing a category classification for the search query based on first training data including a category probability based on product names; (model trained to generate probability values for a set of classification labels for search terms, paragraphs [0026],[0042]; Kota does not expressly show “product names”. However, these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The training steps would be performed the same regardless of the data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). See MPEP 2106.05(g). Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to train using data having any type of content because such data does not functionally relate to the steps claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention).
and training the classification model based on Kota discloses the claimed invention except for “second [training data]”. It would have been obvious to one having ordinary skill in the art at the time the invention was made to perform the same training based on “second” training data, since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. In re Harza, 214 F.2d 669, 774 (CCPA 1960); cf St. Regis Paper Co. v. Bemis, 193 USPQ 8 (7th Cir. 1977) (holding that adding layers to an object was obvious to one of ordinary skill in the art). See MPEP 2144.04(VI)(B)).
calculating, by the classification model, a probability for at least one category related to the search query based on the features of the one or more words and the search query; ((model trained to generate probability values for a set of classification labels for search terms, paragraphs [0026],[0042]).
Kota does not explicitly indicate “a user’s selection of the product search result for the search query”.
However, Wang discloses “a user’s selection of the product search result for the search query” (items from a user search that the user has viewed so far, page 2 & figure 1.1; page 4; note that neither Kota nor Wang expressly show “product [search result]”. However, these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The claimed steps would be performed the same regardless of the data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). See MPEP 2106.05(g). Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to train using data having any type of content because such data does not functionally relate to the steps claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kota and Wang because using the steps claimed would have given those skilled in the art the tools to improve the invention by an interactive and iterative approach to infer a user’s intentions implicitly, and adapt to changing user requirements by gathering relevance feedback from the user (see Wang, abstract). This gives the user the advantage of having better trained classification models to better infer a user’s intentions during search sessions.
Neither Kota nor Wang explicitly indicates “wherein the training of the classification model for executing a category classification for the search query comprises: obtaining distribution information on each of a plurality of categories of one or more words included in the search query; calculating features of the one or more words based on distribution information on each of the plurality of categories;”.
However, Li discloses “wherein the training of the classification model for executing a category classification for the search query comprises: obtaining distribution information on each of a plurality of categories of one or more words included in the search query;” (probability of joint distribution where statistics show probabilities of query “apple” in classified topics, sections 2.3.2.3 - 2.3.3), “calculating features of the one or more words based on distribution information on each of the plurality of categories;” (section 2.3.3., where f() functions show calculations based on distribution probabilities of words in different categories; see also sections 2.3.2.3 – 2.3.3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kota, Wang and Li because using the steps claimed would have given those skilled in the art the tools to improve the invention by being able to adapt to different target categories which may be caused by the dynamic changes observed in both topic taxonomy and content (see Li, abstract). This gives the user the advantage of providing feature-free classification to reduce resource usage in trying to constantly adapt.
Neither Kota, Wang nor Li explicitly indicates “and calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query”.
However, Benyamin discloses “and calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query” (query scored for each category and messages with highest score in this category and that contain some of the matched terms are returned as results, column 6, lines 3-12; see also query score calculations that match keywords of category to terms in the search query, column 6, lines 50-58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kota, Wang, Li and Benyamin because using the steps claimed would have given those skilled in the art the tools to improve the invention by being able to more efficiently search the huge volume of short messages that flow through social media networks (see Benyamin, background). This gives the user the advantage of finding relevant messages more quickly.
As per claim 12,
the Kota discloses the claimed invention except for “second [training data]”. It would have been obvious to one having ordinary skill in the art at the time the invention was made to perform the same training based on “second” training data, since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. In re Harza, 214 F.2d 669, 774 (CCPA 1960); cf St. Regis Paper Co. v. Bemis, 193 USPQ 8 (7th Cir. 1977) (holding that adding layers to an object was obvious to one of ordinary skill in the art). See MPEP 2144.04(VI)(B)).
Kota does not explicitly indicate “selected by a user among search results for a plurality of queries”.
However, Wang discloses “selected by a user among search results for a plurality of queries” (items from a user search that the user has viewed so far, page 2 & figure 1.1; page 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kota and Wang because using the steps claimed would have given those skilled in the art the tools to improve the invention by an interactive and iterative approach to infer a user’s intentions implicitly, and adapt to changing user requirements by gathering relevance feedback from the user (see Wang, abstract). This gives the user the advantage of having better trained classification models to better infer a user’s intentions during search sessions.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kota et al. (‘Kota’ hereinafter) (Publication Number 20210286851) in view of Wang et al. (‘Wang’ hereinafter) (Wang, Haiming, and Kenny Wong. "Personalized search: An interactive and iterative approach." 2014 IEEE World Congress on Services. IEEE, 2014) and further in view of Li et al. (‘Li’ hereinafter) (Li et al., "A feature-free search query classification approach using semantic distance." Expert Systems with Applications 39.12 (2012): 10739-10748) and further in view of Benyamin et al. (‘Benyamin’ hereinafter) (Patent Number 8380697) and further in view of Nie et al. (‘Nie’ hereinafter) (Publication Number 20210065054).
As per claim 11, Kota teaches
the first training data is generated based on a probability of each of a plurality of product names
Neither Kota, Wang, Li nor Benyamin explicitly indicate “being included in product meta information”.
However, Nie discloses “being included in product meta information” (paragraph [0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kota, Wang Li, Benyamin and Nie because using the steps claimed would have given those skilled in the art the tools to improve the invention by providing better training data by providing labels or classification for unlabeled data (see Nie, background). This gives the user the advantage of being able to use training data that is typically unusable.
Response to Arguments
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant argues that the independent claims “focus on the improvement provided by the claimed method in which the method calculates a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query” and that “the additional elements/steps of calculating features of the one or more words based on the distribution information on each of the plurality of categories, calculating, by the trained classification model, information on at least one category related to the search query based on the features of the one or more words and the search query, calculating a plurality of product category related scores based on an appearance of one or more words from the search query in specific product categories regardless of the appearance of the one or more words in the search query and providing a product search result based on the product category related scores, is an improvement in the technology or technical field of using a computer network to provide improved search results including product categories related to a user search query that are not dependent on the frequency of the words in the search query. (See, Paragraphs [0006], [0052] and [0054] of the published application, U.S. Publication No. 2025/0103588” (applicant arguments, pages 8-9). However, Step 2A, Prong Two, of the 2019 PEG requires evaluating any additional elements beyond the judicial exception, individually and in combination, to determine whether they integrate the judicial exception into a practical application, using one or more of the considerations in MPEP §§ 2106.04(d), 2106.05(a)-(c), (e)-(h). The only additional element included in the applicant arguments that was the limitation of “by the trained classification model”, which performs the “calculating” step and was identified as merely the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer under MPEP 2106.05(f). The applicant has not shown how this additional element, or any other additional elements described in the rejections of record, integrate the judicial exception into a practical application and seems to have simply listed most of the claim language as additional elements that describe an improvement in the specification. These arguments are not convincing, since the applicant has not met the requirements of Step 2A, Prong Two, and therefore the rejections of record are maintained.
Applicant’s arguments with respect to the 35 USC 103 rejections of claims 1-9 and 11-15 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. It is noted that the newly added Benyamin reference, in combination with previously cited references, teaches the amended claims as shown in the rejections above.
Conclusion
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/JAY A MORRISON/Primary Examiner, Art Unit 2151