DETAILED ACTION
This rejection is in response to Request for Continued Examination filed on 05/22/2026.
Claims 17-22 are currently pending and have been examined.
Claims 17-22 are new.
Claims 1-16 are cancelled.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN 2022116903239 filed on 12/27/2022.
Response to Arguments
Applicant’s arguments, see page 6, filed 05/22/2026, with respect to 35 U.S.C. 112(b) to claims 3 and 10 have been fully considered and are persuasive because the claims are cancelled. The 35 U.S.C. 112(b) to claims 3 and 10 have been withdrawn.
Applicant's arguments filed 05/22/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on page 6-9 of remarks filed 05/22/2026 that the claims are directed to an improvement to technology because the claims recite specific set of rules allowing an electronic device to execute a recommendation without relying on historical purchase data which solves data sparsity and cold start technical failures, bringing improvement to transforming social-network text and clothing style evaluation data into machine usable structured representations, and Claim 17 does not preempt all ways of making a recommendation, Examiner respectfully disagrees.
In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception. See MPEP 2106.04(d)(I).
The courts have also identified limitations that did not integrate a judicial exception into a practical application: merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) and 2106.04(d)(I).
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. See MPEP § 2106.05(a)(II).
To show that the involvement of a computer assists in improving the technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) and 2106.05(a)(II).
While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B). Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1150, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379, 115 USPQ2d 1152, 1158 (Fed. Cir. 2015). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible. Diamond v. Diehr, 450 U.S. 175, 191-92 n.14, 209 USPQ 1, 10-11 n.14 (1981) ("We rejected in Flook the argument that because all possible uses of the mathematical formula were not pre-empted, the claim should be eligible for patent protection"). See also Synopsys v. Mentor Graphics, 839 F.3d at 1150, 120 USPQ2d at 1483; FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1098, 120 USPQ2d 1293, 1299 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1320-21, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); Sequenom, 788 F.3d at 1379, 115 USPQ2d at 1158. Several Federal Circuit decisions, however, have noted the absence of preemption when finding claims eligible under the Alice/Mayo test. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016); Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016); BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1350-52, 119 USPQ2d 1236, 1243-44 (Fed. Cir. 2016). See MPEP § 2106.04(I).
A specific way of achieving a result to mine data and develop and apply mathematical algorithms and models is not a stand-alone consideration in Step 2A Prong Two. Claim 17 merely includes instructions to implement an abstract idea on an electronic device.
The disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement to technology by merely using an electronic device to execute a recommendation without relying on historical purchase data. The claim fails to recite any limitations regarding transforming or converting social-network text and clothing style evaluation data into structural or machine usable structured representations. Applicant’s specification in paragraphs [0003-0004] discuss that existing methods use historical purchase records for clothing recommendations and the current methods rely on users' past clothing consumption behavior, leading to sparse data and cold start issues. However, recommendations without relying on historical purchase records or cold start issues with recommendations are directed to solving a commercial problem regarding item recommendations rather than solving a problem rooted in technology. Therefore, the claims are not directed to a practical application or recite significantly more by using an electronic device as a tool to recommend clothing items.
Whether the present claims preempt the abstract idea is not a standalone test for determining eligibility. The absence of complete preemption in the present claims does not demonstrate that the claims are eligible. Therefore, the claims are not eligible based on the absence of preemption.
With respect to applicant’s arguments on page 9 of remarks filed 05/22/2026 that the claims amount to significantly more than an abstract idea because the ordered combination of specifically recited machine processing operations is not well-understood, routine, or conventional, Examiner respectfully disagrees.
Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? Examiners should answer this question by first identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). This evaluation is made with respect to the considerations that the Supreme Court has identified as relevant to the eligibility analysis, which are introduced generally in Part I.A of this section, and discussed in detail in MPEP § 2106.05(a) through (h). Many of these considerations overlap, and often more than one consideration is relevant to analysis of an additional element. Not all considerations will be relevant to every element, or every claim. Because the evaluation in Step 2B is not a weighing test, it is not important how the elements are characterized or how many considerations apply from this list. It is important to evaluate the significance of the additional elements relative to the invention, and to keep in mind the ultimate question of whether the additional elements encompass an inventive concept. Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: carry over their identification of the additional element(s) in the claim from Step 2A Prong Two. See MPEP § 2106.05(II).
The claims are not analyzed as well-understood, routine, or conventional and therefore, do not invoke Berkheimer. The additional elements (e.g. electronic device and a trained machine learning relationship model) both individually and in combination do not amount to significantly more than the judicial exception because the electronic device is merely used as a tool to recommend the clothing item.
With respect to applicant’s arguments on page 9-10 of remarks filed 05/22/2026 that Agrawal does not teach inferring stable personality trait from social network language patterns, Examiner respectfully disagrees.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., inferring stable personality trait from social network language patternsare not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
With respect to applicant’s arguments on page 10 of remarks filed 05/22/2026 that Agrawal does not teach emotion dictionary, Examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant’s arguments are unpersuasive because Agrawal is attacked individually when the combination of references is relied upon to teach the predefined emotion dictionary.
With respect to applicant’s arguments on page 10-11 of remarks filed 05/22/2026 that the rejection relies upon impermissible hindsight because there is no rationale to combine Agrawal with Zadeh and Zadeh does not improve Agrawal’s operation or solve any problem identified in Agrawal, Examiner respectfully disagrees.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant’s argument is unpersuasive that the rejection relies upon impermissible hindsight because there is no rationale to combine Agrawal with Zadeh and Zadeh does not improve Agrawal’s operation or solve any problem identified in Agrawal. The conclusion of obviousness is not limited to whether the modification improves or solves a problem in the primary reference Agrawal. Agrawal teaches quantifying design styles and extraction but it does not teach computations (e.g. triangular fuzzy numbers and other equations) and predefined emotion dictionary. Zadeh is relied upon to teach the computations and the predefined emotion dictionary to provide reliable information and computations.
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.
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 17-22 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.
There is insufficient antecedent basis for the following limitations in
Claims 17 and 20: the extracted personality language feature elements;
Claims 19, 20, and 22: the proportional preferences of the fashion personality types.
Appropriate correction or clarification is required.
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 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 17-19 are directed to an electronic device and claims 20-22 are directed to a non-transitory computer readable medium (which falls within one of the four statutory categories of invention (process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 17, recites the abstract idea of:
obtain social text data associated with a user from a social network; extract a set of personality language feature elements from the social text data by applying a predefined emotion dictionary;
calculate correlation coefficients between the extracted personality language feature elements and predefined fashion personality types;
generate a key language feature set by filtering out personality language feature elements having correlation coefficients lower than a first threshold;
input the key language feature set …to generate a multidimensional matrix as output data, wherein the multidimensional matrix represents proportional preferences of the predefined fashion personality types for the user;
quantify design styles of a plurality of clothing samples by processing evaluation scores using triangular fuzzy numbers;
for each clothing sample, calculate an overall utility value of a triangular fuzzy number for each design style by:
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wherein UT(AT) represents the overall utility value of a triangular fizzy number,
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22
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= (ci, ai, di), represents the triangular fuzzy numbers for each design style, and m and I are the upper and lower limits of the triangular fizzy numbers, respectively;
calculate a degree of closeness between the clothing sample and the design style based on the calculated overall utility values;
construct a clothing design model that receives the multidimensional matrix as input and outputs a multidimensional dataset comprising classified design elements; and
output a personalized clothing recommendation for the user based on the outputted multidimensional dataset, thereby enabling the recommendation without relying on historical purchase data of the user.
The above-recited limitations amounts to certain methods of organizing human activity as it relates to sales activities and commercial interactions because the claim is directed towards personalized clothing recommendations for users by obtaining social text, extracting features from the social text using a predefined emotion dictionary, and performing calculations (e.g. correlation coefficient, threshold, matrix, triangular fuzzy number, and degree of closeness) to quantify clothing and design styles and output a personalized clothing recommendation. Accordingly, the claim recites an abstract idea. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
Claim 17: An electronic device, comprising: a memory storing computer-executable instructions; and a processor coupled to the memory, wherein the processor is configured to execute the computer-executable instructions to: … into a trained machine learning relationship model to…;
Claim 20: A non-transitory computer-readable storage medium storing computer- executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform operations, the operations comprising: …into a trained machine learning relationship model to …
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
Claim 17: An electronic device, comprising: a memory storing computer-executable instructions; and a processor coupled to the memory, wherein the processor is configured to execute the computer-executable instructions to: … into a trained machine learning relationship model to…;
Claim 20: A non-transitory computer-readable storage medium storing computer- executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform operations, the operations comprising: …into a trained machine learning relationship model to …
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 17-22 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (U.S. Pub. No. 20220051479 A1, hereinafter “Agrawal”) in view of Zadeh et al. (US Pub. No. 20140201126 A1, hereinafter “Zadeh”) in further view of Xie et al. (US Pub. No. 20200167849 A1, hereinafter “Xie”).
Regarding claim 17
Agrawal discloses an electronic device, comprising: a memory storing computer-executable instructions; and a processor coupled to the memory, wherein the processor is configured to execute the computer-executable instructions to (Agrawal, FIG. 1, [0027]: device, processor, memory):
obtain social text data associated with a user from a social network; extract a set of personality language feature elements from the social text data …(Agrawal, [0009]: obtain social media data (e.g. messages and comments) from user; [0047]: social media data (e.g. messages and comments) analyzed to determine sentiment indicators associated with the social media data such as keywords in social media messages; [0054]: sentiment indicators include the emotional indicators);
calculate correlation coefficients between the extracted personality language feature elements and predefined fashion personality types; generate a key language feature set by filtering out personality language feature elements having correlation coefficients lower than a first threshold (Agrawal, [0043]: the emotions may include happiness, sadness, indifference, surprise, disappointment, frustration, or other emotions, and the emotions may each be associated with a score or rating indicating the user sentiment; [0047]: a number of keywords from social media reply messages and comments regarding a sentiment associated with apparel design (e.g., “like,” “love,” “hate,” etc.).; [0048]: a number of replies that fails to satisfy a plurality threshold may be mapped to a score regarding user sentiment associated with the keyword; [0035]: ML models 138 may be configured to receive the output of the natural language processor 134 (or an output of one of the ML models 138) and to generate an output that indicates the apparel design; [0005]: perform natural language processing (NLP) on the text data to interpret the user instructions from the text data; [0006] After performing the NLP, the server may generate an apparel design based on the interpreted user instructions from text data by providing the user instruction to ML model to indicate visual design elements based on user’s instruction; [0008]: user sentiment determined from text data; [0033]: the phrases of the text data may include particular terms that represent visual design elements that contain the information of the user instructions with respect to the type of apparel design requested by the user; [0056]: refine the apparel design process until generation of an apparel design associated with a user sentiment score or rating that satisfies a threshold with higher user sentiment scores or ratings than previous apparel designs; [0070]: sentiment within the social media data scored assigning a particular rating based on failing or satisfying a threshold);
input the key language feature set into a trained machine learning relationship model to generate a multidimensional matrix as output data, wherein the multidimensional matrix represents proportional preferences of the predefined fashion personality types for the user (Agrawal, [0049]: generate vectors that include historical purchases and user sentiment distributions for each user, group users having similar vector value patterns into groups, and generate a matrix of user and vector values for the groups and after the group is identified, the recommendation engine 148 may be configured to perform operations based on user profiles (e.g. user sentiment scores) for other members of the group, such as providing recommendations for apparel associated with historical purchases of the other group members, adding additional visual design elements or apparel characteristics; [0010]: user profile includes user sentiment data and provide user sentiment data for training the ML model);
quantify design styles of a plurality of clothing samples by processing evaluation scores…(Agrawal, [0033]: NLP includes named entity recognition (NER) identifies design elements and apparel base/content and sub-categories from selected words and phrases (e.g. types of designs, sketches, logos, image design elements, text design elements, pattern design elements, color design elements, apparel color, apparel type, apparel size) and each word corresponds to a distance value; [0034]: based on recognized named entities from NLP generate apparel design with the same design elements and apparel base/content and subcategories; [0051]: extract from text data; [0026]: apparel includes clothing);
construct a clothing design model that receives the multidimensional matrix as input …comprising classified design elements (Agrawal, [0082]: neural style transfer network trained on apparel and visual design elements by training functions that generate a gram/style matrix formation, defining the style cost function, assigning style weights to optimize apparel design image generation; [0006]: neural style transfer to combine the visual design elements and apparel content to generate the apparel design; [0037]: training data includes cost function that is received by ML model); and
output a personalized clothing recommendation for the user …(Agrawal, [0049]: recommend apparel designs to user that are likely to appeal to the user).
Agrawal does not teach:
…by applying a predefined emotion dictionary;
using triangular fuzzy numbers;
for each clothing sample, calculate an overall utility value of a triangular fuzzy number for each design style by:
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wherein UT(AT) represents the overall utility value of a triangular fizzy number,
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= (ci, ai, di), represents the triangular fuzzy numbers for each design style, and m and I are the upper and lower limits of the triangular fizzy numbers, respectively;
calculate a degree of closeness between the clothing sample and the design style based on the calculated overall utility values;
and outputs a multidimensional dataset…;…based on the outputted multidimensional dataset, thereby enabling the recommendation without relying on historical purchase data of the user.
However, Zadeh teaches:
…by applying a predefined emotion dictionary (Zadeh, [2914]: detect emotion on the word, e.g. angry and shouting for dictation or transcribing or storage. The templates on the training for the library of words and emotions are stored, based on supervised learning on known samples for future recognition; [2617]: recognition of emotion analysis based on pre-determined data in a database; [2899]: the system does the tagging automatically, e.g. based on the teachings on emotion recognition);
…using triangular fuzzy numbers; for each clothing sample, calculate an overall utility value of a triangular fuzzy number for each design style by:
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wherein UT(AT) represents the overall utility value of a triangular fuzzy number,
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= (ci, ai, di), represents the triangular fuzzy numbers for each design style, and m and I are the upper and lower limits of the triangular fuzzy numbers, respectively (Zadeh, [2468]: probability function in fuzzy set; [1565]: values of attribute includes one or more of fuzzy numbers; [2658]: for clothing and dress, and various attributes are determined such as style and color; [2162]: all methods can apply expertise factor; [2305]: expertise factor using people to tag or give opinion on the test samples, to show the bias or expertise; [0945]: upper and lower probabilities);
calculate a degree of closeness between the clothing sample and the design style based on the calculated overall utility values (Zadeh, [1572]: similarity measure between A and Aα based on fuzzy set; [1385]: looking for degree of similarity, e.g. as a fuzzy parameter for clothing; [2468]: probability function in fuzzy set; [1565]: values of attributes one or more of fuzzy numbers; [2658]: for clothing and dress, and various attributes are determined such as style and color);
and outputs a multidimensional dataset…;…based on the outputted multidimensional dataset (Zadeh, [1767]: multi-valued output; [1443]: calculate multiple dimensional coordinates).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the quantifying the design styles and the extraction of Agrawal with calculations of scores, fuzzy numbers, degrees of closeness, and a predefined emotion dictionary as taught by Zadeh because the results of such a modification would be predictable. Specifically, Agrawal would continue to teach quantifying the design styles and extraction except now calculations of scores, fuzzy numbers, degrees of closeness, and a predefined emotion dictionary is taught according to the teachings of Zadeh to provide reliable information and computations. This is a predictable result of the combination. (Zadeh, [0094-0095]).
The combination of Agrawal and Zadeh does not teach:
thereby enabling the recommendation without relying on historical purchase data of the user.
However, Xie teaches:
thereby enabling the recommendation without relying on historical purchase data of the user (Xie, [0045]: recommending cold start items that have no historical transaction data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the recommendation of Agrawal and Zadeh with enabling the recommendation without relying on historical purchase data as taught by Xie because the results of such a modification would be predictable. Specifically, Agrawal and Zadeh would continue to teach the recommendation except now enabling the recommendation without relying on historical purchase data of the user is taught according to the teachings of Xie to recommend cold start items. This is a predictable result of the combination. (Xie, [0003]).
Regarding claims 18 and 21
The combination of Agrawal, Zadeh, and Xie teaches the electronic device of claim 17, wherein the degree of closeness between the clothing sample and the design style is calculated by:
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wherein ST(A, B) represents the degree of closeness between the clothing sample and the design style, UT(A) represents the overall utility value corresponding to a triangular fuzzy number A and UT(B) represents the overall utility value corresponding to a triangular fuzzy number B (Zadeh, [1572]: similarity measure between A and Aα based on fuzzy set; [1385]: looking for degree of similarity, e.g. as a fuzzy parameter for clothing; [2468]: probability function in fuzzy set; [1565]: values of attributes one or more of fuzzy numbers; [2658]: for clothing and dress, and various attributes are determined such as style and color).
The combination of Agrawal, Zadeh, and Xie is the same as set forth above in claim 17.
Regarding claims 19 and 22
The combination of Agrawal, Zadeh, and Xie teaches the electronic device of claim 17, wherein the clothing design model is constructed based on a correspondence between the proportional preferences of the fashion personality types…(Agrawal, [0082]: neural style transfer network trained on apparel and visual design elements by training functions that generate a gram/style matrix formation, defining the style cost function, assigning style weights to optimize apparel design image generation; [0006]: neural style transfer to combine the visual design elements and apparel content to generate the apparel design; [0037]: training data includes cost function that is received by ML model).
Agrawal does not teach:
… and the calculated degree of closeness.
However, Zadeh teaches:
… and the calculated degree of closeness (Zadeh, [1572]: similarity measure between A and Aα based on fuzzy set; [1385]: looking for degree of similarity, e.g. as a fuzzy parameter for clothing; [2468]: probability function in fuzzy set; [1565]: values of attributes one or more of fuzzy numbers; [2658]: for clothing and dress, and various attributes are determined such as style and color).
The motivation to combine Agrawal, Zadeh, and Xie is the same as set forth above in claim 17.
Regarding claim 20
Agrawal discloses a non-transitory computer-readable storage medium storing computer- executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform operations, the operations comprising: (Agrawal, [0013]: a non-transitory computer-readable storage medium stores instructions that, when executed by one or more processors; FIG. 1, [0027]: device, processor, memory):
obtaining social text data associated with a user from a social network; extracting a set of personality language feature elements from the social text data …(Agrawal, [0009]: obtain social media data (e.g. messages and comments) from user; [0047]: social media data (e.g. messages and comments) analyzed to determine sentiment indicators associated with the social media data such as keywords in social media messages; [0054]: sentiment indicators include the emotional indicators);
calculating correlation coefficients between the extracted personality language feature elements and predefined fashion personality types; generating a key language feature set by filtering out personality language feature elements having correlation coefficients lower than a first threshold (Agrawal, [0043]: the emotions may include happiness, sadness, indifference, surprise, disappointment, frustration, or other emotions, and the emotions may each be associated with a score or rating indicating the user sentiment; [0047]: a number of keywords from social media reply messages and comments regarding a sentiment associated with apparel design (e.g., “like,” “love,” “hate,” etc.).; [0048]: a number of replies that fails to satisfy a plurality threshold may be mapped to a score regarding user sentiment associated with the keyword; [0035]: ML models 138 may be configured to receive the output of the natural language processor 134 (or an output of one of the ML models 138) and to generate an output that indicates the apparel design; [0005]: perform natural language processing (NLP) on the text data to interpret the user instructions from the text data; [0006] After performing the NLP, the server may generate an apparel design based on the interpreted user instructions from text data by providing the user instruction to ML model to indicate visual design elements based on user’s instruction; [0008]: user sentiment determined from text data; [0033]: the phrases of the text data may include particular terms that represent visual design elements that contain the information of the user instructions with respect to the type of apparel design requested by the user; [0056]: refine the apparel design process until generation of an apparel design associated with a user sentiment score or rating that satisfies a threshold with higher user sentiment scores or ratings than previous apparel designs; [0070]: sentiment within the social media data scored assigning a particular rating based on failing or satisfying a threshold);
inputting the key language feature set into a trained machine learning relationship model to generate a multidimensional matrix as output data, wherein the multidimensional matrix represents proportional preferences of the predefined fashion personality types for the user (Agrawal, [0049]: generate vectors that include historical purchases and user sentiment distributions for each user, group users having similar vector value patterns into groups, and generate a matrix of user and vector values for the groups and after the group is identified, the recommendation engine 148 may be configured to perform operations based on user profiles (e.g. user sentiment scores) for other members of the group, such as providing recommendations for apparel associated with historical purchases of the other group members, adding additional visual design elements or apparel characteristics; [0010]: user profile includes user sentiment data and provide user sentiment data for training the ML model);
quantifying design styles of a plurality of clothing samples by processing evaluation scores…(Agrawal, [0033]: NLP includes named entity recognition (NER) identifies design elements and apparel base/content and sub-categories from selected words and phrases (e.g. types of designs, sketches, logos, image design elements, text design elements, pattern design elements, color design elements, apparel color, apparel type, apparel size) and each word corresponds to a distance value; [0034]: based on recognized named entities from NLP generate apparel design with the same design elements and apparel base/content and subcategories; [0051]: extract from text data; [0026]: apparel includes clothing);
constructing a clothing design model that receives the multidimensional matrix as input …comprising classified design elements, wherein the clothing design model is constructed based on a correspondence between the proportional preferences of the fashion personality types and the calculated …(Agrawal, [0081]: generate the apparel design(s) based on the visual design elements, and the convolutional layers 410-414 may be configured to generate the final apparel design; [0082]: neural style transfer network trained on apparel and visual design elements by training functions that generate a gram/style matrix formation, defining the style cost function, assigning style weights to optimize apparel design image generation, and combining the style cost function weight and the gram/style matrix. The total cost function may be defined to combine the content cost function and the style cost function; [0006]: neural style transfer to combine the visual design elements and apparel content to generate the apparel design; [0037]: training data includes cost function that is received by ML model; [0049]: adding additional visual design elements or apparel characteristics associated with historical designs associated with positive scores); and
outputting a personalized clothing recommendation for the user …(Agrawal, [0049]: recommend apparel designs to user that are likely to appeal to the user).
Agrawal does not teach:
…by applying a predefined emotion dictionary;
using triangular fuzzy numbers;
for each clothing sample, calculating an overall utility value of a triangular fuzzy number for each design style by:
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348
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wherein UT(AT) represents the overall utility value of a triangular fizzy number,
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= (ci, ai, di), represents the triangular fuzzy numbers for each design style, and m and I are the upper and lower limits of the triangular fizzy numbers, respectively;
calculating a degree of closeness between the clothing sample and the design style based on the calculated overall utility values;
… the calculated degree of closeness;
and outputs a multidimensional dataset…;…based on the outputted multidimensional dataset, thereby enabling the recommendation without relying on historical purchase data of the user.
However, Zadeh teaches:
…by applying a predefined emotion dictionary (Zadeh, [2914]: detect emotion on the word, e.g. angry and shouting for dictation or transcribing or storage. The templates on the training for the library of words and emotions are stored, based on supervised learning on known samples for future recognition; [2617]: recognition of emotion analysis based on pre-determined data in a database; [2899]: the system does the tagging automatically, e.g. based on the teachings on emotion recognition);
…using triangular fuzzy numbers; for each clothing sample, calculating an overall utility value of a triangular fuzzy number for each design style by:
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51
348
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wherein UT(AT) represents the overall utility value of a triangular fuzzy number,
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23
22
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= (ci, ai, di), represents the triangular fuzzy numbers for each design style, and m and I are the upper and lower limits of the triangular fuzzy numbers, respectively (Zadeh, [2468]: probability function in fuzzy set; [1565]: values of attribute includes one or more of fuzzy numbers; [2658]: for clothing and dress, and various attributes are determined such as style and color; [2162]: all methods can apply expertise factor; [2305]: expertise factor using people to tag or give opinion on the test samples, to show the bias or expertise; [0945]: upper and lower probabilities);
calculating a degree of closeness between the clothing sample and the design style based on the calculated overall utility values;… the calculated degree of closeness (Zadeh, [1572]: similarity measure between A and Aα based on fuzzy set; [1385]: looking for degree of similarity, e.g. as a fuzzy parameter for clothing; [2468]: probability function in fuzzy set; [1565]: values of attributes one or more of fuzzy numbers; [2658]: for clothing and dress, and various attributes are determined such as style and color);
and outputs a multidimensional dataset…;…based on the outputted multidimensional dataset (Zadeh, [1767]: multi-valued output; [1443]: calculate multiple dimensional coordinates).
The motivation to combine Agrawal and Zadeh is the same as set forth above in claim 17.
The combination of Agrawal and Zadeh does not teach:
thereby enabling the recommendation without relying on historical purchase data of the user.
However, Xie teaches:
thereby enabling the recommendation without relying on historical purchase data of the user (Xie, [0045]: recommending cold start items that have no historical transaction data).
The motivation to combine Agrawal, Zadeh, and Xie is the same as set forth above in claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Chen et al. (US Pub. No. 20200320769 A1, hereinafter “Chen”) related to predicting garment or accessory attributes using deep learning techniques, Joung et al. (KR 20220099753 A) related to combining information on the fashion style desired by consumers with the current fashion trends, and non-patent literature, “Using supervised learning to classify clothing brand styles,” related to machine learning techniques to search for fashion products.
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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