DETAILED ACTION
Introduction
This Non-Final Office Action is in response to the application with serial number 19/166,635, filed on September 18, 2025.
Claims 1-9 are amended via preliminary amendment.
Claims 1-9 are pending.
Information Disclosure Statement
The information disclosure statement filed on September 18, 2025, has been considered.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-9 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-9 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: designing a product based on features (as evidenced by exemplary independent claim 1; “extract product features from the graph data and generate a new product based on a new combination of the product features”), and predicting a probability that a customer will purchase the product (as evidenced by exemplary independent claim 1; “predict a purchase probability that a customer having a combination of the customer features will purchase the new product”); abstract ideas. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “generate graph data;” “extract product features from the graph data; and generate a new product based on a new combination of the product features” “add the new product data to the graph data;” “generate a prediction model that predicts a purchase probability;” “extract customer features from the graph data;” “acquire the new product and the combination of the customer features;” and “predict a purchase probability that a customer having the combination of the customer features will purchase the new product.” The steps are all steps for managing personal behavior related to the abstract ideas of designing a product based on features and predicting a probability that a customer will purchase the product that, when considered alone and in combination, are part of the abstract ideas of designing a product based on features and predicting a probability that a customer will purchase the product. The dependent claims further recite steps for managing personal behavior that are part of the abstract ideas of designing a product based on features and predicting a probability that a customer will purchase the product. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes using a graph of product attributes, customer attributes, and purchase data to design a new product with an optimal purchase probability.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a memory and processor in independent claim 1; and a computer readable medium in independent claim 9. No hardware is recited in independent claim 8). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning, but the abstract idea of predicting a probability that a customer will purchase a new product is generally linked to an environment for machine learning for implementation. Therefore, the recitation of machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than the recited abstract idea. See MPEP §2106.05(h). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4-6, 8, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220092413 A1 to Wang et al. (hereinafter ‘WANG’) in view of US 20200005087 A1 to Sewak (hereinafter ‘SEWAK’).
Claim 1 (Currently Amended)
WANG discloses a prediction device comprising: at least one memory configured to store instructions (see ¶[0070]; a module that includes memory that stores code executed by the processor); and
at least one processor configured to execute the instructions (see ¶[0070]; a module that includes memory that stores code executed by the processor) to:
generate graph data including a plurality of nodes and links indicating relationships between the nodes (see abstract; a knowledge graph comprising a plurality of nodes) based on product information (see ¶[0153]; a product node), customer information (see ¶[0116] and [0153]; a customer node with browsing or purchasing history and features that include age, gender, location, education), and a purchase history (see ¶[0153] and Fig. 5B; complete a knowledge graph using the purchase history of customers).
WANG does not specifically disclose, but SEWAK discloses, extract product features from the graph data (see ¶[0078]; define F-scores based on product attributes) and generate a new product based on a new combination of the product features (see abstract; create a new fashion design using computer models and one identified gap determined from the F-scores);
add the new product to the graph data (see abstract and ¶[0020]; train computer models using deep learning computer vision. Predict likely sales of a designed fashion product);
generate a prediction model that predicts a purchase probability from a combination of a product and a customer by machine learning with using the graph data to which the new product is added (see again abstract and ¶[0020]; train computer models using deep learning computer vision. Predict likely sales of a designed fashion product).
WANG further discloses extract customer features from the graph data to which the new product is added and generate a combination of the customer features (see again ¶[0116] and [0153]; a customer node with browsing or purchasing history and features that include age, gender, location, education); and
acquire the new product and the combination of the customer features (see again ¶[0153]; product node and customer node).
WANG does not specifically disclose, but SEWAK discloses, predict a purchase probability that a customer having the combination of the customer features will purchase the new product by using the prediction model (see ¶[0020], [0056], and [0100]; predict the likely sales of a designed fashion product based on the fashion score. Personalized fashion design that includes the F-score of each customer across each fashion sub-category. Different designs can be obtained for different consumer groups).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). SEWAK discloses automated personalized fashion design using purchase history and customer data that determines likely sales of the designed product. It would have been obvious for one of ordinary skill in the art at the time of invention to include the personalized design as taught by SEWAK in the system executing the method of WANG with the motivation to design a product that is likely to be purchased.
Claim 2 (Currently Amended)
The combination of WANG and SEWAK discloses the prediction device according to claim 1.
WANG does not specifically disclose, but SEWAK discloses, wherein the one or more processors output an optimal combination of a customer and a new product or an optimal new product based on a result of the prediction (see ¶[0019]; the system may optimize the new fashion designs that fill the identified gaps based in part on predicted profitability of the new designs and how the new designs affect other fashion products. See also abstract; personalized fashion design).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). SEWAK discloses automated personalized fashion design using purchase history and customer data that determines an optimal product based on profitability. It would have been obvious for one of ordinary skill in the art at the time of invention to include the optimal design as taught by SEWAK in the system executing the method of WANG with the motivation to design the most profitable product.
Claim 4 (Currently Amended)
The combination of WANG and SEWAK discloses the prediction device according to claim 1.
WANG further discloses wherein the one or more processors generate the graph data by combining a customer and a customer feature with a customer feature link, combining a product and a product feature with a product feature link, and combining the product and the customer with a purchase link, based on the product information, the customer information, and the purchase history (see ¶[0116] and [0153]; features of the nodes are stored in the knowledge graph, which includes customer nodes and product nodes. The knowledge graph includes the purchase history of customers. A large number of customer nodes and product nodes are in the knowledge graph).
Claim 5 (Currently Amended)
The combination of WANG and SEWAK discloses the prediction device according to claim 1.
WANG does not specifically disclose, but SEWAK discloses, wherein the one or more processors acquire a predetermined new product from all the generated new products, and the one or more processors predict a purchase probability for the predetermined new product (see ¶[0020], [0056], and [0100]; predict the likely sales of a designed fashion product based on the fashion score. Personalized fashion design that includes the F-score of each customer across each fashion sub-category. Different designs can be obtained for different consumer groups).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). SEWAK discloses automated personalized fashion design using purchase history and customer data that determines likely sales of the designed product. It would have been obvious for one of ordinary skill in the art at the time of invention to include the personalized design as taught by SEWAK in the system executing the method of WANG with the motivation to design a product that is likely to be purchased.
Claim 6 (Currently Amended)
The combination of WANG and SEWAK discloses the prediction device according to claim 1.
WANG does not specifically disclose, but SEWAK discloses, wherein the one or more processors acquire a predetermined combination of customer features from all the generated combinations of customer features (see ¶[0047]; identify new fashion trends, and generate F-scores for fashion products over multiple domains based on fashion data (e.g., based on the attributes of the products, consumer and/or critic comments regarding the fashion products, sales data, merchant preferences, user preferences, etc.). See also ¶[0020], [0056], and [0100]; predict the likely sales of a designed fashion product based on the fashion score. Personalized fashion design that includes the F-score of each customer across each fashion sub-category. Different designs can be obtained for different consumer groups), and
the one or more processors predict a purchase probability for a customer having the predetermined combination of the customer features (see again ¶[0020], [0056], and [0100]; predict the likely sales of a designed fashion product based on the fashion score. Personalized fashion design that includes the F-score of each customer across each fashion sub-category. Different designs can be obtained for different consumer groups).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). SEWAK discloses automated personalized fashion design using purchase history and customer data that determines likely sales of the designed product. It would have been obvious for one of ordinary skill in the art at the time of invention to include the personalized design as taught by SEWAK in the system executing the method of WANG with the motivation to design a product that is likely to be purchased.
Claim 8 (Currently Amended)
WANG discloses a prediction method comprising: generating graph data including a plurality of nodes and links indicating relationships between the nodes (see abstract; a knowledge graph comprising a plurality of nodes) based on product information (see ¶[0153]; a product node), customer information (see ¶[0116] and [0153]; a customer node with browsing or purchasing history and features that include age, gender, location, education), and a purchase history (see ¶[0153] and Fig. 5B; complete a knowledge graph using the purchase history of customers).
WANG does not specifically disclose, but SEWAK discloses, extracting product features from the graph data (see ¶[0078]; define F-scores based on product attributes) and generating a new product based on a new combination of the product features (see abstract; create a new fashion design using computer models and one identified gap determined from the F-scores);
adding the new product to the graph data (see abstract and ¶[0020]; train computer models using deep learning computer vision. Predict likely sales of a designed fashion product);
generating a prediction model that predicts a purchase probability from a combination of a product and a customer by machine learning with using the graph data to which the new product is added (see again abstract and ¶[0020]; train computer models using deep learning computer vision. Predict likely sales of a designed fashion product).
WANG further discloses extracting customer features from the graph data to which the new product is added and generating a combination of the customer features (see again ¶[0116] and [0153]; a customer node with browsing or purchasing history and features that include age, gender, location, education);
acquiring the new product and the combination of the customer features (see again ¶[0153]; product node and customer node).
WANG does not specifically disclose, but SEWAK discloses predicting a purchase probability that a customer having the combination of the customer features will purchase the new product by using the prediction model (see ¶[0020], [0056], and [0100]; predict the likely sales of a designed fashion product based on the fashion score. Personalized fashion design that includes the F-score of each customer across each fashion sub-category. Different designs can be obtained for different consumer groups).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). SEWAK discloses automated personalized fashion design using purchase history and customer data that determines likely sales of the designed product. It would have been obvious for one of ordinary skill in the art at the time of invention to include the personalized design as taught by SEWAK in the system executing the method of WANG with the motivation to design a product that is likely to be purchased.
Claim 9 (Currently Amended)
WANG discloses a non-transitory computer readable recording medium recording a program for causing a computer to execute processing (see ¶[0070]; a module that includes memory that stores code executed by the processor); comprising:
generating graph data including a plurality of nodes and links indicating relationships between the nodes (see abstract; a knowledge graph comprising a plurality of nodes) based on product information (see ¶[0153]; a product node), customer information (see ¶[0116] and [0153]; a customer node with browsing or purchasing history and features that include age, gender, location, education), and a purchase history (see ¶[0153] and Fig. 5B; complete a knowledge graph using the purchase history of customers).
WANG does not specifically disclose, but SEWAK discloses, extracting product features from the graph data (see ¶[0078]; define F-scores based on product attributes) and generating a new product based on a new combination of the product features (see abstract; create a new fashion design using computer models and one identified gap determined from the F-scores);
adding the new product to the graph data (see abstract and ¶[0020]; train computer models using deep learning computer vision. Predict likely sales of a designed fashion product);
generating a prediction model that predicts a purchase probability from a combination of a product and a customer by machine learning with using the graph data to which the new product is added (see again abstract and ¶[0020]; train computer models using deep learning computer vision. Predict likely sales of a designed fashion product).
WANG further discloses extracting customer features from the graph data to which the new product is added and generating a combination of the customer features (see again ¶[0116] and [0153]; a customer node with browsing or purchasing history and features that include age, gender, location, education); and
acquiring the new product and the combination of the customer features (see again ¶[0153]; product node and customer node).
WANG does not specifically disclose, but SEWAK discloses, predicting a purchase probability that a customer having the combination of the customer features will purchase the new product by using the prediction model (see ¶[0020], [0056], and [0100]; predict the likely sales of a designed fashion product based on the fashion score. Personalized fashion design that includes the F-score of each customer across each fashion sub-category. Different designs can be obtained for different consumer groups).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). SEWAK discloses automated personalized fashion design using purchase history and customer data that determines likely sales of the designed product. It would have been obvious for one of ordinary skill in the art at the time of invention to include the personalized design as taught by SEWAK in the system executing the method of WANG with the motivation to design a product that is likely to be purchased.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220092413 A1 to WANG et al. in view of US 20200005087 A1 to SEWAK as applied to claims 1 and 2 above, and further in view of US 20230196390 A1 to Inoue et al. (hereinafter ‘INOUE’).
Claim 3 (Currently Amended)
The combination of WANG and SEWAK discloses the prediction device according to claim 2.
The combination of WANG and SEWAK does not specifically disclose, but INOUE discloses, wherein the one or more processors output, as an optimal combination, a combination having a highest purchase probability among combinations of customers having the combination of the customer features and the new product (see abstract and ¶[0145]; the design evaluation device may calculate the purchase expectation degree (purchase probability) with respect to a plurality of designs to predict the purchase amount, and output and propose a design having the largest purchase amount as an optimal design for the product. Calculate market share and determine the marketability).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). INOUE discloses design evaluation of a new product design that includes outputting the design with the highest purchase probability. It would have been obvious for one of ordinary skill in the art at the time of invention to include the determination of the design with the highest purchase probability as taught by INOUE in the system executing the method of WANG with the motivation to increase market share and profit.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220092413 A1 to WANG et al. in view of US 20200005087 A1 to SEWAK as applied to claim 1 above, and further in view of US 20200159870 A1 to Bowen (hereinafter ‘BOWEN’).
Claim 7 (Currently Amended)
The combination of WANG and SEWAK discloses the prediction device according to claim 1.
The combination of WANG and SEWAK does not specifically disclose, but BOWEN discloses, the one or more processors are further configured to: add a product feature to the new product, wherein the one or more processors predict a purchase probability for the new product to which the product feature is added, and output the added product feature in a case where the purchase probability is equal to or more than a predetermined threshold (see abstract and ¶[0438]; the deep neural network may be used to predict what design elements and design element combinations (including new design elements not previously sold or offered) users are likely to purchase, and based on the predictions automatically generate templates including those design elements and design element combination users are likely to purchase (e.g., with a likelihood of greater than a threshold percentage of users).
WANG discloses a graph neural network that includes product and customer nodes with purchase history that learns relationships and probabilities of predicted features in the nodes (see ¶[0147] and [0153]). BOWEN discloses generating custom products that includes using design elements that users are likely to purchase with respect to a threshold as taught by BOWEN in the system executing the method of WANG with the motivation to optimize the design of a product.
Conclusion
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
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