DETAILED ACTION
Status of Claims
Claims 1-20 submitted on 10/21/2025 are pending and have been examined.
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 provisional Application No. 63/442,287, filed on 01/31/2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1
Claims 1-10 are directed to a machine and claims 11-20 are directed to a process (see MPEP 2106.03).
Step 2A, Prong 1
Claim 11, taken as representative, recites at least the following limitations that recite an abstract idea:
a method comprising:
for a training outfit look, in a sequence of different types of items to recommend an item to match one or more other outfit items in the training outfit look;
determining, based on an anchor item, at least one look template from a plurality of look templates, wherein the at least one look template comprises an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types;
determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate one or more preliminary looks;
determining, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks; and
transmitting, information based on the one or more looks.
The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction, see ¶0003 of the specification. Claims 1 recites similar limitations as claim 11.
Thus, under Prong 1 of Step 2A, claims 1 and 11 recite an abstract idea.
Step 2A, Prong 2
Claim 11 includes the following additional elements that are bolded:
a computer-implemented method comprising:
training a machine learning module by inputting training item images, for a training outfit look, in a sequence of different types of items for the machine learning module to recommend an item to match one or more other outfit items in the training outfit look;
determining, based on an anchor item, at least one look template from a plurality of look templates, wherein the at least one look template comprises an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types;
determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate one or more preliminary looks for the machine learning module;
determining, via the machine learning module, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks; and
transmitting, via a computer network and for display on a user interface, information based on the one or more looks.
Claim 1 includes the same additional elements as claim 11. In addition, claim 1 includes additional elements such as a system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations, and image embedding.
The additional elements recited in claims 1 and 11 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Fig. 2, ¶¶0017-0018 and ¶0043).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1 and 11 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1 and 11 are acknowledged, claims 1 and 11 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claim 1 and 11 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1 and 11 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1 and 11 are ineligible.
Dependent claims 2-10 and 12-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-10 and 12-20 merely further define the abstract limitations of claims 1 and 11 or provide further embellishments of the limitations recited in independent claims 1 and 11. Claims 2-10 and 12-20 do not introduce any further additional elements.
Thus, dependent claims 2-10 and 12-20 are ineligible.
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:
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 8-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polania et al. (US 2020/0257976 A1 [previously cited]) in view of Penner et al. (US 2022/0366476 A1).
Regarding Claim 1, Polania et al., hereinafter, Polania, discloses a system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising (Fig. 1; ¶¶0031-0032[The recommendation modeling computing system 100 includes a processor 102 communicatively connected to a memory 104 via a data bus 106…The memory 104 includes any of a variety of memory devices, such as using various types of computer-readable or computer storage media… Computer storage media can, in some embodiments, include embodiments including entirely non-transitory components.]):
training a machine learning module by inputting training item images, for a training outfit look, in a sequence of different types of items for the machine learning module to recommend an item to match one or more other outfit items in the training outfit look (Fig. 2[elements 202 and 210] and Fig. 7; ¶¶0052-0060[The first subnetwork 210 can be trained on an image database, such as IMAGENET.] in view of ¶0084[Operation 640 includes returning a subset of the scored items 642 as the recommended items 602. The scored items 642 will likely have a range of scores ranging from completely incompatible to completely compatible with the seed item 612. The subset can be selected as the items having the top N highest scores, where N is an integer. The value of N can be fixed or variable. For instance, the retail server 12 can send the seed item 612 with a request for a certain number of recommended items, and N can be set to that certain number. Returning the recommended items 602 can include sending identifiers of the recommended items 602 to the retail server 12. The retail server 12 can then provide the recommended items 602 for display at the user device 14.]; Examiner notes that the items are clothing items and represent different items, see ¶0009);
determining, based on an anchor item for which a page is to be displayed on an online website, at least one look template from a plurality of look templates, wherein the at least one look template comprises an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types (Figs. 5-7[showing a page displayed on an online website]; ¶0080[Operation 610 includes receiving the seed item 612. The seed item 612 is an apparel item on which the recommended items 602 are to be based]; Examiner notes that the seed item is comparable to the anchor item, ¶0077[FIG. 5 illustrates example model predictions for complementary items with the highest (shown in dashed boxes) and lowest (shown in dotted boxes) compatibility scores with respect to the seed item (shown in dot-dashed boxes)] in view of ¶0035[In an example, the recommendation engine 112 can receive as input an identifier of a seed apparel item of a particular apparel category from the retail server 12 and generate recommendations for apparel items from other categories that complement the seed apparel item as output to the retail server 12] in further view of ¶0081 which discloses that the template can include non-accessories and accessories such as shoes; Examiner notes that apparel category is comparable to a super product type);
determining an anchor image embedding for an anchor item image of the anchor item (Figs. 2-3[showing determination of image embeddings for two items]; ¶¶0064-0065[In an example, a first image of the pair of apparel images 202 is provided to the left branch 212 of the first subnetwork 210 of the network 200 and a second image of the pair of apparel images 202 is provided to the right branch 214 of the first subnetwork 210 of the network 200… At operation 340, the compatibility score 232 is obtained as output from the neural network 200. For example, as part of the processing in operation 330, the neural network 200 can provide the compatibility score 232 as output. The compatibility score 232 can be used for any of a variety of useful purposes, including the automatic recommendation of apparel items matching a given item.] in view of ¶¶0011-0012[In an example, the trained first subnetwork includes a left branch configured to generate embeddings for a first image of the pair of apparel images; and a right branch configured to generate embeddings for a second image of the pair of apparel images… receiving a seed item; for each respective item of a plurality of items in an item collection, determining a compatibility score between the seed item and the respective item]);
determining one or more image embeddings of one or more respective complementary items for the anchor item (Figs. 2-3[showing determination of image embeddings for two items]; ¶¶0064-0065[In an example, a first image of the pair of apparel images 202 is provided to the left branch 212 of the first subnetwork 210 of the network 200 and a second image of the pair of apparel images 202 is provided to the right branch 214 of the first subnetwork 210 of the network 200… At operation 340, the compatibility score 232 is obtained as output from the neural network 200. For example, as part of the processing in operation 330, the neural network 200 can provide the compatibility score 232 as output. The compatibility score 232 can be used for any of a variety of useful purposes, including the automatic recommendation of apparel items matching a given item.] in view of ¶0011[In an example, the trained first subnetwork includes a left branch configured to generate embeddings for a first image of the pair of apparel images; and a right branch configured to generate embeddings for a second image of the pair of apparel images.]);
generating, based on the anchor image embedding and based on the one or more image embeddings of the one or more respective complementary items for the anchor item (Fig. 6; ¶0081[Operation 620 includes performing operation 630 for each respective item 624 in an item collection 622. The item collection 622 is a collection of items of apparel. In many examples, the item collection 622 is a data structure (e.g., a list or an array) storing a plurality of identifiers of items of apparel… the item collection 622 is a subset of items of apparel from the item data 130 selected based on the seed item 612. For example, the item collection 622 can be selected as items in the item data 130 that are of an apparel category other than the apparel category of the seed item. For instance, where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified] in view of ¶0025[techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit]);
determining, via the machine learning module, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks (Figs. 5[showing preliminary looks] and 6; ¶¶0082-0084[Operation 630 includes determining a compatibility score 232 of the seed item 612 and the respective item 624. The compatibility score can be determined using the neural network 200 using the process 300. For example, an image associated with the seed item 612 and an image associated with the respective item 624 are combined to form the pair of images 202 and fed into the neural network 200 to obtain the compatibility score 232… Operation 640 includes returning a subset of the scored items 642 as the recommended items 602.] in view of ¶0081[In an example, the item collection 622 is a subset of items of apparel from the item data 130 selected based on the seed item 612. For example, the item collection 622 can be selected as items in the item data 130 that are of an apparel category other than the apparel category of the seed item. For instance, where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified]; see instant specification ¶0024 where it is stated that shoes are an accessory product type); and
transmitting, via a computer network and for display on a user interface, information based on the one or more looks (Figs. 6 and 7; ¶0084[Returning the recommended items 602 can include sending identifiers of the recommended items 602 to the retail server 12. The retail server 12 can then provide the recommended items 602 for display at the user device 14.]).
Although Polania discloses generating one or more preliminary looks, Polania does not explicitly disclose generating one or more preliminary looks for the machine learning module.
However, Penner et al., hereinafter, Penner, teaches generating preliminary looks for a machine learning module (¶0174[The machine learning methods 300 use data on what the user has liked previously 302, based on what the user has saved as preferred outfits 304, in order to generalize on what the user will like in the future 306. Features about the clothing articles serve to assist in the generalization 308. These features may include color, texture, type of article, time since last worn, patterns in the clothing, and other items.]).
The system of Penner is applicable to the system of Polania as they share characteristics and capabilities, namely, they are both targeted to outfit recommendation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the generation of outfits as disclosed by Polania to include generating preliminary looks for a machine learning module as taught by Penner. One of ordinary skill in the art would have been motivated to expand the system of Polania in order to help evaluate and recommend color and fashion solutions (¶0002).
Regarding Claim 2, Polania in view of Penner teaches the system of claim 1, Polania further discloses wherein generating the one or more preliminary looks for the machine learning module comprises:
determining the one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types based on multiple algorithms, based on the anchor image embedding, and based on the one or more image embeddings of the one or more respective complementary items for the anchor item (Fig. 2[showing multiple algorithms that incorporate image embeddings for complementary and anchor items] and Fig. 6; ¶0081 in view of ¶0042[In addition to or instead of the neural network structure described in FIG. 2, the neural network 200 can include a graph-based approach. For example, a graph can include multiple nodes, each corresponding to an apparel item, and neighboring nodes can represent an outfit. The connections between the nodes can be learned as part of the training process. At each training iteration, massages can be passed between the nodes and updates can be made. In an example, gated recurrent units can be used to update hidden states of the nodes. The functions can have hidden or fully-connected layers, among other arrangements. A linear combination of messages can be used to form a final prediction of fashion compatibility. In an example implementation, all nodes in the graph share a same weight]).
Regarding Claim 3, Polania in view of Penner teaches the system of claim 2, Polania further discloses wherein:
determining the one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types comprises: determining one or more first respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types based on a respective graph-based similarity between the anchor item and each of the one or more first respective complementary items (Figs. 2 and 6; ¶0042[In addition to or instead of the neural network structure described in FIG. 2, the neural network 200 can include a graph-based approach. For example, a graph can include multiple nodes, each corresponding to an apparel item, and neighboring nodes can represent an outfit. The connections between the nodes can be learned as part of the training process. At each training iteration, massages can be passed between the nodes and updates can be made.] in view of ¶0023[A compatibility score can be a numeric value indicating how similar the pair of images of apparel items are to pairs of training images tagged as being fashionable together in an outfit.]); and
determining one or more second respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types based on a respective co-purchase signal between the anchor item and each of the one or more second respective complementary items (¶0036[The recommendation engine 112 can analyze data, such as the item data 130, to determine relationships among items to identify items to be recommended. This can include, for example, identifying apparel items that are complementary to other items that have been or may be selected by a user]; Examiner notes that items being complementary is comparable to a co-purchase signal between the items); and
the one or more respective complementary items comprise a union of the one or more first respective complementary items and the one or more second respective complementary items (Figs. 4 and 5[showing the recommendations being made in union for two complementary items in outfit 8]; ¶0077[FIG. 5 illustrates example model predictions for complementary items with the highest (shown in dashed boxes) and lowest (shown in dotted boxes) compatibility scores with respect to the seed item (shown in dot-dashed boxes)]).
Regarding Claim 4, Polania in view of Penner teaches the system of claim 3, Polania further discloses wherein the operations further comprise:
determining the respective graph-based similarity based on a distance between the anchor image embedding and a respective image embedding of the one or more image embeddings of one or more respective complementary items for the anchor item (Fig. 2[image embeddings]; ¶0042[In addition to… the neural network structure described in FIG. 2, the neural network 200 can include a graph-based approach. For example, a graph can include multiple nodes, each corresponding to an apparel item, and neighboring nodes can represent an outfit] in view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure], ¶¶0072-0076 disclose that the similarity is based on a distance between embeddings).
Regarding Claim 5, Polania in view of Penner teaches the system of claim 3, Polania further discloses wherein: determining the one or more second respective complementary items for the anchor item further comprises: determining one or more similar items for the anchor item (Fig. 6; ¶0081[Operation 620 includes performing operation 630 for each respective item 624 in an item collection 622. The item collection 622 is a collection of items of apparel. In many examples, the item collection 622 is a data structure (e.g., a list or an array) storing a plurality of identifiers of items of apparel… the item collection 622 is a subset of items of apparel from the item data 130 selected based on the seed item 612. For example, the item collection 622 can be selected as items in the item data 130 that are of an apparel category other than the apparel category of the seed item. For instance, where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified] in view of ¶0025[techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit]); and
determining the one or more second respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types further based on a respective similar-item co-purchase signal between each of the one or more similar items and each of the one or more second respective complementary items (Fig. 6; ¶0036[The recommendation engine 112 can analyze data, such as the item data 130, to determine relationships among items to identify items to be recommended. This can include, for example, identifying apparel items that are complementary to other items that have been or may be selected by a user]; Examiner notes that items being complementary are comparable to a co-purchase signal between the items and further indicates similarity).
Regarding Claim 6, Polania in view of Penner teaches the system of claim 1, Polania further discloses wherein the operations further comprise: generating training image feature vectors for the training item images (Fig. 2; ¶0011[receive input representative of a pair of apparel images and provide an output representative of a compatibility score… a combiner configured to produce a vector from the pair of features; a second subnetwork configured to forward propagate the vector; and a readout function configured to produce the output representative of a compatibility score based on an output of the second subnetwork.] in view of ¶¶0056-0060[Each entry of the vectors in the set… Using the foregoing, a plurality of training examples can be used to train the neural network 200.] in view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]).
Regarding Claim 8, Polania in view of Penner teaches the system of claim 1, Polania further discloses wherein the operations further comprise:
after the one or more looks are created, re-determining the one or more looks by: choosing a respective simulation anchor item from each of the one or more looks (Figs. 5-7[shows the customer choosing, element 704 shows simulation anchor item selections]; ¶0085[FIG. 7 illustrates a retailer interface 700 useable for providing apparel item recommendations, according to an example implementation. The retailer interface 700 can be presented within a retailer website, such as may be provided by a retail server 12 as noted above. The retailer interface 700 can be presented to a user and include a set of recommended items of apparel (e.g., based on a user selection of a particular item). As seen in the example, a selection of items 1404 can be presented to a user based on information returned to the retail server 12 from the recommendation engine 112 of system 100, in response to user selection of item 702 (e.g., based on a determination of complementarity). The specific recommendations will change according to which item is selected by the user, as noted above] in view of ¶0025[Given that a customer is interested in a seed apparel item (which can be referred to as a query), techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit] in further view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]; Examiner notes that a seed item is comparable to the anchor item and that element 704 on Fig. 7 of Polania is comparable to simulation anchor items which can be selected by a user); and
simulating complementary item determining and accessory determining for the respective simulation anchor item with the anchor item, wherein the one or more remaining non-accessory super product types further comprise one or more major super product types, and the respective simulation anchor item is selected from the one or more respective complementary items in each of the one or more major super product types (Fig. 7; ¶0085[FIG. 7 illustrates a retailer interface 700 useable for providing apparel item recommendations, according to an example implementation. The retailer interface 700 can be presented within a retailer website, such as may be provided by a retail server 12 as noted above. The retailer interface 700 can be presented to a user and include a set of recommended items of apparel (e.g., based on a user selection of a particular item). As seen in the example, a selection of items 1404 can be presented to a user based on information returned to the retail server 12 from the recommendation engine 112 of system 100, in response to user selection of item 702 (e.g., based on a determination of complementarity). The specific recommendations will change according to which item is selected by the user, as noted above.] in view of ¶0025[Given that a customer is interested in a seed apparel item (which can be referred to as a query), techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit] in further view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]; According to ¶0048 of instant specification, “simulation anchor item” is an item that is not the anchor item and “major super product types” may include some or all of the non-accessory product types).
Regarding Claim 9, Polania in view of Penner teaches the system of claim 8, Polania further discloses wherein the operations further comprise one or more of:
after re-determining the one or more looks, ranking the one or more looks based on a color matrix (Fig. 7; ¶0026[Color can be useful in determining compatibility between fashion items. Disclosed examples can explicitly incorporate color information in the feature extraction process and exploit correlations between the feature representations] in view of ¶0022[techniques disclosed herein can be used to identify which apparel items of a set are most compatible with the seed apparel item.]; Examiner notes that “most compatible” indicates ranking of the looks);
after re-determining the one or more looks, determining size availability match among respective items of each of the one or more looks based on sizes available for the anchor item; or
updating the plurality of look templates based on impression signals associated with historical looks created based on the plurality of look templates.
Regarding Claim 10, Polania in view of Penner teaches the system of claim 1, wherein the operations further comprise one or more of:
after the one or more looks are created, ranking the one or more looks based on a color matrix (Fig. 7; ¶0026[Color can be useful in determining compatibility between fashion items. Disclosed examples can explicitly incorporate color information in the feature extraction process and exploit correlations between the feature representations] in view of ¶0022[techniques disclosed herein can be used to identify which apparel items of a set are most compatible with the seed apparel item.]; Examiner notes that “most compatible” indicates ranking of the looks);
after the one or more looks are created, determining size availability match among respective items of each of the one or more looks based on sizes available for the anchor item; or
updating the plurality of look templates based on impression signals associated with historical looks created based on the plurality of look templates.
Regarding Claim 11, Polania discloses a computer-implemented method comprising:
training a machine learning module by inputting training item images, for a training outfit look, in a sequence of different types of items for the machine learning module to recommend an item to match one or more other outfit items in the training outfit look (Fig. 2[elements 202 and 210] and Fig. 7; ¶¶0052-0060[The first subnetwork 210 can be trained on an image database, such as IMAGENET.] in view of ¶0084[Operation 640 includes returning a subset of the scored items 642 as the recommended items 602. The scored items 642 will likely have a range of scores ranging from completely incompatible to completely compatible with the seed item 612. The subset can be selected as the items having the top N highest scores, where N is an integer. The value of N can be fixed or variable. For instance, the retail server 12 can send the seed item 612 with a request for a certain number of recommended items, and N can be set to that certain number. Returning the recommended items 602 can include sending identifiers of the recommended items 602 to the retail server 12. The retail server 12 can then provide the recommended items 602 for display at the user device 14.]);
determining, based on an anchor item, at least one look template from a plurality of look templates, wherein the at least one look template comprises an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types (Figs. 5-7; ¶0080[Operation 610 includes receiving the seed item 612. The seed item 612 is an apparel item on which the recommended items 602 are to be based]; Examiner notes that the seed item is comparable to the anchor item, ¶0077[FIG. 5 illustrates example model predictions for complementary items with the highest (shown in dashed boxes) and lowest (shown in dotted boxes) compatibility scores with respect to the seed item (shown in dot-dashed boxes)] in view of ¶0035[In an example, the recommendation engine 112 can receive as input an identifier of a seed apparel item of a particular apparel category from the retail server 12 and generate recommendations for apparel items from other categories that complement the seed apparel item as output to the retail server 12] in further view of ¶0081 which discloses that the template can include accessories such as shoes; Examiner notes that apparel category is comparable to a super product type);
determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate (Fig. 6; ¶0081[Operation 620 includes performing operation 630 for each respective item 624 in an item collection 622. The item collection 622 is a collection of items of apparel. In many examples, the item collection 622 is a data structure (e.g., a list or an array) storing a plurality of identifiers of items of apparel… the item collection 622 is a subset of items of apparel from the item data 130 selected based on the seed item 612. For example, the item collection 622 can be selected as items in the item data 130 that are of an apparel category other than the apparel category of the seed item. For instance, where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified] in view of ¶0025[techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit]);
determining, via the machine learning module, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks (Figs. 5[showing preliminary looks] and 6; ¶¶0082-0084[Operation 630 includes determining a compatibility score 232 of the seed item 612 and the respective item 624. The compatibility score can be determined using the neural network 200 using the process 300. For example, an image associated with the seed item 612 and an image associated with the respective item 624 are combined to form the pair of images 202 and fed into the neural network 200 to obtain the compatibility score 232… Operation 640 includes returning a subset of the scored items 642 as the recommended items 602.] in view of ¶0081[In an example, the item collection 622 is a subset of items of apparel from the item data 130 selected based on the seed item 612. For example, the item collection 622 can be selected as items in the item data 130 that are of an apparel category other than the apparel category of the seed item. For instance, where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified]; see instant specification ¶0024 where it is stated that shoes are an accessory product type); and
transmitting, via a computer network and for display on a user interface, information based on the one or more looks (Figs. 6 and 7; ¶0084[Returning the recommended items 602 can include sending identifiers of the recommended items 602 to the retail server 12. The retail server 12 can then provide the recommended items 602 for display at the user device 14.]).
Although Polania discloses generating one or more preliminary looks, Polania does not explicitly disclose generating one or more preliminary looks for the machine learning module.
However, Penner teaches generating preliminary looks for a machine learning module (¶0174[The machine learning methods 300 use data on what the user has liked previously 302, based on what the user has saved as preferred outfits 304, in order to generalize on what the user will like in the future 306. Features about the clothing articles serve to assist in the generalization 308. These features may include color, texture, type of article, time since last worn, patterns in the clothing, and other items.]).
The method of Penner is applicable to the method of Polania as they share characteristics and capabilities, namely, they are both targeted to outfit recommendation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the generation of outfits as disclosed by Polania to include generating preliminary looks for a machine learning module as taught by Penner. One of ordinary skill in the art would have been motivated to expand the method of Polania in order to help evaluate and recommend color and fashion solutions (¶0002).
Regarding Claim 12, Polania in view of Penner teaches the computer-implemented method of claim 11, Polania further discloses wherein:
determining the one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types is based on multiple algorithms (Fig. 2[showing multiple algorithms] and Fig. 6; ¶0081 in view of ¶0042[In addition to or instead of the neural network structure described in FIG. 2, the neural network 200 can include a graph-based approach. For example, a graph can include multiple nodes, each corresponding to an apparel item, and neighboring nodes can represent an outfit. The connections between the nodes can be learned as part of the training process. At each training iteration, massages can be passed between the nodes and updates can be made. In an example, gated recurrent units can be used to update hidden states of the nodes. The functions can have hidden or fully-connected layers, among other arrangements. A linear combination of messages can be used to form a final prediction of fashion compatibility. In an example implementation, all nodes in the graph share a same weight]).
Regarding Claim 13, Polania in view of Penner teaches the computer-implemented method of claim 12, Polania further discloses wherein: determining the one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types further comprises:
determining one or more first respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types based on a respective graph-based similarity between the anchor item and each of the one or more first respective complementary items (Figs. 2 and 6; ¶0042[In addition to or instead of the neural network structure described in FIG. 2, the neural network 200 can include a graph-based approach. For example, a graph can include multiple nodes, each corresponding to an apparel item, and neighboring nodes can represent an outfit. The connections between the nodes can be learned as part of the training process. At each training iteration, massages can be passed between the nodes and updates can be made.] in view of ¶0023[A compatibility score can be a numeric value indicating how similar the pair of images of apparel items are to pairs of training images tagged as being fashionable together in an outfit.]); and
determining one or more second respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types based on a respective co-purchase signal between the anchor item and each of the one or more second respective complementary items (¶0036[The recommendation engine 112 can analyze data, such as the item data 130, to determine relationships among items to identify items to be recommended. This can include, for example, identifying apparel items that are complementary to other items that have been or may be selected by a user]; Examiner notes that items being complementary are comparable to a co-purchase signal between the items); and
the one or more respective complementary items comprise a union of the one or more first respective complementary items and the one or more second respective complementary items (Figs. 4 and 5[showing the recommendations being made in union for two complementary items in outfit 8]; ¶0077[FIG. 5 illustrates example model predictions for complementary items with the highest (shown in dashed boxes) and lowest (shown in dotted boxes) compatibility scores with respect to the seed item (shown in dot-dashed boxes)]).
Regarding Claim 14, Polania in view of Penner teaches the computer-implemented method of claim 13, Polania further discloses wherein: determining the one or more first respective complementary items for the anchor item further comprises:
determining an anchor image embedding for an anchor item image of the anchor item (Fig. 2; ¶0049[an embedding can be created from the categories of the apparel images 202. Each possible combination of categories of apparel items in the apparel images 202 can be assigned to a different value as part of the embedding (e.g., a combination of shirt and pants can have a value of one and a combination of shirt and skirt can have a value of two)]; Examiner notes that creating an embedding from an image is comparable to determining an image embedding);
determining a first respective image embedding for a first respective image of each of the one or more first respective complementary items (Fig. 2; ¶0011[In an example, the trained first subnetwork includes a left branch configured to generate embeddings for a first image of the pair of apparel images; and a right branch configured to generate embeddings for a second image of the pair of apparel images] in view of ¶0040 and ¶0096); and
determining the respective graph-based similarity based on a distance between the anchor image embedding and the first respective image embedding (¶0042[In addition to… the neural network structure described in FIG. 2, the neural network 200 can include a graph-based approach. For example, a graph can include multiple nodes, each corresponding to an apparel item, and neighboring nodes can represent an outfit] in view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]).
Regarding Claim 15, Polania in view of Penner teaches the computer-implemented method of claim 13, Polania further discloses wherein: determining the one or more second respective complementary items for the anchor item further comprises:
determining one or more similar items for the anchor item (Fig. 6; ¶0081[Operation 620 includes performing operation 630 for each respective item 624 in an item collection 622. The item collection 622 is a collection of items of apparel. In many examples, the item collection 622 is a data structure (e.g., a list or an array) storing a plurality of identifiers of items of apparel… the item collection 622 is a subset of items of apparel from the item data 130 selected based on the seed item 612. For example, the item collection 622 can be selected as items in the item data 130 that are of an apparel category other than the apparel category of the seed item. For instance, where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified] in view of ¶0025[techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit]); and
determining the one or more second respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types further based on a respective similar-item co-purchase signal between each of the one or more similar items and each of the one or more second respective complementary items (Fig. 6; ¶0036[The recommendation engine 112 can analyze data, such as the item data 130, to determine relationships among items to identify items to be recommended. This can include, for example, identifying apparel items that are complementary to other items that have been or may be selected by a user]; Examiner notes that items being complementary are comparable to a co-purchase signal between the items and further indicates similarity).
Regarding Claim 16, Polania in view of Penner teaches the computer-implemented method of claim 11, Polania discloses further comprising:
generating training image feature vectors for the training item images (Fig. 2; ¶0011[receive input representative of a pair of apparel images and provide an output representative of a compatibility score… a combiner configured to produce a vector from the pair of features; a second subnetwork configured to forward propagate the vector; and a readout function configured to produce the output representative of a compatibility score based on an output of the second subnetwork.] in view of ¶¶0056-0060[Each entry of the vectors in the set… Using the foregoing, a plurality of training examples can be used to train the neural network 200.] in view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]).
Regarding Claim 18, Polania in view of Penner teaches the computer-implemented method of claim 11, Polania discloses further comprising: after the one or more looks are created, re-determining the one or more looks by:
choosing a respective simulation anchor item from each of the one or more looks (Figs. 5-7[shows the customer choosing, element 704 shows simulation anchor item selections]; ¶0085[FIG. 7 illustrates a retailer interface 700 useable for providing apparel item recommendations, according to an example implementation. The retailer interface 700 can be presented within a retailer website, such as may be provided by a retail server 12 as noted above. The retailer interface 700 can be presented to a user and include a set of recommended items of apparel (e.g., based on a user selection of a particular item). As seen in the example, a selection of items 1404 can be presented to a user based on information returned to the retail server 12 from the recommendation engine 112 of system 100, in response to user selection of item 702 (e.g., based on a determination of complementarity). The specific recommendations will change according to which item is selected by the user, as noted above] in view of ¶0025[Given that a customer is interested in a seed apparel item (which can be referred to as a query), techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit] in further view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]; Examiner notes that a seed item is comparable to the anchor item and that element 704 on Fig. 7 of Polania is comparable to simulation anchor items which can be selected by a user); and
simulating complementary item determining and accessory determining for the respective simulation anchor item with the anchor item, wherein the one or more remaining non-accessory super product types further comprise one or more major super product types, and the respective simulation anchor item is selected from the one or more respective complementary items in each of the one or more major super product types (Fig. 7; ¶0085[FIG. 7 illustrates a retailer interface 700 useable for providing apparel item recommendations, according to an example implementation. The retailer interface 700 can be presented within a retailer website, such as may be provided by a retail server 12 as noted above. The retailer interface 700 can be presented to a user and include a set of recommended items of apparel (e.g., based on a user selection of a particular item). As seen in the example, a selection of items 1404 can be presented to a user based on information returned to the retail server 12 from the recommendation engine 112 of system 100, in response to user selection of item 702 (e.g., based on a determination of complementarity). The specific recommendations will change according to which item is selected by the user, as noted above.] in view of ¶0025[Given that a customer is interested in a seed apparel item (which can be referred to as a query), techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit] in further view of ¶0096[For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure]; According to ¶0048 of instant specification, “simulation anchor item” is an item that is not the anchor item and “major super product types” may include some or all of the non-accessory product types).
Regarding Claim 19, Polania in view of Penner teaches the computer-implemented method of claim 18, Polania discloses further comprising one or more of:
after re-determining the one or more looks, ranking the one or more looks based on a color matrix (Fig. 7; ¶0026[Color can be useful in determining compatibility between fashion items. Disclosed examples can explicitly incorporate color information in the feature extraction process and exploit correlations between the feature representations] in view of ¶0022[techniques disclosed herein can be used to identify which apparel items of a set are most compatible with the seed apparel item.]; Examiner notes that “most compatible” indicates ranking of the looks);
after re-determining the one or more looks, determining size availability match among respective items of each of the one or more looks based on sizes available for the anchor item; or
updating the plurality of look templates based on impression signals associated with historical looks created based on the plurality of look templates.
Regarding Claim 20, Polania in view of Penner teaches the computer-implemented method of claim 11, Polania discloses further comprising one or more of:
after the one or more looks are created, ranking the one or more looks based on a color matrix (Fig. 7; ¶0026[Color can be useful in determining compatibility between fashion items. Disclosed examples can explicitly incorporate color information in the feature extraction process and exploit correlations between the feature representations] in view of ¶0022[techniques disclosed herein can be used to identify which apparel items of a set are most compatible with the seed apparel item.]; Examiner notes that “most compatible” indicates ranking of the looks);
after the one or more looks are created, determining size availability match among respective items of each of the one or more looks based on sizes available for the anchor item; or
updating the plurality of look templates based on impression signals associated with historical looks created based on the plurality of look templates.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polania in view of Penner in view of Forsyth et al. (US 2020/0311798 A1 [previously cited]).
Regarding Claim 7, Polania in view of Penner teaches the system of claim 1, Polania further discloses wherein: training the machine learning module comprises:
training a module based on the training item images to generate for the training items; and training the machine learning module (Fig. 2[shows predetermined sequential steps]; ¶0081[where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified] and ¶0025[techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit] in view of ¶0011[receive input representative of a pair of apparel images and provide an output representative of a compatibility score… a combiner configured to produce a vector from the pair of features; a second subnetwork configured to forward propagate the vector; and a readout function configured to produce the output representative of a compatibility score based on an output of the second subnetwork.] in further view of ¶¶0056-0060, ¶0096; Examiner notes that shoes are comparable to an accessory item).
Although Polania discloses training a module, Polania in view of Penner does not explicitly teach training a visual-semantic embedding module and training item texts of the training items to generate visual-semantic embeddings; and training the machine learning module further based on the visual-semantic embeddings.
However, Forsyth et al., hereinafter, Forsyth, teaches training a visual-semantic embedding module and training item texts of the training items to generate visual semantic embeddings and training a machine learning module X (Fig. 4A-4D; ¶0046[FIG. 4A is a flow chart 400 to illustrate a neural network regression flow use of visual semantic embeddings that employs a two-step training procedure, according to an embodiment. As illustrated, the NN regressor model may be trained with use of a multi-layer neural network. As pre-processing steps, the search engine server 120 receives an input image 410 on which to train, which is submitted to the visual semantic embedder 124 (FIG. 1) in order to generate a visual semantic embedding for the input image 410. The search engine server 120 also receives, as an input, a group of words that represent labels describing the product represented by the input image 410 using the terms in the developed lexicon, which was discussed above. In one embodiment, the NN regressor model computes the visual semantic embedding based on a visual-semantic loss between a text-based image embedding of the input image and features represented within the group of words, as will be discussed in more detail with reference to FIG. 4B] in view of ¶0060[In some embodiments, the fully connected layer 464 of neural network processing may employ a visual-semantic loss algorithm 454 that together performs training in which the first set of vectors (or image embedding) is combined with the second set of vectors (or text embedding) in a way that most closely approximates values in the second set of vectors], ¶0119).
The system of Forsyth is applicable to the system of Polania in view of Penner as they share characteristics and capabilities, namely, they are targeted to online clothing recommendation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the training of a machine learning model as taught by Polania in view of Penner to include visual-semantic embeddings and training item texts as taught by Forsyth. One of ordinary skill in the art would have been motivated to expand the system of Polania in view of Penner in order to be cost-efficient and simplify the problem of picking clothes (¶0110).
Regarding Claim 17, Polania in view of Penner teaches the computer-implemented method of claim 11, Polania further discloses wherein: training the machine learning module comprises:
training a module based on the training item images to generate for the training items; and training the machine learning module further (Fig. 2[shows predetermined sequential steps]; ¶0081[where the seed item 612 is a pair of pants (e.g., being in a “pants” or “bottoms” apparel category), the item collection 622 can be apparel items from categories including tops, outerwear, accessories, shoes, and other categories different from the category to which the pair of pants is classified] and ¶0025[techniques disclosed herein can be used to recommend complementary apparel items that match the seed apparel item to form a stylish outfit] in view of ¶0011[receive input representative of a pair of apparel images and provide an output representative of a compatibility score… a combiner configured to produce a vector from the pair of features; a second subnetwork configured to forward propagate the vector; and a readout function configured to produce the output representative of a compatibility score based on an output of the second subnetwork.] in further view of ¶¶0056-0060, ¶0096; Examiner notes that shoes are comparable to an accessory item).
Although Polania discloses training a module, Polania in view of Penner does not explicitly teach training a visual-semantic embedding module and training item texts of the training items to generate visual-semantic embeddings; and training the machine learning module further based on the visual-semantic embeddings.
However, Forsyth teaches training a visual-semantic embedding module and training item texts of the training items to generate visual semantic embeddings and training a machine learning module (Fig. 4A-4D; ¶0046[FIG. 4A is a flow chart 400 to illustrate a neural network regression flow use of visual semantic embeddings that employs a two-step training procedure, according to an embodiment. As illustrated, the NN regressor model may be trained with use of a multi-layer neural network. As pre-processing steps, the search engine server 120 receives an input image 410 on which to train, which is submitted to the visual semantic embedder 124 (FIG. 1) in order to generate a visual semantic embedding for the input image 410. The search engine server 120 also receives, as an input, a group of words that represent labels describing the product represented by the input image 410 using the terms in the developed lexicon, which was discussed above. In one embodiment, the NN regressor model computes the visual semantic embedding based on a visual-semantic loss between a text-based image embedding of the input image and features represented within the group of words, as will be discussed in more detail with reference to FIG. 4B] in view of ¶0060[In some embodiments, the fully connected layer 464 of neural network processing may employ a visual-semantic loss algorithm 454 that together performs training in which the first set of vectors (or image embedding) is combined with the second set of vectors (or text embedding) in a way that most closely approximates values in the second set of vectors], ¶0119).
The method of Forsyth is applicable to the method of Polania in view of Penner as they share characteristics and capabilities, namely, they are all targeted to online clothing recommendation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the training of a machine learning model as taught by Polania in view of Penner to include visual-semantic embeddings and training item texts as taught by Forsyth. One of ordinary skill in the art would have been motivated to expand the method of Polania in view of Penner in order to be cost-efficient and simplify the problem of picking clothes (¶0110).
Response to Arguments
Applicant’s arguments on pages 12-13 of the remarks filed 10/21/2025, with respect to the previous 35
USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on pages 12-13 of the remarks that the amended claims do not fall within Certain Methods of Organizing Human Activity. Examiner respectfully disagrees. According to the MPEP 2106.04(a)(2)(II), the methods of organizing human activity relate to concepts of 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, and business relations) and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). A method comprising: for a training outfit look, in a sequence of different types of items to recommend an item to match one or more other outfit items in the training outfit look; determining, based on an anchor item, at least one look template from a plurality of look templates, wherein the at least one look template comprises an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types; determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate one or more preliminary looks; determining, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks; and transmitting, information based on the one or more looks constitutes a commercial or legal interaction as defined by the MPEP.
Applicant further argues on page 13 of the remarks that the amended claims integrate the abstract idea into a practical application by improving the functioning of a technical field. Examiner respectfully disagrees. As noted previously, the amended claims recite an abstract idea. The mere execution of the abstract idea on generic and high-level components such as a machine learning module, display on a user interface, processor, and memory does not integrate the abstract idea in a practical application, see ¶0017, ¶0018, ¶0029, ¶0043, and Fig. 2 of the instant specification where these components are described at a high-level and as generic. Furthermore, The additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond linking the use of the judicial exception to a particular technological environment. As currently recited, the instant claims are directed to improving the argued business task of a method comprising: for a training outfit look, in a sequence of different types of items to recommend an item to match one or more other outfit items in the training outfit look; determining, based on an anchor item, at least one look template from a plurality of look templates, wherein the at least one look template comprises an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types; determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate one or more preliminary looks; determining, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks; and transmitting, information based on the one or more looks as recited in amended claim 11 or more simply providing “product recommendations” (i.e., the abstract idea).
Furthermore, providing outfit recommendations based on multiple complementary item recommendation, according to the super product type for each candidate complementary item, determining the item image from multiple images for presenting for an outfit recommendation as recited in ¶0077 of the instant specification are also part of the abstract idea and the mere execution of the abstract idea on generic and high level components does not overcome the 101 rejection.
Accordingly, Examiner maintains that the invention is directed to a judicial exception without
significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the 35 USC §101 rejections are maintained.
Applicant’s arguments on page 14 of the remarks filed 10/21/2025, with respect to the previous 35 USC § 102/103 rejections have been fully considered but are moot in view of the new 103 rejection of the amended claims. As no remarks directed to the specific nature of how the previous art failed to teach the previously presented claims, references Polania and Forsyth have been maintained and reference Penner has been added in view of the claim amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/AHOORA LADONI/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689