DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Applicant's “Amendment” filed on 5/20/2026 has been considered.
Rejection to Claims 1-20 under 35 USC 101 have been overcome.
Rejection to Claims 19-20 under 35 USC 112(b) have been overcome.
Claims 1-3, 10-11, 16, 18-20 are amended.
Claims 1-20 are currently pending and have been examined.
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 of this title, 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.
Claims 1-4, 10-12, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. US 20240040201 A1 to Lee in view of U.S. Patent No. US 11922541 B1 to Parasnis in further view of US 2024/0370708 A1 to Raghavan in further view of US 11507996 B1 to Zhou.
Regarding Claim 1, LEE discloses a method for using artificial intelligence (AI)-generated images to search an item catalog, the method comprising:
applying a machine learning model to the item image to generate embeddings for the item image; ([0129] Images of detected objects may be supplied to embedding 336. Embedding 336 may include converting one or more images to lower dimensionality. Embedding 336 may include providing one or more images to a dimensionality reduction model. The dimensionality reduction model may be a machine learning model. The dimensionality reduction model may be configured to reduce dimensionality of similar images in a similar way. For example, embedding 336 may receive as input an image, and generate as output a vector of values. )
generating a plurality of similarity scores by comparing the embeddings for the item image to a plurality of pre-computed embeddings derived from a plurality of images of items in the item catalog; ([0130] Reduced dimensionality image data may be provided to product identification 338. Product identification 338 may identify one or more products associated with the reduced dimensionality representations provided by embedding 336. Product identification 338 may compare reduced dimensionality image data (e.g., provided by embedding 336) to reduced dimensionality image data (e.g., generated from images of products by the same machine learning model as used by embedding 336) of products included in product image index 339. [0130] Product identification 338 may generate one or more indications of products detected in images of the content item (e.g., a list of products that may match products represented in product image index 339) and one or more indications of confidence values (e.g., a confidence that each of the list of products was accurately detected))
based on the plurality of similarity scores, selecting a similar image from the plurality of images; and from the item catalog, selecting an item corresponding to the similar image. ([0130] Product identification 338 may generate one or more indications of products detected in images of the content item (e.g., a list of products that may match products represented in product image index 339) and one or more indications of confidence values (e.g., a confidence that each of the list of products was accurately detected) … image identification module 330 may be configured to generate a list of all products detected in any selected frame, and provide confidence values for each product in each frame selected. Image identification module 330 may generate image-based product data, e.g., one or more identifiers of products, the products identified based on images of a content item.)
But does not explicitly disclose providing a text description of an item to an application programming interface (API) of an Al image generator to generate an item image; receiving the item image from the Al image generator;
receiving a text description of an item from a user via a text input field of an item search feature of a retail website; providing the item image to the user, receiving an updated text description of the item from the user; providing the updated text description and the item image to the API of the AI image generator, wherein the AI image generator retrieves stored data regarding the item image and previously input text descriptions, and generates an updated item image conditioned on both the item image and the updated text description; updated item image; receiving the updated item image from the AI image generator; and recommending the selected item to the user by displaying, via a user interface of the retail website, the similar image and the selected item. LEE does disclose [0047] The fusion model may receive indications of one or more products detected by a model receiving text associated with a content item as input. The fusion model may receive indications of confidence values that the one or more products are included in the text.
PARASNIS, on the other hand, teaches providing a text description of an item to an application programming interface (API) of an Al image generator to generate an item image; receiving the item image from the Al image generator; ([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 15 Ln 35-40] FIG. 21 shows an image 2102 created by a Generative Artificial Intelligence (GAI) tool; [Col 21 Ln 60-65] The content-generation tool also provides an Application Programming Interface (API) to create templates programmatically. Thus, the API includes commands for template creation and also commands for loading a specified template and generating blocks of content that are returned as results to the API call.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
RAGHAVAN, on the other hand, teaches receiving a text description of an item from a user via a text input field of an item search feature of a retail website; providing the item image to the user, receiving an updated text description of the item from the user; providing the updated text description and the item image to the API of the AI image generator, wherein the AI image generator retrieves stored data regarding the item image and previously input text descriptions, and generates an updated item image conditioned on both the item image and the updated text description; updated item image; receiving the updated item image from the AI image generator; and recommending the selected item to the user by displaying, via a user interface of the retail website, the similar image and the selected item. ([0154] During a first drafting iteration, the user 110 can provide a first input prompt 1202 to the client device 102. As shown, the first input prompt 1202 can be a textual description that recites “space slug”. This can be considered as a first attempt by the user 110 to obtain the synthetic content they envision or desire. As described above, the client device 102 can execute the client-side generative AI model 104 on the first input prompt 1202, and such execution can cause the client-side generative AI model 104 to synthesize a first coarse generative output 1204. As shown, the first coarse generative output 1204 can be an image that depicts what appears to be a slug slithering on a surface beneath a sky filled with stars, planets, or other celestial bodies. [0155] the user 110 can provide a second input prompt 1302 to the client device 102. As shown, the second input prompt 1302 can be an edited version of the first input prompt 1202 and can recite “slug flying through outer space”. This can be considered as an updated attempt by the user 110 to obtain the synthetic content they envision or desire. [0158] the prompt-output history 304 can be considered as comprising: all four of the input prompts 1202, 1302, 1402, and 1502; all four of the coarse generative outputs 1204, 1304, 1404, and 1504; and the respective approvals or disapprovals of those four coarse generative outputs. The client device 102 can accordingly generate the finalization instruction 702 and can transmit it to the server device 106. In various aspects, the server device 106 can, as described above, execute the server-side generative AI model 108 on the prompt-output history 304 (and on the device metadata 704, if included). In various instances, such execution can yield a fine generative output 1602. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by RAGHAVAN, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of RAGHAVAN, in order to obtain desired content (RAGHAVAN, [0155]).
ZHOU, on the other hand, teaches recommending the selected item to the user by displaying, via a user interface of the retail website, the similar image and the selected item. ([Col 12 Ln 60-Col 13 Ln 10] a user interface in which visually similar items are presented, according to some embodiments. In some embodiments, the similar items 180 may be used by a component for user interface generation for catalog access 700. The component 700 may be associated with a web server or other back-end system that generates a user interface that permits customers to search, browse, and make purchases from the electronic catalog 110. The component 700 may represent one or more services in a service-oriented system that collaborate to produce user interface elements associated with the electronic catalog 110. For example, the component 700 may generate a “suggested purchases” or “recommended products” pane or widget on a product detail page associated with the electronic catalog 110, e.g., the product detail page for the initial item 120.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by ZHOU, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of ZHOU, in order to generate product recommendations (ZHOU, [Col 5 Ln 10-15]).
Regarding Claim 2, LEE in view of PARASNIS teaches the method of claim 1.
However LEE does not explicitly teach wherein selecting the item corresponding to the similar image comprises: generating a plurality of text similarity scores by comparing embeddings derived from the text description to embeddings derived from textual data associated with the plurality of images of items in the item catalog; generating a combined similarity score for each of the plurality of images by applying a weighted combination of a respective one of the plurality of similarity scores and a respective one of the plurality of text similarity scores; and wherein selecting the similar image from the plurality of images is based on the combined similarity scores.
ZHOU, on the other hand, teaches wherein selecting the item corresponding to the similar image comprises: generating a plurality of text similarity scores by comparing embeddings derived from the text description to embeddings derived from textual data associated with the plurality of images of items in the item catalog; generating a combined similarity score for each of the plurality of images by applying a weighted combination of a respective one of the plurality of similarity scores and a respective one of the plurality of text similarity scores; and wherein selecting the similar image from the plurality of images is based on the combined similarity scores. ([Col 2 Ln 35-65] Sellers may characterize and differentiate items using titles, descriptive text, images, and so on. Customers may search the electronic catalog using search terms or browse categories of items in order to identify desired items. Customers may then purchase, rent, lease, or otherwise engage in transactions regarding particular items with sellers of those items. In some circumstances, a customer may identify a desired item such as an article of clothing or a cell phone case, but the item may not be in stock in the customer's size or may not be available to the specific customer for geographic, legal, regulatory, or other reasons. Using prior approaches, other catalog items may have been recommended to the customer using analysis of textual similarities, e.g., by comparing the title and description of the unavailable item to the titles and descriptions of other items in order to select and recommend similar items that the customer may wish to purchase instead. Similarly, other catalog items may have been recommended to the customer using analysis of purchase histories and/or browse histories of customers who also showed interest in the unavailable item. However, these approaches may yield inaccurate comparisons due to the inherent limitations of the inputs (e.g., item titles, purchase histories, and so on). These prior approaches may thus result in recommendations that fail to reflect the interests of customers, particularly for items where textual descriptions are less important than visual depictions. [Col 12 Ln 1-15] catalog item selection based on visual similarity, including brand affinity graph embedding, according to some embodiments. The system 100 may maintain a brand affinity graph representing relationships between brands. Edges in the graph may be determined using customer usage histories for the catalog, e.g., customer page view or browse histories 501, customer purchase histories 502, customer add-to-cart histories 503, and so on. Information from the brand affinity graph may be used to prioritize and/or rank the candidate items such that items from brands with stronger affinities to the brand of the initial item 120 may be given higher prioritization. [Col 4 Ln 30-35] improving the speed of performing entity-matching tasks by using machine learning techniques to generate similarity scores in real-time or near-real-time;)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by ZHOU, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of ZHOU, in order to generate product recommendations (ZHOU, [Col 5 Ln 10-15]).
Regarding Claim 3, LEE in view of PARASNIS teaches the method of claim 1.
However LEE does not explicitly teach wherein applying the machine learning model to the updated item image to generate embeddings for the updated item image comprises: applying a computer vision algorithm to the updated item image to determine a category of the updated item image; and applying a convolutional neural network to the updated item image to extract image features of the updated item image prior to comparing the updated item image to the plurality of images of items in the item catalog; wherein generating the plurality of similarity scores comprises comparing the image features of the updated item image to image features of images in the item catalog from the determined category.
RAGHAVAN, on the other hand, teaches wherein applying the machine learning model to the updated item image to generate embeddings for the updated item image comprises: applying a computer vision algorithm to the updated item image to determine a category of the updated item image; and applying a convolutional neural network to the updated item image to extract image features of the updated item image prior to comparing the updated item image to the plurality of images of items in the item catalog; wherein generating the plurality of similarity scores comprises comparing the image features of the updated item image to image features of images in the item catalog from the determined category. ([0045] the client device can host a first generative AI model. In various aspects, the first generative AI model can exhibit any suitable deep learning internal architecture. For example, the first generative AI model can include any suitable numbers of any suitable types of layers (e.g., input layer, one or more hidden layers, output layer, any of which can be convolutional layers, dense layers, non-linearity layers, pooling layers, batch normalization layers, or padding layers). As another example, the first generative AI model can include any suitable numbers of neurons in various layers (e.g., different layers can have the same or different numbers of neurons as each other). As yet another example, the first generative AI model can include any suitable activation functions (e.g., softmax, sigmoid, hyperbolic tangent, rectified linear unit) in various neurons (e.g., different neurons can have the same or different activation functions as each other). As still another example, the first generative AI model can include any suitable interneuron connections or interlayer connections (e.g., forward connections, skip connections, recurrent connections). [0229-0230] classification schemes or systems can be used to automatically learn and perform a number of functions, actions, or determinations. A classifier can map an input attribute vector, z=(z.sub.1, z.sub.2, z.sub.3, z.sub.4, z.sub.n), to a confidence that the input belongs to a class, as by f (z)=confidence (class). Such classification can employ a probabilistic or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to determinate an action to be automatically performed. [0146] fine generative output 1002 can be considered as also being a function of the device metadata 704. In other words, various characteristics or attributes of the fine generative output 1002 can change depending upon the device metadata 704 [0154] During a first drafting iteration, the user 110 can provide a first input prompt 1202 to the client device 102. As shown, the first input prompt 1202 can be a textual description that recites “space slug”. This can be considered as a first attempt by the user 110 to obtain the synthetic content they envision or desire. As described above, the client device 102 can execute the client-side generative AI model 104 on the first input prompt 1202, and such execution can cause the client-side generative AI model 104 to synthesize a first coarse generative output 1204. As shown, the first coarse generative output 1204 can be an image that depicts what appears to be a slug slithering on a surface beneath a sky filled with stars, planets, or other celestial bodies. [0155] the user 110 can provide a second input prompt 1302 to the client device 102. As shown, the second input prompt 1302 can be an edited version of the first input prompt 1202 and can recite “slug flying through outer space”. This can be considered as an updated attempt by the user 110 to obtain the synthetic content they envision or desire. [0158] the prompt-output history 304 can be considered as comprising: all four of the input prompts 1202, 1302, 1402, and 1502; all four of the coarse generative outputs 1204, 1304, 1404, and 1504; and the respective approvals or disapprovals of those four coarse generative outputs. The client device 102 can accordingly generate the finalization instruction 702 and can transmit it to the server device 106. In various aspects, the server device 106 can, as described above, execute the server-side generative AI model 108 on the prompt-output history 304 (and on the device metadata 704, if included). In various instances, such execution can yield a fine generative output 1602. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by RAGHAVAN, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of RAGHAVAN, in order to obtain desired content (RAGHAVAN, [0155]).
Regarding Claim 4, LEE in view of PARASNIS teaches the method of claim 1.
However LEE does not explicitly teach further comprising: automatically providing the item image to an item design system; and generating, at the item design system, a recommendation for an item design based at least in part on the item image..
PARASNIS, on the other hand, teaches further comprising: automatically providing the item image to an item design system; and generating, at the item design system, a recommendation for an item design based at least in part on the item image.. ([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling. [Col 21 Ln 60-65] The content-generation tool also provides an Application Programming Interface (API) to create templates programmatically. Thus, the API includes commands for template creation and also commands for loading a specified template and generating blocks of content that are returned as results to the API call.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
Regarding Claim 10, the claim recites a method comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Regarding Claim 11, the claim recites a system comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Regarding Claim 12, LEE in view of PARASNIS teaches the method of claim 1.
However LEE does not explicitly teach wherein the item design system is configured to: receive the item image and the text description; store the item image in a collection of images generated by the AI image generator; store the text description in a collection of image descriptions; analyze the collection of images and the collection of image descriptions; and based on the analysis of the collection of images and the collection of image descriptions, generate an item design recommendation..
PARASNIS, on the other hand, teaches wherein the item design system is configured to: receive the item image and the text description; store the item image in a collection of images generated by the AI image generator; ([Col 27 Ln 30-35] selecting a product image from a database of product images based on the identification of the product.) store the text description in a collection of image descriptions; ([Col 25 Ln 20-25] text associated with the selected product in the textual description; ) analyze the collection of images and the collection of image descriptions; and based on the analysis of the collection of images and the collection of image descriptions, generate an item design recommendation. ([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling. [Col 21 Ln 60-65] The content-generation tool also provides an Application Programming Interface (API) to create templates programmatically. Thus, the API includes commands for template creation and also commands for loading a specified template and generating blocks of content that are returned as results to the API call.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
Regarding Claim 16, LEE in view of PARASNIS teaches the system of claim 11.
LEE discloses further comprising the AI image generator, the item design system, and a search engine; wherein the search engine is configured to: apply the machine learning model to compare the item image to the plurality of images of items in the item catalog; and from the plurality of images of items in the item catalog, identify the similar image to the item image.
LEE discloses further comprising, prior to applying the machine learning model to the item image to generate the embeddings for the item image: providing the item image to a user; receiving an updated item description from the user; and providing the updated item description to the API of the Al image generator to update the item image.. ([0130] Output of image identification 330 may be utilized to update metadata of a content item (e.g., to include associations with one or more products, to include one or more product identifiers or indicators, etc.). [0190] Adjusting metadata may include supplementing metadata with one or more product associations, e.g., indications of associated products. Adjusting metadata may include updating captions, e.g., to include products that may have been incorrectly transcribed (e.g., incorrectly transcribed by a machine-generated captioning model). In some embodiments, processing logic may further receive one or more time stamps associated with the content item and one or more products (e.g., a time of a video at which a product is detected in an image of the video). Updating metadata may include adding to metadata an indication of a time at which a product is found in the content item.)
However LEE does not explicitly teach the updated item image to generate embeddings for the updated item image, wherein the machine learning model is the same machine learning model used to generate a plurality of pre-computed embeddings for the plurality of images of items in the item catalog; generate a plurality of similarity scores by comparing the embeddings for the updated item image to the plurality of pre-computed embeddings based on cosine similarity or Euclidean distance between the embeddings in a latent space; and based on the plurality of similarity scores.
ZHOU, on the other hand, teaches the updated item image to generate embeddings for the updated item image, wherein the machine learning model is the same machine learning model used to generate a plurality of pre-computed embeddings for the plurality of images of items in the item catalog; generate a plurality of similarity scores by comparing the embeddings for the updated item image to the plurality of pre-computed embeddings based on cosine similarity or Euclidean distance between the embeddings in a latent space; and based on the plurality of similarity scores. ([Col 2 Ln 35-65] Sellers may characterize and differentiate items using titles, descriptive text, images, and so on. Customers may search the electronic catalog using search terms or browse categories of items in order to identify desired items. Customers may then purchase, rent, lease, or otherwise engage in transactions regarding particular items with sellers of those items. In some circumstances, a customer may identify a desired item such as an article of clothing or a cell phone case, but the item may not be in stock in the customer's size or may not be available to the specific customer for geographic, legal, regulatory, or other reasons. Using prior approaches, other catalog items may have been recommended to the customer using analysis of textual similarities, e.g., by comparing the title and description of the unavailable item to the titles and descriptions of other items in order to select and recommend similar items that the customer may wish to purchase instead. Similarly, other catalog items may have been recommended to the customer using analysis of purchase histories and/or browse histories of customers who also showed interest in the unavailable item. However, these approaches may yield inaccurate comparisons due to the inherent limitations of the inputs (e.g., item titles, purchase histories, and so on). These prior approaches may thus result in recommendations that fail to reflect the interests of customers, particularly for items where textual descriptions are less important than visual depictions. [Col 12 Ln 1-15] catalog item selection based on visual similarity, including brand affinity graph embedding (pre-computed embeddings), according to some embodiments. The system 100 may maintain a brand affinity graph representing relationships between brands. Edges in the graph may be determined using customer usage histories for the catalog, e.g., customer page view or browse histories 501, customer purchase histories 502, customer add-to-cart histories 503, and so on. Information from the brand affinity graph may be used to prioritize and/or rank the candidate items such that items from brands with stronger affinities to the brand of the initial item 120 may be given higher prioritization. [Col 4 Ln 30-35] improving the speed of performing entity-matching tasks by using machine learning techniques to generate similarity scores in real-time or near-real-time;)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by ZHOU, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of ZHOU, in order to generate product recommendations (ZHOU, [Col 5 Ln 10-15]).
Regarding Claim 17, LEE in view of PARASNIS teaches the system of claim 16.
However LEE does not explicitly teach wherein the search engine is included within a retail website..
PARASNIS, on the other hand, teaches wherein the search engine is included within a retail website. ([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
Regarding Claim 18, LEE in view of PARASNIS teaches the system of claim 16.
However LEE does not explicitly teach wherein the search engine is included within a retail website..
PARASNIS, on the other hand, teaches wherein identifying the similar image to the item image comprises identifying a plurality of similar images to the item image; wherein selecting the item corresponding to the similar image comprises selecting a plurality of items; and wherein recommending the selected item to the user comprises recommending each item of a plurality of selected items to the user.
([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling. [Col 24 Ln 45-50] causing presentation in the UI of one or more items generated by the GAI tool.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
Regarding Claim 19, LEE discloses a non-transitory computer readable medium comprising: a processor; and memory storing instructions that, when executed by the processor, causes a retail website to:
apply a machine learning model to the item image to generate embeddings for the item image; ([0129] Images of detected objects may be supplied to embedding 336. Embedding 336 may include converting one or more images to lower dimensionality. Embedding 336 may include providing one or more images to a dimensionality reduction model. The dimensionality reduction model may be a machine learning model. The dimensionality reduction model may be configured to reduce dimensionality of similar images in a similar way. For example, embedding 336 may receive as input an image, and generate as output a vector of values. )
generate a plurality of similarity scores by comparing the embeddings for the item image to a plurality of pre-computed embeddings derived from a plurality of images of items in the item catalog; ([0130] Reduced dimensionality image data may be provided to product identification 338. Product identification 338 may identify one or more products associated with the reduced dimensionality representations provided by embedding 336. Product identification 338 may compare reduced dimensionality image data (e.g., provided by embedding 336) to reduced dimensionality image data (e.g., generated from images of products by the same machine learning model as used by embedding 336) of products included in product image index 339. [0130] Product identification 338 may generate one or more indications of products detected in images of the content item (e.g., a list of products that may match products represented in product image index 339) and one or more indications of confidence values (e.g., a confidence that each of the list of products was accurately detected))
based on the plurality of similarity scores, select a similar image from the plurality of images; and from an item catalog, select an item corresponding to the similar image. ([0130] Product identification 338 may generate one or more indications of products detected in images of the content item (e.g., a list of products that may match products represented in product image index 339) and one or more indications of confidence values (e.g., a confidence that each of the list of products was accurately detected) … image identification module 330 may be configured to generate a list of all products detected in any selected frame, and provide confidence values for each product in each frame selected. Image identification module 330 may generate image-based product data, e.g., one or more identifiers of products, the products identified based on images of a content item.)
But does not explicitly disclose receive a text description of an item from a user via a text input field of a user interface of the retail website; provide the text description to an AI image generator to generate an item image; receive the item image from the AI image generator;
receiving a text description of an item from a user via a text input field of an item search feature of a retail website; providing the item image to the user, receiving an updated text description of the item from the user; providing the updated text description and the item image to the API of the AI image generator, wherein the AI image generator retrieves stored data regarding the item image and previously input text descriptions, and generates an updated item image conditioned on both the item image and the updated text description; updated item image; receiving the updated item image from the AI image generator; and recommending the selected item to the user by displaying, via a user interface of the retail website, the similar image and the selected item.LEE does disclose [0047] The fusion model may receive indications of one or more products detected by a model receiving text associated with a content item as input. The fusion model may receive indications of confidence values that the one or more products are included in the text.
PARASNIS, on the other hand, teaches receive a text description of an item via a text input field of a user interface; provide the text description to an AI image generator to generate an item image; receive the item image from the AI image generator; ([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 15 Ln 35-40] FIG. 21 shows an image 2102 created by a Generative Artificial Intelligence (GAI) tool; [Col 21 Ln 60-65] The content-generation tool also provides an Application Programming Interface (API) to create templates programmatically. Thus, the API includes commands for template creation and also commands for loading a specified template and generating blocks of content that are returned as results to the API call.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
RAGHAVAN, on the other hand, teaches receiving a text description of an item from a user via a text input field of an item search feature of a retail website; providing the item image to the user, receiving an updated text description of the item from the user; providing the updated text description and the item image to the API of the AI image generator, wherein the AI image generator retrieves stored data regarding the item image and previously input text descriptions, and generates an updated item image conditioned on both the item image and the updated text description; updated item image; receiving the updated item image from the AI image generator; and recommending the selected item to the user by displaying, via a user interface of the retail website, the similar image and the selected item. ([0154] During a first drafting iteration, the user 110 can provide a first input prompt 1202 to the client device 102. As shown, the first input prompt 1202 can be a textual description that recites “space slug”. This can be considered as a first attempt by the user 110 to obtain the synthetic content they envision or desire. As described above, the client device 102 can execute the client-side generative AI model 104 on the first input prompt 1202, and such execution can cause the client-side generative AI model 104 to synthesize a first coarse generative output 1204. As shown, the first coarse generative output 1204 can be an image that depicts what appears to be a slug slithering on a surface beneath a sky filled with stars, planets, or other celestial bodies. [0155] the user 110 can provide a second input prompt 1302 to the client device 102. As shown, the second input prompt 1302 can be an edited version of the first input prompt 1202 and can recite “slug flying through outer space”. This can be considered as an updated attempt by the user 110 to obtain the synthetic content they envision or desire. [0158] the prompt-output history 304 can be considered as comprising: all four of the input prompts 1202, 1302, 1402, and 1502; all four of the coarse generative outputs 1204, 1304, 1404, and 1504; and the respective approvals or disapprovals of those four coarse generative outputs. The client device 102 can accordingly generate the finalization instruction 702 and can transmit it to the server device 106. In various aspects, the server device 106 can, as described above, execute the server-side generative AI model 108 on the prompt-output history 304 (and on the device metadata 704, if included). In various instances, such execution can yield a fine generative output 1602. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by RAGHAVAN, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of RAGHAVAN, in order to obtain desired content (RAGHAVAN, [0155]).
ZHOU, on the other hand, teaches recommending the selected item to the user by displaying, via a user interface of the retail website, the similar image and the selected item. ([Col 12 Ln 60-Col 13 Ln 10] a user interface in which visually similar items are presented, according to some embodiments. In some embodiments, the similar items 180 may be used by a component for user interface generation for catalog access 700. The component 700 may be associated with a web server or other back-end system that generates a user interface that permits customers to search, browse, and make purchases from the electronic catalog 110. The component 700 may represent one or more services in a service-oriented system that collaborate to produce user interface elements associated with the electronic catalog 110. For example, the component 700 may generate a “suggested purchases” or “recommended products” pane or widget on a product detail page associated with the electronic catalog 110, e.g., the product detail page for the initial item 120.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by ZHOU, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of ZHOU, in order to generate product recommendations (ZHOU, [Col 5 Ln 10-15]).
Regarding Claim 20, LEE in view of PARASNIS teaches the computer readable medium of claim 19.
However LEE does not explicitly teach wherein the instructions, when executed by the processor, further cause the website to display the item corresponding to the similar image via the user interface..
PARASNIS, on the other hand, teaches wherein the instructions, when executed by the processor, further cause the retail website to display the item corresponding to the similar image via the user interface.. ([Col 10 Ln 20-35] FIG. 12A illustrates the generation of a blog post 1202 with the content-generation tool, according to some example embodiments. The blog post includes the generation of a title, description, and image. The illustrated example shows images in the results panel, and one of the images has been added to the canvas. The prompt to generate the image was: Product shot of Sling Bag, intricate, elegant, glowing lights, highly detailed, digital painting, art station, glamor post, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski, artey freytag; [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
Claims 5-9, 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. US 20240040201 A1 to Lee in view of U.S. Patent No. US 11922541 B1 to Parasnis in further view of US 2024/0370708 A1 to Raghavan in further view of US 11507996 B1 to Zhou in view of U.S. Patent Application No. 2024/0119477 A1 to Best.
Regarding Claim 5, LEE in view of PARASNIS teaches the method of claim 4.
However the combination of LEE and PARASNIS does not explicitly teach further comprising determining, at the item design system, an attribute-based demand forecast for an attribute identified in the item image.
BEST, on the other hand, teaches further comprising determining, at the item design system, an attribute-based demand forecast for an attribute identified in the item image. ([0059] Regression Analysis: Employ regression models to forecast demand for various items based on historical sales data, user behavior, and external factors such as seasonality and economic trends. [0065] Content-Based Filtering: Propose items based on their attributes and features, aligning them with user preferences.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 6, LEE in view of PARASNIS teaches the method of claim 5.
However the combination of LEE and PARASNIS does not explicitly teach wherein the recommendation for the item design is based at least in part on a clustering of the item image with a plurality of generated images and on the attribute- based demand forecast for the attribute identified in the item image.
BEST, on the other hand, teaches wherein the recommendation for the item design is based at least in part on a clustering of the item image with a plurality of generated images and on the attribute- based demand forecast for the attribute identified in the item image.. ([0068] Leverage clustering algorithms to group items with similar characteristics or demand patterns, facilitating tailored strategies for different clusters to maximize profitability.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 7, LEE in view of PARASNIS teaches the method of claim 4.
However the combination of LEE and PARASNIS does not explicitly teach further comprising determining that a user did not purchase the selected item; and wherein generating, at the item design system, the recommendation for the item design is performed in response to determining that the user did not purchase the selected item..
BEST, on the other hand, teaches further comprising determining that a user did not purchase the selected item; and wherein generating, at the item design system, the recommendation for the item design is performed in response to determining that the user did not purchase the selected item. ([0053] The system will track all consumer interactions with an offer by capturing data which includes, date/time/location offer was first pushed; date/time/location of consumer initial response to accept (often referred to as avail) an offer or ignore an offer; date/time/location the consumer redeems an offer; date/time an availed offer expires without being redeemed. The use of Artificial Intelligence (AI), which uses all captured data to enhance the targeting of content, operates within the offer warehouse continually learning and making offer recommendations and predictions. In this way, the AI will provide more effective and relevant content for both merchants and consumers.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 8, LEE in view of PARASNIS teaches the method of claim 1.
However LEE does not explicitly teach further comprising: providing a plurality of text descriptions received from a plurality of users to the API of the Al image generator to generate a plurality of item images; receiving the plurality of item images from the Al image generator; providing the plurality of item images to an item design system; clustering, at the item design system, the plurality of item images to generate a plurality of clusters; and based on a characteristic of one of the plurality of clusters, generate an item design recommendation..
PARASNIS, on the other hand, teaches further comprising: providing a plurality of text descriptions received from a plurality of users to the API of the Al image generator to generate a plurality of item images; receiving the plurality of item images from the Al image generator; providing the plurality of item images to an item design system;. ([Col 5 Ln 30-35] Each canvas 310 includes a collection of one or more prompts 312. The prompt 312 is the text input used to generate content. [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling. [Col 21 Ln 60-65] The content-generation tool also provides an Application Programming Interface (API) to create templates programmatically. Thus, the API includes commands for template creation and also commands for loading a specified template and generating blocks of content that are returned as results to the API call.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
BEST, on the other hand, teaches clustering, at the item design system, the plurality of item images to generate a plurality of clusters; and based on a characteristic of one of the plurality of clusters, generate an item design recommendation.. ([0053] The system will track all consumer interactions with an offer by capturing data which includes, date/time/location offer was first pushed; date/time/location of consumer initial response to accept (often referred to as avail) an offer or ignore an offer; date/time/location the consumer redeems an offer; date/time an availed offer expires without being redeemed. The use of Artificial Intelligence (AI), which uses all captured data to enhance the targeting of content, operates within the offer warehouse continually learning and making offer recommendations and predictions. In this way, the AI will provide more effective and relevant content for both merchants and consumers. [0068] Leverage clustering algorithms to group items with similar characteristics or demand patterns, facilitating tailored strategies for different clusters to maximize profitability.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 9, LEE in view of PARASNIS teaches the method of claim 1.
However LEE does not explicitly teach providing a plurality of text descriptions received from a plurality of users to the API of the Al image generator to generate a plurality of item images; receiving the plurality of item images from the Al image generator; providing the plurality of item images to an item design system; identifying, at the item design system, a first attribute in a first image of the plurality of item images; identifying, at the item design system, a second attribute in a second image of the plurality of item images; and generating an item design recommendation by combining the first attribute and the second attribute...
PARASNIS, on the other hand, teaches providing a plurality of text descriptions received from a plurality of users to the API of the Al image generator to generate a plurality of item images; receiving the plurality of item images from the Al image generator; providing the plurality of item images to an item design system;.. ([Col 5 Ln 30-35] Each canvas 310 includes a collection of one or more prompts 312. The prompt 312 is the text input used to generate content. [Col 7 Ln 35-50] Images are generated with awareness of the context for the user and the user's products or services. Let's say a company which manufactures Pokemon plush toys utilizes the content-generation tool to generate images to run ads. The ad images should have the original plush toys the company manufactures instead of something that company does not sell that may be generated by the GAI tool. To achieve this, models are created for each user, the models being “aware” of the actual look and properties of the user products, so the generated images match perfectly the plush toys company is selling. [Col 21 Ln 60-65] The content-generation tool also provides an Application Programming Interface (API) to create templates programmatically. Thus, the API includes commands for template creation and also commands for loading a specified template and generating blocks of content that are returned as results to the API call.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE, the features, as taught by PARASNIS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify LEE, to include the teachings of PARASNIS, in order to produce content relevant to users' particular needs (PARASNIS, [Col 1 Ln 40-45]).
BEST, on the other hand, teaches identifying, at the item design system, a first attribute in a first image of the plurality of item images; identifying, at the item design system, a second attribute in a second image of the plurality of item images; and generating an item design recommendation by combining the first attribute and the second attribute. ([0053] The system will track all consumer interactions with an offer by capturing data which includes, date/time/location offer was first pushed; date/time/location of consumer initial response to accept (often referred to as avail) an offer or ignore an offer; date/time/location the consumer redeems an offer; date/time an availed offer expires without being redeemed. The use of Artificial Intelligence (AI), which uses all captured data to enhance the targeting of content, operates within the offer warehouse continually learning and making offer recommendations and predictions. In this way, the AI will provide more effective and relevant content for both merchants and consumers. [0068] Leverage clustering algorithms to group items with similar characteristics or demand patterns, facilitating tailored strategies for different clusters to maximize profitability.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 13, LEE in view of PARASNIS teaches the system of claim 11.
However the combination of LEE and PARASNIS does not explicitly teach determine an attribute-based demand forecast using the item image; and generate an item design recommendation based at least in part on clustering the image with the plurality of generated images and the attribute-based demand forecast using the image.
BEST, on the other hand, teaches determine an attribute-based demand forecast using the item image; ([0059] Regression Analysis: Employ regression models to forecast demand for various items based on historical sales data, user behavior, and external factors such as seasonality and economic trends. [0065] Content-Based Filtering: Propose items based on their attributes and features, aligning them with user preferences.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
BEST, on the other hand, teaches and generate an item design recommendation based at least in part on clustering the image with the plurality of generated images and the attribute-based demand forecast using the image. ([0068] Leverage clustering algorithms to group items with similar characteristics or demand patterns, facilitating tailored strategies for different clusters to maximize profitability.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 14, LEE in view of PARASNIS and BEST teaches the system of claim 13.
However the combination of LEE and PARASNIS does not explicitly teach wherein the item design system is configured to cluster the item image with the plurality of generated images based, at least in part, on image similarity between the item image and each of the plurality of generated images.
BEST, on the other hand, teaches wherein the item design system is configured to cluster the item image with the plurality of generated images based, at least in part, on image similarity between the item image and each of the plurality of generated images. ([0068] Leverage clustering algorithms to group items with similar characteristics or demand patterns, facilitating tailored strategies for different clusters to maximize profitability.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Regarding Claim 15, LEE in view of PARASNIS and BEST teaches the system of claim 14.
However the combination of LEE and PARASNIS does not explicitly teach wherein the item design system is configured to cluster the item image with the plurality of generated images further based on a similarity between the text description and text used to generate the plurality of generated images.
BEST, on the other hand, teaches wherein the item design system is configured to cluster the item image with the plurality of generated images further based on a similarity between the text description and text used to generate the plurality of generated images. ([0068] Leverage clustering algorithms to group items with similar characteristics or demand patterns, facilitating tailored strategies for different clusters to maximize profitability.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by LEE and PARASNIS, the features, as taught by BEST, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BEST, in order to provide improved recommendations (BEST, [0005]).
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been considered but are moot in view of new grounds of rejection, necessitated by Applicant’s amendment.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 11am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
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