DETAILED ACTION
Status of Claims
Claims 1-20 submitted on 12/23/2025 are pending and have been examined. Claims 1, 2, 8, 9, 15, and 16 have been amended.
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
No foreign priority or domestic benefit was claimed by the applicant and the application has been examined with respect to its filing date of 03/12/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/21/2026 has been considered by the examiner.
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 1-7 are directed to a process, claims 8-14 are directed to an article of manufacture, and claims 15-20 are directed to a machine (see MPEP 2106.03).
Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
A method comprising:
receiving, a user request from a user for additional content that is related to an object that the user has created, generated, or purchased;
capturing:
processing the captured image to extract a plurality of transform-invariant features from the image;
constructing, for the object, an object graph of transform-invariant features (GTIF) product-token by encoding spatial relationships among the extracted transform-invariant features;
wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image, such that the GTIF product-token remains invariant under two-dimensional geometric transformations of the object in the captured image;
determining whether the object GTIF product-token matches a particular pair of a set of GTIF product-token pairs;
wherein the set of GTIF product-token pairs comprises one or more pairs comprising known GTIF product-token and data determined for a user, or an event;
in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair;
wherein the particular additional content comprises to the additional content;
wherein determining the particular additional content includes performing a search for the particular additional content based on at least the particular pair to improve and optimize the accuracy and relevancy of the search.
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. Claims 8 and 15 recites similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1, 8, and 15 recite an abstract idea.
Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
A method comprising:
receiving, using a client application executing on a user device, a user request from a user for additional content that is related to an object that the user has created, generated, or purchased;
capturing, using a camera of the user device, an image of the object:
processing the captured image using a computer vision algorithm to extract a plurality of transform-invariant features from the image, the computer vision algorithm comprising at least one of: scale-invariant feature transform (SIFT), speeded up robust features (SURF), or simultaneous localization and mapping (SLAM);
constructing, for the object, an object graph of transform-invariant features (GTIF) product-token by encoding spatial relationships among the extracted transform-invariant features;
wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image, such that the GTIF product-token remains invariant under two-dimensional geometric transformations of the object in the captured image;
determining whether the object GTIF product-token matches a particular pair of a set of GTIF product-token pairs;
wherein the set of GTIF product-token pairs comprises one or more pairs comprising known GTIF product-token and data determined for a user, a user device, or an event;
in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair, and displaying the particular additional content on the user device;
wherein the particular additional content comprises hyperlinks to the additional content;
wherein determining the particular additional content includes performing a search for the particular additional content based on at least the particular pair to improve and optimize the accuracy and relevancy of the search.
Claims 8 and 15 include the same additional elements as claim 1. In addition, claim 8 includes additional elements such as one or more non-transitory computer readable storage media storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform. In addition, claim 15 includes additional elements such as a custom product computer system generator comprising: a memory unit; one or more processors; and a custom product computer storing one or more instructions, which, when executed by one or more processors, cause the one or more processors to perform.
The additional elements recited in claims 1, 8, and 15 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 of electronic applications and computer vision algorithms (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. 9, ¶0499 and ¶0712).
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, 8, and 15 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1, 8, and 15 are acknowledged, claims 1, 8, and 15 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, 8, and 15 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, 8, and 15 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, 8, and 15 are ineligible.
Dependent claims 3, 5, 10, 12, 17, and 19 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 3, 5, 10, 12, 17, and 19 merely further define the abstract limitations of claims 1, 8, and 15 or provide further embellishments of the limitations recited in independent claims 1, 8, and 15. Claims 3, 5, 10, 12, 17, and 19 do not introduce any further additional elements. Thus, dependent claims 3, 5, 10, 12, 17, and 19 are ineligible.
Furthermore, it is noted that certain dependent claims recite additional elements supplemental to those recited in independent claims 1, 8, and 15: generated using advanced computer-based techniques (claims 2, 9, and 16), a server (claim 4, 11, and 18), based on GPS location data (claims 6, 13, and 20), and instructions on computing machinery (claims 7, 14, and 20). However, these elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 2, 4, 6, 7, 9, 11, 13, 14, 16, 18, and 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.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6, 8-10, 13, and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuang et al. (US 20220084308 A1 [previously cited]) in view of Lenahan et al. (US 2014/0100991 A1).
Regarding Claim 1, Kuang et al., hereinafter, Kuang, discloses a method comprising:
receiving, using a client application executing on a user device, a user request from a user for additional content that is related to an object (¶0096[when browsing an App, a user may find that the App recommends a new dress of the season. The user may want to purchase, on another shopping website, a dress similar to the new dress. In this case, the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images.]);
capturing, using a camera of the user device, an image of the object (¶¶0096-0098[the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images]);
processing the captured image using a computer vision algorithm to extract a plurality of transform-invariant features from the image, the computer vision algorithm comprising at least one of: scale-invariant feature transform (SIFT), speeded up robust features (SURF), or simultaneous localization and mapping (SLAM) (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j] in view of ¶0096 which discloses capturing an image of an object; Examiner notes that features extracted using SIFT are comparable to transform-invariant features);
constructing, for the object, an object graph of transform-invariant features (GTIF) product-token by encoding spatial relationships among the extracted transform-invariant features (Fig. 7[depicting a graph of features with spatial relationship among the features]; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph]);
wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image, such that the GTIF product-token remains invariant under two-dimensional geometric transformations of the object in the captured image (Fig. 7[depicting a non-directed graph with nodes and edges]; ¶¶0046-0049[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph… In training a graph neural network, first, images on each scale in the preset multiple scales corresponding respective to any two labeled sample images in a sample image library may be acquired. Then, feature extraction may be performed respectively on the acquired images, acquiring multiple sample feature maps of the two sample images corresponding to respective scales. In addition, a similarity between two sample feature maps corresponding to each target scale combination may be computed. A sample undirected graph may be established according to similarities between sample feature maps corresponding to respective target scale combinations] in view of ¶0098 and ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features);
determining whether the object GTIF product-token matches a particular pair of a set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein the set of GTIF product-token pairs comprises one or more pairs comprising known GTIF product-token and data determined for a user, a user device, or an event (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair, and displaying the particular additional content on the user device (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price] in view of ¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code]; Examiner notes that images of similar home appliances are comparable to additional content);
wherein the particular additional content comprises to the additional content (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price]);
wherein determining the particular additional content includes performing a search for the particular additional content based on at least the particular pair to improve and optimize the accuracy and relevancy of the search (Fig. 9; ¶¶0097-0105[With the method of the S101 to the S104 of an embodiment herein, the image of a dress similar to the new dress, that the user may want to purchase, may be found by directly searching the shopping website… a similarity between images may be measured combining local features of the first image and the second image on different scales, improving a precision and robustness of the matching.] in view of ¶0002).
Although Kuang discloses an object that the user has identified, Kuang does not explicitly disclose an object that the user has created, generated, or purchased.
Although Kuang discloses websites for additional content, Kuang does not explicitly disclose hyperlinks to additional content.
However, Lenahan et al., hereinafter, Lenahan, teaches identifying similar items to an object that a user has purchased and hyperlinks to additional content (Fig. 2; ¶0138[In personalizing the experience of the user, the personalization modules 230 may, for example, use items that the user wants, items that the user owns, items that the user has previously purchased using marketplace applications 126, a location of the user, past item searches that the user has performed, and any themes that the user has indicated they would like to find more items related to.] in view of ¶0141[The social network entries may include one or more hyperlinks that may automatically redirect a user's browser to a particular marketplace listing.]).
The method of Lenahan is applicable to the method of Kuang as they share characteristics and capabilities, namely, they are both targeted to determining relevant products for a user. 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 extraction of features and determining relevant items as disclosed by Kuang to include objects that a user has purchased and hyperlinks as taught by Lenahan. One of ordinary skill in the art would have been motivated to expand the method of Kuang in order to receive search results that are tailored to the user's personal preferences based on social and purchasing information known about the user (Abstract).
Regarding Claim 2, Kuang in view of Lenahan teaches the method of Claim 1, Kuang discloses wherein a GTIF product-token for a product is a complex data structure that is generated using advanced computer-based techniques that include one or more: encoding spatial representations of certain features identified in the product, or determining a set of invariant features that are specific to the product; wherein the invariant features are features that remain invariant of any 2D transformation performed on the features of the product (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j]; Examiner notes that features extracted using SIFT are comparable to transform-invariant features);
wherein the set of GTIF product-token pairs comprises one or more of: a pair comprising a
known GTIF product-token and a location data determined for a location of a user device, a pair comprising known GDF product-token associated with a user of the user device and one or more social relationships defined for the user. a pair comprising known time based data associated with one or more events defined for the user and the one or more events, a pair comprising a GT1F product token and a representation of a physical object detected by a camera or sensors and communicated to the user device, or a pair comprising a GTIF product token and a representation of a digital object provided by the user device (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]).
Regarding Claim 3, Kuang in view of Lenahan teaches the method of Claim 1, Kuang discloses wherein a GTIF product-token for a product, of a pair of the set of GTIF product-token pairs, represents one or more of:
one or more of relationships between a plurality of transform-invariant features identified for the product, or one or more relationships between the plurality of transform-invariant features identified for the product and other transform-invariant features identified for other products (Fig. 7; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph.] in view of ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features).
Regarding Claim 6, Kuang in view of Lenahan teaches the method of Claim 1, Kuang discloses wherein a product has a plurality transform invariant features and a corresponding plurality of GTIF product-tokens (Fig. 7; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph.] in view of ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features);
wherein a GTIF product-token is used to determine whether the GTIF product-token matches a particular pair of the set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein a GTIF product-token pair comprises additional context data that include one or more of: location data determined based on GPS location data obtained from one or more of: the location of the user device, a photo, an address of an event, or an address of customers or users; social relationship data of a creator or a recipient of the product; or time based data determined based on one or more of: a time of an event, a time when a photo was taken, or a time when a message was sent (Figs. 9 and 13; ¶¶0096-0097[a user may find that the App recommends a new dress of the season. The user may want to purchase, on another shopping website, a dress similar to the new dress. In this case, the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images… With the method of the S101 to the S104 of an embodiment herein, the image of a dress similar to the new dress, that the user may want to purchase, may be found by directly searching the shopping website. Accordingly, the user may place an order to carry out the purchase.]; Examiner notes that a dress of the season is comparable to a time of an event).
Regarding Claim 8, Kuang discloses one or more non-transitory computer readable storage media storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform (Claim 15[A non-transitory computer-readable storage medium, having stored thereon computer-executable instructions which, when executed by a processor, implement]):
receiving, using a client application executing on a user device, a user request from a user for additional content that is related to an object (¶0096[when browsing an App, a user may find that the App recommends a new dress of the season. The user may want to purchase, on another shopping website, a dress similar to the new dress. In this case, the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images.]);
capturing, using a camera of the user device, an image of the object (¶¶0096-0098[the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images]);
processing the captured image using a computer vision algorithm to extract a plurality of transform-invariant features from the image, the computer vision algorithm comprising at least one of: scale-invariant feature transform (SIFT), speeded up robust features (SURF), or simultaneous localization and mapping (SLAM) (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j] in view of ¶0096 which discloses capturing an image of an object; Examiner notes that features extracted using SIFT are comparable to transform-invariant features);
constructing, for the object, an object graph of transform-invariant features (GTIF) product-token by encoding spatial relationships among the extracted transform-invariant features (Fig. 7[depicting a graph of features with spatial relationship among the features]; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph]);
wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes c01Tespond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image, such that the GTIF product-token remains invariant under two-dimensional geometric transformations of the object in the captured image (Fig. 7[depicting a non-directed graph with nodes and edges]; ¶¶0046-0049[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph… In training a graph neural network, first, images on each scale in the preset multiple scales corresponding respective to any two labeled sample images in a sample image library may be acquired. Then, feature extraction may be performed respectively on the acquired images, acquiring multiple sample feature maps of the two sample images corresponding to respective scales. In addition, a similarity between two sample feature maps corresponding to each target scale combination may be computed. A sample undirected graph may be established according to similarities between sample feature maps corresponding to respective target scale combinations] in view of ¶0098 and ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features);
determining whether the object GTIF product-token matches a particular pair of a set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein the set of GTIF product-token pairs comprises one or more of: pairs comprising known GTIF product-token and data determined for a user, a user device. or an event (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair, and displaying the particular additional content on the user device (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price] in view of ¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code]; Examiner notes that images of similar home appliances are comparable to additional content);
wherein the particular additional content comprises to the additional content (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price]);
wherein determining the particular additional content includes performing a search for the particular additional content based on at least the particular pair, to improve and optimize the accuracy and relevancy of the search (Fig. 9; ¶¶0097-0105[With the method of the S101 to the S104 of an embodiment herein, the image of a dress similar to the new dress, that the user may want to purchase, may be found by directly searching the shopping website… a similarity between images may be measured combining local features of the first image and the second image on different scales, improving a precision and robustness of the matching.] in view of ¶0002).
Although Kuang discloses an object that the user has identified, Kuang does not explicitly disclose an object that the user has created, generated, or purchased.
Although Kuang discloses websites for additional content, Kuang does not explicitly disclose hyperlinks to additional content.
However, Lenahan teaches identifying similar items to an object that a user has purchased and hyperlinks to additional content (Fig. 2; ¶0138[In personalizing the experience of the user, the personalization modules 230 may, for example, use items that the user wants, items that the user owns, items that the user has previously purchased using marketplace applications 126, a location of the user, past item searches that the user has performed, and any themes that the user has indicated they would like to find more items related to.] in view of ¶0141[The social network entries may include one or more hyperlinks that may automatically redirect a user's browser to a particular marketplace listing.]).
The system of Lenahan is applicable to the system of Kuang as they share characteristics and capabilities, namely, they are both targeted to determining relevant products for a user. 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 extraction of features and determining relevant items as disclosed by Kuang to include objects that a user has purchased and hyperlinks as taught by Lenahan. One of ordinary skill in the art would have been motivated to expand the system of Kuang in order to receive search results that are tailored to the user's personal preferences based on social and purchasing information known about the user (Abstract).
Regarding Claim 9, Kuang in view of Lenahan teaches the one or more non-transitory computer readable storage media of Claim 8, Kuang discloses wherein a GTIF product-token for a product is a complex data structure that is generated using advanced computer-based techniques that include one or more: encoding spatial representations of certain features identified in the product, or determining a set of invariant features that are specific to the product; wherein the invariant features are features that remain invariant of any 2D transformation performed on the features of the product (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j]; Examiner notes that features extracted using SIFT are comparable to transform-invariant features).
wherein the set of GTIF product-token pairs comprises one or more of: a pair comprising a known GTIF product-token and a location data determined for a location of a user device, a pair comprising known GTIF product-token associated with a user of the user device and one or more social relationships defined for the user, a pair comprising known time based data associated with one or more events defined for the user and the one or more events. a pair comprising a GTIF product token and a representation of a physical object detected by a camera or sensors and communicated to the user device, or a pair comprising a GTIF product token and a representation of a digital object provided by the user device (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]).
Regarding Claim 10, Kuang in view of Lenahan teaches the one or more non-transitory computer readable storage media of Claim 8, Kuang discloses wherein a GTJF product-token for a product, of a pair of the set of GTJF product-token pairs, represents one or more of: one or more of relationships between a plurality of transform-invariant features identified for the product, or one or more relationships between the plurality of transform-invariant features identified for the product and other transform-invariant features identified for other products (Fig. 7; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph.] in view of ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features).
Regarding Claim 13, Kuang in view of Lenahan teaches the one or more non-transitory computer readable storage media of Claim 8, Kuang discloses wherein a product has a plurality transform invariant features and a corresponding plurality of GTIF product-tokens (Fig. 7; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph.] in view of ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features);
wherein a GTIF product-token is used to determine whether the GTIF product-token matches a particular pair of the set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein a GTIF product-token pair comprises additional context data that include one or more of: location data determined based on GPS location data obtained from one or more of: the location of the user device, a photo, an address of an event, or an address of customers or users; social relationship data of a creator or a recipient of the product; or time based data determined based on one or more of: a time of an event, a time when a photo was taken, or a time when a message was sent (Figs. 9 and 13; ¶¶0096-0097[a user may find that the App recommends a new dress of the season. The user may want to purchase, on another shopping website, a dress similar to the new dress. In this case, the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images… With the method of the S101 to the S104 of an embodiment herein, the image of a dress similar to the new dress, that the user may want to purchase, may be found by directly searching the shopping website. Accordingly, the user may place an order to carry out the purchase.]; Examiner notes that a dress of the season is comparable to a time of an event).
Regarding Claim 15, Kuang discloses a custom product computer system generator comprising: a memory unit; one or more processors; and a custom product computer storing one or more instructions, which, when executed by one or more processors, cause the one or more processors to perform (Fig. 1; ¶0009[According to an aspect herein, a device for image search includes a processor and memory. The memory is adapted to storing instructions executable by the processor. The processor is adapted to implementing, by calling the executable instructions stored in the memory, any method for image search of the first aspect herein.]):
receiving, using a client application executing on a user device, a user request from a user for additional content that is related to an object (¶0096[when browsing an App, a user may find that the App recommends a new dress of the season. The user may want to purchase, on another shopping website, a dress similar to the new dress. In this case, the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images.]);
capturing, using a camera of the user device, an image of the object (¶¶0096-0098[the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images]);
processing the captured image using a computer vision algorithm to extract a plurality of transform-invariant features from the image, the computer vision algorithm comprising at least one of: scale-invariant feature transform (SIFT), speeded up robust features (SURF), or simultaneous localization and mapping (SLAM) (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j] in view of ¶0096 which discloses capturing an image of an object; Examiner notes that features extracted using SIFT are comparable to transform-invariant features);
constructing, for the object, an object graph of transform-invariant features (GTIF) product-token by encoding spatial relationships among the extracted transform-invariant features (Fig. 7[depicting a graph of features with spatial relationship among the features]; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph]);
wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image, such that the GTIF product-token remains invariant under two-dimensional geometric transformations of the object in the captured image (Fig. 7[depicting a non-directed graph with nodes and edges]; ¶¶0046-0049[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph… In training a graph neural network, first, images on each scale in the preset multiple scales corresponding respective to any two labeled sample images in a sample image library may be acquired. Then, feature extraction may be performed respectively on the acquired images, acquiring multiple sample feature maps of the two sample images corresponding to respective scales. In addition, a similarity between two sample feature maps corresponding to each target scale combination may be computed. A sample undirected graph may be established according to similarities between sample feature maps corresponding to respective target scale combinations] in view of ¶0098 and ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features);
determining whether the object GTIF product-token matches a particular pair of a set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein the set of GTIF product-token pairs comprises one or more pairs comprising known GTIF product-token and data determined for a user. a user device, or an event (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair, and displaying the particular additional content on the user device (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price] in view of ¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code]; Examiner notes that images of similar home appliances are comparable to additional content);
wherein the particular additional content comprises to the additional content (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price]);
wherein determining the particular additional content includes performing a search for the particular additional content based on at least the particular pair to improve and optimize the accuracy and relevancy of the search (Fig. 9; ¶¶0097-0105[With the method of the S101 to the S104 of an embodiment herein, the image of a dress similar to the new dress, that the user may want to purchase, may be found by directly searching the shopping website… a similarity between images may be measured combining local features of the first image and the second image on different scales, improving a precision and robustness of the matching.] in view of ¶0002).
Although Kuang discloses an object that the user has identified, Kuang does not explicitly disclose an object that the user has created, generated, or purchased.
Although Kuang discloses websites for additional content, Kuang does not explicitly disclose hyperlinks to additional content.
However, Lenahan teaches identifying similar items to an object that a user has purchased and hyperlinks to additional content (Fig. 2; ¶0138[In personalizing the experience of the user, the personalization modules 230 may, for example, use items that the user wants, items that the user owns, items that the user has previously purchased using marketplace applications 126, a location of the user, past item searches that the user has performed, and any themes that the user has indicated they would like to find more items related to.] in view of ¶0141[The social network entries may include one or more hyperlinks that may automatically redirect a user's browser to a particular marketplace listing.]).
The system of Lenahan is applicable to the system of Kuang as they share characteristics and capabilities, namely, they are both targeted to determining relevant products for a user. 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 extraction of features and determining relevant items as disclosed by Kuang to include objects that a user has purchased and hyperlinks as taught by Lenahan. One of ordinary skill in the art would have been motivated to expand the system of Kuang in order to receive search results that are tailored to the user's personal preferences based on social and purchasing information known about the user (Abstract).
Regarding Claim 16, Kuang in view of Lenahan teaches the custom product computer system generator of Claim 15, Kuang discloses wherein a GTIF product-token for a product is a complex data structure that is generated using advanced computer-based techniques that include one or more: encoding spatial representations of certain features identified in the product, or determining a set of invariant features that are specific to the product; wherein the invariant features are features that remain invariant of any 2D transformation performed on the features of the product (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j]; Examiner notes that features extracted using SIFT are comparable to transform-invariant features).
wherein the set of GTIF product-token pairs comprises one or more of: a pair comprising a known GTIF product-token and a location data determined for a location of a user device, a pair comprising known GTIF product-token associated with a user of the user device and one or more social relationships defined for the user, a pair comprising known time based data associated with one or more events defined for the user and the one or more events, a pair comprising a GTIF product token and a representation of a physical object detected by a camera or sensors and communicated to the user device, or a pair comprising a GTIF product token and a representation of a digital object provided by the user (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]).
Regarding Claim 17, Kuang in view of Lenahan teaches the custom product computer system generator of Claim 15, Kuang discloses wherein a GTIF product-token for a product, of a pair of the set of GTIF product-token pairs, represents one or more of: one or more of relationships between a plurality of transform-invariant features identified for the product, or one or more relationships between the plurality of transform-invariant features identified for the product and other transform-invariant features identified for other products (Fig. 7; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph.] in view of ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features).
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuang in view of Lenahan in view of Ong et al. (US 9,800,473 B2 [previously cited]).
Regarding Claim 4, Kuang in view of Lenahan teaches the method of Claim 1, Kuang discloses wherein the set of GTJF product-token pairs is on the user device from a server hosted (¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code] in view of ¶0126[The device 1400 may operate based on an operating system stored in the memory 1432, such as a Windows Server™, a Mac OS X™, a Unix™, a Linux™, a FreeB SD™, etc.]).
Although Kuang discloses a user device and a server, Kuang in view of Lenahan does not explicitly teach that the product-token pairs are preloaded and updated on a device hosted by a collaboration platform.
However, Ong et al., hereinafter, Ong, teaches a collaboration platform and features being preloaded and updated on a device (Col. 6, lines 3-40[The virtual space may be an empty virtual space or pre-loaded with one or more widgets that may be commonly used for many performance issues… Virtual collaboration space functionality is provided based on user input at step 430.]).
The method of Ong is applicable to the method of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 device and server as taught by Kuang in view of Lenahan to include a collaboration platform and preloading and updating features as taught by Ong. One of ordinary skill in the art would have been motivated to expand the method of Kuang in view of Lenahan in order to monitor and report data from Java virtual machines (JVM) to a controller as part of application performance monitoring (Abstract).
Regarding Claim 11, Kuang in view of Lenahan teaches the one or more non-transitory computer readable storage media of Claim 8, Kuang discloses wherein the set of GTIF product-token pairs is on the user device from a server hosted (¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code] in view of ¶0126[The device 1400 may operate based on an operating system stored in the memory 1432, such as a Windows Server™, a Mac OS X™, a Unix™, a Linux™, a FreeB SD™, etc.]).
Although Kuang discloses a user device and a server, Kuang in view of Lenahan does not explicitly teach that the product-token pairs are preloaded and updated on a device hosted by a collaboration platform.
However, Ong teaches a collaboration platform and features being preloaded and updated on a device (Col. 6, lines 3-40[The virtual space may be an empty virtual space or pre-loaded with one or more widgets that may be commonly used for many performance issues… Virtual collaboration space functionality is provided based on user input at step 430.]).
The system of Ong is applicable to the system of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 device and server as taught by Kuang in view of Lenahan to include a collaboration platform and preloading and updating features as taught by Ong. One of ordinary skill in the art would have been motivated to expand the system of Kuang in view of Lenahan in order to monitor and report data from Java virtual machines (JVM) to a controller as part of application performance monitoring (Abstract).
Regarding Claim 18, Kuang in view of Lenahan teaches the custom product computer system generator of Claim 15, Kuang discloses wherein the set of GTIF product-token pairs is on the user device from a server hosted (¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code] in view of ¶0126[The device 1400 may operate based on an operating system stored in the memory 1432, such as a Windows Server™, a Mac OS X™, a Unix™, a Linux™, a FreeB SD™, etc.]).
Although Kuang discloses a user device and a server, Kuang in view of Lenahan does not explicitly teach that the product-token pairs are preloaded and updated on a device hosted by a collaboration platform.
However, Ong teaches a collaboration platform and features being preloaded and updated on a device (Col. 6, lines 3-40[The virtual space may be an empty virtual space or pre-loaded with one or more widgets that may be commonly used for many performance issues… Virtual collaboration space functionality is provided based on user input at step 430.]).
The system of Ong is applicable to the system of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 device and server as taught by Kuang in view of Lenahan to include a collaboration platform and preloading and updating features as taught by Ong. One of ordinary skill in the art would have been motivated to expand the system of Kuang in view of Lenahan in order to monitor and report data from Java virtual machines (JVM) to a controller as part of application performance monitoring (Abstract).
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuang in view of Lenahan in view of Luo et al. (US 2021/0312533 A1 [previously cited]).
Regarding Claim 5, Kuang in view of Lenahan teaches the method of Claim 1, Kuang discloses further comprising: in response to determining that the object GTIF product-token does match any pair of the set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]):
accessing a second set of GTIF product-tokens pairs that is from the set of GTIF product-tokens pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
determining whether the object GTIF product-token matches a second particular pair of the second set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
in response to determining that the object GTIF product-token matches the second particular pair, determining particular additional content based on the second particular pair, and displaying the particular additional content on the user device (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price] in view of ¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code]; Examiner notes that images of similar home appliances are comparable to additional content).
Although Kuang discloses invariant features extracted from images in order to determine other images, Kuang in view of Lenahan does not explicitly teach determining that the product-token does not match and accessing a second set of product-tokens that is different.
However, Luo et al., hereinafter, Luo, teaches product-tokens that do not match and pairs that are different from a set of pairs (Fig. 20; ¶0075[The database 120 also stores data of products in a product table 316, which enables the product catalog system 124 to perform operations related to providing an augmented reality experience with respect to a product (e.g., a given physical item that may be available for purchase or sale). In an example, the product table 316 includes a directory (e.g., listing) of products and their associated product identifiers, which can be compared against product metadata provided by the product catalog system 124. As described further herein, the product catalog system 124 can determine product metadata associated with a particular product.], ¶¶0108-0109[The product catalog system 124 can receive at least the product ID from the client device 102 (or the product catalog system 124 as discussed below), and perform a lookup, search, or select operation on the product table 316 to retrieve the product metadata from the database 120... In an example, the product catalog system 124 can send a request message to a respective server for obtaining metadata related to a given physical item. The request message may include, for example, the product ID.], ¶0202[At operation 2006, the product identification module 704 extracts product metadata based on the determined object. In an embodiment, extracting the product metadata based on the determined object further includes comparing the identified object to a library of objects, each object from the library of objects including associated metadata with product information corresponding to a product, determining that the identified object matches a particular object from the library of object] in view of ¶0104 which discloses that multiple databases may exist and be queried from).
The method of Luo is applicable to the method of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 invariant feature extraction as taught by Kuang in view of Lenahan to include features that do not match as taught by Luo. One of ordinary skill in the art would have been motivated to expand the method of Kuang in view of Lenahan in order to identify various objects or features captured in a wide range of changing conditions (¶0029).
Regarding Claim 12, Kuang in view of Lenahan teaches the one or more non-transitory computer readable storage media of Claim 8, Kuang discloses further comprising:
in response to determining that the object GTIF product-token does match any pair of the set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]):
accessing a second set of GTIF product-tokens pairs that is from the set of GTIF product-tokens pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
determining whether the object GTIF product-token matches a second particular pair of the second set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
in response to determining that the object GTIF product-token matches the second particular pair, determining particular additional content based on the second particular pair, and displaying the particular additional content on the user device (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price] in view of ¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code]; Examiner notes that images of similar home appliances are comparable to additional content).
Although Kuang discloses invariant features extracted from images in order to determine other images, Kuang in view of Lenahan does not explicitly teach determining that the product-token does not match and accessing a second set of product-tokens that is different.
However, Luo teaches product-tokens that do not match and pairs that are different from a set of pairs (Fig. 20; ¶0075[The database 120 also stores data of products in a product table 316, which enables the product catalog system 124 to perform operations related to providing an augmented reality experience with respect to a product (e.g., a given physical item that may be available for purchase or sale). In an example, the product table 316 includes a directory (e.g., listing) of products and their associated product identifiers, which can be compared against product metadata provided by the product catalog system 124. As described further herein, the product catalog system 124 can determine product metadata associated with a particular product.], ¶¶0108-0109[The product catalog system 124 can receive at least the product ID from the client device 102 (or the product catalog system 124 as discussed below), and perform a lookup, search, or select operation on the product table 316 to retrieve the product metadata from the database 120... In an example, the product catalog system 124 can send a request message to a respective server for obtaining metadata related to a given physical item. The request message may include, for example, the product ID.], ¶0202[At operation 2006, the product identification module 704 extracts product metadata based on the determined object. In an embodiment, extracting the product metadata based on the determined object further includes comparing the identified object to a library of objects, each object from the library of objects including associated metadata with product information corresponding to a product, determining that the identified object matches a particular object from the library of object] in view of ¶0104 which discloses that multiple databases may exist and be queried from).
The system of Luo is applicable to the system of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 invariant feature extraction as taught by Kuang in view of Lenahan to include features that do not match as taught by Luo. One of ordinary skill in the art would have been motivated to expand the system of Kuang in view of Lenahan in order to identify various objects or features captured in a wide range of changing conditions (¶0029).
Regarding Claim 19, Kuang in view of Lenahan teaches the custom product computer system generator of Claim 15, Kuang discloses further comprising: in response to determining that the object GTIF product-token does match any pair of the set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]):
accessing a second set of GTIF product-tokens pairs that is from the set of GTIF product-tokens pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
determining whether the object GTIF product-token matches a second particular pair of the second set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired… a user may like a home appliance in an offline physical store. The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
in response to determining that the object GTIF product-token matches the second particular pair, determining particular additional content based on the second particular pair, and displaying the particular additional content on the user device (Figs. 9 and 13; ¶¶0091-0099[The user may want to search a website for a similar product. In this case, the user may take a picture of the home appliance in the physical store using user equipment such as a cell phone, take the acquired image as the first image, go to the website to be searched, and take all images in the website as the second images… Likewise, with the method of the S101 to the S104 of an embodiment herein, images and prices of similar home appliances may be found by directly searching the website. The user may select to purchase a home appliance of a competitive price] in view of ¶0029[The method may be used on machine equipment or device for performing image search, or may be implemented by a processor by running a computer-executable code]; Examiner notes that images of similar home appliances are comparable to additional content).
Although Kuang discloses invariant features extracted from images in order to determine other images, Kuang in view of Lenahan does not explicitly teach determining that the product-token does not match and accessing a second set of product-tokens that is different.
However, Luo teaches product-tokens that do not match and pairs that are different from a set of pairs (Fig. 20; ¶0075[The database 120 also stores data of products in a product table 316, which enables the product catalog system 124 to perform operations related to providing an augmented reality experience with respect to a product (e.g., a given physical item that may be available for purchase or sale). In an example, the product table 316 includes a directory (e.g., listing) of products and their associated product identifiers, which can be compared against product metadata provided by the product catalog system 124. As described further herein, the product catalog system 124 can determine product metadata associated with a particular product.], ¶¶0108-0109[The product catalog system 124 can receive at least the product ID from the client device 102 (or the product catalog system 124 as discussed below), and perform a lookup, search, or select operation on the product table 316 to retrieve the product metadata from the database 120... In an example, the product catalog system 124 can send a request message to a respective server for obtaining metadata related to a given physical item. The request message may include, for example, the product ID.], ¶0202[At operation 2006, the product identification module 704 extracts product metadata based on the determined object. In an embodiment, extracting the product metadata based on the determined object further includes comparing the identified object to a library of objects, each object from the library of objects including associated metadata with product information corresponding to a product, determining that the identified object matches a particular object from the library of object] in view of ¶0104 which discloses that multiple databases may exist and be queried from).
The system of Luo is applicable to the system of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 invariant feature extraction as taught by Kuang in view of Lenahan to include features that do not match as taught by Luo. One of ordinary skill in the art would have been motivated to expand the system of Kuang in view of Lenahan in order to identify various objects or features captured in a wide range of changing conditions (¶0029).
Claim(s) 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuang in view of Lenahan in view of Bourdev et al. (US 7,440,587 B1 [previously cited]).
Regarding Claim 7, Kuang in view of Lenahan teaches the method of Claim 1, Kuang discloses wherein finding additional content that is related to the object is the search that requires comparisons between non-directed graphs having a plurality of nodes, wherein the nodes represent transform invariant features, and wherein a number of transform invariant features (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein a GTIF product-token is generated using one or more of: a scale-invariant feature transform feature recognition method (SIFT), a simultaneous localization and mapping feature recognition method (SLAM), or a speed up robust features feature recognition method (SURF) (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j]).
Although Kuang discloses comparisons between non-directed graphs, Kuang in view of Lenahan does not explicitly teach wherein a time for comparison performed as a series of instructions on computing machinery increases based on a number of comparisons, and wherein features exceeds practical limits of user interaction time.
However, Bourdev et al., hereinafter, Bourdev, teaches time consuming comparisons that exceed practical limits of user interactions (Col. 1, lines 40-60[Other applications can take more time, but need the best intermediate results, such as a computer-assisted person tagging system, in which the user can start correcting the tag assignments before the system has analyzed all images in full. Hence, in some cases comprehensive detection may take more time than the system can allow, and in other cases it is better for the system to spend more time in the hope of finding more instances of the object. Unfortunately, the speed/detection rate tradeoff is hard-coded in traditional systems and cannot be changed dynamically.]).
The method of Bourdev is applicable to the method of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 invariant feature extraction as taught by Kuang in view of Lenahan to include time consuming feature comparisons as taught by Bourdev. One of ordinary skill in the art would have been motivated to expand the method of Kuang in view of Lenahan in order to recognize the presence of the object of interest within a two-dimensional window of a suitable aspect ratio (Col. 1, lines 10-25).
Regarding Claim 14, Kuang in view of Lenahan teaches the one or more non-transitory computer readable storage media of Claim 8, Kuang discloses wherein finding additional content that is related to the object is the search that requires comparisons between non-directed graphs having a plurality of nodes, wherein the nodes represent transform invariant features, and wherein a number of transform invariant features (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein a GTIF product-token is generated using one or more of: a scale-invariant feature transform feature recognition method (SIFT), a simultaneous localization and mapping feature recognition method (SLAM), or a speed up robust features feature recognition method (SURF) (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j]).
Although Kuang discloses comparisons between non-directed graphs, Kuang in view of Lenahan does not explicitly teach wherein a time for comparison performed as a series of instructions on computing machinery increases based on a number of comparisons, and wherein features exceeds practical limits of user interaction time.
However, Bourdev teaches time consuming comparisons that exceed practical limits of user interactions (Col. 1, lines 40-60[Other applications can take more time, but need the best intermediate results, such as a computer-assisted person tagging system, in which the user can start correcting the tag assignments before the system has analyzed all images in full. Hence, in some cases comprehensive detection may take more time than the system can allow, and in other cases it is better for the system to spend more time in the hope of finding more instances of the object. Unfortunately, the speed/detection rate tradeoff is hard-coded in traditional systems and cannot be changed dynamically.]).
The system of Bourdev is applicable to the system of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 invariant feature extraction as taught by Kuang in view of Lenahan to include time consuming feature comparisons as taught by Bourdev. One of ordinary skill in the art would have been motivated to expand the system of Kuang in view of Lenahan in order to recognize the presence of the object of interest within a two-dimensional window of a suitable aspect ratio (Col. 1, lines 10-25).
Regarding Claim 20, Kuang in view of Lenahan teaches the custom product computer system generator of Claim 15, Kuang discloses wherein a product has a plurality transform invariant features and a corresponding plurality of GTIF product-tokens (Fig. 7; ¶0046[as shown in FIG. 7, for example, each node of the target undirected graph may correspond to a similarity. Each similarity may correspond to a target scale combination. An edge of the target undirected graph may be represented by a weight between two nodes. The weight may be a normalized weight subjected to normalization processing. Similarity between two images may be represented more intuitively through the target undirected graph.] in view of ¶0037; Examiner notes that performing feature extraction using a SIFT mode is comparable to extracting transform-invariant features);
wherein a GTIF product-token is used to determine whether the GTIF product-token matches a particular pair of the set of GTIF product-token pairs (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein a GTIF product-token pair comprises additional context data that include one or more of: location data determined based on GPS location data obtained from one or more of: the location of the user device, a photo, an address of an event, or an address of customers or users; social relationship data of a creator or a recipient of the product; or time based data determined based on one or more of: a time of an event, a time when a photo was taken, or a time when a message was sent (Figs. 9 and 13; ¶¶0096-0097[a user may find that the App recommends a new dress of the season. The user may want to purchase, on another shopping website, a dress similar to the new dress. In this case, the image of the new dress provided by the App may be taken as the first image, and images of all dresses provided by the shopping website may be taken as the second images… With the method of the S101 to the S104 of an embodiment herein, the image of a dress similar to the new dress, that the user may want to purchase, may be found by directly searching the shopping website. Accordingly, the user may place an order to carry out the purchase.]; Examiner notes that a dress of the season is comparable to a time of an event);
wherein finding additional content that is related to the object is the search that requires comparisons between non-directed graphs having a plurality of nodes, wherein the nodes represent transform invariant features, and wherein a number of transform invariant features (Figs. 9 and 13; ¶¶0091-0099[When the probability of the similarity is greater than a preset threshold, it may be determined that the second image is a target image matching the first image… By searching all images in the image library in the mode, the target image matching the first image may be acquired] in view of ¶0031[The first image may be a target image to which a match is to be searched for. The second image is any image in an image library.]);
wherein a GTIF product-token is generated using one or more of: a scale-invariant feature transform feature recognition method (SIFT), a simultaneous localization and mapping feature recognition method (SLAM), or a speed-up robust features (SURF) feature recognition method (Fig. 3A-3C and 4; ¶¶0036-0037[For example, for any scale in a set of scales {1, 2, . . . L}, feature extraction may be performed respectively on an image of a level i of the image pyramid of the first image and an image of a level j of the image pyramid of the second image using a Scale Invariant Feature Transform (SIFT) mode or a trained neural network, acquiring the first feature map corresponding to the first image on a scale i and the second feature map corresponding to the second image on a scale j]).
Although Kuang discloses comparisons between non-directed graphs, Kuang in view of Lenahan does not explicitly teach wherein a time for comparison performed as a series of instructions on computing machinery increases based on a number of comparisons, and wherein features exceeds practical limits of user interaction time.
However, Bourdev teaches time consuming comparisons that exceed practical limits of user interactions (Col. 1, lines 40-60[Other applications can take more time, but need the best intermediate results, such as a computer-assisted person tagging system, in which the user can start correcting the tag assignments before the system has analyzed all images in full. Hence, in some cases comprehensive detection may take more time than the system can allow, and in other cases it is better for the system to spend more time in the hope of finding more instances of the object. Unfortunately, the speed/detection rate tradeoff is hard-coded in traditional systems and cannot be changed dynamically.]).
The system of Bourdev is applicable to the system of Kuang in view of Lenahan as they share characteristics and capabilities, namely, they are all targeted to providing online services to customers. 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 invariant feature extraction as taught by Kuang in view of Lenahan to include time consuming feature comparisons as taught by Bourdev. One of ordinary skill in the art would have been motivated to expand the system of Kuang in view of Lenahan in order to recognize the presence of the object of interest within a two-dimensional window of a suitable aspect ratio (Col. 1, lines 10-25).
Response to Arguments
Applicant’s arguments on pages 12-18 of the remarks filed 12/23/2025, with respect to the previous 35
USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on page 14 15, 17, and 18 of the remarks that the amended claims integrate the abstract idea into a practical application to “improve and optimize the accuracy and relevance of the search for the particular additional content by performing the search using at least a particular pair of GTIF product-token pairs” (Remarks page 14). Examiner respectfully disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states "the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." The MPEP further states that "[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art," and that, "conversely, if the specification explicitly sets forth an improvement but in a conclusory manner the examiner should not determine the claim improves technology" (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC V. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was " allowing computers to produce 'accurate and realistic lip synchronization and facial expressions in animated characters " and it was " the incorporation of the claimed rules, not the use of the computer, that "improved [the] existing technological process" by allowing the automation of further tasks". McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of determining additional content based on a pair of GTIF product-token pair. This is illustrated in specification paragraphs [0001-0004] which discusses that the invention is related to shopping. The paragraphs 0226, 0227, 0234, and 0235 cited by the applicant are recited at a high level and an improvement to a technical field is not reflected in the claims.
Although the claims include computer technology such as a client application executing on a user device, a camera of the user device, a computer vision algorithm, scale-invariant feature transform (SIFT), speeded up robust features (SURF), simultaneous localization and mapping (SLAM), hyperlinks processors, and memory units, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the determining additional content based on a pair of GTIF product-token pair. The claimed process, while arguably resulting in a more accurate process for identifying and recommending items and providing information, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. As such, the claims are not eligible.
Applicant further argues on page 16 of the remarks that the amended claims are not directed to organizing human activity, Examiner respectfully disagrees. According to the MPEP 2106.04, the question of whether a claim is “directed to” a judicial exception in Step 2A is now evaluated using a two-prong inquiry. Prong One asks if the claim “recites” an abstract idea, law of nature, or natural phenomenon. Under that prong, the mere inclusion of a judicial exception such as a method of organizing human activity in a claim means that the claim “recites” a judicial exception (see MPEP 2106.04 [“The mere inclusion of a judicial exception such as a mathematical formula (which is one of the mathematical concepts identified as an abstract idea in MPEP § 2106.04(a)) in a claim means that the claim "recites" a judicial exception under Step 2A Prong One.”]). Additionally, MPEP 2106.04 instructs examiners to refer to the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) (i.e., mathematical concepts, certain methods of organizing human activities, and mental processes) in order to identify abstract ideas. As noted above and in the previous office action, the claims recite identifying and recommending items. This is an abstract idea because it is a concept of business relations which makes it a method of organizing human activity (i.e., one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2)).
Applicant argues on pages 16-17 of the remarks that the amended claims integrate the abstract idea into a practical application and provide a technical improvement. Examiner respectfully disagrees. A method comprising: receiving, a user request from a user for additional content that is related to an object that the user has created, generated, or purchased; capturing: processing the captured image to extract a plurality of transform-invariant features from the image; constructing, for the object, an object graph of transform-invariant features (GTIF) product-token by encoding spatial relationships among the extracted transform-invariant features; wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image, such that the GTIF product-token remains invariant under two-dimensional geometric transformations of the object in the captured image; determining whether the object GTIF product-token matches a particular pair of a set of GTIF product-token pairs; wherein the set of GTIF product-token pairs comprises one or more pairs comprising known GTIF product-token and data determined for a user, or an event; in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair; wherein the particular additional content comprises to the additional content; wherein determining the particular additional content includes performing a search for the particular additional content based on at least the particular pair to improve and optimize the accuracy and relevancy of the search are all part of the abstract idea. As previously mentioned, the mere application of the abstract idea on generic and high-level components does not integrate the abstract idea or provide a technical improvement. These components are described at a high level and as generic in Fig. 9, ¶0499 and ¶0712 of the instant specification.
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 pages 18-23 of the remarks filed 12/23/2025, with respect to the previous 35 USC § 102/103 rejections have been fully considered but are mostly moot in view of the new 103 rejection of the amended claims.
Applicant argues on page 19 of the remarks that Kuang fails to disclose “constructing, for the object, an object GTIF product-token by encoding spatial relationships among the extracted transform-invariant features; wherein the GTlF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image.” Examiner respectfully disagrees. Kuang discloses establishing a target undirected graph according to similarities that correspond to respective target scale combinations. Furthermore, similarity between a first feature map and a second feature map located respectively at any two spatial locations is computed corresponding to a target scale combination. According to Kuang, similarity between feature maps corresponds to similarities between a first and second image which is performed in order to determine a match, see Fig.7, ¶¶0038-0054 and ¶0098 which discloses capturing an image. The aforementioned process is comparable to that of “constructing, for the object, an object GTIF product-token by encoding spatial relationships among the extracted transform-invariant features; wherein the GTIF product-token is a data structure representing a non-directed graph whose nodes correspond to the extracted plurality of transform-invariant features extracted from the captured image and whose edges correspond to spatial relationships between the features extracted from the captured image.”
Applicant argues on page 21 of the remarks that Kuang fails to disclose “receiving ... a user request from a user for additional content that is related to an object that the user has created, generated, or purchased.” Examiner respectfully disagrees. Kuang discloses a user who browses a digital application which recommends dresses. Kuang describes that the user can find a similar dress on another application by performing a similarity search using the images of the dresses in question, see ¶0096 of Kuang. This is comparable to “receiving… a user request from a user for additional content that is related to an object.” Kuang does not explicitly disclose the newly added feature of an object “that the user has created, generated or purchased.” However, the newly added reference Lenahan describes determining similar products to an object that the user has previously purchased, see ¶0138 of Lenahan. The method of Lenahan is applicable to the method of Kuang as they share characteristics and capabilities, namely, they are both targeted to determining relevant products for a user. 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 extraction of features and determining relevant items as disclosed by Kuang to include objects that a user has purchased and hyperlinks as taught by Lenahan.
Applicant argues on page 21 of the remarks that Kuang fails to disclose “in response to determining that the object GTIF product-token matches the particular pair, determining particular additional content based on the particular pair...; wherein the particular additional content comprises hyperlinks to the additional content.” Examiner respectfully disagrees. Kuang discloses a user who searches a website for a similar product to an object of interest. Kuang describes that the user may take a picture of a home appliance in a physical store using their cell phone. The image taken by the user is used by the system disclosed by Kuang in order to perform product image matching and finding additional product or contents based on the match and displaying the found product to the user, see Figs. 9 and 13 and ¶0029, ¶¶0091-0099 of Kuang. Kuang does not explicitly disclose the newly added feature of “hyperlinks” to additional content. However, the newly added reference of Lenahan teaches hyperlinks to additional content that redirect a user’s browser to a marketplace listing, see ¶0141 of Lenahan. The method of Lenahan is applicable to the method of Kuang as they share characteristics and capabilities, namely, they are both targeted to determining relevant products for a user. 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 extraction of features and determining relevant items as disclosed by Kuang to include objects that a user has purchased and hyperlinks as taught by Lenahan.
As per MPEP 2111, the pending claims must be given their broadest reasonable interpretation consistent with the specification. Applicant’s arguments regarding constructing a graph with nodes and edges are a narrow interpretation of the claims. Furthermore, according to the MPEP 2111.01(II), it is improper to import claim limitations from the specification when interpreting the claims under broadest reasonable interpretation.
Accordingly, reference Kuang, Ong, Luo, and Bourdev have been maintained and reference Lenahan 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHOORA LADONI whose email is Ahoora.Ladoni@uspto.gov and telephone number is (703) 756-5617. The examiner can normally be reached M-F 0900–1700 ET.
Examiner interviews are available via telephone, in-person and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AHOORA LADONI/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
Prosecution Insights