Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 6/10/2026. Claims 1-11 are currently pending and have been examined. Claims 12-20 stand withdrawn. Claims 1-3, 5 have been amended.
Examiner recommends that Applicant cancel the withdrawn, unelected claims.
Election
Applicant’s remarks regarding the restriction requirement have been considered but are not persuasive.
Applicant “continues to assert that the inventions as claimed are capable of use together, share materially overlapping modes of operation, and are obvious variants of one another. As set forth in the traversal filed December 22, 2025, Group I recites "associating videos of a video library with product codes and personas" as a foundational step that overlaps directly with the video analysis and persona assignment operations recited in Group II. Similarly, Group III's step of "maintaining a video library tagged with metadata associated with personas and product codes" is the direct result of and relies upon the preprocessing and association steps recited in Groups I and II. The specification describes a single integrated system in which these operations function together as part of a unified workflow.” Applicant further argues that “The three groups share the same fundamental mode of operation - analyzing video content, associating that content with product codes and personas, and deploying that content during a customer checkout - and are therefore not materially distinct in function or effect.”
Examiner respectfully disagrees, and refers to the Response provided to the arguments in the Non-Final Rejection. Applicant’s arguments are not persuasive because the groups are directed to independent or distinct inventions. The three inventions, while each generally related to e-commerce video presentation, are directed to three distinct, alternative embodiments which process, select, and display videos in different ways. Each of the groups recites distinct steps not present in the other groupings, and distinct capabilities as part of a distinct workflow. Rather than being directed to the same invention for solving the same problem, the three inventions are directed to three different, alternative, embodiments for analyzing, modifying, and presenting videos in three different ways in three different situations; for instance, Group I recites specific steps for pre-processing videos and performing real-time interactions with a recommendation service, while Group II recites an alternative embodiment in which product codes are linked or mapped to videos in a particular manner and personas are generated for videos based on frequency counts, including the user of a machine learning model trained in a particular way; while Group III recites yet another alternative embodiment in which a loyalty profile, loyalty account, and loyalty system are used at a checkout/POS terminal to generate recommendations. In summation, each of the 3 alternative embodiments recites unique steps not present in the other groups, and provides alternative means to perform each stage of a video-presentation sequence. The three groupings provide three different inventions claiming three different techniques for video presentation, with three different sets of operations to achieve distinct results.
The requirement is still deemed proper and is therefore maintained as FINAL.
Claim Objections
Claim 1 is objected to for the following informality: “wherein the known persona reflects one or more preferences of a customer” should read “…of the customer.”
Appropriate correction is required.
Claim 5 is objected to for the following informality: “a recommendation service” should read “the recommendation service.”
Claim 7 is objected to for the following informality: “according a relevance” should read “…according to a relevance.”
Appropriate correction is required.
Claim Rejection – 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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-11 are directed to a process. Therefore, claims 1-11 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claim 1 recites at least the following limitations that are believed to recite an abstract idea:
preprocessing content of a content library by applying techniques to each content to identify product objects and non-product objects within each content, and associating the content with product codes and personas;
identifying a customer engaged in a checkout;
obtaining a known persona linked to the customer, wherein the known persona reflects one or more preferences of a customer obtained through historical interactions with the customer;
providing a transaction history of the customer in real time to a recommendation service and receiving at least one recommended product code returned from the recommendation service based on the transaction history of the customer;
generating a playlist from the content by filtering the content based on a match between the product codes and the at least one recommended product code and a match between the personas and the known persona; and
presenting at least one content from the playlist to the customer during the checkout, wherein the at least one content is presented on a display associated with processing the checkout that is not being used by a transaction display for the checkout.
The above limitations recite the concept of a personalized content suggestion. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-11 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
Videos
Vision-based algorithms
Frames of videos
A screen of a display
a terminal or a user device
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 5-9, 11 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 2-4, 10 these claims are similar to the independent claims except that they recite the further additional elements of metadata, video frames, a touch interaction with a display. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
Videos
Vision-based algorithms
Frames of videos
A screen of a display
a terminal or a user device
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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 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.
Claim Rejection – 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 1, 3-11 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao et al (US 20160005097 A1), hereinafter Hsiao, in view of Feng et al (US 20180005297 A1), hereinafter Feng, and further in view of Metz et al (US 20220270137 A1), hereinafter Metz.
Regarding claim 1, Hsiao discloses a method, comprising:
preprocessing videos of a video library by applying vision-based algorithms to each video to identify product objects and non-product objects within frames of each video, and associating the videos with product codes and personas (Hsiao: “features may be extracted from one or more product-related images based, at least in part, on one or more interest points within segmented regions of a product-related image and may comprise, for example, one or more texture-type features and/or one or more color-type features. … partition any suitable representation of a product-related image (e.g., a saliency map, edge map, etc.) into one or more regions, such as to facilitate and/or support processing. …segmentation may help with identifying and/or extracting a region of interest, such as indicative of a product” [0038] – “one or more regions of interest may, for example, be detected so as to remove less preferred and/or less relevant image-related content (e.g., a background, retailer's name, non-product-related pixels, etc.). ” [0062] - “ utilize one or more real-time and/or near real-time indexing techniques, for example, so as to keep a suitable index ” [0039] – The content may be video content. [0026] - It is recognized that plurality of features corresponding to the depicted product may correspond to at least a product code and a persona.);
identifying a customer engaged in a checkout (Hsiao: “one or more products may be recommended as a result of accessing a web site, such as, before, during, or after a checkout” [0041]);
obtaining a known persona linked to the customer, wherein the known persona reflects one or more preferences of a customer obtained through historical interactions with the customer (Hsiao: “a user's image-related browsing history as well as one or more user personal preferences (e.g., styles, designs, etc.) characterized via one or more previously purchased products may be collected. One or more features of non-preferred products, such as viewed but not purchased products, for example may also be collected in a suitable manner (e.g., in a log, database, etc.)” [0054]);
providing a transaction history of the customer to a recommendation service and receiving at least one recommended product code returned from the recommendation service based on the transaction history of the customer (Hsiao: “ranking function(s) 128 may be capable of learning a style of a product that a user may prefer and/or context of a purchase based, at least in part, on user's purchasing and/or viewing history,” [0040] – “user personal preferences (e.g., styles, designs, etc.) characterized via one or more previously purchased products may be collected” [0054] – “a user's personal preference score may, for example, be derived by ranking user's preferred (e.g., purchased, etc.) as well as non-preferred (e.g., viewed but not purchased, etc.) products” [0055]);
generating a playlist from the videos by filtering the videos based on a match between the product codes and the at least one recommended product code and a match between the personas and the known persona (Hsiao: “a personalized metric, such as a user's personal preference score descriptive of one or more user preferences, for example, may be derived from a user's past browsing history and/or one or more personal preferences so as to arrive at a resulting user-preferred feature space. At times, a user's personal preference score may, for example, be derived by ranking user's preferred (e.g., purchased, etc.) as well as non-preferred (e.g., viewed but not purchased, etc.) products using any suitable learning models and/or processes.” [0055] – “a recommendation may, for example, account for a contextual aspect of an on-line behavior and/or purchase, such as via a suitable contextual metric and/or measure. … observed on-line behavior and/or purchase-related preferences, such as more frequently viewed product categories and/or average price of products purchased in these categories, for example, may be ranked, such as via a suitable learner function, … product-related images … may be ranked …Based, at least in part, on obtained rankings, in some instances, a contextual metric and/or measure comprising a weighted average of a visual, text, and/or user preference scores may, for example, be derived and used, at least in part, as a final recommendation score so as to suggest one or more products to an on-line user.” [0056] – “rank documents and/or on-line products in an order that may, for example, be based, at least in part, on keyword relevance, recency, usefulness, popularity, visual product similarity, personal visual preference, contextual similarity, product price, product type, and/or the like. For example, as discussed below, in some instances, ranking function(s) 128 may be capable of learning a style of a product that a user may prefer and/or context of a purchase based, at least in part, on user's purchasing and/or viewing history” [0040]); and
presenting at least one video from the playlist to the customer during the checkout (Hsiao: “one or more products may be recommended as a result of accessing a web site, such as, before, during, or after a checkout” [0041]),
but does not specifically teach providing a transaction history of the customer in real time; and that the at least one video is presented on a screen of a display associated with a terminal or a user device that is processing the checkout that is not being used by a transaction interface for the checkout.
However, Feng teaches a recommendation service [Abstract], including providing a transaction history of the customer in real time to a transaction service (Feng: “when the user logs onto a website, the recommendation service controller collects browsing records of the user in real time” [0027] – “the recommendation system online tracks user's accessing records on the electronic business website in real time, and carries out recommendation service according to these records” [0043] – “extracting log data and a merchandise purchase history record corresponding to the log data from a Web server, and processing the log data, thereby generating a page browsing sequence reference set; step B: collecting user's log data in real time” [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Hsiao would continue to teach providing a transaction history of the customer to a recommendation service, except that now it would also teach providing a transaction history of the customer in real time to a transaction service, according to the teachings of Feng. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved accuracy of recommendations (Feng: [0019]).
While Hsiao/Feng do not teach that the at least one video is presented on a screen of a display associated with a terminal or a user device that is processing the checkout that is not being used by a transaction interface for the checkout,
Metz teaches a personalized advertisement method [Abstract], including: that the at least one video is presented on a screen of a display associated with a terminal or a user device that is processing the checkout that is not being used by a transaction interface for the checkout (Metz: “During the checkout process, a checkout screen is presented to the user 110 on the display 44 of the user device 10, as shown in FIG. 20 of the drawings. During the checkout process, the merchant offer advertisement 102 selected by the user 110 is displayed to the user” [0108]– “ A creative refers to an image or a series of images aimed to entice a user to convert to a particular brand (e.g., the “ad” part). …This could be a single image, images stringed together, a gif/video, etc.” [0059] – With reference to Figure 20, the ad is presented on a screen, during checkout, that is not used by a transaction/checkout interface while the ad is presented.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Hsiao/Feng would continue to teach presenting at least one video from the playlist to the customer during the checkout, except that now it would also teach that the at least one video is presented on a screen of a display associated with a terminal or a user device that is processing the checkout that is not being used by a transaction interface for the checkout, according to the teachings of Metz. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to attract and convert customers (Metz: [0003]).
Regarding Claim 3, Hsiao/Feng/Metz teach the method of claim 1, wherein preprocessing further includes generating at least one histogram per video, wherein the at least one histogram includes unique tags for corresponding product codes and objects detected in a corresponding video along with frequency counts for the unique tags within frames of the corresponding video (Hsiao: “features of interest… may be extracted, such as from product-related image 302. Here, any suitable feature extraction techniques or approaches, such as scale-invariant feature transform (SIFT), gradient location and orientation histogram (GLOH) … one or more color-type features 328 may, for example, be represented via any suitable color distribution within region of interest 322 and/or image 302, such as a color histogram comprising a frequency of occurrences of color-related values (e.g., how many times a visual word occurs in an image, etc.). … one or more histograms may, for example, be used, at least in part, to visually “summarize” (e.g., characterize, etc.) product 302, such as via counts of one or more visual words (e.g., a bag of words, etc.) within region of interest 322 and/or image 302. Histograms are generally known and need not be described here in greater detail.” [0046]).
Regarding Claim 4, Hsiao/Feng/Metz teach the method of claim 3, wherein generating the at least one histogram per video further includes assigning corresponding personas per video based on a corresponding at least one histogram (Hsiao: “one or more histograms may, for example, be used, at least in part, to visually “summarize” (e.g., characterize, etc.) product 302, such as via counts of one or more visual words (e.g., a bag of words, etc.) within region of interest 322 and/or image 302. Histograms are generally known and need not be described here in greater detail.” [0046] – “a visual-bag-of-words (VBOW)-type processing may be used, at least in part, to facilitate and/or support indexing, searching, and/or retrieval of a product-related image, such as image 302, for example, in connection with one or more color-type features 328 and/or texture-type features 330. For example, features may be normalized in a suitable manner, such as independently, and/or may be concatenated so as to arrive at a visual descriptor using one or more appropriate techniques.” [0047]).
Regarding Claim 5, Hsiao/Feng/Metz teach the method of claim 1, wherein providing further includes
providing the transaction history in real time to a recommendation service (Feng: “when the user logs onto a website, the recommendation service controller collects browsing records of the user in real time” [0027] – “the recommendation system online tracks user's accessing records on the electronic business website in real time, and carries out recommendation service according to these records” [0043] – “extracting log data and a merchandise purchase history record corresponding to the log data from a Web server, and processing the log data, thereby generating a page browsing sequence reference set; step B: collecting user's log data in real time” [0018]) and
receiving real-time recommended product recommendations from the recommendation service during the checkout (Metz: “the retrieved ordered list 100 of merchant offer advertisements are filtered and groomed. More specifically, during the filtering and grooming processes that, preferably, occurs in real-time, the advertisement system 4 filters out ineligible campaigns, removes any duplicate merchant offer advertisements (e.g., the same merchant offer advertisements or multiple merchant offer advertisements from the same merchant), and adds a degree of randomness.” [0105])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Metz with Feng and with Hsiao for the reasons identified above with respect to claim 1.
Regarding Claim 6, Hsiao/Feng/Metz teach the method of claim 1, wherein generating further includes filtering the videos based on a first match between the product codes and the at least one recommended product code and a second match between the personas and the known persona (Hsiao: “a personalized metric, such as a user's personal preference score descriptive of one or more user preferences, for example, may be derived from a user's past browsing history and/or one or more personal preferences so as to arrive at a resulting user-preferred feature space. At times, a user's personal preference score may, for example, be derived by ranking user's preferred (e.g., purchased, etc.) as well as non-preferred (e.g., viewed but not purchased, etc.) products using any suitable learning models and/or processes.” [0055] – “a recommendation may, for example, account for a contextual aspect of an on-line behavior and/or purchase, such as via a suitable contextual metric and/or measure. … observed on-line behavior and/or purchase-related preferences, such as more frequently viewed product categories and/or average price of products purchased in these categories, for example, may be ranked, such as via a suitable learner function, … product-related images … may be ranked …Based, at least in part, on obtained rankings, in some instances, a contextual metric and/or measure comprising a weighted average of a visual, text, and/or user preference scores may, for example, be derived and used, at least in part, as a final recommendation score so as to suggest one or more products to an on-line user.” [0056] – “rank documents and/or on-line products in an order that may, for example, be based, at least in part, on keyword relevance, recency, usefulness, popularity, visual product similarity, personal visual preference, contextual similarity, product price, product type, and/or the like. For example, as discussed below, in some instances, ranking function(s) 128 may be capable of learning a style of a product that a user may prefer and/or context of a purchase based, at least in part, on user's purchasing and/or viewing history” [0040]).
Regarding Claim 7, Hsiao/Feng/Metz teach the method of claim 6, wherein filtering further includes scoring and ranking the videos in the playlist according a relevance to the known persona and a likelihood of purchase based on the at least one recommended product code (Hsiao: “rank documents and/or on-line products in an order that may, for example, be based, at least in part, on keyword relevance, recency, usefulness, popularity, visual product similarity, personal visual preference, contextual similarity, product price, product type, and/or the like. … ranking function(s) 128 may be capable of learning a style of a product that a user may prefer and/or context of a purchase based, at least in part, on user's purchasing and/or viewing history, … ranking function(s) 128 may be employed, in whole or in part, to re-rank one or more previous recommendations, such as with respect to visually similar products, for example, such as to account for and/or accommodate a user's personal taste. ” [0040] – “a region of interest may comprise (e.g., be representative of), for example, a product or a portion thereof that may be more likely to be preferred by one or more users, such as for purposes of purchasing and/or on-line shopping” [0031]).
Regarding Claim 8, Hsiao/Feng/Metz teach the method of claim 7, wherein scoring further includes providing the at least one video to the terminal or the user device as a highest scored video from the playlist (Hsiao: “ based, at least in part, on a user's personal preference score, a visual product similarity-type recommendation may, for example, be adjusted (e.g., re-ranked, etc.), such as via a trained ranking function (e.g., Ranking SVM, etc.), such as utilizing a weighted preference score” [0055] – “a graphical user interface (GUI) capable of displaying a web document, such as 602 comprising image 604, may communicate with a computing platform …to bring up product-related recommendations 610 and/or 612, ranked” [0060]).
Regarding Claim 9, Hsiao/Feng/Metz teach the method of claim 6, wherein filtering further includes randomly selecting the at least one video from the playlist and providing to the terminal or the user device (Metz: “advertisement system 4 filters out ineligible campaigns, removes any duplicate merchant offer advertisements (e.g., the same merchant offer advertisements or multiple merchant offer advertisements from the same merchant), and adds a degree of randomness. … the advertisement system 4 communicates the list 100 of merchant offer advertisements 102 to the checkout widget 96 and displays the list 100 of merchant offer advertisements 102 on the display 44 of the user device” [0105-0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Metz with Hsiao/Feng for the reasons identified above with respect to claim 1.
Regarding Claim 10, Hsiao/Feng/Metz teach the method of claim 1 further comprising, providing an interactive element in the at least one video that allows the customer to directly add a particular recommended product associated with the at least one video to the checkout through touch interaction with the display (Metz: “button (shown in FIG. 21 by the button titled “Explore Offers” and shown in FIGS. 22 and 23 by the button titled “Explore Additional Offers”) that is actuatable by the device user 110 to generate a list of merchant offer advertisements” [0149] – “The device user 110 may select one or more merchant offer advertisements 102 from the list 100 of merchant offer advertisements 102 by utilizing the user interface 42 of the user device 10 and clicking on the merchant offer advertisement ” [0085]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Metz with Hsiao/Feng for the reasons identified above with respect to claim 1.
Regarding Claim 11, Hsiao/Feng/Metz teach the method of claim 1 further comprising, logging interactions of the customer with the at least one video including any interactions with interactive elements of the at least one video and updating a loyalty profile associated with the customer based on the interactions (Metz: “Analytics data may include data related to the user's prior merchant offer advertisement 102 selections, declined merchant offer advertisements 102 and/or merchant offers (e.g., the merchant offer advertisements and/or merchant offers that were passed up by the device user 110), purchased merchant offers, clickstream data, all data/events relating to purchase made through the checkout widget … The analytics data may be cached” [0090] – “The device user 110 may select one or more merchant offer advertisements 102 from the list 100 of merchant offer advertisements 102 by utilizing the user interface 42 of the user device 10 and clicking on the merchant offer advertisement ” [0085]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Metz with Hsiao/Feng for the reasons identified above with respect to claim 1.
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Hsiao/Feng/Metz, and further in view of Crossley et al (US20200134320 A1), hereinafter Crossley.
Regarding Claim 2, Hsiao/Feng/Metz teach the method of claim 1, wherein data is indexed for retrieval during the checkout (Hsiao: “features may be extracted from one or more product-related images based, at least in part, on one or more interest points within segmented regions of a product-related image and may comprise, for example, one or more texture-type features and/or one or more color-type features. … partition any suitable representation of a product-related image (e.g., a saliency map, edge map, etc.) into one or more regions, such as to facilitate and/or support processing. …segmentation may help with identifying and/or extracting a region of interest, such as indicative of a product” [0038] – “one or more regions of interest may, for example, be detected so as to remove less preferred and/or less relevant image-related content (e.g., a background, retailer's name, non-product-related pixels, etc.). ” [0062] - “ utilize one or more real-time and/or near real-time indexing techniques, for example, so as to keep a suitable index ” [0039] – “one or more products may be recommended as a result of accessing a web site, such as, before, during, or after a checkout” [0041]),
But do not teach that preprocessing further includes tagging the videos with metadata that includes the product codes and the personas and indexing the videos based on the metadata for retrieval.
However, Crossley teaches a video platform for purchasing products (Crossley: [0007]), including that preprocessing further includes tagging the videos with metadata that includes the product codes and the personas and indexing the videos based on the metadata for retrieval (Crossley: “The metadata for each tagged video frame may include object or product identification information (e.g., product IDs) for all objects and products associated with the people appearing in the video frame, regardless of whether the objects and products appear in the frame. In other words, the metadata may include object or product IDs for objects or products that don't appear in the frame.” [0045] – “the product server 207 can obtain metadata in real-time using the object recognition server 200 and fetch previously indexed metadata from the product metadata database 209. To access real-time or previously indexed metadata, the product server 207 can send metadata requests to product metadata database 209 to retrieve metadata ” [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Hsiao/Feng/Metz would continue to teach preprocessing videos of a video library by applying vision-based algorithms, wherein data is indexed for retrieval during the checkout, except that now it would also teach that preprocessing further includes tagging the videos with metadata that includes the product codes and the personas and indexing the videos based on the metadata for retrieval, according to the teachings of Crossley. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to increase engagement and purchase click through of products (Crossley: [0007]).
Response to Arguments
Applicant’s arguments filed 6/23/2026 have been fully considered but are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues with reference to Desjardins that the claims recite “Exactly that kind of specific technical improvement,” stating that the claims “recite a specific technical solution to a well-defined technical problem – the inability of prior checkout systems to deliver relevant, personalized video content to customers in real time during a transaction.” Applicant argues that the claims provide “a video preprocessing and persona-matching system the did not exist…and that solves a specific technical problem arising in the context of retail transaction systems,” stating that the claims “recite more than the routine or conventional use of computers; they recite a specific non-conventional technical architecture for solving the identified problem.”
Examiner disagrees. With reference to the rejection above, the argued ability to analyze recommendable content in advance and to filter it for recommendation based on user preference/persona and a product code derived from user transaction history (the argued “dual-criteria playlist filtering”) are part of the abstract idea itself. Rather than providing the solution to a technological problem, the claims at best offer a business improvement rooted solely in the abstract idea by providing a particular technique for abstract data processing to determine and present recommendations. The additional elements are recited at a high level of generality and amount to mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)]. Whereas Desjardins defines a specific technological problem and then claims a solution to that problem, the pending claims at best provide a business improvement to a business need to deliver relevant, personalized content to customers during a transaction, with additional elements providing a general linking to computer technology.
Applicant further argues that that the additional elements “are not merely generic computer components; they define a specific technical process that improves the functioning of the checkout system itself by enabling real-time, persona-matched, product-specific video content delivery that was not possible in prior art systems.”
Examiner disagrees. As addressed above, the ability to provide real-time, persona-matched product specific content delivery at checkout is part of the abstract idea itself, including the argued “persona matching,” “real-time recommendation service,” and “dual-criteria filtering.” The additional elements, such as data being displayed on a screen, the content being videos, etc., are invoked as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)], providing only a general linking to computer technology.
Applicant further argues that the claims provide “a specific, concrete implementation that imposes meaningful limits on any alleged abstract idea and results in a tangible improvement to the checkout experience and to the technical system performing it.”
Examiner disagrees. Similar to the discussion above, the ability to pre-process content to recognized products and determine “personas” of the content, and that this data is “filtered against the customer’s persona to generate a playlist,” which is then presented in a space that is not being used by the transaction, is part of the abstract idea itself, such that any alleged improvement to the checkout experience is at best a business improvement rooted solely in the abstract idea, except for a general linking to computer technology provided by the additional elements.
Claim Rejection – 35 USC 103
Applicant’s arguments with respect to the prior art rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20220277205-A1
US-20150278916-A1
US-20150095455-A1
KR-20220059123-A
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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