Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
This non-final office action is in response to the communication filed on 11/10/2023. Claims 1-26 are currently pending and have been examined below.
Claim Objections
Claim 25 objected to because of the following informalities: “using one or more processors” in line 6 should be replaced with “using the one or more processors.” Appropriate correction is required.
Claim 26 objected to because of the following informalities: “using one or more processors” in line 7 should be replaced with “using the one or more processors.” Appropriate correction is required.
Claim Rejections – 35 U.S.C. 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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, Claim 1 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows:
accessing user-specific data vectors on a plurality of users, wherein the user-specific data vectors include shopping history and video consumption behavior;
developing, a plurality of clusters based on the user-specific data vectors;
associating a user from the plurality of users with one or more clusters from the plurality of clusters;
identifying that the user, from the plurality of users, is viewing media content, wherein the user
is associated with the one or more clusters; and
enabling a purchase of a product for sale to the user, wherein the product for sale is
relevant to the one or more clusters.
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is – the limitations above describe a process of grouping/clustering users based on shopping/consumption history, advertising a product relevant to the cluster and available for purchase to a content viewing user, and enabling the purchase of the product. This is both an advertising activity (i.e., advertising a product based on user history) and a sales activity (i.e., enabling the purchase of the advertised product). Accordingly claim 1 recites an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 1 recites the additional limitations of:
[developing the clusters] using one or more processors;
inserting a container unit into the media content that is being viewed by the user;
populating the container unit with at least one short-form video from a library of short-form videos, wherein the populating is based on the identifying;
[enabling an] ecommerce [purchase of the product];
[the product relevant to] the at least one short-form video, and wherein the ecommerce purchase is accomplished within a short-form video window.
The additional limitations when viewed individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, do not integrate the abstract idea into a practical application because each of the additional elements are recited at high level of generality implementing the abstract idea on a computer (i.e. apply it) or generally linking the use of the judicial exception to a particular technological environment. Specifically:
The recitation of one or more processors to develop the clusters merely generally links the abstract idea to a particular technological environment or merely utilizes a computer as a tool to perform the abstract idea.
Additionally, the limitations inserting a container unit into the media content that is being viewed by the user and populating the container unit with at least one short-form video from a library of short-form videos, wherein the populating is based on the identifying are recited at a high level of generality. Paragraph [0033] of Applicant’s specification recites that “[t]he container unit is inserted in a region of an electronic display executing an application and/or browser” and “container unit comprises a graphics region allocated for representation of short-form videos. The representation can include static and/or motion thumbnails, icons, hyperlinks, and/or other suitable representations.” Examiner notes the broadest reasonably interpretation of these limitations in view of Applicant’s specification encompasses displaying a video targeted to the user on any region of a generic user device display. At this level of generality these limitations merely generally link the abstract idea to a particular technological environment (i.e., a generic user device to play video) or merely utilizes a computer as a tool to perform the abstract idea.
Further, the limitation [enabling an] ecommerce [purchase of the product] is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (e-commerce) or merely utilizes a computer as a tool to perform the abstract idea.
Finally, [the product relevant to] the at least one short-form video, and wherein the ecommerce purchase is accomplished within a short-form video window is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (e.g., a generic video playing app or browser) or merely utilizes a computer as a tool to perform the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and only generally link the use of the judicial exception to a particular technological environment. The same analysis applies here in 2B, i.e., mere instructions to apply an exception in a particular technological environment cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Alice Corp. also establishes that the same analysis should be used for all categories of claims (e.g., product and process claims). Therefore, computer-readable medium claim 25 and system claim 26 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1. The additional limitations in claim 25 (i.e., a computer program product embodied in a non-transitory computer readable medium) and the additional limitations of claim 26 (i.e., a memory and one or more processors) add nothing of substance to the underlying abstract idea. The components are merely providing a particular technological environment to implement the abstract idea.
Dependent claims 2-24 are rejected on a similar rational to the claims upon which they depend. Specifically:
With regard to claims 2-24, the additional limitations only serve to further narrow the abstract idea or generally link the abstract idea to a particular technological environment and do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 nonobviousness.
Claims 1-7, 9, 12, 16-18, 23, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication Number 20210326674 (“Liu”) in view of US Patent Application Publication Number 20210409800 (“Downing”).
Claims 1, 25, and 26
As per claims 1, 25, and 26, Liu teaches a computer-implemented method for video content analysis; a computer program product embodied in a non-transitory computer readable medium for video
content analysis, the computer program product comprising code which causes one or more processors
to perform operations of; and a computer system for video content analysis comprising:
a memory which stores instructions and one or more processors coupled to the memory wherein the one or more processors, when executing the instructions which are stored, are configured to ([0042] “a processor and a memory, the memory storing a plurality of computer programs, the computer programs, when executed by the processor, causing the computer device to implement the content recommendation method.”):
accessing user-specific data vectors on a plurality of users, wherein the user-specific data vectors include shopping history and video consumption behavior ([0009] “determining n groups of seed user vectors according to the target user vector.” And, [0104] “the target user vector is acquired according to a feature of the user.” And, [0157] “The historical behavior feature is used for representing a feature generated by various behaviors when a user uses an Internet service . . . the historical behavior feature includes . . . a purchase behavior.” And, [0187] “user vector that is generated by user representation learning and that can represent a user interest is used as input content for look-alike learning, historical clicks of the user are used as samples to train the user vector.” And, [0186] “a user feature of each user and an interaction behavior of each user (such as . . . watching a video.” And, [0237] “the application determines the degree of interest of the user in the video according to a ratio of a time length of viewing the video by the user to a full length of the video. For example, if the time length of viewing by the user is 100% of the full length of the video, it is considered that the user is interested in the video.”);
developing, using one or more processors, a plurality of clusters based on the user-specific data vectors ([0175] “clustering the L seed user vectors into K sets by using a K-means clustering algorithm.” And, [0288] “cluster the L seed user vectors into K sets by using a target clustering algorithm.”);
associating a user from the plurality of users with one or more clusters from the plurality of clusters ([0010] “calculate a similarity between the target user vector and each group of seed user vectors.” And, [0099] “capture some seed user vectors most similar to the target user vector.” And, [0175] “clustering the L seed user vectors into K sets by using a K-means clustering algorithm.” And, [0288] “cluster the L seed user vectors into K sets by using a target clustering algorithm.”);
identifying that the user, from the plurality of users, is viewing media content, wherein the user is associated with the one or more clusters ([0186] “a user feature of each user and an interaction behavior of each user (such as . . . watching a video.” And, [0237] “the application determines the degree of interest of the user in the video according to a ratio of a time length of viewing the video by the user to a full length of the video. For example, if the time length of viewing by the user is 100% of the full length of the video.” And, [0010] “calculate a similarity between the target user vector and each group of seed user vectors.” And, [0099] “capture some seed user vectors most similar to the target user vector.” And, [0175] “clustering the L seed user vectors into K sets by using a K-means clustering algorithm.”);
wherein the [selected content] is relevant to the one or more clusters ([0080] “calculating a similarity between a target user vector and a seed user vector corresponding to candidate recommendation content. For different target users, the attention mechanism can select, from a plurality of seed user vectors, vector information that has a more reference value for content recommendation to a current target user, so as to improve accuracy of recommending content to the target user.”).
Liu does not explicitly teach but Downing teaches:
inserting a container unit into the media content that is being viewed by the user ([0033] “When a video is playing, a container can update product and service objects being shown that correspond with the particular sequence in a video segment.” And, [0104] “Dynamically-created containers can be injected into the wave, where the video is the attracting mechanism and the products and services of the supplemental content may be bought.” And, [0111] “the system tracks start and stop times for duration of video watched.” And, [0057] “interaction information that includes user interactions, such as (1) user interactions with the base content (like watching the content).” And, [0044] “supplemental content may overlay the base content.”).
Liu does not explicitly teach but Downing teaches:
populating the container unit with at least one short-form video from a library of short-form videos, wherein the populating is based on the identifying ([0033] “When a video is playing, a container can update product and service objects being shown that correspond with the particular sequence in a video segment.” [0104] “available video assets (such as the video 805) and items in the database.” And, [0030] “SMART CONTAINER code is normally configured with a video player window, a selection of products or services being offered, and a variety of related video clips.” And, [0104] “Dynamically-created containers can be injected into the wave, where the video is the attracting mechanism and the products and services of the supplemental content may be bought.”);
Liu teaches selecting recommended content relevant to the one or more clusters but does not explicitly teach that the recommended content enables purchase of a product for sale as taught by Downing:
enabling an ecommerce purchase of a product for sale to the user and the at least one short-form video, and wherein the ecommerce purchase is accomplished within a short-form video window ([0030] “SMART CONTAINER code is normally configured with a video player window, a selection of products or services being offered.” And, [0104] “Dynamically-created containers can be injected into the wave, where the video is the attracting mechanism and the products and services of the supplemental content may be bought.” And, [0047] “the supplemental content may be based on . . . a user profile.” And, [0031] “the SMART CONTAINER code may allow a consumer to . . . purchase the object, right there.” And, [0100] “the supplemental content may contain interactive items that allow users to purchase products or services being displayed.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify Liu to include inserting a container unit into the media content that is being viewed by the user; populating the container unit with at least one short-form video from a library of short-form videos, wherein the populating is based on the identifying; enabling an ecommerce purchase of a product for sale to the user and the at least one short-form video, and wherein the ecommerce purchase is accomplished within a short-form video window as taught by Downing “[b]ecause the SMART CONTAINER code handles all the complexity, it can turn the simplest website into an instant e-commerce store” which “enables anyone to transact online without having to deal with the complexity of setting up an e-commerce site” and “[f]or merchants with an e-commerce site, it readily enables a much richer shopping experience” (Downing [0032]).
Claim 2
As per claim 2, Liu further teaches:
creating a user-specific data vector, based on gathering of shopping history, for inclusion in the user-specific data vectors on the plurality of users (([0009] “determining n groups of seed user vectors according to the target user vector.” And, [0104] “the target user vector is acquired according to a feature of the user.” And, [0157] “The historical behavior feature is used for representing a feature generated by various behaviors when a user uses an Internet service . . . the historical behavior feature includes . . . a purchase behavior.” And, [0187] “user vector that is generated by user representation learning and that can represent a user interest is used as input content for look-alike learning, historical clicks of the user are used as samples to train the user vector.”).
Claim 3
As per claim 3, Liu further teaches:
using one or more processors, additional information about the plurality of users, wherein the inferring is based on the gathering, and wherein the additional information is added to the user-specific data vector ([0046] “calculating similarities between a target user vector and a plurality of seed user vectors corresponding to the candidate recommendation content, so as to determine a degree of interest of a target user in the candidate recommendation content.” And, [0187] “user vector that is generated by user representation learning and that can represent a user interest is used as input content for look-alike learning, historical clicks of the user are used as samples to train the user vector.” And, [0186] “a user feature of each user and an interaction behavior of each user (such as . . . watching a video.” And, [0237] “the application determines the degree of interest of the user in the video according to a ratio of a time length of viewing the video by the user to a full length of the video. For example, if the time length of viewing by the user is 100% of the full length of the video, it is considered that the user is interested in the video.”).
Claim 4
As per claim 4, Liu further teaches:
comprising collecting video consumption behavior information on the plurality of users ([0186] “a user feature of each user and an interaction behavior of each user (such as . . . watching a video.” And, [0237] “the application determines the degree of interest of the user in the video according to a ratio of a time length of viewing the video by the user to a full length of the video. For example, if the time length of viewing by the user is 100% of the full length of the video, it is considered that the user is interested in the video.”).
Claim 5
As per claim 5, Liu further teaches:
wherein the collecting is accomplished using online data sources, wherein the online data sources include one or more video sites ([0186] “a user feature of each user and an interaction behavior of each user (such as . . . watching a video.” And, [0230] “video recommendation application, and the video recommendation application recommends related video content to the target user according to an historical interaction between the target user and a video.” And, [0237] “A video application determines a degree of interest of a user in a video.”).
Claim 6
As per claim 6, Liu further teaches:
wherein the online data sources include metadata ([0235] “application may further acquire related identity information reported by the user when registering. For example, the identity information may include gender, age, education background, resident city.”).
Claim 7
As per claim 7, Liu further teaches:
wherein gathering shopping history information on the plurality of users comprises gathering online shopping history ([0235] “application may further acquire related identity information reported by the user when registering. For example, the identity information may include gender, age, education background, resident city.”).
Claim 9
As per claim 9, Liu further teaches:
wherein the shopping history includes shopping demographics ([0157] “The historical behavior feature is used for representing a feature generated by various behaviors when a user uses an Internet service . . . the historical behavior feature includes . . . a purchase behavior.” And, [0235] “application may further acquire related identity information reported by the user when registering. For example, the identity information may include gender, age, education background, resident city.”).
Claim 12
As per claim 12, Liu further teaches:
training and deploying a machine learning model to develop the plurality of clusters ([0185] “the user vector extraction model and the look-alike model are trained by using a training set.” And, [0174] “a target clustering algorithm (for example, K-means clustering) is used for performing clustering processing on seed user vectors.” And, [0175] “clustering the L seed user vectors into K sets by using a K-means clustering algorithm.”).
Claim 16
As per claim 16, Liu does not explicitly teach but Downing teaches:
wherein the enabling an ecommerce purchase of a product for sale to the user includes a representation of the product for sale in an on-screen product card ([0033] “When a video is playing, a container can update product and service objects being shown.” And, [0030] “The consumer can select any of these offered items to get more details, all enclosed within the SMART CONTAINER technology.” And, [0031] “The offered items (products or services) may be items being advertised or sold. Depending on the type, the SMART CONTAINER code may allow a consumer to . . . purchase the object, right there.” And, [0032] “supplemental items in the SMART CONTAINER code called ON-DEMAND merchandise can be offered.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the enabling an ecommerce purchase of a product for sale to the user includes a representation of the product for sale in an on-screen product card as taught by Downing “[b]ecause the SMART CONTAINER code handles all the complexity, it can turn the simplest website into an instant e-commerce store” which “enables anyone to transact online without having to deal with the complexity of setting up an e-commerce site” and “[f]or merchants with an e-commerce site, it readily enables a much richer shopping experience” (Downing [0032]).
Claim 17
As per claim 17, Liu does not explicitly teach but Downing teaches:
wherein the enabling includes a virtual purchase cart ([0033] “When a video is playing, a container can update product and service objects being shown.” And, [0030] “The consumer can select any of these offered items to get more details, all enclosed within the SMART CONTAINER technology.” And, [0031] “The offered items (products or services) may be items being advertised or sold. Depending on the type, the SMART CONTAINER code may allow a consumer to . . . purchase the object, right there.” And, [0032] “supplemental items in the SMART CONTAINER code called ON-DEMAND merchandise can be offered.” And, [0078] “the platform may provide . . . a cart . . . within a single video e-commerce system.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the enabling includes a virtual purchase cart as taught by Downing “[b]ecause the SMART CONTAINER code handles all the complexity, it can turn the simplest website into an instant e-commerce store” which “enables anyone to transact online without having to deal with the complexity of setting up an e-commerce site” and “[f]or merchants with an e-commerce site, it readily enables a much richer shopping experience” (Downing [0032]).
Claim 18
As per claim 18, Liu does not explicitly teach but Downing teaches:
wherein the at least one short-form video displays the virtual purchase cart while the short-form video plays ([0033] “When a video is playing, a container can update product and service objects being shown.” And, [0030] “The consumer can select any of these offered items to get more details, all enclosed within the SMART CONTAINER technology.” And, [0031] “The offered items (products or services) may be items being advertised or sold. Depending on the type, the SMART CONTAINER code may allow a consumer to . . . purchase the object, right there.” And, [0032] “supplemental items in the SMART CONTAINER code called ON-DEMAND merchandise can be offered.” And, [0078] “the platform may provide . . . a cart . . . within a single video e-commerce system.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the at least one short-form video displays the virtual purchase cart while the short-form video plays as taught by Downing “[b]ecause the SMART CONTAINER code handles all the complexity, it can turn the simplest website into an instant e-commerce store” which “enables anyone to transact online without having to deal with the complexity of setting up an e-commerce site” and “[f]or merchants with an e-commerce site, it readily enables a much richer shopping experience” (Downing [0032]).
Claim 23
As per claim 23, Liu does not explicitly teach but Downing teaches:
wherein the enabling an ecommerce purchase further comprises presenting a coupon overlay, to the user, in the at least one short-form video populated in the container unit ([0033] “When a video is playing, a container can update product and service objects being shown.” And, [0030] “The consumer can select any of these offered items to get more details, all enclosed within the SMART CONTAINER technology.” And, [0031] “The offered items (products or services) may be items being advertised or sold. Depending on the type, the SMART CONTAINER code may allow a consumer to . . . purchase the object, right there.” And, [0032] “supplemental items in the SMART CONTAINER code called ON-DEMAND merchandise can be offered.” And, [0031] “Offered items could also include or be associated with discounts or coupons.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the enabling an ecommerce purchase further comprises presenting a coupon overlay, to the user, in the at least one short-form video populated in the container unit as taught by Downing “[b]ecause the SMART CONTAINER code handles all the complexity, it can turn the simplest website into an instant e-commerce store” which “enables anyone to transact online without having to deal with the complexity of setting up an e-commerce site” and “[f]or merchants with an e-commerce site, it readily enables a much richer shopping experience” (Downing [0032]).
Claim 24
As per claim 24, Liu further teaches:
wherein the presenting is based on shopping history ([0009] “determining n groups of seed user vectors according to the target user vector.” And, [0104] “the target user vector is acquired according to a feature of the user.” And, [0157] “The historical behavior feature is used for representing a feature generated by various behaviors when a user uses an Internet service . . . the historical behavior feature includes . . . a purchase behavior.” And, [0187] “user vector that is generated by user representation learning and that can represent a user interest is used as input content for look-alike learning, historical clicks of the user are used as samples to train the user vector.” And, [0186] “a user feature of each user and an interaction behavior of each user (such as . . . watching a video.” And, [0237] “the application determines the degree of interest of the user in the video according to a ratio of a time length of viewing the video by the user to a full length of the video. For example, if the time length of viewing by the user is 100% of the full length of the video, it is considered that the user is interested in the video.”).
Claims 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication Number 20210326674 (“Liu”) in view of US Patent Application Publication Number 20210409800 (“Downing”) as applied to claim 7 above, and in further view of US Patent Application Publication Number 20140297363 (“Vemana”).
Claim 8
As per claim 8, Liu does not explicitly teach but Vemana teaches:
wherein the gathering is accomplished by offline data sources, wherein the offline data sources include one or more brick-and-mortar retail databases ([0049] “[s]ales data may include purchase history and fulfillment data received from the . . . physical retail sites associated with the enterprise system including, for example the store.” And, [0059] “The store represents a brick-and-mortar retail establishment offering various products to its customers.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the gathering is accomplished by offline data sources, wherein the offline data sources include one or more brick-and-mortar retail databases as taught by Vemana in order to “improve[] the customers' experience and increases the probability that they will purchase products during their visits, thus yielding higher conversion rates and revenues” and to “help[] merchant significantly improve their understanding of their customers and the customers' needs” (Vemana [0006]).
Claim 10
As per claim 10, Liu does not explicitly teach but Vemana teaches:
wherein the offline data sources are shared via a data exchange ([0049] “[s]ales data may include purchase history and fulfillment data received from the . . . physical retail sites associated with the enterprise system including, for example the store.” And, [0059] “The store represents a brick-and-mortar retail establishment offering various products to its customers” and “transmit information received at checkout including sales data, customer loyalty data, inventory data, etc., to the enterprise system for processing”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu, Downing, and Vemana to include wherein the offline data sources are shared via a data exchange as taught by Vemana in order to “improve[] the customers' experience and increases the probability that they will purchase products during their visits, thus yielding higher conversion rates and revenues” and to “help[] merchant significantly improve their understanding of their customers and the customers' needs” (Vemana [0006]).
Claim 11
As per claim 11, Liu does not explicitly teach but Vemana teaches:
comprising forming a taxonomy, for the plurality of users, of products purchased, wherein the taxonomy includes purchase details ([0047] “analytics data may include location and timing data associated with purchases and actions of a user.” And, [0048] “analytics data may include any information about managed customers and/or any customer who contacts the call center; this data can include past purchases, purchase patterns and trends.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include comprising forming a taxonomy, for the plurality of users, of products purchased, wherein the taxonomy includes purchase details as taught by Vemana in order to “improve[] the customers' experience and increases the probability that they will purchase products during their visits, thus yielding higher conversion rates and revenues” and to “help[] merchant significantly improve their understanding of their customers and the customers' needs” (Vemana [0006]).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication Number 20210326674 (“Liu”) in view of US Patent Application Publication Number 20210409800 (“Downing”) as applied to claim 12 above, and in further view of US Patent Application Publication Number 20230325630 (“Wu”).
Claim 13
As per claim 13, Liu does not explicitly teach but Wu teaches:
wherein the training includes weighting shopping history or video consumption behaviors within the user-specific data vectors ([0151] “weight parameters trained using historical transaction data can be used to update the user node vector representation.” And, [0154] “weight parameters trained using historical transaction data can be used to predict the features of a possible future interaction.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the training includes weighting shopping history or video consumption behaviors within the user-specific data vectors as taught by Wu in order to “improve efficiency and prevent undesirable events (e.g., fraud)” ([0136]).
Claim 14
As per claim 14, Liu does not explicitly teach but Wu teaches:
wherein the training includes hints, based on prior knowledge of shopping history ([0151] “weight parameters trained using historical transaction data can be used to update the user node vector representation.” And, [0154] “weight parameters trained using historical transaction data can be used to predict the features of a possible future interaction.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu, Downing, and Wu to include wherein the training includes weighting shopping history or video consumption behaviors within the user-specific data vectors as taught by Wu in order to “improve efficiency and prevent undesirable events (e.g., fraud)” ([0136]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication Number 20210326674 (“Liu”) in view of US Patent Application Publication Number 20210409800 (“Downing”) as applied to claim 1 above, and in further view of US Patent Application Publication Number 20170206551 (“Gupta”).
Claim 15
As per claim 15, Liu does not explicitly teach but Gupta teaches:
wherein the associating a user from the plurality of users is accomplished using hash tables ([0051] “The clustering module is representative of functionality to compute “K” (e.g., 1000) representative user latent vectors, e.g., by performing K-means clustering. A hash table is then computed by the clustering module that maps each user to a corresponding cluster identifier.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the associating a user from the plurality of users is accomplished using hash tables as taught by Gupta “in order to control recommendation of items that accurately meet a user's requirements, tastes, or preferences and in this way help the user locate desired items as part of an enriched user experience” (Gupta [0023]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication Number 20210326674 (“Liu”) in view of US Patent Application Publication Number 20210409800 (“Downing”) as applied to claim 17 above, and in further view of US Patent Application Publication Number 20220122161 (“Perera”).
Claim 19
As per claim 19, Liu does not explicitly teach but Perera teaches:
wherein the virtual purchase cart covers a portion of the at least one short-form video ([claim 1] “cause the product information to be displayed as a second overlay on the video, wherein the user can access the product information within the second overlay, and in response to receiving a user input for adding a specified product from the set of products to a shopping cart, add the specified product to the shopping cart within the second overlay; and generate a configuration file having the interactive layer for the video.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the virtual purchase cart covers a portion of the at least one short-form video as taught by Perera in order to “provides a convenient way for the user to purchase a product from a video by significantly reducing a video-to-cart journey time and minimizing computing resources otherwise required to make the purchase, thereby providing an improvement over prior interactive video techniques” (Perera [0005]).
Claims 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication Number 20210326674 (“Liu”) in view of US Patent Application Publication Number 20210409800 (“Downing”) as applied to claim 1 above, and in further view of US Patent Application Publication Number 20220248109 (“CHANDRA”).
Claim 20
As per claim 20, Liu does not explicitly teach but CHANDRA teaches:
wherein the populating the container unit further comprises, for each cluster within the plurality of clusters, building a video play list, wherein the video play list includes one or more related videos to the cluster from the library of short-form videos ([0066] “comparing to similar users across similar clusters.” And, [0067] “the platform may create an advertisement playlist comprising multiple descriptor videos or influence videos on the basis of various parameters including the preferences, the parameters, the user profile.”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu and Downing to include wherein the populating the container unit further comprises, for each cluster within the plurality of clusters, building a video play list, wherein the video play list includes one or more related videos to the cluster from the library of short-form videos as taught by CHANDRA in order to “provid[e] better experience to the user by delivering targeted content hooks and the advertisement playlist” (CHANDRA [0073]).
Claim 21
As per claim 21, Liu does not explicitly teach but CHANDRA teaches:
wherein the building a video play list is based on hints, wherein the hints include biographic information, demographic information, geographic information, or shopping history ([0066] “comparing to similar users across similar clusters.” And, [0067] “the platform may create an advertisement playlist comprising multiple descriptor videos or influence videos on the basis of various parameters including the preferences, the parameters, the user profile.” And, [0074] “evaluating how others in similar demographic or economic profile have reacted.” And, [0016] “the one or more parameters to determine the one or more descriptor videos . . . comprise a user profile, the preferences, user's location . . . a history of products purchased by the user and/or user clicks on the content hooks, the descriptor videos and/or the influencer videos.).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu, Downing, and Chandra to include wherein the building a video play list is based on hints, wherein the hints include biographic information, demographic information, geographic information, or shopping history as taught by CHANDRA in order to “provid[e] better experience to the user by delivering targeted content hooks and the advertisement playlist” (CHANDRA [0073]).
Claim 22
As per claim 22, Liu does not explicitly teach but CHANDRA teaches:
wherein the building a video playlist includes an ability to replace the one or more related videos with one or more alternate videos from the library of short form videos ([0066] “comparing to similar users across similar clusters.” And, [0067] “the platform may create an advertisement playlist comprising multiple descriptor videos or influence videos on the basis of various parameters including the preferences, the parameters, the user profile.” And, [0074] “evaluating how others in similar demographic or economic profile have reacted.” And, [0016] “the one or more parameters to determine the one or more descriptor videos . . . comprise a user profile, the preferences, user's location . . . a history of products purchased by the user and/or user clicks on the content hooks, the descriptor videos and/or the influencer videos.).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to modify the combination of Liu, Downing, and Chandra to include wherein the building a video playlist includes an ability to replace the one or more related videos with one or more alternate videos from the library of short form videos as taught by CHANDRA in order to “provid[e] better experience to the user by delivering targeted content hooks and the advertisement playlist” (CHANDRA [0073]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication Number 20190075339 (“Smith”) discloses a process for a viewer to view video content augmented with targeted advertisements
US Patent Publication Number 11051067 (“Baxter”) discloses an in-video shopping system receiving a user request during playback of a video, to add at least one item displayed in an interactive shopping overlay to an electronic shopping cart and subsequently completely a checkout process from the shopping car from within the interactive shopping overlay
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/ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622