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 6/24/2026.
Claims 1, 31-41, 43-45, 47-50, and 52 are currently pending and have been examined. Claims 2-30, 42, 46, 51, and 53-92 stand cancelled. Claims 1, 31, 35-36, 38-40, 44, 52 have been amended. The claim objections have been overcome by amendment.
Priority
Applicant’s claim of priority to provisional US application 63354582 is acknowledged. The claims are therefore afforded an effective filing date of 6/22/2022.
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.
Claim 1 is 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, claim 1 is directed to a machine. Therefore, claim 1 is 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:
receive a branded product interaction; and
activate, trigger, or present the product interaction at a visual display, wherein the branded product interaction comprises a visual interaction;
performs measurements or operations relating to the user with the branded product interaction with a specific authenticated branded product;
to transmit the branded product interaction associated with the specific authenticated branded product;
an interaction model, a product model, and/or a user model;
generating the branded product interaction associated with the specific branded product depicted in a rendering of a first user and one or more branded product;
receives at least one image of a first user and a candidate product;
receives input data associated with the at least one image including user data, and context data;
determines or receives product type data;
evaluates the at least one image and input data using at least two distinct authentication modes selected from tag reading, watermark detection, and vision to determine whether the candidate product is an authentic branded product based on product data, user data, and product data associated with user data, wherein the at least two distinct authentication modes are selected based on the product type;
receives product data associated with an authenticated branded product;
updates a user model and a product model based on real-time feedback from user engagement and authentication events;
determines one or more product interaction types to generate based on the interaction model, branded product data, product type data, and/or context data;
generates one or more product interactions only upon successful authentication using the two distinct authentication modes, the one or more product interactions comprising one or more visual elements of the visual interaction, wherein the product interaction comprises adjusting profile images, data, or image position based on the at least one image associated with the authentic branded product or the first user;
evaluates displaying a product interaction indication in association with a representation of the at least one image of the first user and the one or more branded product;
receives a second user engagement with the representation of the at least one image of the first user and the one or more branded product;
provides the one or more product interactions to the user to activate, trigger, or present the product interaction at the visual display for display of the one or more visual elements as part of the product interaction; and
displays the product interaction as a visualization wherein the visualization comprises the at least one image and provides the product interaction.
The above limitations recite the concept of personalized content. 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, claim 1 recites 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:
A computer system comprising: a user device comprising a hardware processor and an interface; a least one controller; a communication interface; one or more non-transitory memory; and a hardware processor programmed with executable instructions
At least one video
A trained model
RFID tag reading, BLE tag reading, and computer vision analysis
Streams or screen position
Displaying on the interface
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.
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:
A computer system comprising: a user device comprising a hardware processor and an interface; a least one controller; a communication interface; one or more non-transitory memory; and a hardware processor programmed with executable instructions
At least one video
A trained model
RFID tag reading, BLE tag reading, and computer vision analysis
Streams or screen position
Displaying on the interface
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.
Claims 31-41, 43-45, 47-50, & 52 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 31-41, 43-45, 47-50, & 52 are directed to a process. Therefore, claims 31-41, 43-45, 47-50, & 52 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 31 recites at least the following limitations that are believed to recite an abstract idea:
receiving input data that includes at least one image of a first user and a candidate product;
receiving data related to the at least one image of the first user and the candidate product;
identifying the first user associated with the at least one image;
determining or receiving product type data;
authenticating based on the rendering and the data, whether the candidate product displayed is a branded product by:
Detecting and reading product identifiers using at least two distinct authentication modes, wherein the at least two distinct authentication modes are selected based on the product type data;
analyzing, by matching product features to a branded product data store;
Verifying, product authenticity using a combination of data and image data
retrieving additional data associated with the product;
generating a branded product interaction to activate, trigger, or present the product interaction, wherein the branded product interaction comprises adjusting profile images, data, or image position based on the at least one image associated with the authentic branded product or the first user; and
causing display of the product interaction as a visualization, wherein the visualization comprises the at least one image and provides the branded product interaction.
The above limitations recite the concept of personalized content. 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 31-41, 43-45, 47-50, & 52 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:
The method being computer implemented
A hardware processor
Video
Metadata
Sensor data
Streams or screen position
An electronic device displaying data on a user interface
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 36-41, 43-44, 47-50, and 52 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 32-35, and 45, these claims are similar to the independent claims except that they recite the further additional elements of sensors, computer vision, the video being “one or more of still image, snapshot, moving image, real-time live stream, near real-time stream, recorded video stream, a virtual reality snapshot, a virtual reality stream, a recorded virtual reality stream, an augmented reality snapshot, an augmented reality stream, and/or a recorded augmented reality stream”, and “one or more of a 1 D (linear) barcode, 2D barcode, 3D barcode, watermark, microtext, hologram, forensic taggants, a sensor, circuit, printed code, UV code, infrared code, printed smart tag, smart token, RFID technology tag, BLE tag, and/or RTLS tag,” . 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:
The method being computer implemented
A hardware processor
Video
Metadata
Sensor data
Streams or screen position
An electronic device displaying data on a user interface
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.
Claims 1, 31-41, 43-45, 47-50, & 52 are rejected under 35 U.S.C. 103 as being unpatentable over Systrom et al (US 20140278998 A1), hereinafter Systrom, in view of Jain et al (US20180154400A1), hereinafter Jain, and further in view of Grabiner et al (US9053616B2) hereinafter Grabiner.
Regarding Claim 1, Systrom discloses a computer system for
providing a user device with a branded product interaction to activate, trigger, or present the branded product interaction at a visual display, the branded product interaction associated with a specific authenticated branded product depicted in at least one video of a first user and one or more branded product (Systrom: “set a hotspot in the image around the identified product.” [0032] – “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020] – “The image can be a digital or electronic image that is … dynamic, such as a video” [0023]),
the system comprising:
a user device comprising a hardware processor, and an interface (Systrom: [0120]), wherein the user device is configured to receive a branded product interaction; and activate, trigger, or present the product interaction at a visual display, wherein the branded product interaction comprises a visual interaction (Systrom: “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]);
at least one controller that performs measurements or operations relating to the user device with the branded product interaction with a specific authenticated branded product (Systrom: “The systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions can be executed by computer-executable components integrated with the application,” [0140] – “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image.” [0032]);
a communication interface to transmit the branded product interaction associated with the specific authenticated branded product (Systrom: “The systems and methods of the embodiments can be embodied …as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions can be executed by computer-executable components integrated with the …communication interface,” [0140] – “display a visual cue of a hotspot within the image when the image is viewed, by a user, within his personal social feed ” [0090]);
one or more non-transitory memory storing a trained interaction model, a product model, and/or a user model (Systrom: “The computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory,” [0140] – “Block S120 can implement … other machine vision and/or machine learning technique to automatically identify a product” [0032]);
the hardware processor programmed with executable instructions for generating the branded product interaction associated with the specific branded product depicted in a rendering of a first user and one or more branded product (Systrom: “the computer system can upload a photo and associated tags over a distributed network, such as over the Internet, and one or more processors throughout the distributed network can implement one or more Blocks of first method” [0022] – “Block S250A can … set the first hotspot” [0086]), wherein the hardware processor:
receives at least one video [image] of a first user and a candidate product (Systrom: “the image is an amateur (i.e., unofficial) image, such as a digital photograph captured with a Smartphone and uploaded to the Social networking system, by a user, though a native application executing on the Smartphone …a first private user uploads the image that includes an amateur photograph of a woman on a beach wearing a bathing suit by Brand Y and holding a branded soda can by Brand X.” [0020] – “a first user can capture a photographic image through a camera integrated into a Smartphone and upload the photographic image to his social feed within the social networking system” [0025] – See also Figure 13. - “The image can be a digital or electronic image that is … dynamic, such as a video” [0023]);
receives input data associated with the at least one video including user data, and context data (Systrom: “Block S110 can upload an amateur candid photograph, from a first user, to the first user's personal Social feed within the Social networking system, and Block S120 can receive a shoe brand tag, for a pair of shoes shown in the image, from the first user, …and a clothing item tag, for a clothing item shown in the image,” [0027] – “Block S120 analyzes images features, exchangeable image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032]);
evaluates the at least one video and input data using at least two distinct authentication methods including computer vision analysis, to determine whether the candidate product is an authentic branded product based on product data, user data, and product data associated with user data (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchangeable image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032] – “Block S120 can implement an object image detection algorithm to identify a region of the image associated with a product, brand, designer, store, merchant, model, etc. Block S120 can then automatically generate the tag for the image or prompt the first user to enter or confirm the tag. For example, Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033]);
receives product data associated with an authenticated branded product (Systrom: “Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033] – “associate the hotspot defined in the image through Blocks S120 and S130 with a particular merchant to enable access to merchant-related information through the hotspot.” [0038]);
updates a user model and a product model based on real-time feedback from user engagement and authentication events (Systrom: automatically generate the tag for the image or prompt the first user to enter or confirm the tag. … generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033] – “associate a tag received from a user, brand, etc. with all or a portion of the region to define a hotspot within the image” [0035] – “user profile can also store other information provided by the user, for example, images or videos. Images of users can be tagged with identification information of users …A user profile in the user profile store 804 can also maintain references to actions by the corresponding user performed on content items in the content store” [0125]);
determines one or more product interaction [hotspot] types to generate based on the interaction model, branded product data, product type data, and/or context data (Systrom: “Block S250A can select the particular electronic storefront through which the first user has previously shopped, that carries multiple brands preferred by the first user, that retains past shipping and billing information of the first user, etc. and set the first hotspot to link to the particular electronic storefront.” [0086] – “select the border color and/or border thickness based on the type and/or number of hotspots in the image. For example, Block S180 can apply a two-pixel wide green border to an image with one product tag, a two pixel wide orange border to an image with one brand tag, a four-pixel wide green border to an image with two product tags, and a two-pixel wide orange border inside a two-pixel wide green border to an image with one brand tag and one product tag.” [0067]);
generates one or more product interactions only upon successful authentication using the two distinct authentication modes, the one or more product interactions comprising one or more visual elements of the visual interaction (Systrom: “Generally, Block S130 functions to associate a tag received from a user, brand, etc. with all or a portion of the region to define a hotspot within the image.” [0035] – “Block S250A can select the particular electronic storefront through which the first user has previously shopped, that carries multiple brands preferred by the first user, that retains past shipping and billing information of the first user, etc. and set the first hotspot to link to the particular electronic storefront.” [0086] – With reference to Figure 1, it is recognized that Step S120, the “object image detection,” must happen in order for Step130 to act on the detected object. ),
wherein the product interaction comprises adjusting profile images, streams, or screen position based on the at least one video associated with the authentic branded product or the first user (Systrom: “the hotspots as discrete (i.e., non-overlapping), partially overlapping, or fully overlapping regions of the image” [0081] – “display callouts for each hotspot in the image, such as a product or brand description in response to a mouse-over event over a tagged product or a sidebar description with an arrow pointing to a related item within the image.” [0056] – “display visual cues of different hotspots in the image based on a characteristic of a user viewing the image” [0087] See Figure 11);
evaluates displaying a product interaction indication in association with a representation of the at least one video of the first user and the one or more branded product (Systrom: “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]);
receives a second user engagement with the representation of the at least one video of the first user and the one or more branded product (Systrom: “the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]);
provides the one or more product interactions to the user device to activate, trigger, or present the product interaction at the visual display for display of the one or more visual elements as part of the product interaction (Systrom: “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020] – The user views the hotspot on the image on a device [0091].); and
displays the product interaction as a visualization on the interface, wherein the visualization comprises the at least one video and provides the product interaction (Systrom: “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020] – “ display visual cues of different hotspots in the image based on a characteristic of a user viewing the image” [0087] – See Figure 13).
While Systrom further teaches identifying products using RFID tag reading [0109] along with machine vision [0108], it does not specifically teach that the at least two distinct authentication modes selected from: RFID tag reading, BLE tag reading, watermark detection, and computer vision analysis; determinizing or receiving product type data; or that the at least two distinct authentication modes are selected based on the product type data.
However, Jain teaches systems for identification of items [0004], including that the at least two distinct authentication modes are selected from: RFID tag reading, BLE tag reading, watermark detection, and computer vision analysis (Jain: “various machine-vision techniques can be performed on an image of the first respective item to recognize such, and/or machine-readable information (e.g., … unique identifier wireless read from a … RFID tag) can be read from the first respective item or a label … to recognize such.” [0135]).
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, Systrom would continue to teach evaluating the at least one video and input data using at least two distinct authentication methods including computer vision analysis, to determine whether the candidate product is an authentic branded product based on product data, user data, and product data associated with user data, except that now it would also teach including that the at least two distinct authentication methods are selected from: RFID tag reading, BLE tag reading, watermark detection, and computer vision analysis, according to the teachings of Jain. 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 automatically recognized item features (Jain: [0036]).
While Systrom/Jain do not teach determining or receiving product type data; and that the at least two distinct authentication modes are selected based on the product type data, Grabiner teaches product-authenticity determination methods [Col. 1], including:
determining or receiving product type data (Grabiner: “determining by the image capture and communication device a type for each of the one or more environmental monitors on the product label” CLM 1); and
that the at least two distinct authentication modes are selected based on the product type data (Grabiner: “based on the respective determined types of each of the one or more environmental monitors on the product label, analyzing image features of the one or more environmental monitors, the different types of environmental monitors having different image features which are analyzed using different image analysis techniques selected based on the respective type and features of the environmental monitor” CLM 1).
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, Systrom/Jain would continue to teach evaluates the at least one video and input data using at least two distinct authentication modes selected from: RFID tag reading, BLE tag reading, watermark detection, and computer vision analysis, to determine whether the candidate product is an authentic branded product based on product data, user data, and product data associated with user data, except that now it would also teach determinizing or receiving product type data; and that the at least two distinct authentication modes are selected based on the product type data, according to the teachings of Grabiner. 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 verify the authenticity of a product (Grabiner: Col. 7).
Regarding Claim 31, Systrom discloses a computer implemented method for
generating output instructions for a product interaction with a branded product to activate, tripper, or present the product interaction (Systrom: “set a hotspot in the image around the identified product.” [0032] – “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]),
the method comprising:
receiving, using a hardware processor, input data that includes at least one video of a first user [image] and the candidate product (Systrom: “the image is an amateur (i.e., unofficial) image, such as a digital photograph captured with a Smartphone and uploaded to the Social networking system, by a user, though a native application executing on the Smartphone …a first private user uploads the image that includes an amateur photograph of a woman on a beach wearing a bathing suit by Brand Y and holding a branded soda can by Brand X.” [0020] – “a first user can capture a photographic image through a camera integrated into a Smartphone and upload the photographic image to his social feed within the social networking system” [0025] – See also Figure 13. - “The image can be a digital or electronic image that is … dynamic, such as a video” [0023]);
receiving, using the hardware processor, metadata related to the at least one video of the first user and the candidate product (Systrom: “Block S110 can upload an amateur candid photograph, from a first user, to the first user's personal Social feed within the Social networking system, and Block S120 can receive a shoe brand tag, for a pair of shoes shown in the image, from the first user, …and a clothing item tag, for a clothing item shown in the image,” [0027] – “Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032]);
identifying, using the hardware processor, the first user associated with the at least one video (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032]);
authenticating, using the hardware processor, based on the rendering and the metadata, whether the candidate product displayed is a branded product (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032] – “Block S120 can implement an object image detection algorithm to identify a region of the image associated with a product, brand, designer, store, merchant, model, etc. Block S120 can then automatically generate the tag for the image or prompt the first user to enter or confirm the tag. For example, Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033]) by:
detecting and reading product identifiers using at least two distinct authentication modes (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchangeable image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032] – “Block S120 can implement an object image detection algorithm to identify a region of the image associated with a product, brand, designer, store, merchant, model, etc. Block S120 can then automatically generate the tag for the image or prompt the first user to enter or confirm the tag. For example, Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033]);
analysing, by matching product features to a branded product database (Systrom: “receives a text-based descriptor of a product visible in the image, access a database of template images of a product based on the descriptor, and implement template matching to identify the product in the image. ” [0032]);
verifying, product authenticity using a combination of text and image data (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchangeable image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image. …receives a text-based descriptor of a product visible in the image, access a database of template images of a product based on the descriptor, and implement template matching to identify the product in the image” [0032]);
retrieving additional data associated with the product (Systrom: “Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033] – “associate the hotspot defined in the image through Blocks S120 and S130 with a particular merchant to enable access to merchant-related information through the hotspot.” [0038]);
generating a branded product interaction to activate, trigger, or present the product interaction (Systrom: “set a hotspot in the image around the identified product.” [0032] – “Block S250A can select the particular electronic storefront through which the first user has previously shopped, that carries multiple brands preferred by the first user, that retains past shipping and billing information of the first user, etc. and set the first hotspot to link to the particular electronic storefront.” [0086] – “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]),
wherein the branded product interaction comprises adjusting profile images, streams, or screen position based on the at least one video associated with the authentic branded product or the first user (Systrom: “the hotspots as discrete (i.e., non-overlapping), partially overlapping, or fully overlapping regions of the image” [0081] – “display callouts for each hotspot in the image, such as a product or brand description in response to a mouse-over event over a tagged product or a sidebar description with an arrow pointing to a related item within the image.” [0056] – “display visual cues of different hotspots in the image based on a characteristic of a user viewing the image” [0087] See Figure 11);
causing an electronic device to display the product interaction as a visualization on a user interface, wherein the visualization comprises the at least one video and provides the branded product interaction (Systrom: “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020] – “ display visual cues of different hotspots in the image based on a characteristic of a user viewing the image” [0087] – See Figure 13. The user uses a computing device and interacts via an interface [0022].).
While Systrom further teaches identifying products using RFID tag reading [0109] along with machine vision [0108], it does not specifically teach that the combination of data is a combination of sensor and image data; determining or receiving product type data; and that the at least two distinct authentication modes are selected based on the product type data.
However, Jain teaches systems for identification of items [0004], including that the combination of data is a combination of sensor and image data (Jain: “various machine-vision techniques can be performed on an image of the first respective item to recognize such, and/or machine-readable information (e.g., … unique identifier wireless read from a … RFID tag) can be read from the first respective item or a label … to recognize such.” [0135]).
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, Systrom would continue to teach verifying, product authenticity using a combination of text and image data, except that now it would also teach that the combination of data is a combination of sensor and image data, according to the teachings of Jain. 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 automatically recognized item features (Jain: [0036]).
While Systrom/Jain do not teach determining or receiving product type data; and that the at least two distinct authentication modes are selected based on the product type data, Grabiner teaches product-authenticity determination methods [Col. 1], including:
determining or receiving product type data (Grabiner: “determining by the image capture and communication device a type for each of the one or more environmental monitors on the product label” CLM 1); and
that the at least two distinct authentication modes are selected based on the product type data (Grabiner: “based on the respective determined types of each of the one or more environmental monitors on the product label, analyzing image features of the one or more environmental monitors, the different types of environmental monitors having different image features which are analyzed using different image analysis techniques selected based on the respective type and features of the environmental monitor” CLM 1).
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, Systrom/Jain would continue to teach evaluates the at least one video and input data using at least two distinct authentication modes selected from: RFID tag reading, BLE tag reading, watermark detection, and computer vision analysis, to determine whether the candidate product is an authentic branded product based on product data, user data, and product data associated with user data, except that now it would also teach determinizing or receiving product type data; and that the at least two distinct authentication modes are selected based on the product type data, according to the teachings of Grabiner. 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 verify the authenticity of a product (Grabiner: Col. 7).
Regarding Claim 32, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising performing measurements using one or more sensors for receiving the input depicting the first user and the one or more displayed candidate product (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032]).
Regarding Claim 33, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising transmitting control signals to one or more sensors to perform measurements using the one or more sensors for receiving the input depicting the first user and the one or more displayed candidate products (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032]).
Regarding Claim 34, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising using computer vision to extract product data from image or video data received as the input to authenticate whether the one or more displayed candidate product is associated with the branded product to determine one or more authenticated displayed product (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032]).
Regarding Claim 35, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 wherein the at least one video is one or more of still image, snapshot, moving image, real-time live stream, near real-time stream, recorded video stream, a virtual reality snapshot, a virtual reality stream, a recorded virtual reality stream, an augmented reality snapshot, an augmented reality stream, and/or a recorded augmented reality stream (Systrom: “The image can be a digital or electronic image that is static, such as a color, black-and-white, or sepia photograph, or dynamic, such as a video, meme, or other visual image.” [0023] – “a communication channel can be a “stream” through which a series of content items posted, uploaded, or otherwise provided to the social networking system are shared with one or more users. In this example, the stream can also be updated as one or more users add content items to the stream.” [0137].).
Regarding Claim 36, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 the method further comprising evaluating whether the at least one video of the first user and the candidate product contains a sufficient visible portion of the candidate product to identify whether the candidate product is an authentic branded product (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image.” [0032] – “Block S150B can filter out images of a quality below a threshold quality, … or images that fail to meet any other criteria. Block S150B can also implement machine vision and/or machine learning techniques to identify and filter out unsuitable or less desirable images.” [0047]).
Regarding Claim 37, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 wherein the candidate product is an item of apparel (Systrom: “uploads the image that includes an amateur photograph of a woman on a beach wearing a bathing suit by Brand Y and holding a branded soda can by Brand X.” [0020] – “a pair of shoes shown in the image, …a clothing item shown in the image,” [0027]).
Regarding Claim 38, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 the method further comprising identifying whether there are more than one candidate products within the input data and if there are more than one candidate products authenticating each of the candidate products displayed (Systrom: “Block S110 can upload an amateur candid photograph, from a first user, to the first user's personal Social feed within the Social networking system, and Block S120 can receive a shoe brand tag, for a pair of shoes shown in the image, from the first user, a Soda brand tag and a product tag for a soda can, shown in the image, from a second user, a vehicle manufacturer tag for a vehicle, shown in the image, from a third user, and a clothing item tag, for a clothing item shown in the image, from a fourth user.” [0027] – “select the border color and/or border thickness based on the type and/or number of hotspots in the image. For example, Block S180 can apply a two-pixel wide green border to an image with one product tag, a two pixel wide orange border to an image with one brand tag, a four-pixel wide green border to an image with two product tags, and a two-pixel wide orange border inside a two-pixel wide green border to an image with one brand tag and one product tag.” [0067] – See Figure 13.).
Regarding Claim 39, Systrom/Jain/Grabiner teach the computer implemented method of claim 38 further comprising selecting one of the more than one authenticated products to generate a branded product interaction for based on metadata associated with a second user, the metadata comprising one or more of a user region, user size, user purchase history, user wish list, user viewed product list, user activity, user activity history, user planned activity, user preference associated with color, feel state, activity, and/or fabric type (Systrom: “Block S110 can upload an amateur candid photograph, from a first user, to the first user's personal Social feed within the Social networking system, and Block S120 can receive a shoe brand tag, for a pair of shoes shown in the image, from the first user, a Soda brand tag and a product tag for a soda can, shown in the image, from a second user, a vehicle manufacturer tag for a vehicle, shown in the image, from a third user, and a clothing item tag, for a clothing item shown in the image, from a fourth user.” [0027] – “visual cues can be substantially identical but indicate the presence of different hotspots within the image based on characteristics of the first and second users.. display a first pulsing virtual button over the first region (e.g., first hotspot) of the image when viewed by the first user, and Block S280A can display a second pulsing virtual button over the second region (e.g., second hotspot) of the image when viewed by the second user. … The first and second visual cues can be substantially identical or otherwise substantially unique, such as dependent on (e.g., unique to) an associated item (e.g., product or brand) in the image, a preference of a user, a number of items advertised to a user in one image, a time of day or season, or a user characteristic or history. …adjust a visual cue for a particular hotspot in the image based on how many times a user has viewed the image and/or visual cue, a device on which a user views the image (e.g., laptop computer, smartphone, or tablet), a browser or native application through which a user views the image, past user responses (e.g., likes, reposts, comments) to the image, a similar image, the brand, the product, or a similar product” [0091])
Regarding Claim 40, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 wherein identifying, using the hardware processor, the first user associated with the at least one video further comprises identifying an anonymous user instance and validating the product authenticity based on specific aspects related to the product (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image.” [0032] – “Block S120 can implement an object image detection algorithm to identify a region of the image associated with a product, brand, designer, store, merchant, model, etc. Block S120 can then automatically generate the tag for the image or prompt the first user to enter or confirm the tag. For example, Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033]).
Regarding Claim 41, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprises: receiving one or more of a user model, and product model, and/or a retail model; evaluating the candidate product against one or more of a product model, a user model, and/or pre- authenticated branded product metadata associated with user data to authenticate whether the candidate product is a branded product (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image. In another example, Block S120 receives a text-based descriptor of a product visible in the image, access a database of template images of a product based on the descriptor, and implement template matching to identify the product in the image.” [0032] – “Block S120 can implement an object image detection algorithm to identify a region of the image associated with a product, brand, designer, store, merchant, model, etc. Block S120 can then automatically generate the tag for the image or prompt the first user to enter or confirm the tag. For example, Block S120 can generate a set of potential tags for the image based on the object image detection algorithm, and the method can prompt the first user to select a preferred tag or a proper match for the image from the set of potential tags.” [0033]).
Regarding Claim 43, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising evaluating whether the authentic branded product is available in a specific region, gender designation, market, color, size, pattern, version, membership level, and/or season (Systrom: “Block S160A can selectively callout a Subset of tagged items in the image. Such as based on user purchasing history, user location and stock at a nearby brick and-mortar retail location, a perceived user interest, a user demographic, etc.,” [0056] – “Once the location of the user is determined, Block S160B can select a local retailer that is suitably close to the user, such as a retailer that is within five mile” [0062]).
Regarding Claim 44, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 where authenticating, using the hardware processor, based on the at least one video and the metadata, whether the candidate product is a branded product, further comprises evaluating the candidate product against one or more of a product data model, and/or a pre-authenticated branded product metadata associated with user data (Systrom: “Block S120 can implement object recognition, character recognition, template matching, edge detection, and/or any other machine vision and/or machine learning technique to automatically identify a product or brand represented in the image. In one example, Block S120 analyzes images features, exchange able image file format (exif) data of the image, location data, Social context (e.g., user check-ins), and any other relevant image meta to generate the tag for the image. In another example, Block S120 receives a text-based descriptor of a product visible in the image, access a database of template images of a product based on the descriptor, and implement template matching to identify the product in the image.” [0032]).
Regarding Claim 45, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising instructions for authenticating the candidate product is a branded product by detecting and reading one or more of a 1 D (linear) barcode, 2D barcode, 3D barcode, watermark, microtext, hologram, forensic taggants, a sensor, circuit, printed code, UV code, infrared code, printed smart tag, smart token, RFID technology tag, BLE tag, and/or RTLS tag within, attached to, or on the candidate product (Systrom: “The tag received in Block S120 can also include a product or brand description, name, Stock keeping unit (SKU) number, bar code, or other identifier of the product or brand. In this implementation, Block S120 can analyze the tag (e.g., key word extraction) to extract a brand or product identifier from the tag and then attach a link or pointer to a respective region of the image based on the identifier extracted from the tag.” [0029] – “Block S120 receives a text-based descriptor of a product visible in the image, access a database of template images of a product based on the descriptor, and implement template matching to identify the product in the image.” [0032]).
Regarding Claim 47, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising instructions to add to a graphical representation associated with the first user input an indicator that there are one or more product interactions associated with the first user input (Systrom: “set a hotspot in the image around the identified product.” [0032] – “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020] – See Figure 13.).
Regarding Claim 48, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 47 the method further comprising receiving an input from a second user engaging with the interaction indicator and providing the product interaction to the second user (Systrom: “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]).
Regarding Claim 49, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 the method further comprising receiving metadata associated with a second user the metadata comprising one or more of a user region, user size, user purchase history, user wishlist, user viewed product list, user activity, user activity history, user planned activity, user preference associated with color, feel state, activity, and/or fabric type (Systrom: “Block S120 can also receive multiple tags from one or more users or brands. For example, Block S110 can upload an amateur candid photograph, from a first user, to the first user's personal Social feed within the Social networking system, and Block S120 can receive a shoe brand tag, for a pair of shoes shown in the image, from the first user, a Soda brand tag and a product tag for a soda can, shown in the image, from a second user, a vehicle manufacturer tag for a vehicle, shown in the image, from a third user, and a clothing item tag, for a clothing item shown in the image, from a fourth user.” [0027] – “BlockS250A selects the first link for the first hotspot substantially in real time. … Block S250A retrieves user data stored by the Social networking system and implements the user data to select the electronic store that is particularly relevant to the first user. For example, Block S250A can analyze user transaction history, …browsing history, … user shopping trends, …and/or shipping preferences, etc.” [0086]).
Regarding Claim 50, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 further comprising instructions to determine a substitute branded product based on one or more of the authenticated displayed product availability in an inventory, availability in a preferred region, availability in a preferred size, availability in a preferred option, availability for a preferred gender, and/or an updated version associated with the branded product (Systrom: “Block S370 can similarly communicate additional product information, such as …information or locations of local merchants that carry the product, similar products by other brands, local or online sale offers for the product, another local merchant offering the product for sale and a local or overage product price, other products that complement the product (e.g., shoes that pair well with a pair of pants),” [0112] – “see how the product fits, or see what other products, styles, or accessories function with or complement the product.” [0100] – “Block S250A can analyze … user shopping trends, a user interest, other brands or products of interest to the first user (e.g., another product which the first user may purchase with the first product), …From this information, Block S250A can select a particular electronic storefront, from a list of available or preferred electronic storefronts, that is particularly relevant to the first user.” [0086]).
Regarding Claim 52, Systrom/Jain/Grabiner teach the computer implemented method for generating output instructions for a product interaction with a branded product of claim 31 wherein the product interaction further comprises one or more of adding the displayed branded product to a view history, adding the displayed branded product to a customized storefront, adding identification values associated with the displayed branded product to an incentive system, displaying a closet tour associated with the branded product, displaying environmental and/or sustainability factors associated with a branded product, displaying a 360 degree view of the product, displaying an interactive simulation of the product, displaying a pop-up with additional product details for the displayed branded product, providing a link to purchase the displayed branded product, providing a link to receive a special offer related to the displayed branded product, displaying an interactive product site for the displayed branded product, providing a link to related products for the displayed branded product, providing a link to other products related to the user associated with the displayed branded product, displaying activities associated with the displayed branded product, displaying activities associated with the first user associated with the displayed branded product, displaying communities associated with the displayed branded product, and/or displaying communities associated with the first user associated with the displayed branded product (Systrom: “set a hotspot in the image around the identified product.” [0032] – “Block S250A can select the particular electronic storefront through which the first user has previously shopped, that carries multiple brands preferred by the first user, that retains past shipping and billing information of the first user, etc. and set the first hotspot to link to the particular electronic storefront.” [0086] – “when a third user clicks on the first portion of the image, Block S160A can direct the third user to a social feed of Brand X within the social networking system, and, when the third user clicks on the second portion of the image, Block S160B can direct the third user to an online store in which the third user can order an identical or similar swimsuit.” [0020]).
Response to Arguments
Applicant's arguments filed 6/24/2026 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that the claims recite computer components that are “inextricably tied to the claimed subject matter,” and “are not methods of organizing human activity because they are carried out or implemented by hardware components of a computer.” Applicant makes reference to MPEP 2106.04, which found a particular method of rearranging icons on a GUI to not recite an abstract idea.
Examiner disagrees. With reference to the rejection above, the claims recite steps that amount to a concept for personalized content. 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, claim 1 recites an abstract idea. The mere fact that computer-related additional elements are recited in the claims does not “inextricably tie” the abstract idea to computer technology.
Applicant further argues that “the pending subject matter employs any information derived from judicial exceptions to adjust profile images, streams, or screen position within a visualization displayed at an electronic device. Thus, any judicial exceptions are integrated into a practical application,” with reference to Example 46.
Examiner disagrees. The argued ability to adjust images or positioning within displayed information is part of the abstract idea itself, except for the recitation of computer-related elements at a high level of generality, such that any alleged improvement stems solely from the abstract idea alone. The additional elements, rather than integrating the abstract idea into a practical application, are invoked as mere instructions to apply the abstract idea to a technological environment, e.g. for displayed information to be presented on an interface, for an image to be a video, etc. [MPEP 2106.05(f)]. The additional elements therefore provide only a general linking to computer technology.
Applicant further argues that the cited art do not “disclose the pending subject matter. Accordingly, the claims as a whole amount to significantly more than any judicial exceptions found therein.”
Examiner disagrees, and notes that the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103, and that prior art considerations and 101 eligibility have “separate and distinct requirements” [MPEP 2106.05] With reference to the rejection above, the additional elements are invoked as mere instructions to apply the abstract idea to a technological environment, such that the claims do not qualify as significantly more than the abstract idea [MPEP 2106.05(f)].
Claim Rejection – 35 USC §103
Applicant argues that the cited references do not disclose that “the branded product interaction comprises adjusting profile images, streams, or screen position based on that at least one video associated with the authentic branded product or the first user as now claimed.”
Examiner disagrees. With reference to the rejection above, Systrom teaches generating hotspots that define a region within an image [0035] (which may be a video [0023]), which trigger information about the highlighted product when moused over by the user [0056]. The image is modified/adjusted to include the hotspots and presented to the user, with different hotspots being presented based on characteristics of the user, and products in the video/image [0056]. For instance, different regions can be highlighted based on items present in the image, user preferences, user history or interactions, and past user responses to an image [0091]. Thus, the presented branded product interaction involves presenting adjusted/modified video frames based on the products present and user preferences/history.
Applicant further argues that the references fail to teach Claim 39, stating that “nothing in Systrom suggests selecting one product for prioritization based on any user metadata.”
Examiner disagrees. With reference to the rejection above, Systrom teaches that the selection of different hotspots to present within the video/image based on user characteristics or history, adjusting the visual cues for which products are highlighted based on user interaction data or preferences. [0091]. Thus, Systrom teaches selecting a product to prioritize with a hotspot based on stored user data. The system also arranges the images themselves based on this data, “ arrange images in the image feed displayed to the user such that images most relevant to the user and/or most likely to evoke a positive reaction from the user are displayed first on the user's mobile computing device.” [0115].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Avedissian et al (US 20160205431 A1) teaches systems for augmenting videos with information and purchase links based on recognized products.
Hedges et al (US 20100241528 A1) teaches systems of verifying authenticity of listed items on a website.
Brager et al (US 11861528 B1) teaches systems for detecting products that infringe on authentic designs.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J SULLIVAN whose telephone number is (571)272-9736. The examiner can normally be reached Mon - Fri 9-5 ET.
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/T.J.S./Examiner, Art Unit 3689
/ARIELLE E WEINER/Primary Examiner, Art Unit 3689
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