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 4/16/2026. Claims 21, 23-34, 36-41 are currently pending and have been examined. Claims 1-20, 22, and 35 stand cancelled. Claims 21, 40, 41 have been amended.
Applicant is invited to request a telephonic interview prior to next response.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/16/2026 has been entered.
Priority
Applicant’s Claim of priority to US Patent Application 17108137 is acknowledged. The claims are therefore afforded an effective filing date of 12/01/2020.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21, 23-32, and 41-42 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 21, 23-32, and 41-42 are directed to a process. Therefore, claims 21, 23-32, and 41-42 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 21 recites at least the following limitations that are believed to recite an abstract idea:
Receiving, from a user, initial data associated with an initial one or more user interactions with one or more of a plurality of resources accessed via a means to collect the initial data as the one or more plurality of resources are accessed and interacted with;
Determining a vehicle suggestion to provide to the user based on the initial data by:
Parsing the initial data to identify and extract, from the initial data, an initial vehicle image set including one or more vehicle images;
identifying, from the initial vehicle image set, a first plurality of vehicle traits;
determining a first value for each of the first plurality of vehicle traits; and
determining the vehicle suggestion based on the first value for each of the first plurality of vehicle traits;
based on each occurrence of a next one or more user interactions with a same or a different one or more of the plurality of resources, iteratively receiving next data associated with a next one or more user interactions with one or more of the plurality of resources accessed via the means to collect the next data as the one or more of the plurality of resources are accessed and interacted with; and
upon receiving the next data, determining an updated vehicle suggestion to provide to the user based on the next data by:
parsing the next data to identify and extract, from the next data, a next vehicle image set including one or more vehicle images;
identifying, from the next vehicle image set, a second plurality of vehicle traits, wherein the second plurality of vehicle traits include at least a portion of the first plurality of vehicle traits that are overlapping vehicle traits included in the first plurality of vehicle traits and the second plurality of vehicle traits;
determining a second value for each of the second plurality of vehicle traits;
adjusting the first value for at least the portion of the first plurality of vehicle traits that are overlapping vehicle traits based on the second value; and
determining the updated vehicle suggestion to provide to the user based on the adjusted first value for at least the portion of the first plurality of vehicle traits that are overlapping vehicle traits, the first value for non-overlapping vehicle traits of the first plurality of vehicle traits of the first plurality of vehicles, and the second value for non-overlapping vehicle traits of the second plurality of vehicles.
The above limitations recite the concept of personalized recommendations. 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 21, 23-32, and 41-42 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
One or more web applications executing on or more devices
Website data
Steps being automatic
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 23-26, and 29-31 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 27-28, 32, and 41-42 these claims are similar to the independent claims except that they recite the further additional elements of social network sites/accounts, a trained machine learning algorithm, a trained convolutional neural network having a plurality of layers including an input layer, a plurality of convolutional layers, and an output layer, convolutional operations, and a computer system. 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
One or more web applications executing on or more devices
Website data
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 33-34, 36-39 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 33-34, 36-39 are directed to a process. Therefore, claims 33-34, 36-39 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 33 recites at least the following limitations that are believed to recite an abstract idea:
iteratively receiving, from the user, data based on user interactions with a plurality of resources accessed via means to collect the data as the plurality of resources are accessed and interacted with, wherein the data includes a first vehicle image set of one or more vehicle images received based on a first one or more user interactions with a first one or more of the plurality of resources, and a second vehicle image set of one or more vehicle images received based on a second one or more user interactions with a second one or more of the plurality of resources;
upon the receipt of a first portion of the data that includes the first vehicle image set, determining a vehicle suggestion by:
Parsing the first portion of the data to identify and extract, from the first portion of the data, the first vehicle image set;
Identifying a first plurality of vehicle traits from the first vehicle image set;
determining a first value of each of the first plurality of vehicle traits; and
determining the vehicle suggestion based on the first value of each of the first plurality of vehicle traits;
upon the receipt of a second portion of the data that includes the second vehicle image set, determining an updated vehicle suggestion by:
parsing the second portion of the website data to identify and extract, from the second portion of the website data, the second vehicle image set;
Identifying a second plurality of vehicle traits from the second vehicle image set;
Determining a second value for each of the second plurality of vehicle traits;
adjusting the first value of at least a portion of the first plurality of vehicle traits that are overlapping vehicle traits included in the first plurality of vehicle traits and the second plurality of vehicle traits; and
determining the updated vehicle suggestion based on the adjusted first value for at least the portion of the first plurality of vehicle traits that are overlapping vehicle traits, the first value for non-overlapping vehicle traits of the first plurality of vehicle traits, and the second value for non-overlapping vehicle traits of the second plurality of vehicle traits; and
transmitting, to the user, a notification indicating the updated vehicle suggestion.
The above limitations recite the concept of personalized recommendations. 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 33-34, 36-39 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
One or more web applications executing on one or more devices
Website data
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 34, and 37-38 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 36 these claims are similar to the independent claims except that they recite the further additional elements of social network sites/accounts. 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
One or more web applications executing on one or more devices
Website data
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.
Claim 40 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 40 is directed to a process. Therefore, claim 40 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 40 recites at least the following limitations that are believed to recite an abstract idea:
Receiving, from the user, first data associated with a first one or more user interactions with a first set of one or more of a plurality of resources accessed via means to collect the first data as the first set of one or more of the plurality of resources are accessed and interacted with;
Determining an item suggestion to provide to the user based on the first data by:
Parsing the first data to identify and extract, from the first data, a first image set including one or more images of an item of a particular type;
identifying, from the first image set, a first plurality of traits associated with the item;
determining a first value for each of the first plurality of traits; and
determining the item suggestion based on the first value for each of the first plurality of traits;
based on an occurrence of a second one or more user interactions with the first set or a second set of one or more of the plurality of resources, receiving second data associated with the second one or more user interactions with the first set or the second set of one or more of the plurality of resources accessed via means to collect the second data as the first set or the second set of one or more of the plurality of resources are accessed and interacted with; and
upon receiving the second data, determining an updated item suggestion to provide to the user based on the second data by:
parsing the second data to identify and extract, from the second data, a second image set including one or more images of the item of the particular type;
identifying, from the second image set, a second plurality of traits associated with the item, wherein the second plurality of traits include at least a portion of the first plurality of traits that are overlapping traits included in the first plurality of traits and the second plurality of traits;
determining a second value for each of the second plurality of traits;
adjusting the first value for at least the portion of the first plurality of traits that are overlapping traits based on the second value; and
determining the updated item suggestion based on the adjusted first value for at least the portion of the first plurality of traits that are overlapping traits, the first value for non-overlapping traits of the first plurality of traits, and the second value for non-overlapping traits of the second plurality of traits.
The above limitations recite the concept of personalized recommendations. 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 40 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:
The method being computer-implemented
One or more web applications executing on one or more devices
Website data
Steps being performed automatically
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:
The method being computer-implemented
One or more web applications executing on one or more devices
Website data
Steps being performed automatically
These additional limitations 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.
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.
Claim Rejection – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 21, 23-24, and 26-39, & 42 are rejected under 35 U.S.C. 103 as being unpatentable over Wilkinson et al (US 20170301001 A1), hereinafter Wilkinson, in view of August et al (US 20200098032 A1), hereinafter August.
Regarding Claim 21, Wilkinson discloses a computer-implemented method for providing a product suggestion to a user based on image analysis, the method comprising:
receiving, from one or more web applications executing on one or more devices associated with a user, initial website data associated with an initial one or more user interactions with one or more of a plurality of resources accessed via the one or more web applications, the one or more web applications configured to collect the initial website data as the one or more of the plurality of resources are accessed and interacted with (Wilkinson: “the system detects a video content being viewed by a user. …the display device may be coupled to a content source such as … the Internet, a social media server, a streaming video content provider” [0184] – “allow the user to indicate an interest in an item being displayed in the video content …detect the content displayed on the display device … record audio and/or video snippets of the content being displayed to identify the content and the content segmented currently being view” [0175] – See also [0075-0077]);
determining a product suggestion to provide to at least one of the one or more devices based on the initial website data by:
Parsing the initial website data to identify and extract, from the initial website data, an initial product image set including one or more product images [video segment] (Wilkinson: “the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184]);
identifying, from the initial product image set, a first plurality of product traits [category/characteristic] (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product.” [0189]);
determining a first value for each of the first plurality of product traits (Wilkinson: “the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192]); and
determining the product suggestion based on the first value for each of the first plurality of product traits (Wilkinson: “the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]);
based on each occurrence of a next one or more user interactions with a same or a different one or more of the plurality of resources, iteratively receiving, from the one or more web applications, next website data associated with a next one or more user interactions with one or more of the plurality of resources accessed via the one or more web applications, the one or more web applications configured to collect the next website data as the one or more of the plurality of resources are accessed and interacted with (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2001, the system detects a video content being viewed by a user.” [0184] – See also [0188]); and
upon receiving the next website data, automatically determining an updated product suggestion to provide to at least one of the one or more devices based on the next website data by:
parsing the next website data to identify and extract, from the next website data, a next product image set including one or more product images (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2002, the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184]);
identifying, from the next product image set, a second plurality of product traits, wherein the second plurality of product traits include at least a portion of the first plurality of product traits that are overlapping product traits included in the first plurality of product traits and the second plurality of product traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2003, the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product.” [0189] – “after steps 2001 and/or 2002, the system may be configured to update the customer vectors associated with the user in the customer vectors database based on one or more characteristics of the video content view by the user and/or the item. For example, if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188]);
determining a second value for each of the second plurality of product traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2004, the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “In step 2006, the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192]);
adjusting the first value for at least the portion of the first plurality of product traits that are overlapping product traits based on the second value (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “after steps 2001 and/or 2002, the system may be configured to update the customer vectors associated with the user in the customer vectors database based on one or more characteristics of the video content view by the user and/or the item. For example, if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188]); and
determining the updated the product suggestion to provide to at least one of the one or more devices based on the adjusted first value for at least the portion of the first plurality of product traits that are overlapping product traits, the first value for non-overlapping product traits of the first plurality of product traits, and the second value for non-overlapping product traits of the second plurality of product traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2007, the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “In step 2008, the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]),
but does not specifically teach that the product is a vehicle.
However, August teaches personalized product recommendations (August: Title, Abstract), including that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
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, Wilkinson would continue to teach identifying an initial product image set & determining a product suggestion, except that now it would also teach that the product is a vehicle, according to the teachings of August. 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 identify and present relevant vehicles to users (August: [0003]).
Regarding Claim 23, Wilkinson/August teach the computer-implemented method of claim 21, wherein identifying the first plurality of product traits or the second plurality of product traits comprises: identifying one or more first-level product traits and one or more second-level product traits (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product. … the category may comprise potato chips and/or pickle flavored snack foods. … the associated categories may be eggs, olive oil, and black pepper” [0189]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 24, Wilkinson/August teach the computer-implemented method of claim 23, wherein the one or more first-level vehicle traits include one or more of a make, a model, a body style, a color, a door count, or a seat count (August: “vehicle characteristics may include characteristics such as the following: vehicle body type, vehicle size, transmission type, color, price, style, safety features, information features, entertainment features, comfort features, safety rating, reliability rating, fuel efficiency, or other characteristics of vehicles” [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 25, Wilkinson/August teach the computer-implemented method of claim 23, further comprising: identifying a product identification based on the one or more first-level product traits; and determining the one or more second-level product traits based on product identification (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product. … the category may comprise potato chips and/or pickle flavored snack foods. … the associated categories may be eggs, olive oil, and black pepper” [0189]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 26, Wilkinson/August teach the computer-implemented method of claim 23, wherein the one or more second-level vehicle traits include one or more of an engine type, a manufacturing region, a manufacturing year, or a vehicle price (August: “vehicle characteristics may include characteristics such as the following: vehicle body type, vehicle size, transmission type, color, price, style, safety features, information features, entertainment features, comfort features, safety rating, reliability rating, fuel efficiency, or other characteristics of vehicles” [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 27, Wilkinson/August teach the computer-implemented method of claim 21, wherein the plurality of resources include one or more social network sites, and at least one of the initial website data or the next website data includes data from one or more social network accounts associated with the user received from the one or more social network sites (Wilkinson: “the system detects a video content being viewed by a user. …the display device may be coupled to a content source such as … the Internet, a social media server, a streaming video content provider” [0184] – “the interaction records 502 can pertain to the social networking behaviors of the monitored person including such things as their “likes,” their posted comments, images, and tweets, affinity group affiliations, their on-line profiles, their playlists and other indicated “favorites,” and so forth. ” [0077]).
Regarding Claim 28, Wilkinson/August teach the computer-implemented method of claim 27, wherein at least one interaction of the initial one or more user interactions or the next one or more user interactions includes an association of a product image with the one or more social network accounts, and the product image is included in the initial product image set or the next product image set, respectively (Wilkinson: “the system detects a video content being viewed by a user. …the display device may be coupled to a content source such as … the Internet, a social media server, a streaming video content provider” [0184] – “the interaction records 502 can pertain to the social networking behaviors of the monitored person including such things as their “likes,” their posted comments, images, and tweets, affinity group affiliations, their on-line profiles, their playlists and other indicated “favorites,” and so forth. ” [0077]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 29, Wilkinson/August teach the computer-implemented method of claim 21, wherein the first value for each of the first plurality of product traits is a first weighted value associated with a frequency that each of the first plurality of product traits appears in the initial product image set (Wilkinson: “For at least some behaviors of interest that general (or specific) frequency of occurrence can serve as a significant indication of a person's corresponding partialities.” [0089] – “if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188] – See [0093-0097] for specific details on the vector length/weighting based on frequency.),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 30, Wilkinson/August teach the computer-implemented method of claim 29, wherein determining the product suggestion comprises: generating a matrix of the first plurality of product traits based on each first weighted value; and determining the product suggestion based on the matrix (Wilkinson: “the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192] – “use the aforementioned partiality vectors 1307 and the vectorized product characterizations 1304 to define a plurality of solutions that collectively form a multidimensional surface” [0142]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 31, Wilkinson/August teach the computer-implemented method of claim 29, wherein each first weighted value includes a weight based on more recently viewed product images from the initial product image set (Wilkinson: “the age of the information (where, for example the older (or newer, if desired) data is preferred or weighted more heavily than the newer (or older, if desired) data.” [0122]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding Claim 32, Wilkinson/August teach the computer-implemented method of claim 21, wherein determining the vehicle suggestion further comprises: determining the vehicle suggestion via a trained machine learning algorithm (August: “To determine vehicle recommendations for the user, the server 140 may apply a machine learning model, which has previously been trained.” [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 21.
Regarding claim 33, Wilkinson discloses a computer-implemented method for providing a product suggestion to a user based on image analysis, the method comprising:
iteratively receiving, from one or more web applications executing on one or more devices associated with the user, website data based on user interactions with a plurality of resources accessed via the one or more web applications, the one or more web applications configured to collect the website data as the plurality of resources are accessed and interacted with (Wilkinson: “the system detects a video content being viewed by a user. …the display device may be coupled to a content source such as … the Internet, a social media server, a streaming video content provider” [0184] – “allow the user to indicate an interest in an item being displayed in the video content …detect the content displayed on the display device … record audio and/or video snippets of the content being displayed to identify the content and the content segmented currently being view” [0175] – See also [0075-0077] – “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194]),
wherein the website data includes a first product image set of one or more product images [video segment] received based on a first one or more user interactions with a first one or more of the plurality of resources, and a second product image set of one or more product images received based on a second one or more user interactions with a second one or more of the plurality of resources (Wilkinson: “the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184] – “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194]);
upon the receipt of a first portion of the website data that includes the first product image set, determining a product suggestion by:
parsing the first portion of the website data to identify and extract from the first portion of the website data the first product image set [video segment] (Wilkinson: “the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184]);
identifying a first plurality of product traits [category/characteristic] from the first product image set (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product.” [0189]);
determining a first value of each of the first plurality of product traits (Wilkinson: “the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192]); and
determining the product suggestion based on the first value of each of the first plurality of product traits (Wilkinson: “the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]);
upon the receipt of a second portion of the website data that includes the second product image set, determining an updated product suggestion by:
parsing the second portion of the website data to identify and extract, from the second portion of the website data, the second product image set [video segment] (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184]);
identifying a second plurality of product traits from the second product image set (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2003, the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product.” [0189] – “after steps 2001 and/or 2002, the system may be configured to update the customer vectors associated with the user in the customer vectors database based on one or more characteristics of the video content view by the user and/or the item. For example, if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188]);
determining a second value for each of the second plurality of product traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2004, the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “In step 2006, the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192]);
adjusting the first value of at least a portion of the first plurality of product traits that are overlapping product traits included in the first plurality of product traits and the second plurality of product traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “after steps 2001 and/or 2002, the system may be configured to update the customer vectors associated with the user in the customer vectors database based on one or more characteristics of the video content view by the user and/or the item. For example, if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188] – “In step 2007, the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “In step 2008, the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]); and
determining the updated product suggestion based on the adjusted first value for at least the portion of the first plurality of product traits that are overlapping product traits, the first value for non-overlapping product traits of the first plurality of product traits, and the second value for non-overlapping product traits of the second plurality of product traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2007, the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “In step 2008, the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]); and
transmitting, to at least one of the one or more devices of the user, a notification indicating the updated product suggestion (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2007, the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “In step 2008, the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]),
but does not specifically teach that the product is a vehicle.
However, August teaches personalized product recommendations (August: Title, Abstract), including that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
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, Wilkinson would continue to teach the website data includes a first product image set of one or more product images & determining a product suggestion, except that now it would also teach that the product is a vehicle, according to the teachings of August. 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 identify and present relevant vehicles to users (August: [0003]).
Regarding Claim 34, Wilkinson/August teach the computer-implemented method of claim 33, wherein identifying the first plurality of product traits comprises:
identifying one or more first-level product traits (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product. … the category may comprise potato chips and/or pickle flavored snack foods. … the associated categories may be eggs, olive oil, and black pepper” [0189]);
identifying a product identification based on the one or more first-level product traits; and determining one or more second-level product traits based on the product identification (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product. … the category may comprise potato chips and/or pickle flavored snack foods. … the associated categories may be eggs, olive oil, and black pepper” [0189]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 33.
Regarding Claim 36, Wilkinson/August teach the computer-implemented method of claim 33, wherein: the plurality of resources include one or more social network sites, the website data includes data based on user interactions from one or more social network accounts associated with the user received from the one or more social network sites, at least one of the user interactions includes an association of a product image with the one or more social network accounts, and the product image is included one of the first product image set or the second product image set (Wilkinson: “the system detects a video content being viewed by a user. …the display device may be coupled to a content source such as … the Internet, a social media server, a streaming video content provider” [0184] – “the interaction records 502 can pertain to the social networking behaviors of the monitored person including such things as their “likes,” their posted comments, images, and tweets, affinity group affiliations, their on-line profiles, their playlists and other indicated “favorites,” and so forth. ” [0077]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 33.
Regarding Claim 37, Wilkinson/August teach the computer-implemented method of claim 33, wherein the first value for each of the first plurality of product traits is a first weighted value associated with a frequency that each of the first plurality of product traits appears in in the first product image set (Wilkinson: “For at least some behaviors of interest that general (or specific) frequency of occurrence can serve as a significant indication of a person's corresponding partialities.” [0089] – “if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188] – See [0093-0097] for specific details on the vector length/weighting based on frequency.),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 33.
Regarding Claim 38, Wilkinson/August teach the computer-implemented method of claim 37, wherein determining the vehicle suggestion comprises: generating a matrix of the first plurality of product traits based on each first weighted value; and determining the product suggestion based on the matrix (Wilkinson: “the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192] – “use the aforementioned partiality vectors 1307 and the vectorized product characterizations 1304 to define a plurality of solutions that collectively form a multidimensional surface” [0142]),
wherein August further teaches that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 33.
Regarding Claim 39, Wilkinson/August teach the computer-implemented method of claim 33, wherein determining the vehicle suggestion further comprises: determining the vehicle suggestion via a trained machine learning algorithm (August: “To determine vehicle recommendations for the user, the server 140 may apply a machine learning model, which has previously been trained.” [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine August with Wilkinson for the reasons identified above with respect to claim 33.
Regarding Claim 42, Wilkinson/August teach the computer-implemented method of claim 21,
wherein a computer system implementing the method is associated with an entity, at least one of the plurality of resources is independent of the entity (Wilkinson: “ the interaction records 502 can pertain to the social networking behaviors of the monitored person including such things as their “likes,” their posted comments, images, and tweets, affinity group affiliations, their on-line profiles, their playlists and other indicated “favorites,” and so forth. ” [0077] – “Upon detecting, (for example, based upon purchases, social media, or other relevant inputs) that this person is aspirating” [0167] – “the system 2100 may identify user partialities using data obtained from other sources outside of a customer's purchase history. For example, partialities may be identified based on calendar appointments, charitable donations, age, and profession, among many others. ” [0216]), and
the method further comprises: receiving, from the one or more devices associated with the user, an indication granting the entity access to website data of the user associated with the plurality of resources; and based on the grant of access, receiving the initial website data and the next website data (Wilkinson: “the control circuit monitors a person's behavior over time. The range of monitored behaviors can vary with the individual and the application setting. By one approach, only behaviors that the person has specifically approved for monitoring are so monitored.” [0075]).
Claim 41 is rejected under 35 U.S.C. 103 as being unpatentable over Wilkinson, in view of August, and further in view of Suresh et al (US 20200050202 A1), hereinafter Suresh.
Regarding Claim 41, Wilkinson/August teach the computer-implemented method of claim 21, wherein determining the product suggestion comprises: determining the product suggestion based on the initial product image set and the first value for each of the first plurality of product traits (Wilkinson: “the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]),
wherein August further teaches:
that the product is a vehicle (August: “identifying a plurality of vehicle recommendations associated with available vehicles matching at least some of the preferred vehicle characteristics” [0004]), and
determining the vehicle suggestion using a trained machine learning model (August: “o determine vehicle recommendations for the user, the server 140 may apply a machine learning model, which has previously been trained.” [0046]).
However, Wilkinson/August do not explicitly teach determining the suggestion using a convolutional neural network having a plurality of layers, including an input layer, a plurality of convolutional layers, and an output layer, wherein determining the suggestion using the convolutional neural network comprises: receiving as first input, via the input layer and by a first convolutional layer of the plurality of convolutional layers comprising a first set of learnable filters, an image of the one or more images of the initial image set; applying, by the first convolutional layer, a first convolution operation to the first input to generate a first map associated with the first set of learnable filters; receiving as second input, by a second convolutional layer of the plurality of convolutional layers subsequent to the first convolutional layer and comprising a second set of learnable filters, only a restricted subarea of the first convolutional layer output; and applying, by the second convolutional layer, a second convolution operation to the second input to generate a second map associated with the second set of learnable filters for output to a next layer of the plurality of layers.
However, Suresh teaches methods for receiving and classifying images to determine suggestions (Suresh: Abstract, [0057]), including:
determining the suggestion using a convolutional neural network having a plurality of layers, including an input layer, a plurality of convolutional layers, and an output layer (Suresh: “The processor may include a convolutional neural network module. The processor may be configured to use the convolutional neural network module to identify objects in the images.” [0014] – “When the input layer receives an input, it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and output, allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.” [0122] – “the deep learning model includes one or more convolutional layers” [0136]),
wherein determining the suggestion using the convolutional neural network comprises:
receiving as first input, via the input layer and by a first convolutional layer of the plurality of convolutional layers comprising a first set of learnable filters, an image of the one or more images of the initial image set (Suresh: “When the input layer receives an input, it passes on a modified version of the input to the next layer.” [0122] – “applying a convolution function to the input image using one or more filters” [0136] – “The convolutional layer has a variety of parameters that consist of a set of learnable filters” [0144]);
applying, by the first convolutional layer, a first convolution operation to the first input to generate a first map associated with the first set of learnable filters (Suresh: “CNNs may include local or global pooling layers, which combine the outputs of neuron clusters. Pooling layers may also consist of various combinations of convolutional and fully connected layers, with pointwise nonlinearity applied at the end of or after each layer. A convolution operation on small regions of input is introduced to reduce the number of free parameters” [0143] – “The convolutional layer has a variety of parameters that consist of a set of learnable filters” [0144] – “layers may also have any suitable configuration known in the art (e.g., max pooling layers) and are generally configured for reducing the dimensionality of the feature map generated by the one or more convolutional layers while retaining the most important features.” [0136] – “CNNs first convolve the input image with a small filter to generate feature maps (each pixel on the feature map is a neuron corresponds to a receptive field). …A subsampling layer computes the max or average over small windows in the previous layer to reduce the size of the feature map” [0140]);
receiving as second input, by a second convolutional layer of the plurality of convolutional layers subsequent to the first convolutional layer and comprising a second set of learnable filters, only a restricted subarea of the first convolutional layer output (Suresh: “there are many layers between the input and output, allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.” [0122] – “Each node in a convolutional layer of the hierarchical probabilistic graph can take a linear combination of the inputs from nodes in the previous layer, and then applies a nonlinearity to generate an output and pass it to nodes in the next layer. … CNNs first convolve the input image with a small filter to generate feature maps (each pixel on the feature map is a neuron corresponds to a receptive field). …A subsampling layer computes the max or average over small windows in the previous layer to reduce the size of the feature map” [0140]); and
applying, by the second convolutional layer, a second convolution operation to the second input to generate a second map associated with the second set of learnable filters for output to a next layer of the plurality of layers (Suresh: “The convolutional layer has a variety of parameters that consist of a set of learnable filters” [0144] – “layers may also have any suitable configuration known in the art (e.g., max pooling layers) and are generally configured for reducing the dimensionality of the feature map generated by the one or more convolutional layers while retaining the most important features.” [0136] – “Each node in a convolutional layer of the hierarchical probabilistic graph can take a linear combination of the inputs from nodes in the previous layer, and then applies a nonlinearity to generate an output and pass it to nodes in the next layer. … CNNs first convolve the input image with a small filter to generate feature maps (each pixel on the feature map is a neuron corresponds to a receptive field). …A subsampling layer computes the max or average over small windows in the previous layer to reduce the size of the feature map” [0140]).
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, Wilkinson/August would continue to teach determining the vehicle suggestion using a trained machine learning model, except that now it would also teach determining the suggestion using a convolutional neural network having a plurality of layers, including an input layer, a plurality of convolutional layers, and an output layer, wherein determining the suggestion using the convolutional neural network comprises: receiving as first input, via the input layer and by a first convolutional layer of the plurality of convolutional layers comprising a first set of learnable filters, an image of the one or more images of the initial image set; applying, by the first convolutional layer, a first convolution operation to the first input to generate a first map associated with the first set of learnable filters; receiving as second input, by a second convolutional layer of the plurality of convolutional layers subsequent to the first convolutional layer and comprising a second set of learnable filters, only a restricted subarea of the first convolutional layer output; and applying, by the second convolutional layer, a second convolution operation to the second input to generate a second map associated with the second set of learnable filters for output to a next layer of the plurality of layers, according to the teachings of Suresh. 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 performance of a neural network (Suresh: [0143]).
Claim Rejection – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 40 is rejected under 35 U.S.C. 102 as being anticipated by Wilkinson.
Regarding Claim 40, Wilkinson discloses a computer-implemented method for providing an item suggestion to a user based on image analysis, the method comprising:
Receiving, from one or more web applications executing on one or more devices associated with the user, first website data associated with a first one or more user interactions with a first set of one or more of a plurality of resources accessed via the one or more web applications, the one or more web applications configured to collect the first website data as the first set of one or more of the plurality of resources are accessed and interacted with (Wilkinson: “the system detects a video content being viewed by a user. …the display device may be coupled to a content source such as … the Internet, a social media server, a streaming video content provider” [0184] – “allow the user to indicate an interest in an item being displayed in the video content …detect the content displayed on the display device … record audio and/or video snippets of the content being displayed to identify the content and the content segmented currently being view” [0175] – See also [0075-0077]);
Determining an item suggestion to provide to at least one of the one or more devices based on the first website data by:
parsing the first website data to identify and extract, from the first website data, a first image set including one or more images [video segment] of an item of a particular type (Wilkinson: “the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184]);
identifying, from the first image set, a first plurality of traits [category/characteristic] associated with the item traits (Wilkinson: “the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product.” [0189]);
determining a first value for each of the first plurality of traits (Wilkinson: “the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192]); and
determining the item suggestion based on the first value for each of the first plurality of traits (Wilkinson: “the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]);
based on an occurrence of a second one or more user interactions with the first set or a second set of one or more of the plurality of resources, receiving, from the one or more web applications, second website data associated with the second one or more user interactions with the first set or the second set of one or more of the plurality of resources accessed via the one or more web applications, the one or more web applications configured to collect the second website data as the first set or the second set of one or more of the plurality or resources are accessed and interacted with (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2001, the system detects a video content being viewed by a user.” [0184]); and
upon receiving the second website data, automatically determining an updated item suggestion to provide to at least one of the one or more devices based on the second website data by:
parsing the second website data to identify and extract, from the second website data, a second image set including one or more images of the item of the particular type (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2002, the system identifies an item associated with a current segment of the video content viewed by the user. In some embodiments, the system may be configured to first identify the content and/or content segment based on metadata, audio, and/or video analysis.” [0184]);
identifying, from the second image set, a second plurality of traits associated with the item, wherein the second plurality of traits include at least a portion of the first plurality of traits that are overlapping traits included in the first plurality of traits and the second plurality of traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2003, the system determines a product category associated with the item identified in step 2002. … product category may comprise a more generic description of the item and/or a categorical characteristic of the product.” [0189] – “after steps 2001 and/or 2002, the system may be configured to update the customer vectors associated with the user in the customer vectors database based on one or more characteristics of the video content view by the user and/or the item. For example, if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188]);
determining a second value for each of the second plurality of traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2004, the system retrieves product characteristic vectors associated with a plurality of products in the product category … the vectorized product characterizations may comprise one or more of vectors associated with customer values, preferences, affinities, and/or aspirations in reference to the products.” [0190] – “In step 2006, the system determines an alignment between the customer vectors and product vectors associated with a plurality of products in the category. … the alignment between a product and the customer may be determined by adding, subtracting, multiplying, and/or dividing the magnitudes of the corresponding vectors in the customer vectors and product characterization vectors. … alignment scores for each vector may be added and/or averaged to determine an overall customer alignment score for a product.” [0192]);
adjusting the first value for at least the portion of the first plurality of traits that are overlapping traits based on the second value (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “after steps 2001 and/or 2002, the system may be configured to update the customer vectors associated with the user in the customer vectors database based on one or more characteristics of the video content view by the user and/or the item. For example, if the customer repeatedly watches New England Patriots play in NFL games, the system may determine that the customer has an affinity to the Patriots.” [0188]); and
determining the updated the item suggestion based on the adjusted first value for at least the portion of the first plurality of traits that are overlapping traits, the first value for non-overlapping traits of the first plurality of traits, and the second value for non-overlapping traits of the second plurality of traits (Wilkinson: “steps 2001-2008 may be repeated as a customer views a video content and/or when a customer indicates an interest in an item in the video content.” [0194] – “In step 2007, the system selects a recommended product from a plurality of products based on the alignments between the customer value vectors and the product characteristic vectors for each of the plurality of products. … the item selected may correspond to the item with the highest alignment to the customer vectors.” [0193] – “In step 2008, the system initiates an offer of the recommended product to the customer. … cause a product ordering user interface for the recommended product to be displayed on a user interface device to the user.” [0194]).
Response to Arguments
Applicant's arguments filed 4/16/2026 have been fully considered but are not persuasive.
Claim Rejections – 35 USC § 101
Applicant argues that “the claims as a whole integrate the abstract idea into a practical application,” arguing that the steps of parsing website data to identify and extract images sets “are additional elements reciting a specific and particular technical implementation of a solution to the problems resulting from the limitations of the manually selectable filters provided by traditional vehicle search user interfaces.” Applicant contends that such a solution is provided by “determining and iteratively updating vehicle or item suggestions based on passive user interactions with online resources.”
Examiner disagrees. With reference to the rejection above, the steps of parsing data to identify and extract image steps, and the argued ability to determine and iteratively update updating vehicle or item suggestions based on passive user interactions with resources, are part of the abstract idea itself, and are part of a concept for personalized recommendations that falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that it recites commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. At best, the alleged improvements stemming from these limitations are business improvements rooted solely in the abstract idea rather than technological ones addressing a problem specific to computer technology. The additional elements, e.g. the data being website data, are recited at a very high level of generality, and are invoked as mere instructions to apply the abstract idea to a technological environment, providing only a general linking to computer technology [MPEP 2106.05(f)].
Applicant further argues that claim 41 “introduces how consecutive convolutional layers of the convolutional neural network process input data to achieve an improvement to the model itself,” with use of a CNN providing a technological improvement by allowing the system to “reduce the number of free parameters, allowing a network to be deeper with fewer parameters.” Applicant argues that this ability of CNNs “improves machine learning itself.”
Examiner disagrees. As acknowledged by Applicant, convolutional operations reduce the number of free parameters – relying on known technology for its known purpose does not improve machine learning functionality, nor did Applicant invent the ability of a convolutional neural network for perform convolutional operations. Rather, Applicant is invoked this additional elements to create a general linking between the abstract idea and this computer technology [MPEP 2106.05(f)]. Rather than improving the neural network, the claim merely recites known aspects of the neural network, at best providing to the item suggestion method the improved speed or efficiency inherent to this general purpose computing competent [MPEP 2106.05(a)]. Whereas Desjardins identifies a specific problem in ML technology and claims a specific technological solution to that problem, Claim 41 merely invokes a generic neural network to perform its known functions.
Claim Rejection – 35 USC §§ 102 & 103
Applicant argues with respect to Claim 40 that “Wilkinson does not disclose that when the steps 2001-2008 are performed or repeated to select or update a recommended product that each of the three specific and particular values claimed are used in the determination.” Applicant notes that these values are “the adjusted first value for at least a portion of the first plurality of traits that are overlapping traits, the first value for non-overlapping traits of the first plurality of traits, and the second value for non-overlapping traits of the second plurality of traits.”
Examiner disagrees. With reference to the rejection above, Wilkinson teaches that, as the user views content, the customer vector is updated based on characteristics of the video content viewed. [0188] This is repeated as the user views new content or expresses interest [0194], as the user views a plurality of different content items, such as nature documentaries, Patriots football games [0188], a cooking show [0195], and content featuring a range of consumer goods [0189]. The process involves detecting changes to the user’s routine [0082]. The system updates the customer vectors each time a new content item is watched, and notes when a type of content has been watched repeatedly [0188]. Thus, the system tracks traits of each content item viewed, with at least one of these traits being repeated/overlapping between content items in order to establish that the trait has been seen “repeatedly.” Similarly, as each item of content is tracked, any content that does not yet have a repeated trait has a non-repeated or non-overlapping trait represented in the customer vector. These traits, representing customer preferences and affinities for each of the plurality of content items viewed by a user, are stored as a “plurality of partiality vectors for a particular customer.” In other words, Wilkinson tracks a plurality of traits, and acts upon those that repeat; thus, there is at least one repeating/overlapping trait, with each new and previous trait also being tracked for recommendation purposes. Without further clarification to the claims, Wilkinson teaches that traits of a plurality of viewed/interacted content items are recorded, with some being repeated in subsequent content and other traits not being repeated. This is represented as a multi-dimensional vector or surface [0142], as illustrated in Figure 12, which contains various levels of partiality to different products, wherein a higher level of partiality represents a repeated/overlapping interest in the product trait across multiple items of content. Wilkinson further teaches that the process repeats as the user views new content or indicates interest with items [0194], with values representing each of the items a user has viewed or indicated interest in, as represented by the dimensions of the customer value vectors, being used to select an item having the highest alignment with the customer based on all the items they have interacted with or indicated interest in [0193], which is in turn presented as a new recommended product displayed to the user [1094]. In particular, when the system adjusts the first value to indicate that is an overlapping/recurring trait in multiple items engaged with by the user, i.e. when they “repeatedly watch” content with a particular trait, the system determines that they have an affinity which is represented in the customer vector [0188].
Applicant further argues with respect to claims 21 and 33 are allowable over prior art “for the same or similar reasons described above” with respect to Claim 40.
Examiner disagrees for the reasons addressed in the rejection and the response above with respect to Claim 40.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bhardwaj et al (US 20130085893 A1) teaches systems for making recommendations based on images provided by the user, including associating a confidence value with features extracted from the images.
Reference U (NPL – see attached) discusses image-based product recommendations.
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/T.J.S./
Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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