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 11/4/2025. Claims 21, 23-24, and 26-42 are currently pending and have been examined. Claims 1-20, 22, and 35 stand cancelled; claims 22 and 35 are newly cancelled. Claims 41-42 have been newly entered Claims 21, 33-34, and 39-40 have been amended.
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.
Information Disclosure Statement
The information disclosure statement filed 12/30/2022 was received and has been considered.
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;
identifying, 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;
determining a vehicle suggestion to provide to the user 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;
identifying, 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
updating the 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
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, 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;
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;
determining a vehicle suggestion based on the first value of each of the first plurality of vehicle traits;
upon the receipt of the second vehicle image set, updating the vehicle suggestion by:
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
updating the 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;
identifying, 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;
determining an item suggestion to provide to the user 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;
identifying, 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
updating the item suggestion to provide to the user 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
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
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]);
identifying, 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]);
determining a product suggestion to provide to at least one of the one or more devices 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]);
identifying, 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
updating 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]);
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]);
determining a 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 the second product image set, updating the product suggestion by:
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
updating the 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 Mohamed (US 20180374138 A1).
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 trained neural network, wherein the trained neural network is a convolutional neural network having a plurality of layers, including one or more convolutional layers that each apply a convolution operation to a respective input received by the convolutional layer for output to a next layer of the plurality of layers.
However, Mohamed teaches systems and methods for personalized recommendations (Mohamed: Abstract), including determining the suggestion using a trained neural network (Mohamed: “training a deep reinforcement learning system based on the historic user online actions data and the purchase confirmations of the user to enable the deep reinforcement learning system to provide one or more purchase recommendations to the user” [0004] – “when deep reinforcement learning system 105 is trained based on historic user online actions data, purchase conversions (confirmations) data, historic data pertained to other similar users, and other information, deep reinforcement learning system 105 cause presenting more targeted purchase recommendations to users ” [0047]),
wherein the trained neural network is a convolutional neural network having a plurality of layers, including one or more convolutional layers that each apply a convolution operation to a respective input received by the convolutional layer for output to a next layer of the plurality of layers (Mohamed: “The deep reinforcement learning system can be based on any applicable neutral network, including … convolutional neural network” [0035] – “Generally, neural networks are machine-learning algorithms that employ one or more layers, including an input layer, an output layer, and one or more hidden layers. At each layer (except the input layer), an input value is transformed in a non-linear manner to generate a new representation of the input value. The output of each hidden layer is used as an input to the next layer in the network, i.e., the next hidden layer or the output layer.” [0034]).
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 trained neural network, wherein the trained neural network is a convolutional neural network having a plurality of layers, including one or more convolutional layers that each apply a convolution operation to a respective input received by the convolutional layer for output to a next layer of the plurality of layers, according to the teachings of Mohamed. 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 effectively product relevant products to users (Mohamed: [0002]).
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]);
identifying, 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]);
determining an item suggestion to provide to at least one of the one or more devices 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]);
identifying, 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
updating the item 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 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 11/4/2025 have been fully considered but are not persuasive.
Claim Rejections – 35 USC § 101
Applicant argues that “each of the independent claims covers a particular solution to the problems resulting from the limitations of the manually selectable filters provided by traditional vehicle search user interfaces.” Applicant argues that “Enabling the up-to-date vehicle suggestion through the above-described specific and particular manner of iteratively receiving and processing website data results in many efficiencies and improvements over the traditional search user interfaces, and thus exemplifies an improvement to the technology of recommendation systems.”
Examine respectfully disagrees. With reference to the rejection above, the alleged “improvement to providing vehicle suggestions to a user” stem from the abstract idea itself, rather than the additional elements, such that they at best amount to a business improvement rather than a technological improvement. The additional elements, rather that providing an improvement to the functioning of the technology or to the technical field, are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], providing only a general linking to computer technology.
Applicant further argues with respect to dependent claim 41 that “use of a convolutional neural network as claimed to determine a vehicle suggestion provides an improvement to computer functionality,” specifically that “the reduction in number of free parameters caused by the convolution operation allows the network to be deeper with fewer parameters, and thus improves the neural network.”
Examiner disagrees. The claims do not improve a neural network or its functionality, but merely use it to perform the abstract idea; invoking the CNN merely as a tool to perform the claimed method, with any alleged improvement in speed or efficiency stemming solely from the capabilities of the general-purpose computer elements [MPEP 2106.05(a)]. These additional elements, including the CNN, do not integrate the abstract idea into a practical application, but are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], providing only a general linking to computer technology.
Claim Rejection – 35 USC § 102
Examiner notes that the arguments regarding 102 refer to Claim 21 (Remarks, Page 21). This is understood to be inadvertent, with the 102 arguments being directed to Claim 40.
Applicant argues with respect to Claim 40 that “while the customer value vectors used by Wilkinson’s system as part of the basis for recommended product selection are updatable based on one or more characteristics of the video content viewed by the user and/or the item, Wilkinson generally fails to teach or suggest how the vectors are updated, let alone in the manner recited by the claims. In other words, Wilkinson fails to explicitly teach or suggest that the customer value vectors include values for traits of items and those values are determined, used, and/or adjusted based on whether traits identified from a most recent content view and/or item are overlapping or non-overlapping traits from traits identify from a previous content view and/or item.”
Examiner disagrees. Wilkinson discloses methods for content-based product recommendations [Abstract], wherein an application on the user’s device, such as one executing over the Internet, detects video content/resources being viewed by a user [0184], and records identifying information therefrom as data on the web content [0175]. Image/video analysis is performed on the imagery/video segments to identify the content [0184] and is used to categorize products/items in the content and characteristics thereof [0189]. For each of these characteristics, the system retrieves vectors representing values associated with “customer values, preferences, affinities, and/or aspirations in reference to the products” [0190] and determines an alignment between these and customer vectors [0192] in order to select a recommended product based on the highest alignment [0193], which is presented on a UI of the user device for the user to view and order [0194]. This process is repeated as the user continues to engage with content [0194], and, as a customer’s affinities or preferences are determined over time based on the characteristics of the content they watch, the system will “update the customer vectors” to reflect attributes, such as a category repeatedly watched, associated with multiple interactions of the user with different content items [0188]. Future recommendations, based on the customer vector, will then be based on the updated customer vector reflecting all attributes detected in the user’s content history, including repeated/overlapping attributes across different web data/videos, and new attributes which are specific to the new piece of content. In other words, Wilkinson teaches a system where, as a user iteratively engages with content, attributes of this content are determined and the user’s preferences/affinities are updated; new, non-overlapping attributes between different content will be updated to the vector, and, as provided in an example in [0188], attributes that occur “repeatedly” in different content items can add up in the user vector to represent an affinity.
With respect to Applicant’s argument that Wilkinson does not teach how the vectors are updated, or that “the customer value vectors include values for traits of items,” Examiner notes that Wilkinson explains that customer vectors include representations of customer preferences and affinities [0180], and the system “update[s] the customer vectors … in the customer vector database 1914 based on the content detected by the content monitoring device,” [0177]. The exact adjustments to the vector values are discussed further in detail in at least [0159]. Paragraph [0188] provides a specific example where the method will “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” – When a user is engaging with a new video content, which is detected to bear an attribute/category that the user has engaged with “repeatedly,” the system determines that an affinity exists and accordingly updates the user vector; that is, based on the vector representing a past engagement with media featuring a characteristic, the system determines that a new engagement with media featuring that characteristic indicates a repeated, or overlapping, interest in that characteristic in different engagements, and updates the user vector accordingly. Thus, as recommendations are based on the user vector, future recommendations will be updated to represent this adjusted value/affinity.
Claim Rejection – 35 USC §103
Applicant further argues that “for at least the reasons described above, …the cited references of record fail to render obvious independent claim 21 or any dependent claims thereof.”
Examiner disagrees for the reasons addressed in the rejection & response above.
Applicant further argues that “for at least the reasons described above, …the cited references of record fail to render obvious independent claim 33 or any dependent claims thereof.”
Examiner disagrees for the reasons addressed in the rejection & response above.
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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689