Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-20 have been examined.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are moot in view of the new ground(s) of rejection. On 1/26/26, Applicant amended the claims. Applicants Remarks address these features. See the new 103 with new citations and motivation to Otten that address these new features.
Also, since the Applicant was given the opportunity and has failed to traverse the Examiner's assertion of Official Notice, with the 12/15/24 response Applicant did not comment on Official Notice, the common knowledge or well known in the art statement is taken to be admitted prior art (see MPEP 2144.03.C).
Also, in light of the 2/21/25 claim amendments and remarks, the 101 is no longer found to apply. Dynamically changing the browser based preference options that are presented on the user interface based on survey and history information in combination with the vectors, weighting, and machine learning and other features was found substantive to pass 101.
Claim Rejections - 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.
Claims 1-4, 6-11, 13-17, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over by Lee (20180157499) in view of Johri (20180189597) in view of Otten (11270168).
Claims 1, 9, 15. Lee discloses a system for providing personalized vehicle content, the system comprising:
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to (Fig. 1):
store a plurality of vehicle images in an image repository (Figs. 1, 2a, 2b; [47]; “[30]… and which images related to the physical object should be initially displayed to the user.”).
Lee does not explicitly disclose present, via a user interface associated with an application executed on a client device, a survey. However, Lee discloses present, via a user interface associated with an application executed on a client device (see application at [17]), an opportunity for the user to provide specific user information and user preferences. Lee discloses “[25]…user-provided data such…” and “[37]…declarative information about the user that was explicitly provided by the user to the online system” including biographic, demographic, and other types of descriptive information, hobbied, favorites, preferences ([25, 37]). While not called a survey in Lee, this is functionally equivalent to a survey. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made that Lee’s “[25]…user-provided data such…” and “[37]…declarative information about the user that was explicitly provided by the user to the online system” functions like a survey. One would have been motivated to do this in order learn information about the user for better targeting (as Lee says).
Lee further discloses wherein the user interface is configured to track information related to a historical pattern of electronic activities associated with a user of the client device that relate to a prospective vehicle transaction for the user (see historical data and car at [28]; see infer and action log at [37, 41]; also for client see client and log [42, 46] and note the application is on the client [23, 42]);
receive, from the client device, the information related to the historical pattern of electronic activities (see historical data and car at [28]; see infer and action log at [37, 41]; also for client see client and log [42, 46] and note the application is on the client [23, 42]) and a response to the survey (see citations for survey above).
Lee further discloses determine, based on the information related to the historical pattern of electronic activities and the response to the survey ([25, 37, 30] note that [25, 37] user information directly provided by the user and inferred from historical activity go into user profile and the profile is used to better target/present content), one or more vehicle attributes (“[4]… Social information describing a user includes, for example, the user's profile information, interactions with the online system… a car has customizable features including the color of the car, the type of transmission of the car (automatic or manual), wheel style, and so on. The online system determines values for various customizable features that the user is likely to be interested in based on social information of the user.”; “[28]… The online system 100 identifies the attributes or features of the car that the user is likely to be interested in further customizing based on the user profile information and social information of the user”; also see [30] cars and [28] and history and cars);
generate a weighted feature dataset, wherein the weighted feature dataset is represented as a vehicle feature math model associated with the one or more vehicle attributes (“[26]… a score indicative of a likelihood that a feature of a physical object is of interest to a user.… weighted aggregations of feature values may be used to predict user preferences”, also see weight and feature at [57], also see different car features at [24]; note the math models and weights at [26, 57]).
Lee does not explicitly disclose a vehicle feature vector that includes an array of elements. However, Lee discloses machine learning techniques [5, 6, 26] and image techniques (see the system tagging images [37], also see automatically customizing an image [29]; also see clicking image in newsfeed [34] and the car is initially displayed based on user favorite car color and also top few favorite colors [34]). And, Johri discloses machine learning and computer vision [2] and labelling images as a car/vehicle (see car at [48]) and user profiles and preferences [3, 20, 24] and using computer vision to extract features from an image [2] and a feature vector that includes an array of elements ([42]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Johri’s computer vision and machine learning techniques for further image classifying and use and profile and likes and cars to Lee’s machine learning and images and profile and likes and cars. One would have been motivated to do this in order to better use images related to user preferences.
Lee further discloses and wherein generating the weighted feature dataset is based on:
mapping a portion of the information related to the historical pattern of electronic activities to a browser-based preference dataset and applying a first influence factor to a portion of the one or more vehicle attributes that are included in the browser-based preference dataset (color, transmission, wheel based on social information [4]; present customizing options based on predicted preferences and based on social information [5]; present customizing options based on user profile information which as shown above are based on user history/log/interactions: “[22]… The user interface 182 may present a widget, for example, a drop down list of various colors allowing a user to provide a selection of a color. The online system 100 selects the colors presented in the drop down list of the user interface 182 based on user profile information or social information of the user”; Also, and score and level of interest and presenting the options reads on influence factor [26, 28, 34, 53, 55, 56]).
Also, and score and level of interest and presenting the options reads on influence factor (highlighted copy of text citations preceding): “[26]… The online system 100 uses the machine learning models 140 to generate metadata 150 used for customization of the physical objects and for customization of user interface 182. In an embodiment, a machine learning model 140 determines a score indicative of a likelihood that a feature of a physical object is of interest to a user.
[28]… The online system 100 determines, based on historical data of matching users, various colors of cars that similar users have shown interest in and ranks these colors based on a score predicted by the machine learning model 140, the score indicating a likelihood of the user being interested in a color. The online system 100 presents a widget, for example, a drop down list listing these colors ranked based on the score determined by the machine learning model 140.
[34]… In an embodiment, the interactive user interface 210 presents the values of the top few options of a customizable feature of the car in an order based on a score for each value by the machine learning model 140. For example, the interactive user interface 210 may present values that were ranked higher by the machine learning model 140 more prominently, for example, above the lower ranked values. Similarly, the interactive user interface 210 presents customizable widgets in an order determined by a score for each feature determined by the machine learning model 140, the score indicating a level of interest of the user in the customizable feature.
[0053] The machine learning models 140 are trained to provide a score or value (e.g. a Boolean or a score classifier) indicating a user's predicted preference for a particular feature variation
[0055] In one embodiment, a machine learning model 140 receives information identifying a user and a value for a customizable feature of a customizable physical object and generates a score indicative of the likelihood that the user is likely to be interested in the input value for the input customizable feature. Accordingly, the user is most likely to select an input value of the customizable feature that has the highest score as predicted by the machine learning model when presented with a plurality of values for the customizable feature via the user interface 210.
[56]… For example, the user interface generation module 375 configures a widget for selecting a color for a car for presenting to a user as follows. The user interface generation module 375 determines a score for each color for the user using the machine learning model 140. The score indicates a likelihood that the user would select the color for the car. The user interface generation module 375 ranks the various values, i.e., colors based on the score. The user interface generation module 375 selects the top few values based on the ranking. The user interface generation module 375 orders the selected values based on the scores, for example, to present a higher ranked value above a lower ranked value.”.
Lee further discloses mapping a portion of data derived from the response to the survey to a user-specified vehicle preference dataset and applying a second influence factor to a portion of the one or more vehicle attributes that are included in the user- specified vehicle preference dataset (present customizing options based on user profile information which as shown above are based on directly provided user information/answers functionally equivalent to a survey: “[22]… The user interface 182 may present a widget, for example, a drop down list of various colors allowing a user to provide a selection of a color. The online system 100 selects the colors presented in the drop down list of the user interface 182 based on user profile information or social information of the user.”; Also, score and level of interest and presenting the options reads on influence factor: [26, 28, 34, 53, 55, 56], note the highlighted versions of these score citations preceding, also note that there are multiple scores for multiple characteristics).
Lee further discloses identify, using a machine learning model [5, 6, 26], a most preferred vehicle associated with the user (see machine learning and predicted user preferences and most likely to appeal to the user at [5]) based on a comparison between the weighted feature dataset and information associated with each vehicle in a vehicle inventory (see weight and machine learning model at [26, 57]), a most preferred vehicle associated with the user ([30, 53]).
Lee does not explicitly disclose and vectorized information associated with each vehicle in a vehicle inventory. However, Lee discloses scores and weights [26, 57]. And, Johri discloses machine learning and vectors (Abstract) and weighting factors [33] and vectors of features [43] and vectors and comparing for similarity [44] and scores and vectors [45, 51, 52, 55]. And, Johri discloses comparing scores [54]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Johri’s weighting factors and scores and vectors and comparing scores to Lee’s scores and weights so scores can be compared that related to weights and vectors. One would have been motivated to do this in order to better user machine learning and comparisons to predict preferences (as Lee shows at [5]).
Lee further discloses identify, among the plurality of vehicle images stored in the image repository, a vehicle image that is a closest match with respect to the most preferred vehicle ([30, 53]).
Examiner notes that computer vision is described in Applicant Spec at “[31]… Additionally, or alternatively, the personalization system may use one or more machine learning techniques, such as a computer vision technique, to derive one or more of the attributes or features associated with the images in the image repository. For example, the personalization system may obtain a vehicle image that is associated with metadata indicating that the make and model of the depicted vehicle is a Honda Accord associated with an EX trim level, and may use computer vision techniques to determine the color of the depicted vehicle and/or the year of the depicted vehicle (e.g., based on distinctive design features).”. And, this is the only description of computer vision that could be found. Lee discloses machine learning techniques [5, 6, 26] and further discloses using computer techniques to derive one or more attributes associated with the plurality of vehicle images (see favorite color and infer at [25], note vehicle and color at [24]; also see the system tagging images [37], also see automatically customizing an image [29]; also see clicking image in newsfeed [34] and the car is initially displayed based on user favorite car color and also top few favorite colors [34] and, also, this click would go into the action log [37]). Lee does not explicitly disclose using computer vision techniques to derive one or more attributes associated with the plurality of vehicle images. However, Johri discloses machine learning and computer vision [2] and labelling images as a car/vehicle (see car at [48]) and user profiles and preferences [3, 20, 24] and using computer vision to extract features from an image [2]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Johri’s computer vision and machine learning techniques for further image classifying and use and profile and likes and cars to Lee’s machine learning and images and profile and likes and cars. One would have been motivated to do this in order to better use images related to user preferences.
Lee does not explicitly disclose wherein the vehicle image that is the closest match with respect to the most preferred vehicle is identified based on the system determining a preferred viewing angle of the vehicle. However, Examiner notes that this feature is generally found in Applicant spec at [33]. And, Lee discloses tracking user interactions with the system to form a user profile to determine user preferences so that a custom image of a car can be presented to the user [4, 6]. And, Lee discloses an action log or history of user online actions that is used to infer user preferences, likes and profiles [41, 48]. And, Lee discloses presenting an initial view of the object/car to the user according to user preferences [7]. And, Lee discloses other views of the vehicle [35]. And, Otten discloses different angles/perspectives or view of the vehicle in the vehicle image (1:57-63; 2:40-45; 3:35-40; 7:64-8:3). And, Otten discloses the consumer selecting a view/angle/perspective of the vehicle for the vehicle image (3:55-3:25, “…In response to receiving input from the consumer through the GUI, such as a selection of a specific vehicle on the dealership's lot and, optionally, a specific view or feature of the vehicle, the widget transmits information identifying the selected vehicle, e.g., a VIN of the vehicle… The widget then renders a display of the a vehicle image and the one or more portions of the feature content that describe the view and/or features included in the displayed vehicle image within the GUI on the dealership's website.”; 9:35-37“…In one embodiment, when providing input to select a specific vehicle, the consumer also selects a view or feature of the vehicle, e.g., a “front view.”.”). Also, in further regards to the system determining a preferred viewing angle, Lee discloses customize car image based on profile [4], profile and historical data an initial image for car presentation [28], and also direct user input and inferences about user interests [37, 41]. And, Otten discloses the above in regards to angle/view and also that a default or initial view/angle can be presented when the user does not select an initial view/angle( 9:45-50 “In an alternative embodiment, the dataset may be included within the widget. In yet another embodiment, a default view is used by the widget for an initial rendering of the display of the selected vehicle.” and also see 11:45-50 “In other embodiments, when the user input does not include the selection of a view or a feature, default settings may be utilized indicating a view and/or feature.”). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Otten’s initial/default view/angle of the vehicle in the vehicle image to Lee’s action log to determine user preferences that inform an initial customized image or view of the vehicle so that the system can present an initial view based on user preference inferences. One would have been motivated to do this in order to better present a customized view of the vehicle of interest to the user (as Lee discloses presenting customized view of interest to the user).
Lee further discloses generate, using the machine learning model and based on identifying the vehicle image that is the closest match with the closest match with respect to the most preferred vehicle (see match at “[65]… The online system 100 identifies a stored content item that represents the best match with the received metadata and returns the identified content item as the content item to be presented via the interactive user interface.”, note machine learning model in [65]) personalized content to include in a message to be sent to the user, wherein the personalized content includes the vehicle image that is the closest match with respect to the most preferred vehicle ([30, 53]; “[30]… and accordingly send a red sports car as content 170 to the user.”; “[65]… The online system 100 identifies a stored content item that represents the best match with the received metadata and returns the identified content item as the content item to be presented via the interactive user interface.”); and
send the message that includes the personalized content to the user (“[33]… The example of FIG. 2A shows a mobile news feed, but the content 170b can be provided to the user in a variety of contexts, such as along a right or left hand side of a page in an advertisement slot, as a banner ad, as a full page ad overlay, in a desktop news feed amongst other news items, among other locations.”; also note text “Auto Palace” in Fig. 2a).
In further regards to claim 9, Lee does not explicitly disclose wherein the preferred viewing angle of the vehicle is assigned more weight than at least one of the one or more vehicle attributes. However, Lee and Otten render obvious user favorites and view/angle of car (see above) and Lee further discloses score and weighted values for feature values of the physical object [26, 57] and score and ranks of different feature values for particular users ([28] and score and rank is interpreted to read on weight). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Lee’s weights/score/rank for feature values for particular users to Lee and Otten’s inferred user features of interest including view/angle. One would have been motivated to do this in order to better present the feature of interest for that user(s).
Claim 2, 10, 16. Lee further discloses the system of claim 1, wherein the one or more processors, to identify the vehicle image that is the closest match with respect to the most preferred vehicle using computer vision techniques to derive one or more attributes associated with the plurality of vehicle images stored in the image repository, are configured to:
determine, using the computer vision techniques and for each of the plurality of vehicle images, a combination of features associated with a vehicle depicted in the respective vehicle image, wherein the vehicle image that is the closest match with respect to the most preferred vehicle is identified based on the combination of features associated with the vehicle depicted in the respective vehicle image being an exact match with respect to a combination of attributes associated with the most preferred vehicle (see model or type like sports car or minivan and also color at [30, 53], see features like DVD at [34]; also note [47] with numerous car features; also note ranked and score at [34, 53, 55, 57, 71, 72] and specific combination of features [65]; also for computer vision techniques see the combination and motivation above).
Claim 3, 11, 17. Lee further discloses the system of claim 1, wherein the one or more processors, to identify the vehicle image that is the closest match with respect to the most preferred vehicle using computer vision techniques to derive one or more attributes associated with the plurality of vehicle images, are configured to: determine that the plurality of vehicle images do not include a vehicle image associated with a combination of features that is an exact match with respect to a combination of attributes associated with the most preferred vehicle; and search the plurality of vehicle images for a vehicle image associated with a prioritized subcombination of features that is an exact match with respect to a prioritized subcombination of the combination of attributes associated with the most preferred vehicle, wherein the vehicle image that is the closest match with respect to the most preferred vehicle is identified based on the prioritized subcombination of features associated with the vehicle image being an exact match with respect to the prioritized subcombination of the combination of attributes associated with the most preferred vehicle ( see ranked and score at [34, 53, 55, 57, 71, 72] and specific combination of features [65]).
Claim 4. Lee further discloses the system of claim 3, wherein a set of features included in the prioritized subcombination of features associated with the vehicle image and the prioritized subcombination of the combination of attributes associated with the most preferred vehicle does not include one or more of a year, a make, a model, a trim, or a color (note the car attributes of wheel style, fuel source, engine model, upholstery at [47] and DVD player at [34] each of which are not in the “not” list preceding; and see ranked and score at [34, 53, 55, 57, 71, 72] and specific combination of features [65]).
Claim 6, 13, 19. Lee further discloses the system of claim 1, wherein the one or more processors are configured to identify the vehicle image that is the closest match with respect to the most preferred vehicle using the computer vision techniques based on a combination of attributes associated with the most preferred vehicle and combinations of features associated with vehicles depicted in the plurality of vehicle images (see silver minivan or red sports car at [30]; see DVD at [34]; see match at [28] and see best match at [65]; also see ranked and score at [34, 53, 55, 57, 71, 72] and specific combination of features at [65]; also for computer vision techniques see the combination and motivation above).
In further regards to claim 19, Lee discloses using the machine learning model ([5, 6, 26]).
Claims 7, 14, 20. Lee does not explicitly disclose the system of claim 1, wherein the one or more processors, to identify the vehicle image that is the closest match with respect to the most preferred vehicle using computer vision techniques to derive one or more attributes associated with the plurality of vehicle images, are configured to: identify, as the vehicle image that is the closest match with respect to the most preferred vehicle (see match at [28] and see best match at [65). Lee does not explicitly disclose a default image associated with a combination of attributes associated with the most preferred vehicle based on none of the plurality of vehicle images being associated with a feature that matches an attribute associated with the most preferred vehicle. However, Lee discloses ranking and scoring attributes/features (see ranked and score at [34, 53, 55, 57, 71, 72] and specific combination of features [65]) and also which images should be displayed [30]. And, Otten discloses default/stock images of vehicles (1:40-62). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Otten’s vehicles and default vehicle images to Lee’s best match and vehicle images. One would have been motivated to do this in order to better present a vehicle image from available images.
Claim 8. Lee further discloses the system of claim 1, wherein the message is an email message, a text message, a direct mail communication, a web notification, or an application-specific message (see send message at [30] and see message types at [59]).
Claims 5, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over by Lee (20180157499) in view of Johri (20180189597) in view of Otten (11270168) in view of Applicant’s Own Admission (Official Notice).
Claims 5, 12, 18. Lee does not explicitly disclose the system of claim 1, wherein the one or more processors, to identify the vehicle image that is the closest match with respect to the most preferred vehicle using computer vision techniques to derive one or more attributes associated with the plurality of vehicle images, are configured to: determine that the plurality of vehicle images include multiple vehicle images associated with a combination or subcombination of features that is an exact match with respect to a combination or subcombination of attributes associated with the most preferred vehicle; and identify, as the vehicle image that is the closest match with respect to the most preferred vehicle, one of the multiple vehicle images that has a highest resolution. However, Lee discloses ranking and scoring attributes/features (see ranked and score at [34, 53, 55, 57, 71, 72] and specific combination of features [65]) and also which images should be displayed [30] and also and also screen resolution and screen size [24]. So, Lee does not explicitly disclose identifying the image with highest resolution. However, Examiner takes Official Notice that it was an old and well known that images of higher resolution can be sent over lower resolution. Advertisements are known to have to a goal to generate a response. And, Lee discloses ads [33] and sending attractive content (see color like preferred silver color at [30]) and tracking response [41]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add choosing a higher resolution image to Lee’s images and computers with different resolutions and screen sizes. One would have been motivated to do this in order to send a better picture and get a better ad response.
Since the Applicant was given the opportunity and has failed to traverse the Examiner's assertion of Official Notice, with the 12/15/24 response Applicant did not comment on Official Notice, the common knowledge or well known in the art statement is taken to be admitted prior art (see MPEP 2144.03.C).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
aa) Holzer default vehicle video and perspective; Price vehicle perspective image; Lee 878 default image of car and perspective view of car based on location of user; Pyati vehicle perspectives [0105] ; Slotky vehicle angles; Strong vehicle perspectives;
Perrier discloses default car views (see 7/18/25 action);
Aaa) Chaurasia, Pyati, Strong discloses feature vector arrays and computer vision techniques;
a) Ye [16, 33] discloses relevant features for present car images based on user preferences;
b) Kim, Slotky disclose default images and cars.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/ARTHUR DURAN/Primary Examiner, Art Unit 3621 2/19/26