DETAILED ACTION
This is a Final Office action is in response to communications filed on May 8th, 2026. Claim 1 is cancelled and claims 2-4, 6, 9 and 15 is/are amended. Claims 2-21 have been examined in this application. The Information Disclosure Statement (IDS) filed on May 8th, 2026 has been acknowledged.
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 .
Claim Rejections - 35 USC § 112 (First Paragraph)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims were amended to include the following limitations:
Independent Claims 2, 9, and 15:
“based at least in part on the bio-feedback data meeting or exceeding a threshold, determining one or more preferences toward the first product and the changed product attribute”
“identifying a second product based on the one or more preferences towards the first product and the changed product attribute, wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold”
Dependent Claim(s) 3:
“identifying, based on the bio-feedback data, a heart rate of the subject”
“wherein the determining the one or more preferences is based at least in part on determining that the heart rate of the subject meets or exceeds the threshold”
Applicant's original disclosure suggests that a recommendation engine may capture bio-feedback from a subject during a virtual try-on session, including a focal point, a line of sight, a facial expression, an utterance, a body movement, a gesture, a pose, and biometrics such as blood pressure, pulse rate, heart rate, pupil dilation, electroencephalogram (EEG), and body temperature. The disclosure further suggests that the recommendation engine may determine a change of a biometric parameter from a biometric measurement, such as an increased pulse rate, a heightened blood pressure, or increased EEG activities, and may transmit a query based on that change to a biometric data table storing biometric data and related emotion indicators. The disclosure suggests that a data entry in the biometric data table may specify a range of pulse rate and an associated emotional state such as "excitement," and that when the changed pulse rate falls within a range of pulse rate specified in the biometric data table, the query result reflects the corresponding emotional state from the data table. The disclosure additionally suggests that a movement pattern may be queried against a movement database to obtain an associated emotion indicator, that words and tone patterns may be queried against an emotion database to obtain an associated emotion indicator, and that where multiple forms of bio-feedback are available the recommendation engine may aggregate them or prioritize one form over another as a primary source. The disclosure further suggests that where the emotion indicator is determined to be positive toward an identified product feature, the recommendation engine transmits a query based on that preferred feature to a product database and receives a product recommendation having the same product feature, and that where the emotion indicator is determined to be negative, the recommendation engine stores the identified product feature as a non-preference and restricts recommendation of products having that feature.
Thus, the disclosure may support capturing plural forms of sensor derived bio-feedback during a virtual try-on session; determining a change in a biometric parameter; querying a stored table that maps a biometric range to a corresponding emotional state; querying stored databases that map movement patterns and verbal expressions to emotion indicators; aggregating or prioritizing among available forms of bio-feedback; classifying a resulting emotion indicator as positive or negative toward an identified product feature; querying a product database using a feature associated with a positive emotion indicator; and storing and restricting a feature associated with a negative emotion indicator. However, the disclosure does not reasonably convey possession of determining one or more preferences based at least in part on the bio-feedback data meeting or exceeding a threshold. Nor does the disclosure describe identifying a second product on the condition that the second product is expected to trigger bio-feedback data that meets or exceeds the threshold. The disclosure further does not describe identifying a heart rate of the subject and determining that the heart rate meets or exceeds the threshold as the basis for determining the one or more preferences. The original disclosure is instead directed to a recommendation engine that classifies an already-captured emotional reaction as positive or negative and then propagates the associated product feature forward. The disclosure describes obtaining a biometric measurement from a wearable device, determining a change in the measured parameter, querying a lookup table in which a stored range of values is associated with a named emotional state, and obtaining the emotion indicator corresponding to the range within which the measured value falls. A range-membership lookup is not a comparison of a measured value against a threshold: the disclosed operation returns a categorical emotion label by locating the measured value inside a bounded interval, whereas the claimed operation conditions the preference determination on the bio-feedback data satisfying or surpassing a single stated value. The disclosure nowhere identifies any such value, nowhere states how it would be set or calibrated, and nowhere describes any step of comparing bio-feedback data to it. The disclosed alternative pathways confirm the absence: the movement database and the emotion database are likewise described as pattern-to-label and expression-to-label mappings, with no comparison operation of any kind.
The original disclosure is instead directed to a recommendation engine that classifies an already-captured reaction and propagates the associated feature forward. As to the threshold, the specification describes only a range with control circuitry; determines “a change of a biometric parameter from the biometric measurement” (0055) such as “an increased pulse rate, a heightened blood pressure, increased EEG activities” (0055), then queries a biometric data table in which “a data entry ... may specify a range of pulse rate and an associated emotional state as 'excitement'”, and obtains the emotion indicator “when the changed pulse rate falls within a range of pulse rate specified in the biometric data table”. Falling within a bounded interval and returning a categorical label is not comparing a value to a threshold; the disclosure identifies no such value, no manner of setting it, and no comparison step. The disclosed alternatives confirm the absence of the movement database and emotion database are described only as pattern to label and expression to label mappings. As to the selection criterion, the disclosure describes recommendation as backward-looking: the engine “determines the product feature 202 that the subject 102 is paying attention to and triggers the bio-feedback as a preferred feature 215”, then “recommendation engine 310 may then transmit a query based on the preferred feature 215 to the product database 219 for a product recommendation 216” (0037) and “receives a product recommendation having the same product feature” (0044). Nothing in the disclosure evaluates a candidate product's prospective effect on the subject's bio-feedback before presentation. Matching a stored feature label is not predicting a physiological response.
The rejection of claim 3 follows for the same reasons and for one additional reason. While the disclosure identifies heart rate among the biometrics that may be obtained from a wearable device, and describes determining an increased pulse rate as a change in a biometric parameter, it associates that change with an emotional state solely by the range-lookup operation described above. The disclosure does not describe determining that a heart rate meets or exceeds any value, and does not describe the preference determination being conditioned on such a determination.
If the original disclosure does describe these functions, Applicant is encouraged to identify the portion of the original disclosure describing them.
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04). Also, See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made. See MPEP § 2161.01(I).
Therefore, the claims are rejected under 112 1st paragraph as failing to comply with the written description requirement as applicant is only entitled to claim the invention the Applicant possessed and disclosed at the time of invention. Hence, independent claims 1, 10, and 19, its dependent claims 2-9, 11-18, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 112 (Second Paragraph)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2-21 is/are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2, 9, and 15 recite that the second product “is expected to trigger the bio-feedback data that meets or exceeds the threshold.” The claims do not recite by whom or what the expectation is formed, any step by which a candidate product is evaluated for its prospective effect on the subject's bio-feedback, or what degree of likelihood satisfies “expected.” The specification describes recommendation only retrospectively - the engine “determines the product feature 202 that the subject 102 is paying attention to and triggers the bio-feedback as a preferred feature 215” - and provides no standard for measuring the requisite degree. See MPEP § 2173.05(b).
Claims 9 and 15 recite “determining ... one or more preferences toward the first product and the changed product attribute meeting or exceeding a threshold.” The threshold clause is appended to the changed product attribute rather than to the bio-feedback data, such that a product attribute, for example, a color or shade, must meet or exceed a threshold. It cannot be determined what quantity is compared. Corresponding claim 2 recites the same limitation as “based at least in part on the bio-feedback data meeting or exceeding a threshold,” placing the clause on the bio-feedback data. It cannot be determined whether claims 9 and 15 recite the same comparison as claim 2 or a different one. See MPEP § 2173.02.
Claims 9 and 15 further recite “the bio-feedback data that meets or exceeds the threshold.” There is insufficient antecedent basis for this limitation, as the only threshold recited in these claims is one that the changed product attribute meets or exceeds. Further, dependent Claim 3 recites “identifying, based on the bio-feedback data, a heart rate of the subject” and that “the heart rate ... meets or exceeds the threshold,” while parent claim 2 recites that the bio-feedback data meets or exceeds that threshold. It cannot be determined whether one comparison or two are required. Dependent claim 6 depends from claim 4 and re-recites the facial area and “a movement pattern comprising a movement at the area,” both already recited in claim 4. It cannot be determined whether a second movement pattern is required or the same one is intended; if the same, the claims conflict, as claim 4 requires the pattern comprise a facial expression while claim 6 requires an eye movement indicative of a gaze.
As such, the claims is/are indefinite under 35 U.S.C. § 112, second paragraph, as it fails to distinctly point out and particularly claim the subject matter which the inventor regards as the invention.
Claim Rejections - 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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 2-14 is/are drawn to method (i.e., a process), and claims 15-21 is/are drawn to system (i.e., a manufacture). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 2: A method comprising:
causing to be captured, using a computing device, visual content data depicting a subject;
receiving, from the computing device, a selection of a first product;
generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid;
transmitting, to the computing device, the first interactive visualization for display at an interface of the computing device;
receiving, from the computing device, an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product;
receiving, from the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device;
based at least in part on the bio-feedback data meeting or exceeding a threshold, determining one or more preferences toward the first product and the changed product attribute;
identifying a second product based on the one or more preferences towards the first product and the changed product attribute, attribute, wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold;
generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid;
and transmitting, to the computing device for display at the interface, the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject.
Claim 9: A method comprising: capturing, at a computing device, visual content data depicting a subject;
detecting, at the computing device, a selection of a first product;
generating, for display at an interface of the computing device and based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid;
detecting, at the computing device, an interaction with the first interactive visualization at the interface that changes a product attribute of the first product;
capturing, at the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device;
based at least in part on the bio-feedback data, determining, at the computing device, one or more preferences toward the first product and the changed product attribute meeting or exceeding a threshold;
identifying, at the computing device, a second product based on the one or more preferences towards the first product and the changed product attribute, wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold;
generating, for display at the interface, a second interactive visualization, comprising modified content depicting the subject at which a representation of the second product is overlaid, wherein the representation of the second product is displayed at least partially over the subject.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, independent claims 2, 9, and 15 recite a commercial product recommendation and personalization practice comprising capturing visual content depicting a subject, receiving a selection of a first product, presenting an interactive visualization in which the first product is overlaid on the subject, receiving an interaction that changes an attribute of the first product, collecting bio-feedback associated with the displayed product attribute, evaluating the bio-feedback to determine the user’s preferences, identifying a second product based on those preferences, and presenting another visualization in which the second product is overlaid on the subject. The amended claims further recite evaluating whether the bio-feedback meets or exceeds a threshold and selecting a second product expected to produce bio-feedback meeting or exceeding that threshold. The recited limitations therefore describe observing and evaluating a potential customer’s reaction to a displayed product or product feature, inferring the customer’s preferences, and using those preferences to select and present another product expected to generate a favorable response. This constitutes product recommendation, customer-preference analysis, personalized marketing, and sales activity and therefore falls within the “certain methods of organizing human activity” grouping of abstract ideas, specifically commercial interactions involving advertising, marketing, and sales activities or behaviors under MPEP § 2106.04(a)(2)(II).
The claims also recite mental process concepts involving observation, evaluation, judgment, and opinion. In particular, determining whether a customer’s reaction is favorable, determining whether the reaction satisfies a threshold, inferring a preference toward a product or product attribute, selecting another product based on that preference, and determining that the other product is expected to produce a qualifying response are evaluative and judgment-based activities. The use of bio-feedback merely supplies another source of information from which the commercial preference determination is made. The threshold limitation further specifies a rule for classifying the observed response and does not change the underlying character of the claimed product recommendation activity. The amended language does not alter the abstract nature of the claims. Rather, “bio-feedback data meeting or exceeding a threshold” further specifies how the customer response is evaluated, while “the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold” further specifies the desired basis or criterion for selecting the recommended product. These limitations refine the abstract process by requiring a threshold qualified customer reaction and a recommendation expected to produce a similar qualifying reaction; they do not recite an improvement to sensor operation, bio-feedback measurement, image processing, computer graphics, interface architecture, or computer functionality.
The commercial and evaluative character of the claimed process is also consistent with the specification, which describes capturing a user’s movement or facial expression pattern, identifying an associated emotion, and recommending a product having a particular feature when the detected bio-feedback indicates a positive emotion. Thus, the bio-feedback and visualization are used to determine consumer preference and provide a product recommendation, rather than to improve the underlying sensor, computing device, or visualization technology. From applicant’s specification, the claimed invention is implemented to “FIG. 2 depicts an illustrative embodiment illustrating aspects of providing a product recommendation based on bio-feedback captured from subject interactions with the simulated visualization described in FIG. 1, according to some embodiments described herein. Diagram 200 shows an example screen of user equipment 114, which illustrates a product recommendation 121 of another lipstick product “Vincent Logo lip stain coral” 122 which has a similar color “coral” with the product “Revlon coral” 113 that has been virtually tried on” and “control circuitry transmits a query based on the product feature (such as the lip color “coral” in FIG. 1) to the product databases (e.g., 219 in FIG. 3), and receives a product recommendation having the same product feature” (see at least [0026 and 0044] of instant specification). The steps under its broadest reasonable interpretation specifically directed to an abstract idea of requiring a threshold qualified customer reaction and a recommendation expected to produce a similar qualifying reaction implemented on generic computer infrastructure, thus characterized as collecting information, analyzing the information to determine preferences, and using the results to present product recommendations, which constitute “certain methods of organizing human activity” and “mental processes”. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
The dependent claims further elaborate on the same abstract idea. Claim 3 recites identifying heart rate and determining whether the heart rate satisfies a threshold, thereby further specifying the customer-response information and comparison used to infer preference. Claims 4, 10, and 16 recite identifying an area at which the product is displayed, identifying movement at that area, and determining preference from the movement. Claims 4, 11, and 17 further specify that the area is a facial area and that the movement is a facial expression. Claims 5, 12, and 18 narrow the observed response to lip movement, while claims 6, 13, and 19 narrow it to eye movement or gaze and storing an association between the gaze and the product. These limitations remain directed to observing customer behavior, associating the behavior with a displayed product, and determining customer preference. Claims 7 and 20 specify that the visual content comprises image data and that the product is overlaid in modified image data. Claims 8, 14, and 21 further specify identifying a product type, determining that the product modifies a facial area, identifying that facial area, and presenting the product overlaid at the modified facial area. These limitations merely specify the informational format and visual context in which the product is presented to the potential customer. They do not recite a particular improved rendering, facial detection, image processing, or sensor technique. The prior rejection similarly determined that the movement, facial expression, lip movement, gaze, product association, image, and facial product limitations further refined the same customer preference and customized product presentation activity. Accordingly, claims 2-21 recite the abstract idea of collecting and evaluating customer-response information to determine product preferences and selecting and presenting personalized product recommendations, which falls within the judicial-exception groupings of “certain methods of organizing human activity” and “mental processes”. As such, the claims are directed to an abstract idea involving organizing human activity related to commerce and product recommendation, which falls within a judicial exception under 35 U.S.C. §101.
Independent claim(s) 15 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 2 and 9 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computing device, sensor, interface, transceiver and control circuitry, interactive visualization, etc. (Claims 2, 9, and 15) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a computing device, sensor, interface, transceiver and control circuitry, interactive visualization, etc. (Claims 2, 9, and 15, and dependent claims 3-8, 10-14, and 16-21) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., receive, capture, detect, generate, transmit, identify, etc. steps performed by a computing device, sensor, interface, transceiver and control circuitry, interactive visualization, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited steps of capturing visual content depicting the subject, receiving or detecting a product selection, generating and displaying the first product visualization, receiving an interaction that changes a product attribute, and capturing bio-feedback data through a sensor merely constitute pre-solution data-gathering and preparatory activity. These steps collect and present the information or stimulus used to perform the abstract process of evaluating the user’s reaction, determining a product preference, and selecting another product. The subsequent steps of generating, transmitting, and displaying the second interactive visualization merely constitute post-solution activity that communicates the result of the abstract product-preference and recommendation determination. Displaying the selected second product over the subject does not alter the underlying recommendation analysis or produce a separate technological result. Accordingly, to the extent these input capture, visualization, transmission, and display limitations are considered additional elements, they merely append insignificant pre-solution data gathering and post-solution output presentation to the judicial exception, additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 3-8, 10-14, and 16-21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent Claim(s) 1, 9, and 15, and dependent claims 3-8, 10-14, and 16-21 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional elements of a computing device, at least one sensor, an interface, transceiver circuitry, control circuitry, and interactive visualizations, considered individually and as an ordered combination, do not amount to significantly more than the judicial exception. The recited steps of capturing or receiving visual content data and bio-feedback data, receiving or detecting product selections and user interactions, comparing the bio feedback data with a threshold, identifying a second product, generating modified visual content, transmitting the interactive visualizations, and displaying product representations merely collect, receive, analyze, transmit, and present the information used to implement the abstract commercial practice of determining customer preferences and recommending products. Additionally, and/or alternatively, the limitations of capturing the subject image, receiving the product selection and changed product attribute, and capturing bio-feedback data merely append insignificant pre-solution data-gathering activity to the judicial exception. Generating, transmitting, and displaying the first and second interactive visualizations merely append insignificant output-presentation or post-solution activity. These functions are similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0007-0008] acknowledges that “The recommendation engine captures an image or video of a subject’s movement (including facial movement) and generates a movement pattern or facial expression pattern from the captured image or video content. The recommendation engine then uses the pattern to identify the movement or facial expression, and then identifies an emotion associated with the identified movement … Based on the particular feature and the identified emotion, the recommendation engine recommends a product having the same particular product feature if the bio-feedback shows positive emotion” (i.e., conventional nature of receiving and transmitting data/messages over a network). This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-8, 10-14, and 16-21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Claims 2-8 merely specify particular types of bio-feedback or user behavior, such as movement patterns, facial expressions, lip movements, or gaze direction, used to infer user preferences. Limiting the abstract idea to specific forms of human behavior or physiological response does not impose a meaningful limit on the abstract idea, as these forms of bio-feedback merely represent additional data inputs to the same preference-analysis process. The dependent claims do not recite any improvement to sensor technology, image processing, or bio-feedback analysis techniques. Instead, they continue to rely on generic computing devices and sensors performing their ordinary functions of detecting movement, facial expressions, or gaze. Claims 10 14 further elaborate on the abstract idea by reciting associations between user behavior and products or product attributes, and by determining preferences based on such associations. The recited associations and preference determinations are performed using generic computer components executing conventional data-processing functions. The claims do not recite any particular data structure, algorithmic improvement, or technical mechanism that enhances the operation of the computing device itself. Instead, they merely automate a process of tracking user interest and drawing conclusions about preferences. Claims 16-21 recite that the visual content data comprises image data and that modified content depicts changes to particular areas of the subject, such as facial areas, based on selected or recommended products. These claims merely specify how the results of the abstract idea are presented or visualized to the user. The claims do not recite any improvement to image rendering, computer graphics, or display technology. Collectively, these dependent claims constitute well-understood, routine, and conventional activities performed by generic computer components and therefore fail to integrate the abstract financial concept into a practical application.it is recited at a high level of generality and does not integrate the judicial exception into a practical application.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 2-21 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status:
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20160275600 (“Adeyoola”) in view U.S. Pub. 20160379225 (“Rider”) in view U.S. Pub. 20140366049 (“Lehtiniemi”).
As per claims 2 and 15, Adeyoola discloses, causing to be captured, using a computing device, visual content data depicting a subject (Examiner interprets that Adeyoola discloses acquiring image data of a user, such as a full-length photograph, using a computing device and processing the image data to generate a virtual body model of the user. Adeyoola further discloses displaying the generated three-dimensional virtual body model on a screen. The Examiner interprets the acquired photograph as “visual content data depicting a subject.”) (“The method may be one in which a user takes, or has taken for them, a single full length photograph of themselves which is then processed by a computer system that presents a virtual body model based on that photograph, together with markers whose position the user can adjust, the markers corresponding to some or all of the following: top of the head, bottom of heels, crotch height, width of waist, width of hips, width of chest. The method may be one where the user enters height, weight and, optionally, bra size. The method may be one comprising a computer system which then generates an accurate 3D virtual body model and displays that 3D virtual body model on screen. The method may be one where a computer system is a back-end server.”) (0376-0378);
receiving, from the computing device, a selection of a first product (Examiner interprets Adeyoola discloses receiving a user selection of a garment from an on-screen library of virtual garments. The selected garment corresponds to the claimed first product) (“user selecting a garment from an on-screen library of virtual garments; (b) a processing system automatically generating an image of the garment combined onto the virtual body model, the garment being sized automatically to be a correct fit; (c) the processing system generating data defining how a physical version of that garment would be sized to provide that correct fit; (d) the system providing that data to a garment manufacturer to enable the manufacturer to make a garment that fits the user”) (0376-0378, 0955-0958);
generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid (Examiner interprets automatically generating and displaying an image of a selected garment combined onto the user’s virtual body model. The Examiner interprets “combined onto” and “displayed” as modified content depicting the subject with an overlaid representation of the product, i.e., Adeyoola discloses automatically generating an image of the selected garment combined with the user’s virtual body model and displaying the combined garment and virtual body model. The Examiner interprets the garment “combined onto” the virtual body model as modified content depicting the subject with a representation of the selected product overlaid on the subject) (“user selecting a garment from an on-screen library of virtual garments; (b) a processing system automatically generating an image of the garment combined onto the virtual body model, the garment being sized automatically to be a correct fit; (c) the processing system generating data defining how a physical version of that garment would be sized to provide that correct fit; (d) the system providing that data to a garment manufacturer to enable the manufacturer to make a garment that fits the user” and “virtual body model includes an image of a user's face, obtained from a digital photograph provided by the user. The method may be one in which the lighting conditions include one or more of: main direction of the lighting; colour balance of the lighting; colour temperature of the lighting; diffusiveness of the lighting. The method may be one in which the type of garment determines simulated weather conditions, such as rain, sunshine, snow, which are then applied to the image of the garment when combined with the virtual body model. The method may be one in which a user can manually select images from the database by operating a control that mimics the effect of changing the lighting conditions in which a garment was photographed. The method may be one in which the image processing system can automatically detect parameters of the lighting conditions applying to the digital face photograph supplied by the user can select matching garment images from the database”) (0376-0378 and 0262-0266, 0955-0958);
transmitting, to the computing device, the first interactive visualization for display at an interface of the computing device (Examiner interprets that Adeyoola discloses generating the combined garment and virtual-body-model visualization using a computer-implemented system, including a back-end server, and displaying the visualization through a virtual fitting-room interface accessible from different computing platforms and devices. The Examiner interprets the delivery of the generated visualization to the user’s device for on-screen display as transmitting the first interactive visualization for display at an interface of the computing device) (“method of generating photo-realistic images of a garment combined onto a virtual body model, in which (a) a user locates a garment on a website; (b) a computer implemented system analyses the image of that garment from the website and then searches and identifies that garment in a database of previously analysed garments and then combines one or more virtual images of the garment from its database onto a virtual body model of the user and then displays to the user that combined garment and virtual body model … virtual fitting room is possible to view and interact with from different types of platforms, where the user will get the virtual fitting room experience from any device used. Using a multi channel approach aids the different core features of the different devices and also the different types of features that you would like to use when you are using a mobile phone for instance”) (0955-0958, also refer to 0376-0378 and 0262-0266);
receiving, from the computing device, an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product (Examiner interprets Adeyoola discloses a virtual fitting-room interface through which the user browses garment types and outfits, selects a garment to remain on the body model, changes among sets of garments, and selects alternative garments to replace or be worn together with a previously selected garment. The Examiner interprets the user-selected garment type, garment variant, outfit combination, or displayed garment configuration as a product attribute or product configuration presented in the visualization. Accordingly, the user’s interaction with the virtual fitting-room interface changes the displayed attribute or configuration of the first product) (“user can select what type of garment they would like to flick through to try out on the body model and also select one of the garments to stay on the body model. The user can also select to flick through whole outfits of garments to be tried on to the body model … browsing tool allows the user to flick vertically to change the set of garments to flick through. It can be understood as several different horizontal rows of garments that the user can alter between … user can select to continue to flick through alternative garments to replace the selected garment or the user can select to flick through alternative garments to be worn together with the selected garment …”) (0394-0396);
generating, based on the first interactive visualization and the second product, a second interactive visualization (Examiner interprets iteratively selecting alternative garments/outfits to be tried on and displayed on the body model, which the Examiner interprets as generating a second interactive visualization with a different (second) product overlaid on the subject/body model; Adeyoola permits selecting another garment and generates another combined garment/body-model image) (“user can select what type of garment they would like to flick through to try out on the body model and also select one of the garments to stay on the body model. The user can also select to flick through whole outfits of garments to be tried on to the body model … browsing tool allows the user to flick vertically to change the set of garments to flick through. It can be understood as several different horizontal rows of garments that the user can alter between … user can select to continue to flick through alternative garments to replace the selected garment or the user can select to flick through alternative garments to be worn together with the selected garment …”) (0394-0396, 0264-0266), wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid (Adeyoola discloses selecting alternative garments or outfits to be tried on and displayed on the virtual body model. Adeyoola further discloses generating a photo-realistic image in which another selected garment is combined onto the user’s virtual body model. The Examiner interprets the resulting combined image as the claimed second interactive visualization containing a representation of the second product overlaid on the subject) (“method of generating photo-realistic images of a garment combined onto a virtual body model, in which (a) a user locates a garment on a website; (b) a computer implemented system analyses the image of that garment from the website and then searches and identifies that garment in a database of previously analysed garments and then combines one or more virtual images of the garment from its database onto a virtual body model of the user and then displays to the user that combined garment and virtual body model”) (0955-0956, 0264-0266, 0394–0396);
and transmitting, to the computing device for display at the interface, the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject (Examiner interprets displaying combined garment images on the virtual body model across devices/platforms such that the garment image is shown as combined onto (overlaid on) the body model, i.e. displaying the representation of the second product at least partially over the subject; Adeyoola transmits/displays a combined body model and selected garment, with the garment overlaid on the virtual body model; Adeyoola discloses displaying the combined garment and virtual-body-model image through different applications, websites, and computing platforms. The garment representation is combined with, and therefore displayed at least partially over, the user’s virtual body model) (“generating and sharing a virtual body model of a person combined with an image of a garment, in which the virtual body model is generated by analysing and processing one or more photographs of a user, and a garment image is generated by analysing and processing one or more photographs of the garment; and in which the virtual body model is accessible or use-able by multiple different applications or multiple different web sites, such that images of the garment can, using any of these different applications or web sites, be seen as combined onto the virtual body model to enable visualization of what the garment will look like when worn … method may be one in which one or more of the different applications or web sites each displays, in association with the image of the garment, a single icon or button which, when selected, automatically causes one or more images of the garment to be combined onto the virtual body model to enable a person to visualize what the garment will look like when worn by them. The method may be one in which the combined image is a 3D photo-real image which the user can rotate and/or zoom. The method may be one in which one of the different web sites is a garment retail web site and the garment is available for purchase from that web site”) (0264-0266, 0377-0378, 0955-0958).
Adeyoola specifically doesn’t discloses, receiving, from the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device, determining one or more preferences toward the first product and the changed product attribute, identifying a second product based on the one or more preferences towards the first product and the changed product attribute, however Rider discloses, receiving, from the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device (The Examiner interprets a detected positive or negative emotional response associated with a product or product feature as indicating a preference toward the product or product feature. When Rider’s sensor-based response analysis is incorporated into Adeyoola’s virtual fitting-room system, the sensed response is associated with the garment attribute or garment configuration displayed when the response is captured) (“user gaze detection module 110 may identify and track a user's gaze while the user 102 is moving about the shopping environment 100. The user gaze detection module 110 may identify products, shelves, advertising displays, or other objects that capture the user's gaze and store an association between the object that the user 102 viewed and metrics about the gaze, such as length of gaze, whether the user 102 looked away and then looked back, or whether the user 102 appeared to be reading portions of the object (e.g., reading an ingredient list on the back of a cereal box). The user gaze detection module 110 may interface with one or more user devices, such as wearable devices (e.g., smartglasses), that are able to track the user's gaze locally and then provide gaze information to the user gaze detection module 110 …” and “user tracking system 106 allows a retailer or other related party to track body language, gaze characteristics, facial emotions, and audible feedback of patrons in order to profile products and the responses that they evoke. Using various inputs such as facial expressions, audible emotions, gaze, group shopping dynamics, interactions with products (e.g., physical interactions, picked up product, scanned barcode, read ingredients, tried product on for size, etc.), detection of interest in similar products (e.g., user performed same actions with competing boxes and bought one of the competing boxes), change of body language in aisle (e.g., walked slower, walked faster, parked the cart so that they can take time looking, etc.), gaze duration, shopping and personal history analysis, and other analytics, the user tracking system 106 may be used to understand a user's shopping behavior and context, identify individual's behavior and paths, and capture emotional responses to provide actionable feedback to manufacturers and retailers”) (0016-0017 and 0019, 0022, 0028, 0047, 0049);
based at least in part on the bio-feedback data meeting or exceeding a threshold, determining one or more preferences toward the first product and the changed product attribute (Examiner notes that the underlined limitation is disclosed by another reference. The Examiner interprets a detected positive or negative emotional response associated with the product or product feature as indicating a preference toward that product or product feature. When applied to Adeyoola, Rider’s response is associated with the product attribute or configuration displayed in the interactive visualization at the time the response is captured) (0019-0022, 0028, 0047, 0049);
identifying a second product based on the one or more preferences towards the first product and the changed product attribute, wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold (Examiner notes that the underlined limitation by another prior art. Examiner interprets that Rider discloses presenting the user with an offer for the original item or a competing or alternative item after analyzing the user’s emotional reaction to the original item. Rider also discloses selecting a sales action based on the detected emotion and the object with which the user is interacting. In one example, Rider advertises an alternative product after determining a likely reason that the user rejected or reacted negatively to the original product) (0021-0022, 0040-0041, 0051-0052).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, receiving, from the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device, determining one or more preferences toward the first product and the changed product attribute, identifying a second product based on the one or more preferences towards the first product and the changed product attribute, as taught by Rider for the purpose to incorporating sensing and response-analysis functions into virtual product visualization to determine how a user responds to a displayed garment attribute or garment configuration and to use the detected response to provide a more relevant product recommendation.
Adeyoola specifically doesn’t discloses, based at least in part on the bio-feedback data meeting or exceeding a threshold, and wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold, however Lehtiniemi discloses, based at least in part on the bio-feedback data meeting or exceeding a threshold (Examiner interprets that Lehtiniemi discloses collecting an emotional response of a user and determining whether the emotional response meets a predefined criterion. Lehtiniemi teaches that the emotional response may include heart rate, facial expression, vocalization, or facial flush. Lehtiniemi further teaches that the predefined criterion may include an emotional response above a predefined threshold, heart rate above a predefined threshold, a facial expression of a predefined type, a vocalization of a predefined type, or a facial flush of a predefined hue) (0004),
wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold (Examiner interprets that Rider, however, discloses storing associations between merchandise and observed emotional responses and selecting a competing or alternative product based on the emotion associated with the first product. Lehtiniemi discloses classifying an emotional or biometric response according to whether the response meets a predefined threshold. The combined teachings would have suggested selecting, as the second product, a product having a stored association with a desired response that satisfies the predefined threshold. A product selected based on such a stored product-response association would be expected to elicit the corresponding threshold-satisfying response when presented to the user.) (0004).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, based at least in part on the bio-feedback data meeting or exceeding a threshold, and wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold, as taught by Lehtiniemi for the purpose using predefined threshold or criterion to cause a sufficiently strong favorable response, rather than selecting products without regard to the strength of the associated response.
As per claims 3, Adeyoola specifically doesn’t discloses, identifying, based on the bio-feedback data, heart rate of the subject, however Rider discloses, identifying, based on the bio-feedback data, heart rate of the subject (Examiner interprets that Rider obtains heart-rate data from a wearable device or heart-rate sensor and uses it to identify or strengthen an inference of emotion) (0021-0022, 0040-0041, 0051-0052).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, identifying, based on the bio-feedback data, heart rate of the subject, as taught by Rider for the purpose to incorporating sensing and response-analysis functions into virtual product visualization to determine how a user responds to a displayed garment attribute or garment configuration and to use the detected response to provide a more relevant product recommendation.
Adeyoola specifically doesn’t discloses, based at least in part on the bio-feedback data meeting or exceeding a threshold, and wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold, however Lehtiniemi discloses, and wherein the determining the one or more preferences is based at least in part on the determining that the heart rate of the subject meets or exceeds the threshold (Examiner interprets that Lehtiniemi discloses collecting an emotional response of a user and determining whether the emotional response meets a predefined criterion. Lehtiniemi expressly compares heart rate with a predefined threshold) (0004).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, and wherein the determining the one or more preferences is based at least in part on the determining that the heart rate of the subject meets or exceeds the threshold, as taught by Lehtiniemi for the purpose using predefined threshold or criterion to cause a sufficiently strong favorable response, rather than selecting products without regard to the strength of the associated response.
As per claims 4, 11, and 17, Adeyoola discloses, identifying an area corresponding to the subject at which the representation of the first product is overlaid, wherein the area corresponding to the subject at which the first product is overlaid comprises a facial area (Examiner interprets that Adeyoola discloses receiving one or more photographs of a user’s face, analyzing the face for facial geometry, selecting a matching hairstyle, and combining an image of the selected hairstyle onto an image of the user’s face. Adeyoola further discloses providing an image of make-up applied to an image of the user’s face. The Examiner interprets the portion of the face at which the hairstyle or make-up representation is applied as the claimed facial area at which the representation of the first product is overlaid) (0379-0385).
Adeyoola doesn’t expressly disclose, and identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area, wherein the movement pattern comprises a facial expression corresponding to the facial area, however Rider discloses, and identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area, wherein the movement pattern comprises a facial expression corresponding to the facial area (Examiner interprets that Rider discloses capturing a user’s facial expressions, head pose, eye gaze, body posture, body language, and other movements using one or more cameras while the user browses merchandise and further discloses analyzing images of the user’s face to identify or infer an emotion while the user browses or views a product and expressly identifies a facial expression from image data to determine the emotion expressed by the person i.e. Rider’s sensor-captured facial expression as a movement pattern comprising movement at the facial area) (0013 and 0015, 0028, 0045);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area, wherein the movement pattern comprises a facial expression corresponding to the facial area, as taught by Rider for the purpose to incorporate camera based facial expression analysis into face-based product visualization to detect a facial expression occurring while a hairstyle or make-up product is displayed at the user’s facial area and to use the detected expression to determine the user’s emotional response to the displayed product, thereby improving preference detection and product recommendations.
As per claims 5, 12, and 18, Adeyoola discloses, wherein the facial area comprises a lip area corresponding to the subject (Examiner interprets that Adeyoola discloses analyzing and annotating facial features, including the mouth. In particular, Adeyoola identifies the endpoints of the mouth by placing respective markers at opposite sides of the mouth. The Examiner interprets the identified mouth region as including the claimed lip area i.e. Adeyoola’s identified mouth region, bounded by the marked endpoints on opposite sides of the mouth, as defining a facial area that includes the subject’s lip area.) (0288-0291).
Adeyoola doesn’t expressly disclose, and wherein the facial expression comprises a lip movement at the lip area, however Rider discloses, and wherein the facial expression comprises a lip movement at the lip area (Examiner interprets Rider discloses capturing and analyzing facial expressions while a user browses or interacts with merchandise. Rider provides an example in which video analysis detects that a user frowns after picking up a product, and the detected frown is associated with emotions such as disgust, frustration, or anger i.e. Rider’s detected frown as a facial expression involving movement of the mouth and lips and, therefore, as a lip movement occurring at the lip area identified by Adeyoola.) (0013 and 0015).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area, wherein the movement pattern comprises a facial expression corresponding to the facial area, as taught by Rider for the purpose to apply facial expression detection to the mouth or lip area to detect movement of the user’s mouth or lips, such as a frown, and to use the detected movement to determine the user’s emotional response and preference toward the displayed product, thereby improving product response analysis and personalized product recommendations.
As per claims 6, 13, and 19, Adeyoola discloses, wherein the area corresponding to the subject at which the first product is overlaid comprises a facial area (Examiner interprets that Adeyoola discloses receiving an image of the user’s face and combining a hairstyle or make-up representation with the image of the user’s face. The Examiner interprets the portion of the user’s face at which the product representation is displayed as the area corresponding to the subject at which the first product is overlaid, wherein that area comprises a facial area) (0379-0385).
Adeyoola doesn’t expressly disclose, and wherein the facial expression comprises a lip movement at the lip area, however Rider discloses, identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area, wherein the movement pattern comprises an eye movement indicative of a gaze directed at the facial area (Examiner interprets Rider discloses capturing eye gaze using cameras, including infrared cameras that determine eye rotation or eye position. Rider further discloses using gaze characteristics to determine a gaze target, wherein the gaze target may be a product, product display, or advertisement i.e. When Rider’s gaze-detection technique is incorporated into Adeyoola’s facial-product visualization, the product displayed at Adeyoola’s facial area constitutes the gaze target. Thus, the detected eye movement is indicative of a gaze directed at the facial area at which the first product is overlaid) (0013 and 0047);
storing an association between the gaze and the first product (Examiner interprets Rider stores an association between the object viewed by the user and gaze metrics, including gaze duration and whether the user looked away and returned i.e. Rider discloses identifying products, displays, shelves, or other objects that capture the user’s gaze and storing an association between the viewed object and gaze metrics. The stored gaze metrics may include gaze duration, whether the user looked away and returned, and whether the user viewed a specific portion of the object) (0016, 0015, 0022);
wherein the determining the one or more preferences is based at least in part on the association between the gaze and the first product (Examiner interprets Rider combines gaze information with emotion information and uses product-associated gaze and emotional responses to profile products and determine whether users are attracted to or put off by a product or product feature i.e. Rider’s determination of interest, attraction, aversion, or another emotional response associated with the viewed product as determining a preference toward the product based at least in part on the stored gaze-product association ) (0017-0019, 0022).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area, wherein the movement pattern comprises a facial expression corresponding to the facial area, as taught by Rider for the purpose to apply gaze tracking and gaze product association technique into facial product visualization to determine whether the user was looking at the product displayed at the facial area and to use the resulting gaze and emotional response information to improve preference detection and personalized product recommendations.
As per claims 7 and 20, Adeyoola discloses, wherein the visual content data comprises image data depicting the subject (Examiner interprets that Adeyoola discloses that a user takes or uploads a digital photograph of himself or herself using a computing device, such as a mobile telephone. The photograph is processed to generate the user’s virtual body model i.e. the full-length photograph or facial photograph of the user as image data depicting the subject) (0376–0378), and wherein the modified content comprises modified image data depicting the subject (Examiner interprets the photograph or body-model image after the garment, hairstyle, or make-up representation is applied as modified image data depicting the subject i.e. automatically generating an image of a selected garment combined onto the user’s virtual body model) (0380, 0384) at which the representation of the first product is overlaid (Examiner interprets automatically generates an image of a selected garment combined onto the user’s virtual body model, combines a selected hairstyle onto an image of the user’s face, and provides an image of make-up applied to the user’s face. The Examiner interprets the resulting combined image as modified image data depicting the subject with the representation of the first product overlaid) (“method may be one in which the virtual body model is generated using one or more of the following: width of chest, height of crotch etc. The method may be one in which a user takes, or has taken for them, a single full length photograph of themselves which is then processed by a computer system that presents a virtual body model based on that photograph, together with markers whose position the user can adjust, the markers corresponding to some or all of the following: top of the head, bottom of heels, crotch height, width of waist, width of hips, width of chest. The method may be one where the user enters height, weight and, optionally, bra size. The method may be one comprising a computer system which then generates an accurate 3D virtual body model and displays that 3D virtual body model on screen. The method may be one where a computer system is a back-end server”) (0376-0378, 0955-0958, 0349).
As per claims 8 and 21, Adeyoola discloses, identifying a product type corresponding to the first product (Examiner interprets identifying the selected product as a hairstyle or make-up product as identifying a product type corresponding to the first product i.e. selects a hairstyle from a library of hairstyles and selects make-up descriptions from a library of make-up descriptions) (“a user has a virtual body model of themselves, the method includes the steps of (a) the user selecting a garment from an on-screen library of virtual garments; (b) a processing system automatically generating an image of the garment combined onto the virtual body model, the garment being sized automatically to be a correct fit; (c) the processing system generating data defining how a physical version of that garment would be sized to provide that correct fit; (d) the system providing that data to a garment manufacturer to enable the manufacturer to make a garment that fits the user …” and “automatically generating hairstyle recommendations, in which a system receives as input one or more photographs of a user's face and then (a) analyses that face for facial geometry and (b) matches the facial geometry to a library of hairstyles, each hairstyle being previously indexed as suitable for one or more facial geometries, and (c) selects one or more optimally matching hairstyles and (d) outputs an image of that optimally matched hairstyle to the user”) (0376-0378 and 0379-0385);
determining, based at least in part on the product type, that the first product modifies a facial area associated with a human body (Examiner interprets Adeyoola applies a hairstyle or make-up product to the user’s face according to the identified product category, Adeyoola determines, based on the product type, that the product modifies a facial area associated with the human body and Adeyoola’s detection and annotation of the face and its facial features as identifying, in the image data, a facial area corresponding to the subject) (“user selecting a garment from an on-screen library of virtual garments; (b) a processing system automatically generating an image of the garment combined onto the virtual body model, the garment being sized automatically to be a correct fit; (c) the processing system generating data defining how a physical version of that garment would be sized to provide that correct fit; (d) the system providing that data to a garment manufacturer to enable the manufacturer to make a garment that fits the user” and “method of generating photo-realistic images of a garment combined onto a virtual body model, in which (a) a user locates a garment on a website; (b) a computer implemented system analyses the image of that garment from the website and then searches and identifies that garment in a database of previously analysed garments and then combines one or more virtual images of the garment from its database onto a virtual body model of the user and then displays to the user that combined garment and virtual body model) (0376-0379 and 0955, 0288-0291);
identifying, in the image data, a facial area corresponding to the subject (“system receives as input one or more photographs of a user's face and then (a) analyses that face for facial geometry and (b) matches the facial geometry to a library of hairstyles, each hairstyle being previously indexed as suitable for one or more facial geometries, and (c) selects one or more optimally matching hairstyles and (d) outputs an image of that optimally matched hairstyle to the user” and “face is being changed to represent the common features for a person of the relevant age”) (0379 and 0625);
and wherein the modified image data depicts the subject having a modified facial area at which the representation of the first product is overlaid (Examiner interprets Adeyoola applies a hairstyle or make-up product to the user’s face according to the identified product category, Adeyoola determines, based on the product type, that the product modifies a facial area associated with the human body and Adeyoola’s detection and annotation of the face and its facial features as identifying, in the image data, a facial area corresponding to the subject i.e. Examiner interprets the hairstyle or make-up image applied to the facial image as a representation of the first product overlaid at the modified facial area) (“FIG. 17; a drawing of the photo positions is shown in FIG. 18. The user can in one example as a short cut click on “back view” to show the backside view of the body model when showing a look. The user can zoom the body model image and see the body model and the garment in closer detail as shown for example in FIG. 24. The user can change the head and the measurements of the body model from the store interface, for instance if the user would like to change to a different hairstyle”) (0317, 0380-0384).
As per claims 9, Adeyoola discloses, capturing, at a computing device, visual content data depicting a subject (The Examiner interprets the digital photograph captured by the user’s computing device as visual content data depicting the subject i.e. a user taking a digital photograph using a computing device, such as a mobile telephone, and using the photograph to generate a virtual body model of the user) (“The method may be one in which a user takes, or has taken for them, a single full length photograph of themselves which is then processed by a computer system that presents a virtual body model based on that photograph, together with markers whose position the user can adjust, the markers corresponding to some or all of the following: top of the head, bottom of heels, crotch height, width of waist, width of hips, width of chest. The method may be one where the user enters height, weight and, optionally, bra size. The method may be one comprising a computer system which then generates an accurate 3D virtual body model and displays that 3D virtual body model on screen. The method may be one where a computer system is a back-end server.”) (0376-0378);
detecting, at the computing device, a selection of a first product (The Examiner interprets the user’s selection of a garment through the virtual fitting-room interface as detecting, at the computing device, a selection of the first product i.e. presenting an on-screen library of virtual garments and allowing a user to select a garment to be combined with the user’s virtual body model) (“user can actively select that an image is to be passed on to the Image Identification engine for garment detection. The user can also in one example indicate on a specific image what portion of the image includes the garment which is to be identified. This could be done for instance by indicating with a click in the middle of the garment or for instance by indicating the perimeters of the garment in the image to assist the Image Identification Engine”) (0973, 096-0299, 0376-0378 and 0394-0396, Figs. 21A-B);
generating, for display at an interface of the computing device and based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid (The Examiner interprets the generated and displayed garment/body-model image as the first interactive visualization, with the garment image constituting the representation of the first product overlaid on the subject i.e. virtual fitting-room system generates and displays an image in which a garment representation is combined onto the user’s body model i.e. the displayed facial image containing the selected hairstyle as the claimed first interactive visualization, with the hairstyle constituting the representation of the first product overlaid on the subject) (“user selecting a garment from an on-screen library of virtual garments; (b) a processing system automatically generating an image of the garment combined onto the virtual body model, the garment being sized automatically to be a correct fit; (c) the processing system generating data defining how a physical version of that garment would be sized to provide that correct fit; (d) the system providing that data to a garment manufacturer to enable the manufacturer to make a garment that fits the user …” and “method of generating photo-realistic images of a garment combined onto a virtual body model, in which (a) a user locates a garment on a website; (b) a computer implemented system analyses the image of that garment from the website and then searches and identifies that garment in a database of previously analysed garments and then combines one or more virtual images of the garment from its database onto a virtual body model of the user and then displays to the user that combined garment and virtual body model) (0376-0372 and 0955-0958, 0264-0266);
detecting, at the computing device, an interaction with the first interactive visualization at the interface that changes a product attribute of the first product (The Examiner interprets the user’s interface interaction that changes the selected garment, garment variant, garment type, outfit, or displayed garment configuration as an interaction that changes a product attribute of the first product i.e. the hairstyle as the first product, the hair color as a product attribute of the hairstyle, and the user’s selection of the hair color as the claimed interaction that changes the product attribute of the first product.) (“user can select what type of garment they would like to flick through to try out on the body model and also select one of the garments to stay on the body model. The user can also select to flick through whole outfits of garments to be tried on to the body model … browsing tool allows the user to flick vertically to change the set of garments to flick through. It can be understood as several different horizontal rows of garments that the user can alter between … user can select to continue to flick through alternative garments to replace the selected garment or the user can select to flick through alternative garments to be worn together with the selected garment …”) (0380, 0394-0396);
generating, for display at the interface, a second interactive visualization (The Examiner interprets Adeyoola’s generation and display of a subsequent image containing an alternative garment combined onto the virtual body model as generating the second interactive visualization with the representation of the second product overlaid on the subject i.e. the generation and display of a subsequent facial image containing an alternative hairstyle as the claimed second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject) (“user can select what type of garment they would like to flick through to try out on the body model and also select one of the garments to stay on the body model. The user can also select to flick through whole outfits of garments to be tried on to the body model … browsing tool allows the user to flick vertically to change the set of garments to flick through. It can be understood as several different horizontal rows of garments that the user can alter between … user can select to continue to flick through alternative garments to replace the selected garment or the user can select to flick through alternative garments to be worn together with the selected garment …”) (0394-0396, 0264-0266, 0955-0958), comprising modified content depicting the subject at which a representation of the second product is overlaid (Examiner interprets that Adeyoola permits selection of an alternative garment and generates another image of the selected alternative garment combined onto the user’s virtual body model) (0379-0382), wherein the representation of the second product is displayed at least partially over the subject (Examiner interprets that the alternative garment image is combined onto the corresponding portion of the virtual body model, the representation of the second product is displayed at least partially over the subject) (“method of generating photo-realistic images of a garment combined onto a virtual body model, in which (a) a user locates a garment on a website; (b) a computer implemented system analyses the image of that garment from the website and then searches and identifies that garment in a database of previously analysed garments and then combines one or more virtual images of the garment from its database onto a virtual body model of the user and then displays to the user that combined garment and virtual body model … virtual fitting room is possible to view and interact with from different types of platforms, where the user will get the virtual fitting room experience from any device used. Using a multi channel approach aids the different core features of the different devices and also the different types of features that you would like to use when you are using a mobile phone for instance, in contrast to sitting at your desk by your computer”) (0955-0958, 0379-0382).
Adeyoola does not expressly disclose, capturing, at the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device, based at least in part on the bio-feedback data, determining, at the computing device, one or more preferences toward the first product and the changed product attribute, identifying, at the computing device, a second product based on the one or more preferences towards the first product and the changed product attribute, wherein the second product is expected, however Rider discloses, capturing, at the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device (Examiner interprets when Rider’s sensing technique is incorporated into Adeyoola’s virtual fitting-room system, the biometric, gaze, facial-expression, posture, or other sensed response captured while the changed garment attribute or configuration is displayed constitutes bio-feedback data associated with the changed product attribute; Rider further discloses using the biometric information to determine or strengthen an inference of a particular emotion and wearable device may sense heart rate, skin temperature, or blood pressure and use that information to infer emotion) (0013, 0015-0019, 0028, 0047, and 0049);
based at least in part on the bio-feedback data, determining, at the computing device, one or more preferences toward the first product and the changed product attribute meeting or exceeding a threshold (The underlined limitation is disclosed by another prior art. The Examiner interprets a positive or negative emotional response associated with the product or product feature as indicating a preference toward that product or feature. When Rider’s sensing technique is incorporated into Adeyoola’s interactive hairstyle visualization, the biometric, gaze, facial-expression, or other sensed response captured while the selected hair color is displayed constitutes bio-feedback data associated with the changed product attribute; Rider further discloses using facial expressions, gaze characteristics, body language, biometric information, and product interactions to profile products and the responses that they evoke. Rider associates observed emotional responses with individual merchandise and uses those responses to determine whether a user is attracted to or put off by a product or product feature) (0017, 0019, 0021-0022, 0028, 0047, and 0049);
identifying, at the computing device, a second product based on the one or more preferences towards the first product and the changed product attribute (The Examiner interprets Rider’s selection of a competing or alternative item based on the detected reaction to the first item as identifying a second product based on the user’s preferences toward the first product and the changed product attribute i.e. Rider further discloses identifying or offering an original, competing, or alternative product based on the user’s emotional reaction to the product with which the user interacted. Rider also selects a sales action based on the detected emotion and the corresponding object and may advertise an alternative product based on the reason the original product was rejected) (0021-0022, 0040-0041, and 0051-0052).
wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold (The underlined limitation is disclosed by another prior art. The Examiner interprets Rider’s selection of a competing or alternative item based on the detected reaction to the first item as identifying a second product based on the user’s preferences toward the first product and the changed product attribute) (0021-0022, 0040-0041, and 0051-0052).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, capturing, at the computing device, bio-feedback data associated with the changed product attribute, wherein the bio-feedback data is captured via at least one sensor communicatively coupled to the computing device, based at least in part on the bio-feedback data, determining, at the computing device, one or more preferences toward the first product and the changed product attribute, identifying, at the computing device, a second product based on the one or more preferences towards the first product and the changed product attribute, wherein the second product is expected, as taught by Rider for the purpose to incorporating sensing and response-analysis functions into virtual product visualization to determine how a user responds to a displayed garment attribute or garment configuration and to use the detected response to provide a more relevant product recommendation.
Adeyoola specifically doesn’t discloses, meeting or exceeding a threshold and to trigger the bio-feedback data that meets or exceeds the threshold, however Lehtiniemi discloses, meeting or exceeding a threshold (The Examiner interprets that Lehtiniemi discloses collecting an emotional response and determining whether the response satisfies a predefined criterion. Lehtiniemi teaches that the emotional response may include heart rate, facial expression, vocalization, or facial flush and that the criterion may include an emotional response above a predefined threshold, heart rate above a predefined threshold, a facial expression of a predefined type, a vocalization of a predefined type, or facial flushing of a predefined hue i.e. Lehtiniemi’s determination that a biometric or emotional response satisfies a predefined criterion as determining that the response meets or exceeds the claimed threshold.) (0004).
wherein the second product is expected to trigger the bio-feedback data that meets or exceeds the threshold (The Examiner interprets selecting a second product having a stored or determined association with a favorable emotional response, together with applying Lehtiniemi’s threshold criterion to that response, as identifying a second product expected to cause a bio-feedback or emotional response that meets or exceeds the threshold) (0004).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, meeting or exceeding a threshold and to trigger the bio-feedback data that meets or exceeds the threshold, as taught by Lehtiniemi for the purpose using predefined response criterion to select an alternative product associated with a qualifying response, thereby improving the reliability of preference determinations and product recommendations.
As per claims 10, and 16, Adeyoola discloses, identifying an area corresponding to the subject at which the representation of the first product is overlaid (Examiner interprets Adeyoola as disclosing a representation of a selected garment combined onto the user’s virtual body model. Because the garment representation occupies and is displayed over a corresponding portion of the virtual body model, Adeyoola inherently identifies the area of the subject at which the representation of the first product is overlaid) (0264-0266, 0376-0378, and 0955-0958).
Adeyoola doesn’t expressly disclose, identifying, based on the bio-feedback data, a movement pattern comprising movement at the area, wherein determining the one or more preferences is based at least in part on the movement pattern at the area, however Rider discloses, identify, based on the bio-feedback data, a movement pattern comprising a movement at the area (Examiner interprets that Rider discloses capturing a user’s movement, facial expressions, head pose, eye gaze, body posture, and body language while the user browses or interacts with merchandise. Rider further analyzes such movements and expressions to determine the user’s emotion toward the product and associates detected emotional responses with the product involved in the interaction) (0013, 0015, 0019, 0028, and 0039-0040);
and wherein the determining the one or more preferences is based at least in part on the movement pattern at the area (The Examiner interprets Rider’s detected movement, facial expression, posture, or other action occurring while the user views or interacts with a product as the claimed movement pattern. When Rider’s movement and emotion detection technique is incorporated into Adeyoola’s interactive visualization system, the detected movement occurring while the product is displayed at the corresponding area of the subject is used to determine the user’s emotional response and, therefore, the user’s preference toward the displayed product) (0013, 0015, 0019, 0028, and 0039-0040).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for captured visual content data depicting a subject; receiving a selection of a first product, generating, based at least in part on the visual content data and the selection, a first interactive visualization comprising modified content depicting the subject at which a representation of the first product is overlaid, transmitting the first interactive visualization for display at an interface of the computing device, receiving an interaction with the first interactive visualization at the interface of the computing device that changes a product attribute of the first product, generating, based on the first interactive visualization and the second product, a second interactive visualization, wherein the second interactive visualization comprises modified content depicting the subject at which a representation of the second product is overlaid, and transmitting the second interactive visualization, wherein the representation of the second product is displayed at least partially over the subject, as disclosed by Adeyoola, identifying, based on the bio-feedback data, a movement pattern comprising a movement at the area and wherein the determining the one or more preferences is based at least in part on the movement pattern at the area, as taught by Rider for the purpose to apply movement and emotion detection technique to interactive visualization so that movement occurring while a product is displayed at the corresponding area of the subject could be detected and used to determine the user’s preference toward the displayed product thus improving preference detection and product recommendation accuracy.
As per claims 14, Adeyoola discloses, wherein the visual content data comprises image data depicting the subject, and wherein the modified content comprises modified image data depicting the subject at which the representation of the first product is overlaid, the method further comprising (The Examiner interprets the facial photograph as image data depicting the subject and the facial photograph containing the overlaid hairstyle as modified image data depicting the subject at which the representation of the first product is overlaid i.e. Adeyoola receives one or more photographs of the user’s face, analyzes the face for facial geometry, and generates an image in which a selected or optimally matched hairstyle is combined onto the image of the user’s face) (“method may be one in which the virtual body model is generated using one or more of the following: width of chest, height of crotch etc. The method may be one in which a user takes, or has taken for them, a single full length photograph of themselves which is then processed by a computer system that presents a virtual body model based on that photograph, together with markers whose position the user can adjust, the markers corresponding to some or all of the following: top of the head, bottom of heels, crotch height, width of waist, width of hips, width of chest. The method may be one where the user enters height, weight and, optionally, bra size. The method may be one comprising a computer system which then generates an accurate 3D virtual body model and displays that 3D virtual body model on screen. The method may be one where a computer system is a back-end server”) (0377-0378 and 0379-0382):
identifying a product type corresponding to the first product (Examiner interprets identification of the selected item as a hairstyle as identifying the product type corresponding to the first product i.e. Adeyoola identifies the product as a hairstyle, presents the user with different hairstyle options, permits the user to filter the available hairstyles, and receives the user’s selection of a hairstyle) (“automatically generating hairstyle recommendations, in which a system receives as input one or more photographs of a user's face and then (a) analyses that face for facial geometry and (b) matches the facial geometry to a library of hairstyles, each hairstyle being previously indexed as suitable for one or more facial geometries, and (c) selects one or more optimally matching hairstyles and (d) outputs an image of that optimally matched hairstyle to the user”) (0379-0381, 0296-0299);
determining, based at least in part on the product type, that the first product modifies a facial area associated with a human body (The Examiner interprets these disclosures as determining, based on the hairstyle product type, that the product modifies a facial area associated with the human body i.e. Adeyoola expressly states that hairstyle data is used so that the hair may be applied to a face in the correct position; when the identified product is a hairstyle, the system combines the hairstyle image onto an image of the user’s face. Adeyoola additionally discloses using hairstyle metadata to set variables so that the hair is applied to the face in the correct location) (0380, 0802);
identifying, in the image data, a facial area corresponding to the subject (“The Examiner interprets Adeyoola’s facial-geometry analysis and facial-landmark identification as identifying, in the image data, the facial area corresponding to the subject i.e. Adeyoola analyzes the facial photograph for facial geometry and identifies facial landmarks including the eyes, mouth, nose, chin, jaw, and ear lobes) (0288-0291, 0379);
and wherein the modified image data depicts the subject having a modified facial area at which the representation of the first product is overlaid (The Examiner interprets the resulting image as modified image data depicting the subject having a modified facial area at which the representation of the selected hairstyle is overlaid i.e. Adeyoola combines the selected hairstyle image onto the image of the user’s face and generates the resulting facial image for display) (0380-0382).
Response to Arguments
With regards to § 101 rejections:
The arguments filed on May 8th, 2026, with respect to the rejection(s) of claims 2-21 under 35 U.S.C 101 have been fully considered but are unpersuasive/moot. The rejection is maintained and updated to address the amended claims.
Applicant’s arguments have been considered but are not persuasive. The claims remain directed to collecting user-response data, evaluating the data according to a threshold, inferring product preferences, and recommending another product, activities that constitute evaluation of information and commercial recommendation processes. The recited computing device, interface, sensor, and circuitry are used only as generic tools to capture, transmit, display, and analyze information according to their ordinary functions. The claims do not recite an improvement to sensor operation, bio-feedback processing, computer functionality, or interactive-display technology. Merely using bio-feedback instead of express user input does not integrate the abstract idea into a practical application, because the bio-feedback is collected and analyzed only to determine preferences and improve product recommendations. The computing device, sensor, transceiver circuitry, control circuitry, and interface are recited at a high level of generality and perform their ordinary functions of capturing data, receiving and transmitting information, processing or comparing information, selecting information according to a result, and displaying the result. The claims do not recite a particular improved sensor arrangement, bio-feedback-processing technique, image-rendering process, data-transmission architecture, or improvement to computer functionality. The additional elements, individually and in combination, therefore do not amount to significantly more than the abstract idea. See MPEP §§ 2106.05(a)–(c), (e)– (h). Therefore, the rejection of claims 2-21 under 35 U.S.C. §101 is maintained.
With regards to § 103 rejections:
Applicant's arguments, see pages 9, filed May 8th, 2026, with respect to the rejection(s) of claims 2-21 under 35 U.S.C 102/103 have been fully considered but are unpersuasive/moots on new ground of rejection.
Applicant’s arguments have been considered but are not persuasive. Applicant argues that the previously applied references fail to teach determining preferences based on bio-feedback meeting or exceeding a threshold and selecting a second product expected to trigger threshold-satisfying bio-feedback. The present rejection, however, relies on Rider for associating sensed emotional or biometric responses with products and selecting a competing or alternative product based on those responses, and on Lehtiniemi for determining whether an emotional or biometric response satisfies a predefined threshold. Applying Lehtiniemi’s threshold criterion to Rider’s product-response associations would have predictably enabled selection of an alternative product associated with a sufficiently strong favorable response. Further, the proposed modification does not remove or replace Adeyoola’s virtual fitting-room functionality, garment or hairstyle visualization, body-model processing, or existing recommendation criteria. Adeyoola continues to capture an image of the user, receive a product selection, modify or overlay the selected product, and display the resulting visualization. Rider merely provides an additional known source of user-preference information; sensor-captured physiological or behavioral responses associated with the displayed product or product attribute. Lehtiniemi supplies a known criterion for distinguishing a sufficiently meaningful response from ordinary variation. Adeyoola may continue to consider body type, garment suitability, or business criteria while additionally using sensor-based preference information to improve the relevance of its recommendations. Thus, the modification supplements Adeyoola’s recommendation process and does not render Adeyoola inoperable, change its principle of operation, or prevent it from performing its intended virtual product-visualization function.
Thus, the dependent claims 2-8, 10-14, and 16-21 that depend from claims 1, 9, and 15 respectively are also moot/unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pat. 20200104703 (“Yun”).
Yun discloses, a methods and systems performed by the device, of recommending products includes: displaying a product selected by the user; obtaining user's facial expression information with respect to the displayed product; determining the user's satisfaction with the displayed product based on the obtained user's facial expression information; selecting a product set to be recommended to the user from among a plurality of product sets based on the determined user's satisfaction; and displaying at least one product included in the selected product set.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GAUTAM UBALE whose telephone number is (571)272-9861. The examiner can normally be reached Mon-Fri. 7:00 AM- 6:30 PM PST.
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/GAUTAM UBALE/
Primary Examiner, Art Unit 3689
Prosecution Insights