DETAILED ACTION
Status of the Application
In the response filed on July 23, 2026, the Applicant amended claims 21, 24, 26, 28, 31, 33, 35, and 38; added claims 44-50; and cancelled claims 23, 25, 30, 32, 37, and 39. Claims 1-20, 22, 29, and 36 were previously cancelled. Claims 21, 24, 26-28, 31, 33-35, 38, and 40-50 are pending and currently under consideration for patentability.
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 .
Response to Amendments and Arguments
v Applicant’s arguments, with respect to the rejection of claims 21, 24, 26-28, 31, 33-35, 38, and 40-50 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 21, 24, 26-28, 31, 33-35, 38, and 40-50 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
“First, with respect to the mental processes characterization…as recited by amended claim 21 cannot practically be performed in the human mind. These steps require computationally analyzing interactions across multiple users, cross-referencing user actions with user boundaries, and algorithmically determining image attributes based on aggregated data from two distinct refined user populations (potential crushes and potential admirers). This is analogous to Example 39 (Facial Detection) from the USPTO's Subject Matter Eligibility Examples, where the claim was found NOT to recite a judicial exception because the steps were not practically performed in the human mind
Examiner respectfully disagrees with Applicant’s first argument.
A human is capable of analyzing user interactions (i.e., making observations in/from data). For example, a human is capable of observing that a first user has set a boundary in their profile (e.g., seeking woman who live in a certain area code), and observing profiles from a list of profiles which profiles satisfy this boundary. A human can also observe indications of profile actions (e.g., indications a second user viewed or liked a first user’s profile, etc.), can determine potential admirers based on actions and boundaries (another observation, or evaluation), and can determine attributes of performance images (an observation/evaluation/judgment) based on the refined population of users (e.g., a human can observe/evaluate characteristics of images that are effective at inducing certain responses from the refined population). Applicant fails to explain why a manual approach to performing these functions could not be performed in the human mind or with paper and pen, even if a less desirable alternative. The claim is not analogous to Examiner 39, which involves a particular process for generating training data sets for multi-stage training of a neural network.
“Second, with respect to the "organizing human activity" characterization, the claims are not merely directed to managing personal behavior or relationships. The specific technical steps of determining attributes of performance images based on cross-referencing potential crushes and potential admirers go beyond organizing human activity, they describe a specific data processing methodology for analyzing image performance across distinct user populations.
Examiner respectfully disagrees with Applicant’s second argument.
The step of determining attributes of performance images (e.g., based on cross-referencing potential crushes and potential admirers) is part of the abstract idea. Insofar as the steps of determining a refined population and then determining attributes of performance images based on the refined population amounts to a “specific data processing methodology”, data processing (i.e., making observations in data) is a non-technical part of an abstract idea.
“amended claim 21 recites a specific ordered combination of steps that produces a concrete, tangible result: photo identification information that is derived from refining a population of users by cross-referencing two distinct user populations (potential crushes determined based on the second user and the boundary of the first user, and potential admirers determined based on the action of the third user and boundaries of the potential admirers). This is not merely "apply it" on a generic computer. The specific manner in which the photo identification information is produced, by determining attributes of performance images based on the refined population of users including both potential crushes and potential admirers, imposes meaningful limits on the judicial exception.
Examiner respectfully disagrees with Applicant’s third argument.
An identification of attributes of performance images is simply information (e.g., an indication of photo attributes, such as an indication of whether a smiles are present in pictures that have been liked) not a tangible result. That the performance images are images that have performance with respect to the refined population does nothing to impose a meaningful limit on the judicial exception, as this is all part of the judicial exception. See In re Smith, No. 2022-1310, 2022 WL 4112730, *3 (Fed. Cir. Sept. 9, 2022 – “But utility is not the test for patent eligibility under the Supreme Court’s cases.”); See SAP, 898 F.3d at 1163 (“We may assume that the techniques claimed are ‘[g]roundbreaking, innovative, or even brilliant,’ but that is not enough for eligibility.”) (citation omitted).
The claims lack additional elements that provide an inventive concept beyond the abstract idea. “Without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014 - ‘If a claim is directed essentially to a method of calculating, using a mathematical formula, even if the solution is for a specific purpose, the claimed method is nonstatutory.”). Applicant has not explained how the identified additional elements integrate the judicial exception into a practical application. The relevant question is whether the claim includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application. The focus of the claim as a whole is directed to a result or effect that itself is the abstract idea.
“Example 42 (Medical Record Updates) from the USPTO's Subject Matter Eligibility Examples is instructive…
Examiner respectfully disagrees with Applicant’s fourth argument.
The claims in Example 42 were not found eligible simply because they provided an improvement. They were found eligible because they recited a combination of additional elements that provided a technical solution to a technical problem. The instant claims are not directed to an analogous technical solution to a technical problem, nor do they recite a combination of additional elements to provide a technical improvement to any technical problem.
“Example 40 Claim 1 (Adaptive Monitoring of Network Traffic Data) is also instructive. In that example, although individual collecting steps might be viewed as mere pre/post-solution activity, the claim as a whole was directed to a particular improvement. Similarly, the ordered combination of steps in amended claim 21, receiving actions, refining a population by determining potential crushes based on the second user and the boundary of the first user, determining potential admirers based on the action of the third user and boundaries of the potential admirers, and then determining attributes of performance images based on the refined population, represents a specific technical approach that provides a particular improvement in generating photo identification information
Examiner respectfully disagrees with Applicant’s fifth argument.
The claims in Example 40 were not found eligible simply because they provided an improvement. They were found eligible because the claim as a whole was determined to be directed to a provided a technical solution to a technical problem. The instant claims are not directed to an analogous technical solution to a technical problem, nor do they recite a combination of additional elements to provide a technical improvement to any technical problem.
“The Examiner has alleged that the receiving/transmitting steps are well-understood, routine, and conventional, taking Official Notice to support this position. Applicant hereby expressly traverses the Examiner's taking of Official Notice. The specific combination of steps recited in the claims, particularly the interplay between determining potential crushes, determining potential admirers, and using both to determine attributes of performance images, is not well-understood, routine, or conventional. Applicant respectfully requests that the Examiner provide documentary evidence to support any assertion that these specific steps, taken in combination, were well- understood, routine, and conventional at the effective filing date. Accordingly, Applicant respectfully requests withdrawal of the rejection of claims 21, 24, 26-28, 31, 33-35, 38, and 40-50 under 35 U.S.C. § 101.
Examiner respectfully disagrees with Applicant’s sixth argument.
First, it is noted that the Examiner’s analysis does not consider the step of “receiving a registration from a first user, the registration including a boundary of the first user” (claims 21, 28, and 35) and/or “receiving an action from the first user on a profile of a second user” (claims 21, 28, and 35) and/or “receiving a plurality of images from the first user” (claims 21, 28, and 35) and/or “transmits a profile of a second user to the first user” (claims 28 and 35) to be “additional” elements that are subject to further analysis under Step 2A prong 2 or Step 2B. The Examiner considers these elements to be part of the abstract idea. Applicant’s argument is therefore unpersuasive, overall. The Examiner’s rejection merely suggested that even if considered to be an “additional” element for the purpose of the eligibility analysis, these elements would 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). The Examiner provided this additional (albeit unnecessary) analysis simply to help expedite prosecution (e.g., if Applicant were somehow able to persuasively argue that these limitations should be considered “additional” elements, which they have not done, they would still not result in the claims being patent eligible under the eligibility analysis). As such, Applicant’s argument is not persuasive.
Second, Applicant’s traversal is not proper. Applicant’s traversal refers generally states that “(t)he specific combination of steps recited in the claims, particularly the interplay between determining potential crushes, determining potential admirers, and using both to determine attributes of performance images, is not well-understood, routine, or conventional. Applicant respectfully requests that the Examiner provide documentary evidence to support any assertion that these specific steps, taken in combination, were well- understood, routine, and conventional at the effective filing date”. The Examiner’s Official Notice did not involve these steps, nor did it suggest all of these steps were well-understood, routine, and conventional. See MPEP 2144.03.C. “A general allegation that the claims define a patentable invention without any reference to the examiner’s assertion of official notice would be inadequate. The Examiner’s Official Notice involved the steps of “receiving a registration from a first user, the registration including a boundary of the first user” (claims 21, 28, and 35 - e.g., a user creating a dating app profile and providing dating preferences) and/or “receiving an action from the first user on a profile of a second user” (claims 21, 28, and 35 – e.g., a first user viewing or liking a second user’s profile/photo in a dating app) and/or “receiving a plurality of images from the first user” (claims 21, 28, and 35 – e.g., a first user uploading images to their dating profile in a dating app) and/or “transmits a profile of a second user to the first user” (claims 28 and 35 – e.g., a first user viewing a second user’s dating profile in a dating app). Because applicant did not specifically point out the supposed errors in the Examiner’s action (specifically, stating why the noticed facts above are not considered to be common knowledge or well-known in the art), Applicant’s traversal is not proper and is inadequate. As such, the Examiner’s common knowledge or well-known in the art statement is taken to be admitted prior art because applicant either failed to traverse the examiner’s assertion of official notice or that the traverse was inadequate (see MPEP 2144.03.C.).
Third, Applicant’s prior alleged traversal was already deficient, and therefore the Examiner’s common knowledge or well-known in the art statement was already taken to be admitted prior. Examiner continues to note that Applicant’s own specification appears to suggest that these steps were conventional (see paragraphs [0017]-[0019] of the as-filed specification).
v Applicant’s arguments, with respect to the rejection of amended claims 21, 28, and 35 under 35 U.S.C. §103 have been considered, but are not persuasive. Applicant only argues against alleged deficiencies in Rana. However, the rejection is/was based on a combination of references, specifically Rana in view of Bhattacharya. Bhattacharya was relied on to disclose the features of determining attributes of performance images based at least in part on the refined population of users. However, Applicant’s argument makes no reference to the Bhattacharya reference, and fails to show how Bhattacharya failed/fails to remedy the deficiency of Rana. Applicant’s argument is therefore not persuasive.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 5/21/26, 6/10/26, 6/25/26, 8/4/26, and 8/12/26 have been considered by the examiner.
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.
v Claim(s) 21, 24, 26-28, 31, 33-35, 38, and 40-50 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claim(s) 21, 24, 26, 27 and 41-46 is/are drawn to methods (i.e., a process), claim(s) 28 and 31, 33, 34, 47, and 48 is/are drawn to apparatus (i.e., a machine/manufacture), and claim(s) 35 and 38, 40, 49, and 50 is/are drawn to non-transitory, computer-readable media (i.e., a machine/manufacture). As such, claims 21, 24, 26-28, 31, 33-35, 38, and 40-50 are drawn to one of the statutory categories of invention (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 21 (representative of independent claim(s) 28 and 35) recites/describes the following steps;
receiving a registration from a first user, the registration including a boundary of the first user;
receiving an action from the first user on a profile of a second user;
refining a population of users, at least in part by: determining potential crushes of the first user, at least in part based on the second user and the boundary of the first user
receiving an action on a portion of a profile of the first user from a third user;
determining potential admirers of the first user, at least in part based on the action of the third user and boundaries of the potential admirers;
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce photo identification information for the first user
transmitting the photo identification information to the first user;
receiving a plurality of images from the first user; and
adding the plurality of images to the profile of the first user
These steps, under its broadest reasonable interpretation, describe or set-forth a business process for managing a social dating profile for a user based on tracked interactions with various dating profiles and images for the first user. Specifically, the process for managing a social dating profile for a user includes receiving an action from the first user on a profile of a second user; refining a population of users, at least in part by determining potential crushes of the first user, at least in part based on the second user and the boundary of the first user; receiving an action on a portion of a profile of the first user from a third user; determining potential admirers of the first user, at least in part based on the action of the third user and boundaries of the potential admirers; determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce photo identification information for the first user (e.g., attributes of photos that induce high level of interactions from potential crushes of the first user and from potential admirers of the first user), providing the first user with an indication of these attributes, receiving images from the first user, and adding the images to their dating profile, which amounts to managing personal behavior or relationships or interactions between people (specifically social activities). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Additionally, and/or alternatively, each of the above-recited steps/functions, under their broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) performing one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions, but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 21 recites an abstract idea (Step 2A – Prong One: YES).
Independent claim(s) 28 and 35 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.
Each of the depending claims likewise recite/describe these steps (by incorporation - 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. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim. The same reasoning is similarly applicable to the limitations in the remaining dependent claims, and their respective limitations are not reproduced here for the sake of brevity.
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 claim(s) recite the additional elements/limitations of
“an apparatus, comprising: a network interface…and a processor configured to…wherein the network interface…and the processor is further configured to…” (claim 28)
“wherein the processor is further configured to…” (claims 31, 33, and 34)
“a non-transitory, computer-readable medium encoded with executable instructions that, when executed by a processing unit, perform operations comprising…” (claim 35)
“determined via at least one of deterministic programming or machine learning” (claims 44, 47, and 49)
The requirement to execute the claimed steps/functions using “an apparatus, comprising: a network interface…and a processor configured to…wherein the network interface…and the processor is further configured to…” (claim 28) and/or “wherein the processor is further configured to…” (claims 31, 33, and 34) and/or “a non-transitory, computer-readable medium encoded with executable instructions that, when executed by a processing unit, perform operations comprising…” (claim 35) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s own disclosure explains that these “additional” elements may be embodied as a general-purpose computer (e.g., the as-filed specification at paragraphs [0165]-[0174] “computing device or the server…server…iPhone…executed by a processor, or other similar machine…computer instructions executed by a processor…any suitable memory…”). 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)).
The recitation of “determined via at least one of deterministic programming or machine learning” (claims 44, 47, and 49) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The deterministic programming or machine learning is used to generally apply the abstract idea without placing any limits on how the deterministic programming or machine learning functions. Rather, these limitations only recite the outcome of “determining attributes of performance images” and do not include any details about how the “determining” is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. 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)).
The recitation of “determined via at least one of deterministic programming or machine learning” (claims 44, 47, and 49) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “via at least one of deterministic programming or machine learning” limits the identified judicial exceptions to making the determination “via at least one of deterministic programming or machine learning”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (deterministic programming or machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. 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)).
In an effort to expedite prosecution, Examiner notes that the recited element(s) of “receiving a registration from a first user, the registration including a boundary of the first user” (claims 21, 28, and 35) and/or “wherein the registration…includes a voice recording of the first user” (claim 50) and/or “receiving an action from the first user on a profile of a second user” (claims 21, 28, and 35) and/or “receiving an action on a portion of a profile of the first user from a third user” (claims 21, 28, and 35) and/or “receiving a plurality of images from the first user” (claims 21, 28, and 35) and/or “transmits a profile of a second user to the first user” (claims 28 and 35), even if considered to be an “additional” element for the purpose of the eligibility analysis (which, the Examiner maintains, they are not), would 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). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element(s) do are deemed “extra-solution” because all uses of the recited judicial exceptions require such data gathering and output, and because such data gathering and solution-outputting/transmission steps have long been held to be insignificant pre/post-solution activity. 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) and (g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. For example, Applicant’s as-filed specification suggests that it is advantageous to implement the claimed business process because doing so can help a user to identify photos to use as part of their dating profile that will increase the chance of them “matching” or inducing a desired reaction/interaction with their target dating candidates, and perhaps that doing so can decrease the time it takes for a user to create their dating profile (see, for example, Applicant’s as-filed disclosure at paragraphs [0001] & [0021]-[0023] & [0026] & [0067]-[0068]). These are non-technical business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., an improved process for generating a user’s dating profile or for suggesting images for a user to add to their dating profile).
Independent claim 21 and dependent claims 24, 26, 27, 38, 40-43, 45, 46, 48, and 50 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claim 21 and dependent claims 24, 26, 27, 38, 40-43, 45, 46, 48, and 50 are further part of the abstract idea as identified by the Examiner for each respective claim (i.e. they are part of the abstract idea recited in each respective claim). For example, claim 24 recites “further comprising: refining the potential crushes of the first user, at least in part based on preferences of the first user.”. This is an abstract limitation which further sets forth the abstract idea encompassed by claim 24. This limitation is not an “additional element”, and therefore it is not subject to further analysis under Step 2A- Prong Two or Step 2B. The same logic applies to each of the other dependent claims, whose limitations are not being repeated here for the sake of brevity and clarity. With respect to the other dependent claims not specifically listed here - each of the limitations/elements recited in these dependent claims other than those identified as being “additional” elements above (at the beginning of the Prong One analysis), are further part of the abstract idea encompassed by each respective dependent claim (i.e. it should be understood that these limitations 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 requirement to execute the claimed steps/functions using “an apparatus, comprising: a network interface…and a processor configured to…wherein the network interface…and the processor is further configured to…” (claim 28) and/or “wherein the processor is further configured to…” (claims 31, 33, and 34) and/or “a non-transitory, computer-readable medium encoded with executable instructions that, when executed by a processing unit, perform operations comprising…” (claim 35) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recitation of “determined via at least one of deterministic programming or machine learning” (claims 44, 47, and 49) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recitation of “determined via at least one of deterministic programming or machine learning” (claims 44, 47, and 49) also serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited element(s) of “receiving a registration from a first user, the registration including a boundary of the first user” (claims 21, 28, and 35) and/or “receiving an action from the first user on a profile of a second user” (claims 21, 28, and 35) and/or “receiving an action on a portion of a profile of the first user from a third user” (claims 21, 28, and 35) and/or “receiving a plurality of images from the first user” (claims 21, 28, and 35) and/or “transmits a profile of a second user to the first user” (claims 28 and 35), even if considered to be an “additional” element for the purpose of the eligibility analysis, would 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; mere post-solution activity in conjunction with an abstract idea). These additional element(s), taken individually or in combination, additionally amount to well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, appended to the judicial exception. These additional elements, taken individually or in combination, are well-understood, routine and conventional to those in the field of online dating and/or matchmaking. These limitations therefore do not qualify as “significantly more”. (see MPEP 2106.05(d)). This conclusion is based on a factual determination. The determination that receiving data/messages over a network is well-understood, routine, and conventional is supported by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of receiving data/messages over a network. Furthermore, Examiner takes Official Notice that these steps were well-understood, routine, and conventional at the effective filing date of the claimed invention. Furthermore, the lack of technical detail/description in Applicant’s own specification provides implicit evidence that these steps were well-understood, routine, and conventional. Finally, Applicant’s own specification appears to suggest that these steps were conventional (see paragraphs [0017]-[0019] of the as-filed specification).
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, and appended with well-understood, routine and conventional activities previously known to the industry.
Independent claim 21 and dependent claims 24, 26, 27, 38, 40-43, 45, 46, 48, and 50 fail to include any additional elements. In other words, each of the limitations/elements recited in independent claim 21 and respective dependent claims 24, 26, 27, 38, 40-43, 45, 46, 48, and 50 is/are further part of the abstract idea as identified by the Examiner for each respective claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed). See explanation above in Step 2A prong 2.
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).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
v Claims 21, 24, 26, 28, 31, 33, 35, 38, and 42-49 rejected under 35 U.S.C. 103 as being unpatentable over Rana et al. (U.S. Patent No. 9,560,156, January 31, 2017 - hereinafter "Rana”) in view of “Tinder is A/B Testing your Face” (Bhattacharya, Ananya; published online on July 21, 2022 at https://qz.com/809681/tinders-machine-learning-algorithms-can-now-serve-your-most-appealing-photos-to-potential-dates - hereinafter “Bhattacharya”)
With respect to claim 21, Rana teaches a method, comprising:
receiving a registration from a first user, the registration including a boundary of the first user; (Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”)
receiving an action from the first user on a profile of a second user; (Fig 3A shows “view profile” button associated with profiles of second users on interface with which the first user can interact, 7:8-15 “Assuming Tom has decided he would like to know more about one of the members whose user profile is presented in FIG. 2C, he may click on the profile photo associated with the selected user profile”)
refining a population of users, at least in part by: determining potential crushes of the first user, at least in part based on the second user and the boundary of the first user (Fig 2C shows profiles of people (e.g., at least a second user) meeting the first user’s boundaries/preferences (i.e., potential crushes of the first user) and per 7:8-15 the first user may perform an action of the profile of this user (see “Assuming Tom has decided he would like to know more about one of the members whose user profile is presented in FIG. 2C, he may click on the profile photo associated with the selected user profile… It will be noted that the information solicited for a user profile using the page shown in FIG. 2C may be used in selecting matches for Tom.”) – as such, the system determines potential crushes of the first user, at least in part based on the second user (e.g., based on their profile attributes matching the boundary conditions of the first user), see also Fig 3A where LadyDi520 is also displayed and identified at a potential crush for the first user, Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”) –Examiner notes that Bhattacharya also discloses refining a population of users, at least in part by wherein the determining the potential crushes of the first user is at least in part based on the boundary of the first user, as Tinder users that were served the first user’s profile/image(s) would only be served the first user’s profile/image(s) if they meet the first user’s boundaries (e.g., location filter)
receiving an action on a portion of a profile of the first user from a third user; (1:60-67 & 2:1-5 “Information related to a user profile and/or user activity of an end user is collected…the information may relate to how others have acted/interacted with the particular user profile (e.g. , number of views, rank/score, appearances on other end user's matches…analyses information against a metric”, 17:56-57 “is the end user showing up other end user’s list of matches at least X number of times? Is the user profile of the end user being viewed at least X number of times?” – viewing the profile requires a click/interaction/”action” as discussed throughout)
boundaries in registrations of potential admirers of the first user (1:60-67 and 7:15-45 show that other people interact with the first user’s profile (e.g., 2nd and 3rd people) and therefore these people were provided with the first user’s profile/image that the system provides these other people (potential admirers) with their matches based on their respective boundaries (search criteria) so the system therefore receives boundaries in registration of these people as well)
determining attributes of images to produce photo identification information for the first user (8:4-46 “successful end users, end users who are able to use the online dating service to find dates or matches… Statistical analysis performed based on the information from and/or related to the user profiles and/or user activities of successful end users… one or more metrics are determined/defined. The metric may be (positively) defined to include one or more characteristics representative of a successful end user, i.e., characteristics of a user profile…metrics may be defined based on or tailored to a cohort… this metric may be tailored to a cohort of woman, e.g., aged 30-35, where the metric may indicate that a typical successful user would upload more than six photos… the coaching mechanism may evaluate the end user based on the cohort to which the end user belongs…” and 18:1-6 show exemplary metrics such as “Does the user have at least X number of profile photos? Does the user have at least X number of head shots? Body shots?” – therefore the system analyzes profile data of successful users in the first user’s cohort (i.e., a population of users) and may determine attributes of images in their profile (e.g., quantity of images depicting headshots, quantity of images depicting body shots, quantity of images in general) that are correlated to their success)
transmitting photo identification information to the first user, (9:3-16 “information from a user profile…of a particular end user can be collected and analyzed against the metric(s). Based on…whether the user profile…exhibits the characteristic(s) indicative of a successful user), a coaching mechanism use the result(s) from the metrics analysis to guide an end user in becoming a successful end user….one or more rules may be provided to generate appropriate, targeted, and/or personalized coaching messages to the end user to provoke the end user to, e.g., change the user profile of the end user…” & 11:55-62 “coaching metric…add another photo, you have too few…” & 12:39-41 “coaching messages which require the user to upload photos, provide longer user profile description, provide photo captions…” & 13:3-9 “a metric may include a characteristic that a user has at least 6 profile photos. The information collected in step 602 may include 5 the number of profile photos a particular end user has. The number of profile photos a particular end user has is analyzed/ evaluated against the metric to determine whether the information meets the metric or not” – therefore the system analyzes the first user’s profile (e.g., their profile photos) to determine whether or not they have the characteristics/attributes correlated with successful matching and if not the system transmits coaching information associated with the metrics such as photo identification information including the attributes of the images (e.g., add a certain number of images to your profile, the attribute of the images is a quantity here). Examiner notes that because quantity of body shots or head shots or inclusion of photo captions are also identified as being metrics the system analyzes, the photo identification information sent to the first user may be coaching relating to adding photos with more headshots or body shots or captions (i.e., photo identification information including attributes of the images such as headshots, bodyshots, captions))
receiving a plurality of images from the first user; and adding the plurality of images to the profile of the first user (Fig 3A shows “add/edit photos” button where first user can input a plurality of images of themselves for addition to their dating profile, 7:35-40 “System 10 may also ask the user to upload one or more photos for the user profile. It will be recognized that the information collected using the web pages illustrated in FIGS. 2E-2G is illustrative only and that any type/amount of information for a user profile may be solicited in the illustrated manner.” – therefore the first user can provide a plurality of images for addition to their profile)
Although Rana discloses transmitting identification information to the first user to identify information that is correlated with greater matching/engagement performance in order to coach the first user (including photo identification information), Rana does not appear to disclose where the photo identification information includes attributes of performance images per se (e.g., attributes of respective images determined to specifically receive or result in certain levels of engagement). Rana further does not appear to disclose where the determination of attributes of performance images is based on the potential crushes of the first user and the potential admirers of the first user (instead, the attributes of the images in the photo identification information are based on the metrics associated with patterns of success associated with the first user’s cohort). Rana does not appear to disclose,
determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information
However, Bhattacharya discloses
determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – Tinder identifies potential admirers of the first user based at least in part on actions from third users on a portion of the first user’s profile (e.g., users that swipe right on the first user’s profile picture that was served to them are potential admirers of the first user – also discloses determining the potential admirers of the first user, at least in part based on boundaries of the potential admirers because the population of users that are potential admirers of the first user that engaged with the first user’s photos are on Tinder and they have been served the first user’s image(s)) and because in Tinder these user’s would only be served the first user’s profile/image(s) if the first user met their boundaries (e.g., location requirement) which are provided as registration of the potential admirers of the first user (as is known requirement of Tinder))
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – therefore discloses determining attributes of performance images (e.g., elements of photos that result in successful/positive engagement, such as inclusion of a dog, smiling state, covering part or all of your face, being in a group phone, and wearing hats or glasses) personalized for a user (e.g., the first user) based on interactions/feedback from a population of users that are potential admirers of the first user (e.g., because they are on Tinder and they have been served the first user’s image(s)) and they interacted/engaged positively on the first user’s image(s)) and potential crushes of the first user (e.g., because these same users were served the first user’s profile/image(s) and because in Tinder these user’s would only be served the first user’s profile/image(s) if they meet the first user’s boundaries (e.g., location and gender preference) and therefore they are also “potential crushes of the first user”) to produce the photo identification)
Bhattacharya suggests it is advantageous to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information, because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rana to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information, as taught by Bhattacharya because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively
Furthermore, as in Bhattacharya it was within the capabilities of one of ordinary skill in the art to modify the method of Rana to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information. Furthermore, as in Bhattacharya, the results of doing so would have been predictable to one of ordinary skill in the art. It would have been predictable to one of ordinary skill in the art that feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
With respect to claim 28, Rana teaches an apparatus, comprising:
a network interface that (3:45-67 & 4:1-60 – computer devices used to send/receive information over the network)
receives a registration from a first user, (Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”)
transmits a profile of a second user to the first user, and receives an action from the first user on the profile of the second user; and (Fig 3A shows “view profile” button associated with profiles of second users on interface with which the first user can interact, 7:8-15 “Assuming Tom has decided he would like to know more about one of the members whose user profile is presented in FIG. 2C, he may click on the profile photo associated with the selected user profile”)
wherein the network interface receives an action on a portion of a profile of the first user from a third user; (1:60-67 & 2:1-5 “Information related to a user profile and/or user activity of an end user is collected…the information may relate to how others have acted/interacted with the particular user profile (e.g. , number of views, rank/score, appearances on other end user's matches…analyses information against a metric”, 17:56-57 “is the end user showing up other end user’s list of matches at least X number of times? Is the user profile of the end user being viewed at least X number of times?” – viewing the profile requires a click/interaction/”action” as discussed throughout)
a processor configured to (3:45-67 & 4:1-60 – computer devices comprising processors)
refine a population of users, at least in part by: determine potential crushes of the first user, at least in part based on the second user and the boundary of the first user (Fig 2C shows profiles of people (e.g., at least a second user) meeting the first user’s boundaries/preferences (i.e., potential crushes of the first user) and per 7:8-15 the first user may perform an action of the profile of this user (see “Assuming Tom has decided he would like to know more about one of the members whose user profile is presented in FIG. 2C, he may click on the profile photo associated with the selected user profile… It will be noted that the information solicited for a user profile using the page shown in FIG. 2C may be used in selecting matches for Tom.”) – as such, the system determines potential crushes of the first user, at least in part based on the second user (e.g., based on their profile attributes matching the boundary conditions of the first user), see also Fig 3A where LadyDi520 is also displayed and identified at a potential crush for the first user, Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”) –Examiner notes that Bhattacharya also discloses refining a population of users, at least in part by wherein the determining the potential crushes of the first user is at least in part based on the boundary of the first user, as Tinder users that were served the first user’s profile/image(s) would only be served the first user’s profile/image(s) if they meet the first user’s boundaries (e.g., location filter)
boundaries in registrations of potential admirers of the first user (1:60-67 and 7:15-45 show that other people interact with the first user’s profile (e.g., 2nd and 3rd people) and therefore these people were provided with the first user’s profile/image that the system provides these other people (potential admirers) with their matches based on their respective boundaries (search criteria) so the system therefore receives boundaries in registration of these people as well)
determine attributes of images to produce photo identification information, (8:4-46 “successful end users, end users who are able to use the online dating service to find dates or matches… Statistical analysis performed based on the information from and/or related to the user profiles and/or user activities of successful end users… one or more metrics are determined/defined. The metric may be (positively) defined to include one or more characteristics representative of a successful end user, i.e., characteristics of a user profile…metrics may be defined based on or tailored to a cohort… this metric may be tailored to a cohort of woman, e.g., aged 30-35, where the metric may indicate that a typical successful user would upload more than six photos… the coaching mechanism may evaluate the end user based on the cohort to which the end user belongs…” and 18:1-6 show exemplary metrics such as “Does the user have at least X number of profile photos? Does the user have at least X number of head shots? Body shots?” – therefore the system analyzes profile data of successful users in the first user’s cohort (i.e., a population of users) and may determine attributes of images in their profile (e.g., quantity of images depicting headshots, quantity of images depicting body shots, quantity of images in general) that are correlated to their success)
wherein the network interface transmits photo identification information to the first user, and (9:3-16 “information from a user profile…of a particular end user can be collected and analyzed against the metric(s). Based on…whether the user profile…exhibits the characteristic(s) indicative of a successful user), a coaching mechanism use the result(s) from the metrics analysis to guide an end user in becoming a successful end user….one or more rules may be provided to generate appropriate, targeted, and/or personalized coaching messages to the end user to provoke the end user to, e.g., change the user profile of the end user…” & 11:55-62 “coaching metric…add another photo, you have too few…” & 12:39-41 “coaching messages which require the user to upload photos, provide longer user profile description, provide photo captions…” & 13:3-9 “a metric may include a characteristic that a user has at least 6 profile photos. The information collected in step 602 may include 5 the number of profile photos a particular end user has. The number of profile photos a particular end user has is analyzed/ evaluated against the metric to determine whether the information meets the metric or not” – therefore the system analyzes the first user’s profile (e.g., their profile photos) to determine whether or not they have the characteristics/attributes correlated with successful matching and if not the system transmits coaching information associated with the metrics such as photo identification information including the attributes of the images (e.g., add a certain number of images to your profile, the attribute of the images is a quantity here). Examiner notes that because quantity of body shots or head shots or inclusion of photo captions are also identified as being metrics the system analyzes, the photo identification information sent to the first user may be coaching relating to adding photos with more headshots or body shots or captions (i.e., photo identification information including attributes of the images such as headshots, bodyshots, captions))
receives a plurality of images from the first user; and the processor is further configured to add the plurality of images to a profile of the first user (Fig 3A shows “add/edit photos” button where first user can input a plurality of images of themselves for addition to their dating profile, 7:35-40 “System 10 may also ask the user to upload one or more photos for the user profile. It will be recognized that the information collected using the web pages illustrated in FIGS. 2E-2G is illustrative only and that any type/amount of information for a user profile may be solicited in the illustrated manner.” – therefore the first user can provide a plurality of images for addition to their profile)
Although Rana discloses transmitting identification information to the first user to identify information that is correlated with greater matching/engagement performance in order to coach the first user (including photo identification information), Rana does not appear to disclose where the photo identification information includes attributes of performance images per se (e.g., attributes of respective images determined to specifically receive or result in certain levels of engagement). Rana further does not appear to disclose where the determination of attributes of performance images is based on the potential crushes of the first user and the potential admirers of the first user (instead, the attributes of the images in the photo identification information are based on the metrics associated with patterns of success associated with the first user’s cohort). Rana does not appear to disclose,
determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information
However, Bhattacharya discloses
determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – Tinder identifies potential admirers of the first user based at least in part on actions from third users on a portion of the first user’s profile (e.g., users that swipe right on the first user’s profile picture that was served to them are potential admirers of the first user – also discloses determining the potential admirers of the first user, at least in part based on boundaries of the potential admirers because the population of users that are potential admirers of the first user that engaged with the first user’s photos are on Tinder and they have been served the first user’s image(s)) and because in Tinder these user’s would only be served the first user’s profile/image(s) if the first user met their boundaries (e.g., location requirement) which are provided as registration of the potential admirers of the first user (as is known requirement of Tinder))
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – therefore discloses determining attributes of performance images (e.g., elements of photos that result in successful/positive engagement, such as inclusion of a dog, smiling state, covering part or all of your face, being in a group phone, and wearing hats or glasses) personalized for a user (e.g., the first user) based on interactions/feedback from a population of users that are potential admirers of the first user (e.g., because they are on Tinder and they have been served the first user’s image(s)) and they interacted/engaged positively on the first user’s image(s)) and potential crushes of the first user (e.g., because these same users were served the first user’s profile/image(s) and because in Tinder these user’s would only be served the first user’s profile/image(s) if they meet the first user’s boundaries (e.g., location and gender preference) and therefore they are also “potential crushes of the first user”) to produce the photo identification)
Bhattacharya suggests it is advantageous to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information, because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Rana to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information, as taught by Bhattacharya because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively
Furthermore, as in Bhattacharya it was within the capabilities of one of ordinary skill in the art to modify the apparatus of Rana to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information. Furthermore, as in Bhattacharya, the results of doing so would have been predictable to one of ordinary skill in the art. It would have been predictable to one of ordinary skill in the art that feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
With respect to claim 35, Rana teaches a non-transitory, computer-readable medium encoded with executable instructions that, when executed by a processing unit, perform operations comprising:
receiving a registration from a first user; (Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”)
transmitting a profile of a second user to the first user; receiving an action from the first user on the profile of the second user; (Fig 3A shows “view profile” button associated with profiles of second users on interface with which the first user can interact, 7:8-15 “Assuming Tom has decided he would like to know more about one of the members whose user profile is presented in FIG. 2C, he may click on the profile photo associated with the selected user profile”)
refining a population of users, at least in part by: determining potential crushes of the first user, at least in part based on the second user and a boundary of the first user, the registration including the boundary (Fig 2C shows profiles of people (e.g., at least a second user) meeting the first user’s boundaries/preferences (i.e., potential crushes of the first user) and per 7:8-15 the first user may perform an action of the profile of this user (see “Assuming Tom has decided he would like to know more about one of the members whose user profile is presented in FIG. 2C, he may click on the profile photo associated with the selected user profile… It will be noted that the information solicited for a user profile using the page shown in FIG. 2C may be used in selecting matches for Tom.”) – as such, the system determines potential crushes of the first user, at least in part based on the second user (e.g., based on their profile attributes matching the boundary conditions of the first user), see also Fig 3A where LadyDi520 is also displayed and identified at a potential crush for the first user, Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”) –Examiner notes that Bhattacharya also discloses refining a population of users, at least in part by wherein the determining the potential crushes of the first user is at least in part based on the boundary of the first user, as Tinder users that were served the first user’s profile/image(s) would only be served the first user’s profile/image(s) if they meet the first user’s boundaries (e.g., location filter)
receiving an action on a portion of a profile of the first user from a third user; (1:60-67 & 2:1-5 “Information related to a user profile and/or user activity of an end user is collected…the information may relate to how others have acted/interacted with the particular user profile (e.g. , number of views, rank/score, appearances on other end user's matches…analyses information against a metric”, 17:56-57 “is the end user showing up other end user’s list of matches at least X number of times? Is the user profile of the end user being viewed at least X number of times?” – viewing the profile requires a click/interaction/”action” as discussed throughout)
boundaries in registrations of potential admirers of the first user (1:60-67 and 7:15-45 show that other people interact with the first user’s profile (e.g., 2nd and 3rd people) and therefore these people were provided with the first user’s profile/image that the system provides these other people (potential admirers) with their matches based on their respective boundaries (search criteria) so the system therefore receives boundaries in registration of these people as well)
determining attributes of images to produce photo identification information; (8:4-46 “successful end users, end users who are able to use the online dating service to find dates or matches… Statistical analysis performed based on the information from and/or related to the user profiles and/or user activities of successful end users… one or more metrics are determined/defined. The metric may be (positively) defined to include one or more characteristics representative of a successful end user, i.e., characteristics of a user profile…metrics may be defined based on or tailored to a cohort… this metric may be tailored to a cohort of woman, e.g., aged 30-35, where the metric may indicate that a typical successful user would upload more than six photos… the coaching mechanism may evaluate the end user based on the cohort to which the end user belongs…” and 18:1-6 show exemplary metrics such as “Does the user have at least X number of profile photos? Does the user have at least X number of head shots? Body shots?” – therefore the system analyzes profile data of successful users in the first user’s cohort (i.e., a population of users) and may determine attributes of images in their profile (e.g., quantity of images depicting headshots, quantity of images depicting body shots, quantity of images in general) that are correlated to their success)
transmitting the photo identification information to the first user (9:3-16 “information from a user profile…of a particular end user can be collected and analyzed against the metric(s). Based on…whether the user profile…exhibits the characteristic(s) indicative of a successful user), a coaching mechanism use the result(s) from the metrics analysis to guide an end user in becoming a successful end user….one or more rules may be provided to generate appropriate, targeted, and/or personalized coaching messages to the end user to provoke the end user to, e.g., change the user profile of the end user…” & 11:55-62 “coaching metric…add another photo, you have too few…” & 12:39-41 “coaching messages which require the user to upload photos, provide longer user profile description, provide photo captions…” & 13:3-9 “a metric may include a characteristic that a user has at least 6 profile photos. The information collected in step 602 may include 5 the number of profile photos a particular end user has. The number of profile photos a particular end user has is analyzed/ evaluated against the metric to determine whether the information meets the metric or not” – therefore the system analyzes the first user’s profile (e.g., their profile photos) to determine whether or not they have the characteristics/attributes correlated with successful matching and if not the system transmits coaching information associated with the metrics such as photo identification information including the attributes of the images (e.g., add a certain number of images to your profile, the attribute of the images is a quantity here). Examiner notes that because quantity of body shots or head shots or inclusion of photo captions are also identified as being metrics the system analyzes, the photo identification information sent to the first user may be coaching relating to adding photos with more headshots or body shots or captions (i.e., photo identification information including attributes of the images such as headshots, bodyshots, captions))
receiving a plurality of images from the first user; and adding the plurality of images to the profile of the first user (Fig 3A shows “add/edit photos” button where first user can input a plurality of images of themselves for addition to their dating profile, 7:35-40 “System 10 may also ask the user to upload one or more photos for the user profile. It will be recognized that the information collected using the web pages illustrated in FIGS. 2E-2G is illustrative only and that any type/amount of information for a user profile may be solicited in the illustrated manner.” – therefore the first user can provide a plurality of images for addition to their profile)
Although Rana discloses transmitting identification information to the first user to identify information that is correlated with greater matching/engagement performance in order to coach the first user (including photo identification information), Rana does not appear to disclose where the photo identification information includes attributes of performance images per se (e.g., attributes of respective images determined to specifically receive or result in certain levels of engagement). Rana further does not appear to disclose where the determination of attributes of performance images is based on the potential crushes of the first user and the potential admirers of the first user (instead, the attributes of the images in the photo identification information are based on the metrics associated with patterns of success associated with the first user’s cohort). Rana does not appear to disclose,
determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information
However, Bhattacharya discloses
determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – Tinder identifies potential admirers of the first user based at least in part on actions from third users on a portion of the first user’s profile (e.g., users that swipe right on the first user’s profile picture that was served to them are potential admirers of the first user – also discloses determining the potential admirers of the first user, at least in part based on boundaries of the potential admirers because the population of users that are potential admirers of the first user that engaged with the first user’s photos are on Tinder and they have been served the first user’s image(s)) and because in Tinder these user’s would only be served the first user’s profile/image(s) if the first user met their boundaries (e.g., location requirement) which are provided as registration of the potential admirers of the first user (as is known requirement of Tinder))
determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – therefore discloses determining attributes of performance images (e.g., elements of photos that result in successful/positive engagement, such as inclusion of a dog, smiling state, covering part or all of your face, being in a group phone, and wearing hats or glasses) personalized for a user (e.g., the first user) based on interactions/feedback from a population of users that are potential admirers of the first user (e.g., because they are on Tinder and they have been served the first user’s image(s)) and they interacted/engaged positively on the first user’s image(s)) and potential crushes of the first user (e.g., because these same users were served the first user’s profile/image(s) and because in Tinder these user’s would only be served the first user’s profile/image(s) if they meet the first user’s boundaries (e.g., location and gender preference) and therefore they are also “potential crushes of the first user”) to produce the photo identification))
Bhattacharya suggests it is advantageous to include determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information, because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the medium of Rana to include instructions for determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information, as taught by Bhattacharya because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Furthermore, as in Bhattacharya it was within the capabilities of one of ordinary skill in the art to modify the medium of Rana to include instructions for determining potential admirers of the first user, at least in part based on the action of the third user and the boundaries of the potential admirers, and determining attributes of performance images, at least in part based on the refined population of users including the potential crushes of the first user and the potential admirers of the first user, to produce the photo identification information. Furthermore, as in Bhattacharya, the results of doing so would have been predictable to one of ordinary skill in the art. It would have been predictable to one of ordinary skill in the art that feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
With respect to claims 24, 31, and 38, Rana and Bhattacharya teach the method of claim 21, the apparatus of claim 28, and the medium of claim 35. Rana further discloses
refining the potential crushes of the first user, at least in part based on preferences of the first user (Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…”– Fig 2d & 2E & 2F also show user registration where the user can also input their preference information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”).
Examiner notes that Bhattacharya also discloses refining the potential crushes of the first user, at least in part based on preferences of the first user, as Tinder users that were served the first user’s profile/image(s) would only be served the first user’s profile/image(s) if they meet the first user’s preferences (e.g., age/gender preferences).
With respect to claims 26 and 33, Rana and Bhattacharya teach the method of claim 21 and the apparatus of claim 28. Rana discloses
the registrations of the potential admirers include the preferences (1:60-67 and 7:15-45 show that other people interact with the first user’s profile (e.g., 2nd and 3rd people) and therefore these people were provided with the first user’s profile/image that the system provides these other people (potential admirers) with their matches based on their respective preferences (search criteria) that are entered when they register similar to the way the first user’s preferences are entered at registration)
Rana does not appear to disclose,
refining the potential admirers, based on preferences of the potential admirers
However, Bhattacharya discloses
refining the potential admirers, based on preferences of the potential admirers (“Tinder… With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – therefore discloses determining attributes of performance images based on feedback from a population of users that are potential admirers of the first user (e.g., because they are on Tinder and they have been served the first user’s image(s)) and because they are Tinder user’s that were served the first user’s image(s) the first user fit’s these potential admirer’s preferences (e.g., gender/age) because Tinder serves you images/profile of potential matches that meet your preferences/boundaries)
Bhattacharya suggests it is advantageous to include refining the potential admirers, based on preferences of the potential admirers, because feedback on specific profile pictures from this specific population of people that are potential admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rana to include refining the potential admirers, based on preferences of the potential admirers, as taught by Bhattacharya because feedback on specific profile pictures from this specific population of people that are potential admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Furthermore, as in Bhattacharya it was within the capabilities of one of ordinary skill in the art to modify the method of Rana to include wherein refining the potential admirers, based on preferences of the potential admirers. Furthermore, as in Bhattacharya, the results of doing so would have been predictable to one of ordinary skill in the art. It would have been predictable to one of ordinary skill in the art that doing so would help users increase their chances at matching or having their photos be engaged with positively, as is needed in Rana.
With respect to claim 42, Rana and Bhattacharya teach the method of claim 21. Rana further discloses
wherein the action from the first user and the action of the third user are performed on a dating service (1:5-20 “an online dating service or platform” – the actions of the first and third user are performed in the online dating service)
With respect to claims 44, 47, and 49, Rana and Bhattacharya teach the method of claim 21, the apparatus of claim 28, and the medium of claim 35. Rana fails to teach, but Bhattacharya further discloses
wherein the attributes of the performance images are determined via at least one of deterministic programming or machine learning (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – therefore discloses wherein the attributes of the performance images are determined via at least one of deterministic programming or machine learning)
Bhattacharya suggests it is advantageous to include wherein the attributes of the performance images are determined via at least one of deterministic programming or machine learning, because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, apparatus, and medium of Rana to include wherein the attributes of the performance images are determined via at least one of deterministic programming or machine learning, as taught by Bhattacharya because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Furthermore, as in Bhattacharya it was within the capabilities of one of ordinary skill in the art to modify the method, apparatus, and medium of Rana to include wherein the attributes of the performance images are determined via at least one of deterministic programming or machine learning. Furthermore, as in Bhattacharya, the results of doing so would have been predictable to one of ordinary skill in the art. It would have been predictable to one of ordinary skill in the art that feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
With respect to claim 45, Rana and Bhattacharya teach the method of claim 21. Rana fails to teach, but Bhattacharya further discloses
wherein the attributes of the performance images identify at least one of a face of a user, lighting, image clarity, or a smile of a user (“Tinder has pointed out that not smiling, covering part or all of your face, being in a group phone, and wearing hats or glasses are deal breakers. With the popular dating app’s latest feature, it’s analyzing the effectiveness of individual users’ photos and adjusting what’s shown in their profiles based on which of their photos get more people to swipe right… aims to maximize your chances of finding a match. Tinder is using machine learning to identify which photos work and which don’t…It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder. Then it analyses the responses by who swipes left (to decline a connection) or right (to agree to one). Based on those responses, it reorders your photos to show your most successful one first… The algorithm also adjusts to the personal preferences of its users. For example, if a picture of you hugging a puppy is your tried-and-tested best bet, that will generally show up as your first photo. But if your profile is shared with someone who almost always swipes left on dog photos, Tinder will pick an alternative image to show them” – therefore discloses that the attributes of the performance images identify at least one of a face of a user or a smile of a user)
Bhattacharya suggests it is advantageous to include wherein the attributes of the performance images identify at least one of a face of a user, lighting, image clarity, or a smile of a use, because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rana to include wherein the attributes of the performance images identify at least one of a face of a user, lighting, image clarity, or a smile of a use, as taught by Bhattacharya because feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
Furthermore, as in Bhattacharya it was within the capabilities of one of ordinary skill in the art to modify the method of Rana to include wherein the attributes of the performance images identify at least one of a face of a user, lighting, image clarity, or a smile of a use. Furthermore, as in Bhattacharya, the results of doing so would have been predictable to one of ordinary skill in the art. It would have been predictable to one of ordinary skill in the art that feedback on specific profile pictures from this specific population of people that are potential crushes/admirers (and for whom the first user is within the target demographic the potential admirer would want to date or match with) is highly valuable as these are the people who will be seeing the first user’s profile/images and this data could increase the first user’s chances at matching or having their photos be engaged with positively.
With respect to claims 46 and 48, Rana and Bhattacharya teach the method of claim 21. Rana fails to teach, but Bhattacharya further discloses
wherein the boundary of the first user includes at least one of a gender, an age range, or a geographical distance (Fig 2a “seeking – woman…between 25 and 33…located in (city/zip code)” & Fig 2C “refine your search…” both show boundaries of a first user in their registration information and the boundary of the first user includes at least one of a gender, an age range, or a geographical distance – Fig 2d & 2E & 2F also show user registration where the user can also input their boundary information (e.g., seeking gender, age range, zip/postal code, height, eye color, hair color, etc.), 6:34-53 “gathering of information from (member) end users to form a user profile…the web page solicits location information, such as a city or zip code, as well as an indication of the end user's gender and an age range and gender preference of persons the end user is interested in "meeting"…”)
v Claims 27, 34, 40, and 50 are rejected under 35 U.S.C. 103 as being unpatentable over Rana in view of Bhattacharya, as applied to claims 21, 28, and 35 above, and further in view of Hopkins et al. (U.S. PG Pub No. 2023/0120441, April 20, 2023- hereinafter "Hopkins”)
With respect to claim 27, Rana and Bhattacharya teach the method of claim 21. Although Rana suggests that a user profile may include video data (7:47-50), Rana does not appear to disclose,
generating a biometric fingerprint of the first user, at least in part based on a verification video of the first user, wherein the registration from the first user includes the verification video
However, Hopkins discloses
generating a biometric fingerprint of the first user, at least in part based on a verification video of the first user, wherein the registration from the first user includes the verification video (“[0028]-[0035] “the image may also be analyzed to determine if the image is an image of different person (e.g., using deep face verification models, by comparing a fingerprint of the image submitted by the user…The user may also be instructed to submit a video (e.g., a file of a recorded video or a live streaming video) of the user reading a script provided by the system…The submitted videos may then be analyzed, as part of a verification process, to determine if the video includes a video of a person reading the script…the image of the face of the person in the video is compared with the image of the face in the still image/photograph submitted to be used as the profile image to ensure that the facial image in the video is of the same person as in the still image (e.g., using deep face verification models). If the facial images do not match, optionally the user may be prevented from establishing an account and/or user system services to communicate with other users…If the user's image meets the image criteria and the user and the user may enabled to use social networking and communication features, examples of which are described here… a match filter may be provided via which a user can request to only be shown matches with other users that have been verified using the facial image process” – therefore the system generates biometric fingerprints of a user from their verification video and compares it to other photos of the user to verify the person is real and is the same person, [0077] “the process may compare the face in the video recording with the face in one or more of the still photographs provided for the user profile and determine if the faces match (are of the same person). For example, a face may be detected and localized in a video frame, facial landmarks may be detected (e.g., eyes, nose, mouth), the face may be aligned (as may be the face in the still photograph), the pixel values of the face image in the video frame and the face image in the still photograph may be transformed into compact and discriminative feature vectors (sometimes referred to as a template), and at block 318 the still photograph and video templates may be compared to produce a similarity score that indicates the likelihood that the still photograph template and the video template belong to the same subject. Optionally, a trained neural network may be utilized to determine if the face in the video and the face in a still photograph are the same face” – the feature vectors representing the landmarks and pixel values of the user’s face are a biometric fingerprint of the user consistent with Applicant’s own specification at [0060] where it suggest a biometric fingerprint represents facial geometry, see also [0062]-[0063] “ a verification rule may specify that the face in the video must match a face in a still image submitted by the user (e.g., a photograph to be used as a profile photo). For example, the verification service 212A may include a deep neural network trained to identify matching faces…different nodal points on a human face identified in the video may normalized, and the distance between nodal points many be measured. Similarly, different nodal points on a human face identified in the still image may normalized, and the distance between nodal points many be measured. The nodal distances from the video and the still image may be compared, and a determination may be made whether there is a match (e.g., the distances are within a specified threshold of each other). If there is a match, this portion of the verification process may be satisfied. If there is not a match, this portion of the verification process may have failed, and the user may be inhibited from creating an account on the system 100 and/or utilizing certain system 100 resources” comparing biometric fingerprint of the first user from the video and images, [0011]-[0012] & [0125] video is part of user registration, [0022] “online social networking platforms (e.g., dating platforms) have outdated user profiles or profiles of users…certain user profiles contain false information (e.g., facial images, gender, age, interests, location, height, weight, religion, etc.) and are used to create a fictional online persona to fool other users and to lure other users into what would otherwise be unwanted online communications and potentially dangerous in-person meetings” & [0103] “Certain user interfaces may be presented via a dedicated social networking application (e.g., a dating application)” – therefore the verification video is added to a user profile of a dating service when they register for the dating service)
Hopkins suggests it is advantageous to include generating a biometric fingerprint of the first user, at least in part based on a verification video of the first user, wherein the registration from the first user includes the verification video, because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service ([0022])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rana in view of Bhattacharya to include generating a biometric fingerprint of the first user, at least in part based on a verification video of the first user, wherein the registration from the first user includes the verification video, as taught by Hopkins because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service.
With respect to claims 34 and 40, Rana and Bhattacharya teach the apparatus of claim 28 and the medium of claim 35. Although Rana suggests that a user profile may include video data (7:47-50), Rana does not appear to disclose,
wherein the processor is further configured to generate a biometric fingerprint of the first user, at least in part based on a verification video of the first user, the registration from the first user includes the verification video, and the photo identification information includes the biometric fingerprint of the first user
However, Hopkins discloses
wherein the processor is further configured to generate a biometric fingerprint of the first user, at least in part based on a verification video of the first user, the registration from the first user includes the verification video, and the photo identification information includes the biometric fingerprint of the first user (“[0028]-[0035] “the image may also be analyzed to determine if the image is an image of different person (e.g., using deep face verification models, by comparing a fingerprint of the image submitted by the user…The user may also be instructed to submit a video (e.g., a file of a recorded video or a live streaming video) of the user reading a script provided by the system…The submitted videos may then be analyzed, as part of a verification process, to determine if the video includes a video of a person reading the script…the image of the face of the person in the video is compared with the image of the face in the still image/photograph submitted to be used as the profile image to ensure that the facial image in the video is of the same person as in the still image (e.g., using deep face verification models). If the facial images do not match, optionally the user may be prevented from establishing an account and/or user system services to communicate with other users…If the user's image meets the image criteria and the user and the user may enabled to use social networking and communication features, examples of which are described here…a match filter may be provided via which a user can request to only be shown matches with other users that have been verified using the facial image process” – therefore the system generates biometric fingerprints of a user from their verification video and compares it to other photos of the user to verify the person is real and is the same person, [0077] “the process may compare the face in the video recording with the face in one or more of the still photographs provided for the user profile and determine if the faces match (are of the same person). For example, a face may be detected and localized in a video frame, facial landmarks may be detected (e.g., eyes, nose, mouth), the face may be aligned (as may be the face in the still photograph), the pixel values of the face image in the video frame and the face image in the still photograph may be transformed into compact and discriminative feature vectors (sometimes referred to as a template), and at block 318 the still photograph and video templates may be compared to produce a similarity score that indicates the likelihood that the still photograph template and the video template belong to the same subject. Optionally, a trained neural network may be utilized to determine if the face in the video and the face in a still photograph are the same face” – the feature vectors representing the landmarks and pixel values of the user’s face are a biometric fingerprint of the user consistent with Applicant’s own specification at [0060] where it suggest a biometric fingerprint represents facial geometry, see also [0062]-[0063] “ a verification rule may specify that the face in the video must match a face in a still image submitted by the user (e.g., a photograph to be used as a profile photo). For example, the verification service 212A may include a deep neural network trained to identify matching faces…different nodal points on a human face identified in the video may normalized, and the distance between nodal points many be measured. Similarly, different nodal points on a human face identified in the still image may normalized, and the distance between nodal points many be measured. The nodal distances from the video and the still image may be compared, and a determination may be made whether there is a match (e.g., the distances are within a specified threshold of each other). If there is a match, this portion of the verification process may be satisfied. If there is not a match, this portion of the verification process may have failed, and the user may be inhibited from creating an account on the system 100 and/or utilizing certain system 100 resources” comparing biometric fingerprint of the first user from the video and images, [0011]-[0012] & [0125] video is part of user registration, [0022] “online social networking platforms (e.g., dating platforms) have outdated user profiles or profiles of users…certain user profiles contain false information (e.g., facial images, gender, age, interests, location, height, weight, religion, etc.) and are used to create a fictional online persona to fool other users and to lure other users into what would otherwise be unwanted online communications and potentially dangerous in-person meetings” & [0103] “Certain user interfaces may be presented via a dedicated social networking application (e.g., a dating application)” – therefore the verification video is added to a user profile of a dating service when they register for the dating service)
Hopkins suggests it is advantageous to include wherein the processor is further configured to generate a biometric fingerprint of the first user, at least in part based on a verification video of the first user, the registration from the first user includes the verification video, and the photo identification information includes the biometric fingerprint of the first user, because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service ([0022])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus and medium of Rana in view of Bhattacharya to include wherein the processor is further configured to generate a biometric fingerprint of the first user, at least in part based on a verification video of the first user, the registration from the first user includes the verification video, and the photo identification information includes the biometric fingerprint of the first user, as taught by Hopkins because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service.
With respect to claim 50, Rana and Bhattacharya teach the medium of claim 35. Although Rana suggests that a user profile may include video data (7:47-50), Rana does not appear to disclose,
wherein the registration from the first user includes a voice recording of the first user
However, Hopkins discloses
wherein the registration from the first user includes a voice recording of the first user. (“[0028]-[0035] “the image may also be analyzed to determine if the image is an image of different person (e.g., using deep face verification models, by comparing a fingerprint of the image submitted by the user…The user may also be instructed to submit a video (e.g., a file of a recorded video or a live streaming video) of the user reading a script provided by the system…my name is…The submitted videos may then be analyzed, as part of a verification process, to determine if the video includes a video of a person reading the script…the image of the face of the person in the video is compared with the image of the face in the still image/photograph submitted to be used as the profile image to ensure that the facial image in the video is of the same person as in the still image (e.g., using deep face verification models). If the facial images do not match, optionally the user may be prevented from establishing an account and/or user system services to communicate with other users…If the user's image meets the image criteria and the user and the user may enabled to use social networking and communication features, examples of which are described here…a match filter may be provided via which a user can request to only be shown matches with other users that have been verified using the facial image process” – therefore the registration from the first user includes a voice recording of the first user (e.g., video of the user reading a script), [0061] “a user attempting to establish an account or use certain system 100 services, may be required to record a video/audio of the user reading the script” [0011]-[0012] & [0125] video is part of user registration, [0103] “Certain user interfaces may be presented via a dedicated social networking application (e.g., a dating application)” – therefore the verification video is added to a user profile of a dating service when they register for the dating service)
Hopkins suggests it is advantageous to include wherein the registration from the first user includes a voice recording of the first user, because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service ([0022])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the medium of Rana in view of Bhattacharya to include wherein the registration from the first user includes a voice recording of the first user, as taught by Hopkins because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service.
v Claims 41, and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Rana in view of Bhattacharya, as applied to claim 21 above, and further in view of Reinisch et al. (U.S. PG Pub No. 2022/0198825 June 23, 2022- hereinafter "Reinisch”)
With respect to claim 41, Rana and Bhattacharya teach the method of claim 21.
Rana does not appear to disclose,
wherein the photo identification information includes detailed facial geometry
However, Reinisch discloses
wherein the photo identification information includes detailed facial geometry ([0025]-[0026] & [0052] & [0064] image recognition and/or biometric image processing involves determining detailed facial geometry and per [0072] & [0084] the system applies the detailed facial geometry for use applying image recognition to images being selected for profile pictures to validate these images are authentic (i.e., the photo identification information includes detailed facial geometry))
Reinisch suggests it is advantageous to include wherein the photo identification information includes detailed facial geometry, because doing so can help verify that the users of the dating service are real people and are really who they say they are, and that their profile pictures are real images of them which can increase safety and satisfaction with the dating service ([0022]-[0026] & [0034] & [0052] & [0064] & [0072] & [0084])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rana in view of Bhattacharya to include wherein the photo identification information includes detailed facial geometry, as taught by Reinisch because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service.
With respect to claim 43, Rana and Bhattacharya teach the method of claim 21. Rana further discloses
wherein the registration includes a photograph (6:15-18 “End user 12 can access website 22 via the communications network 14 (which in the example presented comprises the Internet) using endpoint 13, register, and create a profile on the site”, Fig 2e “upload photos”, 7:35-36 “System 10 may also 35 ask the user to upload one or more photos for the user profile")
Rana does not appear to disclose,
a verification photograph
and the photo identification information includes the verification photograph
However, Reinisch discloses
a verification photograph and the photo identification information includes the verification photograph ([0072] & [0084] the verification photograph submitted and verified and used for applying image recognition to images being selected for profile pictures to validate these images are authentic (i.e., the photo identification information includes the verification photograph))
Reinisch suggests it is advantageous to include a verification photograph and the photo identification information includes the verification photograph, because doing so can help verify that the users of the dating service are real people and are really who they say they are, and that their profile pictures are real images of them which can increase safety and satisfaction with the dating service ([0022]-[0026] & [0034] & [0052] & [0064] & [0072] & [0084])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rana in view of Bhattacharya to include a verification photograph and the photo identification information includes the verification photograph, as taught by Reinisch because doing so can help verify that the users of the dating service are real people and are really who they say they are, which can increase safety and satisfaction with the dating service.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
El Daher et al. (U.S. PG Pub No. 2015/0287146, October 8, 2015) teaches an online dating service where users can receive feedback on their dating profile and photos from their target match demographic.
Wolf et al. (U.S. PG Pub No. 2014/0006395, January 2, 2014) teaches an online dating service where users can receive feedback on their dating profile and photos from their target match demographic.
Futterman et al. (U.S. PG Pub No. 2023/0409631, December 21, 2023) teaches using AI/ML to determining which dating profile photo characteristics result in maximum interaction/likes and automatically analyzing a user’s photos to recommend a best photo that will maximize engagement.
Kuchka et al. (U.S. PG Pub No. 2020/0233854, July 23, 2020) teaches analyzing dating profiles to identify characteristics associated with successful people and recommending profile modifications (including photo recommendations) to users to help increase their matching success.
Ferdinand (U.S. PG Pub No. 2017/0109853 April 20, 2017) teaches users uploading a verification image/video and verifying the authenticity of the image/video and using this image/video in combination with facial recognition to ensure dating profile photos being uploaded/selected are authentic
“Choosing Good Dating Profile Pictures With Helpfull” (published online on March 26, 2023 at https://helpfull.com/feedback-types/dating-profile and retrieved using Internet Archive Wayback Machine on September 3, 2025) discloses determining attributes of performance images to produce photo identification information
“The dating app pictures that will get you the most right-swipes revealed” (Hoosie, Rachel; published April 21, 2017 at https://www.the-independent.com/life-style/love-sex/dating-app-hinge-pictures-most-right-swipes-men-women-love-relationships-a7694916.html - hereinafter "Hoosie”) discloses determining attributes of performance images to produce photo identification information
Conclusion
No claim is allowed
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M DETWEILER whose telephone number is (571)272-4704. The examiner can normally be reached on Monday-Friday from 8 AM to 5 PM ET.
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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621
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