DETAILED ACTION
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/09/2026 has been entered.
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 .
Examiner’s Comment
This Action is in response to the Request for Continued Examination filed on 04/09/2026 with Amended Claims and Applicant's Remarks filed on 03/12/2026.
Applicant has amended claims 21-27, 29-34, and 36-40 according to Amendments filed on 03/12/2026. Claims 21-40 are pending and currently under consideration for patentability.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 01/21/2026, 02/11/2026, 05/21/2026, 06/10/2026, and 06/25/2026 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.
Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 21-40 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 21-26 recite a method; claims 27-33 recite a computer-readable medium; and 34-40 recite a system. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 21-40 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 21, 27, and 34 recite limitations directed to the abstract idea including displaying an on-screen portion of a profile of a target in an online dating service to a seeker; determining that an interest indicated by the on-screen portion of the profile of the target is a potential interest of the seeker; determining an existing interest of the seeker from a profile of the seeker; determining that the potential interest is not the existing interest of the seeker; and adding the potential interest of the seeker to the profile of the seeker in the online dating service, at least in part based on an input from the seeker. These further limitations are not seen as any more than the judicial exception. Claims 21, 27, and 34 also recite additional limitations including “displaying an on-screen portion of a profile; and in an online dating service”. A method of adding potential interest of a seeker to a profile of the seeker based on input is considered to be an abstract idea, specifically, certain methods of organizing human activity; such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) because the claims are directed to managing interaction between people such as a target and seeker in order to determine interests and add said interests to a profile. Furthermore, the method of adding potential interest of a seeker to a profile of the seeker is considered to fall under another abstract idea, specifically, Mental Processes; such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to displaying data (i.e. profile of a target), determining data (i.e. interest of a target), determining data (i.e. existing interest of seeker), determining data (i.e. potential interest is not existing interest), and adding data to a profile (i.e. adding potential interest of seeker to profile of seeker). Therefore, under Step 2A, Prong I, claims 21, 27, and 34 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 21, 27, and 34 also recite additional limitations including “displaying an on-screen portion of a profile; and in an online dating service”. These additional limitations are seen as merely using an apparatus and computer-readable medium to describe the environment in which the abstract idea takes place which is seen as additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process. These are not found to integrate the judicial exception into a practical application because they are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f), adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g), and generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. on-screen portion of a profile and online dating service, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 21, 27, and 34 also recite additional limitations including “displaying an on-screen portion of a profile; and in an online dating service”. These additional limitations do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 21, 27, and 34 do not include additional elements or a combination of elements that result in the claims amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe the system as “Similarly, any of the potential processing elements, modules, and machines described in this Specification should be construed as encompassed within the broad term "processor."” (See ¶ [00173] of the Applicant’s specification) for implementing the apparatus and/or computer-readable medium, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 22-26, 28-33, and 34-40 further recite the method of claim 21, computer-readable medium of claim 27, and system of claim 34. Dependent claims 22-26, 28-33, and 34-40 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 21, 27, and 34. For example, claims 22-26, 28-33, and 34-40 describe the limitations for a method of adding potential interest of a seeker to a profile of the seeker – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 22-26, 28-33, and 34-40, there are no additional elements introduced. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication 2024/0370943 to Gulati in view of U.S. Publication 2021/0065314 to Storment.
Claims 21-26, 27-33, and 34-40 are method, computer-readable media, and system claims, respectively, with substantially indistinguishable features between each group. For purposes of compact prosecution, the Office has grouped the common method, system and non-transitory computer readable storage medium claims in applying applicable prior art.
With respect to Claim 21:
Gulati teaches:
A method, comprising: displaying an on-screen portion of a profile of a target in an online dating service to a seeker (i.e. displaying dater’s dating matches to dater) (Gulati: ¶ [0038] “In one embodiment, social media platform 208 may provide a dating service that enables users to view and/or connect with potential dating matches (i.e., recommended dating matches). The dating service may select potential dating matches for a user (e.g., algorithmically) using any type or form of recommended match selection system. In some examples, the dating service may enable a user to create a digital dating profile. The digital dating profile may be presented, via an interface of a dating application ( e.g., within a digital profile card), to additional users of the dating service for whom the user has been selected as a potential dating match. Additionally, the digital dating profiles of the additional users may be presented to the user (e.g., where each digital dating profile is presented in a different digital profile card).” Furthermore, as cited in Fig. 11 and ¶ [0060] “In some examples, a shareable dating profile creation process may enable user 206 to easily create shareable versions of user 206's dating profile that are configured for multiple different social media channels. FIG. 11 depicts an exemplary creation flow that may be initiated from a dating profile editor interface 1100 by selecting a share element 1102. A selection of share element 1102 via user input may surface a share interface 1104 that includes multiple different channels to which the shareable version of user 206's dating profile may be shared. A selection of a channel may be configured to trigger media module 212 to (1) generate a shareable version of user 206's dating profile that corresponds to the selected channel and/or (2) share the generated shareable version of user 206's dating profile to the selected channel.”);
determining that an interest indicated by the on-screen portion of the profile of the target is a potential interest of the seeker (i.e. determining an interest of the dating match is a common or potential interest of the dater) (Gulati: ¶ [0017] “In some examples, the dating-analytics dashboard may include a common interests section that shows interests of the dater that overlap with interests of the dater's dating matches ( e.g., 80% of accepted matches have cats as a common interest, 20% have surfacing as a common interest, etc.” Furthermore, as cited in ¶ [0047] “For example, media module 212 may (1) determine a percentage of user 206's dating matches (e.g., all matches recommended by social media platform 208, matches accepted by user 206, and/or mutual matches) that share a particular interest with user 206 and (2) provide the determined percentage as part of information 224 presented within dashboard 222 (e.g., within a common interests section such as common interests section 900 presented in FIG. 9).”);
determining an existing interest of the seeker from a profile of the seeker (i.e. determining interests of the user or the dater) (Gulati: ¶ [0017] “In some examples, the dating-analytics dashboard may include a common interests section that shows interests of the dater that overlap with interests of the dater's dating matches ( e.g., 80% of accepted matches have cats as a common interest, 20% have surfacing as a common interest, etc.” Furthermore, as cited in ¶ [0047] “For example, media module 212 may (1) determine a percentage of user 206's dating matches (e.g., all matches recommended by social media platform 208, matches accepted by user 206, and/or mutual matches) that share a particular interest with user 206 and (2) provide the determined percentage as part of information 224 presented within dashboard 222 (e.g., within a common interests section such as common interests section 900 presented in FIG. 9).”);
adding the potential interest of the seeker to the profile of the seeker in the online dating service, at least in part based on an input from the seeker (i.e. more interests are added to the user’s profile in order to reach a broader audience) (Gulati: Element 900 in Fig. 9 and ¶ [0047] “For example, media module 212 may (1) determine a percentage of user 206's dating matches (e.g., all matches recommended by social media platform 208, matches accepted by user 206, and/or mutual matches) that share a particular interest with user 206 and (2) provide the determined percentage as part of information 224 presented within dashboard 222 (e.g., within a common interests section such as common interests section 900 presented in FIG. 9).” Furthermore, as cited in Fig. 11 and ¶ [0060] “In some examples, a shareable dating profile creation process may enable user 206 to easily create shareable versions of user 206's dating profile that are configured for multiple different social media channels. FIG. 11 depicts an exemplary creation flow that may be initiated from a dating profile editor interface 1100 by selecting a share element 1102. A selection of share element 1102 via user input may surface a share interface 1104 that includes multiple different channels to which the shareable version of user 206's dating profile may be shared. A selection of a channel may be configured to trigger media module 212 to (1) generate a shareable version of user 206's dating profile that corresponds to the selected channel and/or (2) share the generated shareable version of user 206's dating profile to the selected channel.”).
Gulati does not explicitly disclose determining that the potential interest is not the existing interest of the seeker.
However, Storment further discloses determining that the potential interest is not the existing interest of the seeker (i.e. determining that the actual interest is not the current interest of the user based on other selected profiles and adding the actual interest to the user’s profile or curating user’s profile with actual interest) (Storment: ¶ [0019] “The neural network of the algorithm may be operable to determine the user's actual interest, continuously update the curated profile and generate a list of profiles for the users next session with the application that reflect the curated profile. Actual interest is what a user selects in comparison to what the user believes they want. The machine learning algorithm may further, retrieve the time, communication characteristics, and whether a link was requested. The computing system may further correlate the parameters of each data set with the decision made by the user in the profile. The correlations between the characteristics of different matches selected by the user may be stored as various similarity matrices. A Laplacian score method may be applied to the similarity matrix. A filter may be used to evaluate eigenmaps and local projection. A multi-cluster feature selection may be used to compare the various matrices and formulate a regression to determine a group of clusters in the data set. The group of clusters may be, for example, non-limiting, least to most interesting; In other embodiments, a unified model language can be determined and used for clustering the various data sets. A statistical analysis using a k-means clustering algorithm may be applied to the data set, and a root mean squared may be utilized to measure the accuracy of the regression and linearization of the data. The manipulated data may then determine the actual interest of the user more accurately than the preferences expressly entered by the user in their profile questionnaire, and a new set of parameters are incorporated with interest defined by the user, such parameters are unknown to the user to generate the curated profile. Additionally, the computing system may modify the interest defined by the user to further align with the actions a user has taken when selecting a profile.” Furthermore, as cited in ¶ [0069] “The communication characteristics function 2135 may evaluate communications between a first and second user, the information may be used to assist other functions and determine if a correlation is causation or coincidence. The clustering function 2136 may be operable to group various parameters to determine user interests simultaneously. The function re-sorts similarities and difference 2137 and may be operable to parse and linearize data from the clustering function 2136 and determine what combinations of profile characteristics are attractive to a user. The statistical analysis 2138 may perform various statistical calculations to measure the relevancy of each function performed by the machine-learning algorithm 2100. The comparison of pre-processing data and statistical analysis 2121 data will reveal what types of user-profiles a first user finds interesting. The function of determining interest with a large deviation for user interest 2122 measures if the changes are too significant from the user's current interest as indicated by the cotemporaneous selections made by the user and determine the efficiency of the machine-learning algorithm 2100. The function to update user interest 2123 creates a new set of parameters, the parameters are used in the function to group profiles of interest 2124, and the machine learning algorithm 2100 generates a new list 2125 for the group profiles of interest function 2124. Once a new list has been generated, the computing system may export the list to the application 2300 for viewing by the user.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Storment’s determining that the potential interest is not the existing interest of the seeker to Gulati’s adding the potential interest of the seeker to the profile of the seeker in the online dating service, at least in part based on an input from the seeker. One of ordinary skill in the art would have been motivated to do so in order to “determine the actual interest of the user more accurately than the preferences expressly entered by the user in their profile.” (Storment: ¶ [0019]).
With respect to Claims 27 and 34:
All limitations as recited have been analyzed and rejected to claim 21. Claim 27 recites “A non-transitory, computer-readable medium encoded with executable instructions that, when executed by a processing unit, perform operations comprising:” (Gulati: ¶ [0091]) the steps of method claim 21. Claim 34 recites “An apparatus, comprising: a memory that stores an instruction; and at least one processor configured to execute the instruction to cause the apparatus to at least cause” (Gulati: ¶ [0091]) the steps of method claim 21. Claims 27 and 34 do not teach or define any new limitations beyond claim 21. Therefore they are rejected under the same rationale.
With respect to Claim 22:
Gulati teaches:
The method of Claim 21, further comprising: prompting the seeker to confirm the potential interest (i.e. asking user if they would like to add the more interests such as dogs, concerts, food, surfing, etc.) (Gulati: Element 900 in Fig. 9 and ¶ [0047] “For example, media module 212 may (1) determine a percentage of user 206's dating matches (e.g., all matches recommended by social media platform 208, matches accepted by user 206, and/or mutual matches) that share a particular interest with user 206 and (2) provide the determined percentage as part of information 224 presented within dashboard 222 (e.g., within a common interests section such as common interests section 900 presented in FIG. 9).”).
With respect to Claims 28 and 35:
All limitations as recited have been analyzed and rejected to claim 22. Claims 28 and 35 do not teach or define any new limitations beyond claim 22. Therefore they are rejected under the same rationale.
With respect to Claim 23:
Gulati teaches:
The method of Claim 21, further comprising: determining the on-screen portion of the profile of the target (i.e. determining viewing pane of profile for match or target) (Gulati: ¶ [0028] “A newsfeed post may also display a text-based caption, metadata content ( e.g., content describing users that have been tagged in the newsfeed post, a timestamp, etc.), information indicating the source of the newsfeed post ( e.g., the name of the creator of the post, a profile image, etc.), and/or a digital special effect ( e.g., a digital sticker, a filter, an augmented reality element, etc.). Such information and/or features may be displayed (and/or a menu corresponding to such information and/or features may be displayed) within the viewing pane (e.g., over the primary content), within the viewing pane and/or may be visually associated with the viewing pane (e.g., displayed beneath the viewing pane).”); and
determining a selected portion of the profile of the target to determine the potential interest (i.e. determining user selected/accepted profile of match/target) (Gulati: ¶ [0058] “In one embodiment, the profile card may include a feature (e.g., such as the accepting or rejecting elements presenting in FIG. 8) that enables the additional user to accept or reject user 206 as a potential dating match. In one example, a selection of the link from an additional user who has a dating account with dating application 210 may additionally trigger a profile card of the additional user to be added to a queue of recommended dating matches presented to user 206.”).
With respect to Claims 29 and 36:
All limitations as recited have been analyzed and rejected to claim 23. Claims 29 and 36 do not teach or define any new limitations beyond claim 23. Therefore they are rejected under the same rationale.
With respect to Claim 24:
Gulati teaches:
The method of Claim 21, further comprising: determining a previous, off-screen portion of the profile of the target to determine a potential interest of the seeker (Examiner notes that “off-screen” is described in the Applicant’s specification in ¶¶ [00113]-[00115] as portions of the profile that have not been viewed yet or at all) (i.e. determining interests such as overlapping location, mutual friends, common age range, which are off-screen portions of the profile that are not viewable from the profile card) (Gulati: Figs. 8 and 9 and ¶ [0048] “As another example, media module 212 may (1) identify locations and/or ages associated with user 206's dating matches (e.g., all matches recommended by social media platform 208, matches accepted by user 206, and/or mutual matches) and (2) provide the locations within a matches-locations summary, and/or the ages within a matches-ages summary, as part of information 224 provided within dashboard 222 (e.g., within a locations summary section such as location summary section 902 in FIG. 9 and/or an age summary section such as age summary section 904 in FIG. 9.”).
With respect to Claims 30 and 37:
All limitations as recited have been analyzed and rejected to claim 24. Claims 30 and 37 do not teach or define any new limitations beyond claim 24. Therefore they are rejected under the same rationale.
With respect to Claim 25:
Gulati does not explicitly disclose the method of Claim 21, further comprising: performing image recognition on an image displayed in the on-screen portion of the profile of the target to determine a potential interest of the seeker.
However, Storment further discloses performing image recognition on an image displayed in the on-screen portion of the profile of the target to determine a potential interest of the seeker (i.e. applying image recognition to a plurality of images or on-screen portions of selected second profiles to determine visual features or characteristics or interests of the user in order to curate the profile or add interests) (Storment: ¶ [0018] “Accordingly, it is an object of the present invention to provide a machine learning algorithm operable for importing user characteristics, and compare and sort similarities and differences from the profiles selected by the user, the computing system may be operable to analyze a profile image data and determine through image recognition and image transformation. Image recognition algorithms through machine learning are capable of determining the background space and setting of the profile image, where image transformation algorithms may through machine learning be capable of determining the angle of the image. The machine-learning algorithm of the image transformation may be of the supervised type, configured in a nonlimiting fashion, as a convolutional neural network for image recognition, steps include a first locally trained filter set to extract visual features over the input of the image, testing using a control data set, and exporting of image data removed to isolate visual features. The trained filter may include a plurality of various facial features types, body types, and human attributes. The control data set is processed by the computing system and checked to ensure the computing system has accurately interpreted the data set. The computing system isolates the human image and exports the human image for image transformation, a background of the image may be analyzed to determine image characteristics, where image characteristics may include, hue, saturation, and value; the image analysis may further, determine the ratio of characteristics, the analysis may then be operable store the results of the image analysis. Further, the image transformation may use methods of position determination and analyze the real-estate of facial features absorbed in the pixels plane of the image; the features may then be categorized and stored with the data collected from the image recognition process. The image data may be utilized in developing a user's curated profile over time, correlated the features of the images that a consistent among selected profiles.” Furthermore, as cited in ¶ [0069] “The time characteristics 2133 function may be operable to determine correlations of a user success when selecting a match, and create new parameters for the machine learning algorithm 2100 to use when generating a list of potential matches. The evaluation of image characteristics 2134 function may be capable of determining the types of profile images a user may select for matching, and create a new set of visual parameters. Such parameters may include skin tone, hair type, hair length, ear size, eyebrow shape, facial hair, the shape of the face, angle of a photo, setting of a photo, colors in the photo, facial expression, and other features. The communication characteristics function 2135 may evaluate communications between a first and second user, the information may be used to assist other functions and determine if a correlation is causation or coincidence. The clustering function 2136 may be operable to group various parameters to determine user interests simultaneously. The function re-sorts similarities and difference 2137 and may be operable to parse and linearize data from the clustering function 2136 and determine what combinations of profile characteristics are attractive to a user. The statistical analysis 2138 may perform various statistical calculations to measure the relevancy of each function performed by the machine-learning algorithm 2100. The comparison of pre-processing data and statistical analysis 2121 data will reveal what types of user-profiles a first user finds interesting. The function of determining interest with a large deviation for user interest 2122 measures if the changes are too significant from the user's current interest as indicated by the cotemporaneous selections made by the user and determine the efficiency of the machine-learning algorithm 2100. The function to update user interest 2123 creates a new set of parameters, the parameters are used in the function to group profiles of interest 2124, and the machine learning algorithm 2100 generates a new list 2125 for the group profiles of interest function 2124.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Storment’s performing image recognition on an image displayed in the on-screen portion of the profile of the target to determine a potential interest of the seeker to Gulati’s adding the potential interest of the seeker to the profile of the seeker in the online dating service, at least in part based on an input from the seeker. One of ordinary skill in the art would have been motivated to do so in order to “determine the actual interest of the user more accurately than the preferences expressly entered by the user in their profile.” (Storment: ¶ [0019]).
With respect to Claims 31 and 38:
All limitations as recited have been analyzed and rejected to claim 25. Claims 31 and 38 do not teach or define any new limitations beyond claim 25. Therefore they are rejected under the same rationale.
With respect to Claim 26:
Gulati teaches:
The method of Claim 21, further comprising: determining an unseen, off-screen portion of the profile of the target to determine a potential interest of the seeker (Examiner notes that “off-screen” is described in the Applicant’s specification in ¶¶ [00113]-[00115] as portions of the profile that have not been viewed yet or at all) (i.e. determining interests such as overlapping location, mutual friends, common age range, which are off-screen portions of the profile that are not viewable from the profile card) (Gulati: Figs. 8 and 9 and ¶ [0048] “As another example, media module 212 may (1) identify locations and/or ages associated with user 206's dating matches (e.g., all matches recommended by social media platform 208, matches accepted by user 206, and/or mutual matches) and (2) provide the locations within a matches-locations summary, and/or the ages within a matches-ages summary, as part of information 224 provided within dashboard 222 (e.g., within a locations summary section such as location summary section 902 in FIG. 9 and/or an age summary section such as age summary section 904 in FIG. 9.”).
With respect to Claims 32 and 39:
All limitations as recited have been analyzed and rejected to claim 26. Claims 32 and 39 do not teach or define any new limitations beyond claim 26. Therefore they are rejected under the same rationale.
With respect to Claims 33 and 40:
All limitations as recited have been analyzed and rejected to claim 21. Claims 33 and 40 do not teach or define any new limitations beyond claim 21. Therefore they are rejected under the same rationale.
Response to Arguments
Applicant’s arguments see pages 9-11 of the Remarks disclosed, filed on 03/12/2026, with respect to the 35 U.S.C. § 101 rejection(s) of claim(s) 21-40 have been considered but are not persuasive. The Applicant asserts “Indeed, "Adding interests to a user's profile can be a burdensome part of the onboarding process." Id., para. [0092]. Thus, according to Applicant's specification, "Some implementations of the algorithm 500 allow for interests to be added to a user's profile while the user primarily is viewing profiles , rather than completing the onboarding process." Id… Accordingly, independent Claim 21 includes features that reflect the improvement identified in the specification. Consistent with the Office policy announced in Ex Parte Desjardins, independent Claim 21 and all associated dependent claims are therefore directed to eligible subject matter. Appeal No. 2024-000567 (PTAB Sept. 26, 2025, Appeals Review Panel Decision) (designated precedential Nov. 4, 2025).” The Examiner respectfully disagrees. Merely automating the manual process of creating a profile by adding/recommending interests for the user is not an improvement to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a). Furthermore, Claims 21, 27, and 34 recite limitations directed to the abstract idea including displaying an on-screen portion of a profile of a target in an online dating service to a seeker; determining that an interest indicated by the on-screen portion of the profile of the target is a potential interest of the seeker; determining an existing interest of the seeker from a profile of the seeker; determining that the potential interest is not the existing interest of the seeker; and adding the potential interest of the seeker to the profile of the seeker in the online dating service, at least in part based on an input from the seeker. These further limitations are not seen as any more than the judicial exception. Claims 21, 27, and 34 also recite additional limitations including “displaying an on-screen portion of a profile; and in an online dating service”. A method of adding potential interest of a seeker to a profile of the seeker based on input is considered to be an abstract idea, specifically, certain methods of organizing human activity; such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) because the claims are directed to managing interaction between people such as a target and seeker in order to determine interests and add said interests to a profile. Furthermore, the method of adding potential interest of a seeker to a profile of the seeker is considered to fall under another abstract idea, specifically, Mental Processes; such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to displaying data (i.e. profile of a target), determining data (i.e. interest of a target), determining data (i.e. existing interest of seeker), determining data (i.e. potential interest is not existing interest), and adding data to a profile (i.e. adding potential interest of seeker to profile of seeker). Therefore, the rejection(s) of claim(s) 21-40 under 35 U.S.C. § 101 is maintained above with an updated analysis.
Applicant’s arguments see pages 11-12 of the Remarks disclosed, filed on 03/12/2026, with respect to the 35 U.S.C. § 102(a)(1) rejection(s) of claim(s) 21-24, 26-30, 32-37, and 39-40 over Gulati have been considered but are moot because the arguments do not apply to the new ground(s) of rejection is made in view of U.S. Publication 2021/0065314 to Storment. Examiner notes that the Applicant has amended the claims to clarify “potential interest”, however, the “common interest” recited in Gulati reads on potential interest because this common interest is also a potential interest of the seeker/dater. Furthermore, Gulati does not explicitly teach that the potential interest is not the existing interest of the seeker/dater. Storment discloses this in at least ¶¶ [0019] [0069]; determining that the actual interest is not the current interest of the user based on other selected profiles and adding the actual interest to the user’s profile or curating user’s profile with actual interest (See pages 9-11 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The following reference are cited to further show the state of the art:
U.S. Publication 2011/0072085 to Standley for disclosing A computer-implemented method of and system for matching a first and a second different independently-created descriptions of a shared event involving at least two persons is described. The method comprises: receiving each of the first and second descriptions of the shared event respectively from each of a first and a second user's communications device; generating a first set of descriptive variables from the first received description and a second set of descriptive variables from the second received description, the first and second set of variables including the location of the event, the time/date of the event and at least one variable describing the appearance of at least one of the at least two persons; determining a matching value for each variable common to both the first and second sets; using the matching values of the first and second sets to establish the likelihood of whether the first and second descriptions describe the same event; and sending the results of the using step to each of the first user's communication device and the second user's communications device.
U.S. Publication 2007/0220045 to Morris for disclosing A media discovery module (MDM) is described that facilitates a user's access to desired media items. The MDM presents a series of arrays of media items to the user. The user's selection within any array governs the composition of media items that are presented in a subsequent array. Different linking criteria can define the relationship among arrays. In one case, the MDM uses a time-based linking criterion, allowing a user to examine a media item at increasing levels of temporal granularity. In another case, the MDM uses a subject matter-based criterion, allowing a user to examine one or more media items at increasing levels of detail with respect to a subject matter index. The MDM can employ various tools to establish the relationship among media items, including image analysis tools, audio analysis tools, metadata analysis tools, and so on.
U.S. Publication 2022/0261853 to Publicover for disclosing Targeted Content solutions can be provided using a variety of techniques. Targeted Content can be provided in place of generic advertisements on a first device or on personal computing devices. Targeted Content can be presented during, or in place of, generic advertisements in Content (e.g., television content, streaming content, etc.). Targeted Content can be provided in individual and/or group environments. In a group environment, Users and/or Devices can be grouped into a shared advertising group and Targeted Content can be selected based on Profiles of one or more members of the group. Feedback can be received regarding Targeted Content and payout amount can be determined.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner's supervisor, Waseem Ashraf, can be reached at (571) 270-3948.
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Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
June 26, 2026
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