CTNF 19/217,431 CTNF 83234 DETAILED ACTION Application No. 19/217,431 filed on 05/23/2025 has been examined. In this Office Action, claims 1-20 are pending. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-20 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below: At Step 1 : Regarding with independent claims 1, 10, 16, it recites receiving a set of attributes associated with a user, wherein the set of attributes are indicative of user interests based on content consumption of the user; generating, via a machine learning model, tailored content comprising a visual representation of a set of content characteristics determined based on the set of attributes, wherein the machine learning model utilizes training data comprising content items of the set of content characteristics; and displaying, by a user interface, the tailored content tailored to the user. At Step 2A, Prong One : The limitation of “receiving a set of attributes associated with a user, wherein the set of attributes are indicative of user interests based on content consumption of the user” recites a mental process because human mind can receive attributes associated with a user by observation, evaluation and judgment. The “wherein” limitation only describes that the set of attributes associated with a user reflects the user’s interests and does not recite any other functionalities. Therefore, the limitation recites a mental process. The limitation of “generating, via a machine learning model, tailored content comprising a visual representation of a set of content characteristics determined based on the set of attributes, wherein the machine learning model utilizes training data comprising content items of the set of content characteristics” recites a mental process because it involves generating tailored content based on the set of attributes through observation, evaluation and judgment. A person can observe a set of attributes, evaluate those attributes to identify relevant content characteristics and use judgment to create tailored content including a visual representation that reflects those characteristics. The “wherein” limitation only describes that the model utilizes training data and does not recite any other functionalities. Therefore, the limitation recites a mental process. At Step 2A, Prong Two: The claims recite additional elements of " one or more processors; and at least one memory storing instructions, that when executed by the one or more processors", “A non-transitory computer readable medium storing instructions” to perform steps. However, the limitations merely amount to 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, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology. The limitation of “displaying, by a user interface, the tailored content tailored to the user” is insignificant extra-solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g). Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. At Step 2B: The conclusions for the mere implementation using a computer are carried over and does not provide significantly more. “displaying, by a user interface, the tailored content tailored to the user” is well-known, routine and conventional activities (WURC) as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9". Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101. In regards with claims 2, 4, 14, 17, “wherein the displaying comprises displaying, by the user interface, the tailored content in a content feed associated with the user (claim 2)” and “generating a second tailored content item in response to user input by the user interface, wherein the second tailored content item comprises at least one variation of the tailored content (claims 4, 14, 17)” is a recitation of an insignificant extra-solution activity. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not a mount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) See MPEP 2106.05 (g) examples of activities that the courts have found to be insignificant extra solution activity, Mere Data Gathering: Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754, receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9". The addition of insignificant extra-solution activity which does not a mount to an inventive concept. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. In regards with claims 3, 13, 20, wherein prior to the generating the tailored content: training the machine learning model with the training data based on the set of content characteristics, wherein the set of content characteristics define one or more different characteristics of the visual representation, as drafted, is a process that, under its broadest reasonable interpretation covers the process of process of mental processes. The claim has not added any additional elements that could integrate the judicial exception into a practical application or provide significantly more than the abstract idea. In regards with claims 5, 15, 18, providing a selection within, or associated with, the content feed to update the tailored content; and updating the tailored content by including a new content characteristic in the visual representation, in response to detecting an initiation of the selection, as drafted, is a process that, under its broadest reasonable interpretation covers the process of process of mental processes. The claim has not added any additional elements that could integrate the judicial exception into a practical application or provide significantly more than the abstract idea. In regards with claim 6, wherein the selection provides at least one of a prompt indicating at least one modification to the tailored content, a box to receive information, input received by the user interface, or content describing the at least one modification of the tailored content is a recitation of an insignificant extra-solution activity. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not a mount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) See MPEP 2106.05 (g) examples of activities that the courts have found to be insignificant extra solution activity, Mere Data Gathering: Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754, receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9". The addition of insignificant extra-solution activity which does not a mount to an inventive concept. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. In regards with claim 7, defining the set of content characteristics based on determining a location of the user, as drafted, is a process that, under its broadest reasonable interpretation covers the process of process of mental processes. The claim has not added any additional elements that could integrate the judicial exception into a practical application or provide significantly more than the abstract idea. In regards with claims 8-9, 12, 19, wherein the set of content characteristics comprises one or more of a time period, a place, an aesthetic feature, an interest, a character, or a cultural moment and wherein the visual representation comprises at least one of an image, a video, a color scheme, or a social media post, as drafted, is a process that, under its broadest reasonable interpretation covers the process of process of mental processes. The claim has not added any additional elements that could integrate the judicial exception into a practical application or provide significantly more than the abstract idea. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramesh et al (US 2024/0212716 A1) in view of Cassal et al (US 2025/0291612 A1, which claiming the priority of provisional application no: 63/564840, filed on Mar.13, 2024) . As per claim 1, Ramesh teaches a method comprising: receiving a set of attributes associated with a user, wherein the set of attributes are indicative of user interests based on content consumption of the user ([0040]-[0041], e.g., disclose wherein obtaining the set of user attributes can involve receiving a content consumption history of the user and having received the content consumption history, the structured data collector can analyze the content consumption history to determine one or more user attributes to include in the set of user attributes); Ramesh does not explicitly teach generating, via a machine learning model, tailored content comprising a visual representation of a set of content characteristics determined based on the set of attributes, wherein the machine learning model utilizes training data comprising content items of the set of content characteristics; and displaying, by a user interface, the tailored content tailored to the user. However, Cassal teaches generating, via a machine learning model ([0080]-[0082], e.g., describe that the Al UI create component uses a machine learning model that is trained based on prior user interactions, content, and feedback), tailored content ([0071], explains that the system personalizes or tailored content based on predicted user interests and behaviors) comprising a visual representation of a set of content characteristics determined based on the set of attributes ([0060]-[0068], e.g., explain that the generated content includes interface elements and media content presented visually in the UI, determines multiple characteristics used to generate content, including demographic content, user interests and determining demographic attributes and signals associated with user activity and device/ application usage, determining user attributes (e.g., demographic information, user profile data) and using those attributes along with activity signals to drive personalization), wherein the machine learning model utilizes training data comprising content items of the set of content characteristics ([0080]-[0082], e.g., disclose that the machine learning model is trained using training data that includes content data, prior generated UI outputs, and user interaction data. This training data inherently includes content items and associated characteristics used by the model to learn how to generate personalized UI content); and displaying, by a user interface, the tailored content tailored to the user ([0071]-[0074], explain that the generated UI is personalized and tailored to the specific user based on predicted interests and behaviors and [0077], describe displaying this tailored content while the user interacts with the interface). Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Cassal with the teachings of Ramesh in order to enabling a system to facilitates efficient and reliable mechanisms that provide personalized user interfaces that is tailored to particular or specific users based on attributes of a user (Cassal). As per claim 2, wherein the displaying comprises displaying, by the user interface, the tailored content in a content feed associated with the user (figs.4A, 5A and 5B show a social network feed with multiple posts arranged in a scrollable interface for a specific user and [0066], describe presenting tailored content within user interface, Cassal). As per claim 3, wherein prior to the generating the tailored content, the method further comprises: training the machine learning model with the training data based on the set of content characteristics, wherein the set of content characteristics define one or more different characteristics of the visual representation ([0064]-[0064], describe user attributes, demographic content and activity signals that define characteristics and ([0080]-[0082], e.g., disclose that the machine learning model is trained using training data that includes content data, prior generated UI outputs, and user interaction data. This training data inherently includes content items and associated characteristics used by the model to learn how to generate personalized UI content, Cassal). As per claim 4, further comprising: generating a second tailored content item in response to user input by the user interface, wherein the second tailored content item comprises at least one variation of the tailored content ([0071]-[0077], disclose wherein generates or updates content items as the user interacts with the interface and wherein that content may be rearranged, modified, or replaced based on user behavior, which constitutes a variation of previously generated tailored content, Cassal). As per claim 5, further comprising: providing a selection within, or associated with, the content feed to update the tailored content ([0075]-[0077], disclose wherein modifying displayed content based on user interaction with interface elements); and updating the tailored content by including a new content characteristic in the visual representation, in response to detecting an initiation of the selection ([0075]-[0077], disclose modifying displayed content based on user interaction with interface elements and [0060]-(0064) and [0076] indicate user-selectable UI controls allow interaction, and the system updates content by incorporating new user inputs/signals(new characteristics) into the generated output, Cassal). As per claim 6, wherein the selection provides at least one of a prompt indicating at least one modification to the tailored content, a box to receive information input received by the user interface, or content describing the at least one modification of the tailored content (figs. 4A-4B show UI elements such as “what’s on your mind?” input filed and other interactive elements, the input filed shown in the figures is text box for receiving user input and [0076]-[0077] show modifies or updates content based on the user provided input and interactions, Cassal). As per claim 7, further comprising: defining the set of content characteristics based on determining a location of the user ([0049], e.g., utilize received/detected demographic data to determine one or more attributes of a user(s) wherein demographic data include a location of a user and [0060]-[0063], e.g., explain that demographic content and device signals are used to determine inputs for personalization. The location is a standard subset of such demographic/contextual attributes used to define characteristics of generated content, Cassal). As per claim 8, wherein the set of content characteristics comprises one or more of a time period, a place, an aesthetic feature, an interest, a character, or a cultural moment ([0060], discloses demographic and contextual user information, which includes location/place-based attributes, Cassal). As per claim 9, wherein the visual representation comprises at least one of an image, a video, a color scheme, or a social media post (Figs. 4A-5C and [0065], show posts containing images and media content including a video displayed in the feed, Cassal). Regarding claims 10, 16, claims 10, 16 are rejected for substantially the same reason as claim 1 above. As per claim 11, wherein the content consumption is associated with social media activity of the user ([0064], e.g., frequently post in photos and reels (e.g., short-form videos) associated with the social network, the AI UI create component may also determine based on the user activity data that the user in this example may have high engagement with posts of other users, Cassal). Regarding claims 13, 20, claims 13, 20 are rejected for substantially the same reason as claim 3 above. Regarding claims 12, 19, claims 12, 19 are rejected for substantially the same reason as claims 8-9 above. Regarding claims 14, 17, claims 14, 17 are rejected for substantially the same reason as claim 4 above. Regarding claims 15, 18, claims 15, 18 are rejected for substantially the same reason as claim 5 above. It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any wav. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]] . Citation of Pertinent Prior Arts The prior art made of record and not relied upon in form PTO-892, if any, is considered pertinent to applicant's disclosure. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohammad A Sana whose telephone number is (571)270-1753. The examiner can normally be reached Monday-Friday 9-5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at 5712724098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Mohammad A Sana/Primary Examiner, Art Unit 2166 Application/Control Number: 19/217,431 Page 2 Art Unit: 2166 Application/Control Number: 19/217,431 Page 3 Art Unit: 2166 Application/Control Number: 19/217,431 Page 4 Art Unit: 2166 Application/Control Number: 19/217,431 Page 5 Art Unit: 2166 Application/Control Number: 19/217,431 Page 6 Art Unit: 2166 Application/Control Number: 19/217,431 Page 7 Art Unit: 2166 Application/Control Number: 19/217,431 Page 8 Art Unit: 2166 Application/Control Number: 19/217,431 Page 9 Art Unit: 2166 Application/Control Number: 19/217,431 Page 10 Art Unit: 2166 Application/Control Number: 19/217,431 Page 11 Art Unit: 2166 Application/Control Number: 19/217,431 Page 12 Art Unit: 2166 Application/Control Number: 19/217,431 Page 13 Art Unit: 2166
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