CTNF 18/535,551 CTNF 83114 DETAILED ACTION 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. Claims 1-20 are presented and claims 1-20 are pending. Drawings The drawings received on 11 December 2023 are accepted by the Examiner. Priority Application 18535551 has PRO 63/386,916 12/11/2022. This Office Action is Non-Final. Information Disclosure Statement The information disclosure statement (IDS) submitted on May 20, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements has been considered by the examiner. Claims rejection 35 U.S.C. 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. Step 1 (All Claims) According to the first part of the analysis, in the instant case, claims 1-17 are directed to system; claims 18-19 are directed to method. Claim 20 is directed to non-transitory medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A, Prong 1 (Claim 1, 18 and 20) Regarding representative claim 1, the following limitation is an abstract idea: Exemplary claim 1 recites “deriving, via a client device, a date, or a combination thereof, a music recommendation, a sound recommendation, or a combination thereof, for the selection of the photographic filter or the virtual lens". The limitation is merely associating data to photographic filter, which can be mentally performed. The limitation of “deriving, via a model, a date” under its broadest reasonable interpretation and absent any further technical detail, the limitation “via a model” does not required any particular sequence or complexity of operations and therefore the broadest reasonable interpretation of this limitation places it within the capability of human mind. Step 2A Prong 2 (Claims 1, 18, and 20) Claim 1 recites the additional elements of “one or more hardware processors” and “at least one memory”. These are high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. The limitations of “receiving, via a client device, a selection of a photographic filter or a virtual lens”; “providing the music recommendation, the sound recommendation, or the combination thereof, to the client device” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Step 2B: With respect to the all additional limitations above are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);"; MEPE 2106.05(d)(iv). Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;MPEP 2106.05(f) apply mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. The dependent claims have been considered and do not appear to provide additional requirements that overcome the claims from being abstract or providing additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding claim 2 limitation “a machine learning model”. This limitation under its broadest reasonable interpretation is recited as at a high level of generality. Note that absent any further technical detail, the limitation “a machine learning model” does not required any particular sequence or complexity of operations and therefore the broadest reasonable interpretation of this limitation places it within the capability of human mind. The judicial exception is not integrated into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application Regarding claim 3 limitation “training the machine learning model on a training data set”. This limitation under its broadest reasonable interpretation is recited as at a high level of generality. Note that absent any further technical detail, the limitation “a machine learning model” does not required any particular sequence or complexity of operations and therefore the broadest reasonable interpretation of this limitation places it within the capability of human mind. The judicial exception is not integrated into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application Regarding claims 4-8 limitation “training data set”. This limitation under its broadest reasonable interpretation is recited as at a high level of generality for example as a general way the human mind can determine date. Note that absent any further technical detail, the limitation “training data set” does not required any particular sequence or complexity of operations and therefore the broadest reasonable interpretation of this limitation places it within the capability of human mind. The judicial exception is not integrated into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Regarding claim 9 limitation “linear regression model configured to apply a linear regression derivation or a probability-based model configured to apply a statistical probability derivation to derive the music recommendation” under its broadest reasonable interpretation is recited as at a high level of generality for example as a general way the human mind can think of the music recommendation. Note that absent any further technical detail, the limitation “training data set” does not required any particular sequence or complexity of operations and therefore the broadest reasonable interpretation of this limitation places it within the capability of human mind. The judicial exception is not integrated into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Regarding claim 10 limitation the additional element of “executing a first query to determine” is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);"; MEPE 2106.05(d)(iv). Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;MPEP 2106.05(f) apply mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. Regarding claim 11-12 limitation “deriving via the date the music recommendation” under its broadest reasonable interpretation and absent any further technical detail, the limitation “via a model” does not required any particular sequence or complexity of operations and therefore the broadest reasonable interpretation of this limitation places it within the capability of human mind. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. Regarding claim 13 limitation “wherein the media interrelationship recommendation query” the additional element of “media interrelationship recommendation query” is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);"; MEPE 2106.05(d)(iv). Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;MPEP 2106.05(f) apply mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. Regarding claim 14 limitation “the photographic filter is configured to position a media overlay on an image captured by a camera system” is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);"; MEPE 2106.05(d)(iv). Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;MPEP 2106.05(f) apply mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. Regarding claim 15 limitation “the virtual lens is configured to position an augmented reality (AR) content on an image captured by a camera system” is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);"; MEPE 2106.05(d)(iv). Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;MPEP 2106.05(f) apply mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. Regarding claims 16-17 limitations “wherein the client device is configured to display a graphical user interface providing a carousel control for the selection of the photographic filter or of the virtual lens”; “wherein the carousel control comprises an icon comprising a visual representation of the photographic filter” are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);"; MEPE 2106.05(d)(iv). Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;MPEP 2106.05(f) apply mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function. Therefore under step 2B remains insignificant extra-solution activity that does not provide significantly more. Looking at the claim as a whole does not change this conclusion and the claim is ineligible. Claims rejection 35 U.S.C. 103 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. Claims 1-3, 14-18 and 20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over White et al. (US 20210049529 A1) in view of Boyd et al. (US 20220094653 A1). Regarding claims 1, 18 and 20 White discloses a system, comprising: one or more hardware processors; and at least one memory storing instructions that cause the one or more hardware processors to perform operations (see White paragraph [0024]\, The processing device 128 may correspond to a device including a processor and memory that executes or otherwise runs an application to perform one or more steps as described herein) comprising: receiving, via a client device, a selection of a photographic filter or a virtual lens ( see White paragraph [0040], an application a user is interacting with may include a “capture task” functionality to acquire an image via a camera, the device's camera, or in another manner. At 806 , the image may be processed and/or filtered to enhance the ability of task extraction and retrieval process) ; deriving, via a model, a date, or a combination thereof… (see White paragraph [0040], an application a user is interacting with may include a “capture task” functionality to acquire an image via a camera, the device's camera, or in another manner. At 806 , the image may be processed and/or filtered to enhance the ability of task extraction and retrieval process; The method 800 may proceed to 808 where contextual information may be extracted from the image. As previously discussed, the contextual information may correspond to, but is not limited to, a medium on which text is written, a type of medium, a color of ink that is used, a person associated with the image, a person in the image, a positional layout of text in the image, and delineation between different portions of text such that task and subtask identification may be more easily achieved. At 810 , contextual information retrieved from the image may be associated with a user and/or other entity. As one example, the contextual information may be associated with a building, a room in a building, an individual, and user account, a user's identify, an user's schedule, and other user information. Such information may be specific to the user (e.g., a user capturing an image of a couple of sticky notes in the user's office) while other information may be associated with or otherwise correspond to multiple people if a large whiteboard meant to be shared). White discloses , see White paragraph [0045], for a user to add more tasks, the user may simply select or otherwise activate the button 1116 causing an application associated with the graphical user interface to interact with a camera or microphone for example. As further depicted in FIG. 11, the tasks 1104 and 1108 generally correspond to the tasks discussed with respect to FIG. 2. Thus, the tasks displayed in FIG. 11 correspond to tasks that have been previously extracted. A user may be able to change a task name 1111 and/or other parameters of task. Boyd expressly discloses deriving, via a client device, a… or a combination thereof, a music recommendation, a sound recommendation, or a combination thereof, for the selection of the photographic filter or the virtual lens (see Boyd paragraph [0015], providing a system that allows a user to associate a sound or audio clip with an image that is shared with other users. When the receiving users access the image or images with which the sound has been associated, the recipients are presented with the images while the associated sound is played back) ; and providing the music recommendation, the sound recommendation, or the combination thereof, to the client device (see Boyd paragraph [0015], Once the user is satisfied with the sound or audio clip that has been selected, the user can confirm association of the sound with the image and a graphical element is embedded in the image to visually alert the user that a sound has been associated with the image). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the method of White to have deriving, via a client device, a… or a combination thereof, a music recommendation, a sound recommendation. Here, combining Boyd with White , which are both related to content processing improves White by reducing the number of screens and interfaces a user has to navigate through to find a graphical element to share with other users and sounds that are played with the graphical elements (see Boyd paragraph [0017]) . Regarding claims 2 and 19, White discloses wherein the model comprises a machine learning model (see White paragraph [0033], The context extractor 316 and/or the task identification/formatting engine 320 may utilize one or more machine learning techniques to and/or one or more template matching techniques to determine which elements 504 - 532 may be linked to one another). Regarding claim 3, White discloses wherein the instructions comprise instructions that cause the one or more hardware processors to perform operations comprising training the machine learning model on a training data set (see White paragraph [0033], The context extractor 316 and/or the task identification/formatting engine 320 may utilize one or more machine learning techniques to and/or one or more template matching techniques to determine which elements 504 - 532 may be linked to one another) . Regarding claim 14, White discloses, wherein the photographic filter is configured to position a media overlay on an image captured by a camera system (see Boyd paragraph [036], enable a user to augment (e.g., annotate or otherwise modify or edit) media content associated with a message. For example, the augmentation system 208 provides functions related to the generation and publishing of media overlays for messages processed by the messaging system 100 . The augmentation system 208 operatively supplies a media overlay or augmentation (e.g., an image filter) to the messaging client 104 based on a geolocation of the client device 102 ). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the method of White to have de wherein the photographic filter is configured to position a media overlay on an image captured by a camera system. Here, combining Boyd with White , which are both related to content processing improves White by reducing the number of screens and interfaces a user has to navigate through to find a graphical element to share with other users and sounds that are played with the graphical elements (see Boyd paragraph [0017]) . Regarding claim 15, Boyd discloses, wherein the virtual lens is configured to position an augmented reality (AR) content on an image captured by a camera system (see Boyd paragraph [0076],an augmented reality content item may be a real-time special effect and sound that may be added to an image or a video). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the method of White to have the virtual lens is configured to position an augmented reality (AR) content on an image captured by a camera system. Here, combining Boyd with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Boyd paragraph [0001]) . Regarding claim 16, Boyd discloses, wherein the client device is configured to display a graphical user interface providing a carousel control for the selection of the photographic filter or of the virtual lens (see Boyd paragraph [0036], A media overlay may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo) at the client device 102 . For example, the media overlay may include text or image that can be overlaid on top of a photograph taken by the client device 102 . In another example, the media overlay includes an identification of a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In another example, the augmentation system 208 uses the geolocation of the client device 102 to identify a media overlay). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the method of White to have the virtual lens is configured to position an augmented reality (AR) content on an image captured by a camera system. Here, combining Boyd with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Boyd paragraph [0001]) . Regarding claim 17, Boyd discloses, wherein the carousel control comprises an icon comprising a visual representation of the photographic filter or of the virtual filter, and wherein the music recommendation, the sound recommendation, or the combination thereof, is displayed alongside the icon ( see Boyd paragraph [0036], A media overlay may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo) at the client device 102 . For example, the media overlay may include text or image that can be overlaid on top of a photograph taken by the client device 102 . In another example, the media overlay includes an identification of a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In another example, the augmentation system 208 uses the geolocation of the client device 102 to identify a media overlay) . It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the method of White to have the virtual lens is configured to position an augmented reality (AR) content on an image captured by a camera system. Here, combining Boyd with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Boyd paragraph [0001]) . Claims 4-13 are rejected under AIA 35 U.S.C. 103 as being unpatentable over White et al. (US 20210049529 A1) in view of Boyd et al. (US 20220094653 A1) further in view Cama et al. (US 20170124074 A1). Regarding claim 4, Cama expressly discloses wherein the training data set comprises a number of times that a song, a song snippet, a sound, or a combination thereof, is selected to be played alongside the photographic filter, the virtual lens, or a combination thereof (see Cama paragraph [0020],store and provide data surrounding the musical preferences of the user through the selections made by the user while the respective web or application service was running This data may include information relating to the songs, albums and artists listened to, the number of times specific songs were repeated or listened through the entire length, favorite or saved songs, favorite genre, as well as disliked or songs that were skipped when they were played on the streaming services). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have wherein the training data set comprises a number of times that a song, a song snippet, a sound, or a combination thereof, is selected. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 5, Cama expressly discloses wherein the training data set comprises a geographic location where the song, the song snippet, or the combination thereof, was played alongside the photographic filter, the virtual lens, or the combination thereof (see Cama paragraph [0055], Contexts which a sub profile having its own context specific music parameters may include contexts such as time of day, day of the week, GPS location, including home, office, vacation, travelling in a vehicle, whether in a group of individuals or listening solo, the touch point device the music recommendation engine is being accessed from, listener's mood and the specific selection of a user defined sub-profiles). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have geographic location where the song, the song snippet, or the combination thereof, was played alongside the photographic filter. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 6, Cama expressly discloses, wherein the training data set comprises the number of times that the song, the song snippet, the sound, or a combination thereof, is selected by members of a social network to be played alongside the photographic filter, the virtual lens, or the combination thereof (see Cama paragraph [0018], Data sources may include both publicly available and private structured files, unstructured files, databases, data sets, data sheets, spreadsheets, XML files, flat files, computer logs, network performance logs, search engine logs, social media, data marts, data warehouses, web sites and web services and any other data source known in the art, especially any data sources that provide historical data regarding the user's musical preferences. In FIG. 1, each of the data sources are displayed by reference number 103 a , 103 b , 103 c . . . 103 n . The music recommendation engine is not limited to collecting information from a particular number of data sources, in fact the more data sources available, the more precise that a recommendation delivered by the music recommendation engine may be). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have wherein the training data set comprises the number of times that the song, the song snippet, the sound. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 7, Cama expressly discloses, wherein the members of the social network comprise a friends group of a user of the client device (see Cama paragraph [0055], Contexts which a sub profile having its own context specific music parameters may include contexts such as time of day, day of the week, GPS location, including home, office, vacation, travelling in a vehicle, whether in a group of individuals or listening solo, the touch point device the music recommendation engine is being accessed from, listener's mood and the specific selection of a user defined sub-profiles; see Cama paragraph [0022],the demographic information may be derived from one or more of the data sources, as part of the data being collected. Examples of demographic information about the user may include the user's age, marital status, education level, income, location, perceived socio-economic status, and other family members or relatives the user may be related to). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have wherein the training data set comprises the number of times that the song, the song snippet, the sound. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 8, Cama expressly discloses, wherein training the machine learning model comprises continuously training the machine learning model by using a current data set (see Cama paragraph [0065] if a user profile already exists, the music recommendation engine may select the profile and proceed to perform the method under step 207 to update the user profile with the newly collected user information of step 205 and make recommendations in accordance with the remaining steps of this method; see Cama paragraph [0065] the analytics engine may apply one or more of the algorithms of the machine learning models 124 described below. Predictive analytics may be used to determine which characteristics of the user preference data, sometimes referred to as predictors, in a data set are clustered together). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have wherein continuously training the machine learning model by using a current data set. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 9, Cama discloses, wherein the model comprises a linear regression model configured to apply a linear regression derivation or a probability-based model configured to apply a statistical probability derivation to derive the music recommendation, the sound recommendation, or the combination thereof (see Cama paragraph [0036], regression algorithms that may be used by the analytics engine 118 may include ordinary least square regression (OLSR), linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines (MARS), and locally estimated scatterplot smoothing (LOESS).; see Cama paragraph [0036], one or more mathematical techniques or algorithms applied to the set of user preference data 101 to determine the probability that the user may enjoy the proposed musical recommendation. The analytics engine may apply one or more of the algorithms of the machine learning models 124 described below. Predictive analytics may be used to determine which characteristics of the user preference data, sometimes referred to as predictors, in a data set are clustered together). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have a linear regression model configured to apply a linear regression derivation. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 10, Cama discloses, wherein deriving via the date the music recommendation, the sound recommendation, or the combination thereof, comprises executing a first query to determine holiday, a national day, an international day, an event, or a combination thereof (see Cama paragraph [0057], the music recommendation may be classified differently depending on the type of recommendation being delivered. The recommendation may be considered a public recommendation, or a private recommendation. Public recommendations may include publicly held music events, such as concerts, festivals, meet and greets album signings, book tours or any other public music event). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have executing a first query to determine holiday, a national day, an international day, an event. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 11, Cama discloses, wherein deriving via the date the music recommendation, the sound recommendation, or the combination thereof, comprises executing a second query using results from the first query to determine music, sounds, or a combination thereof, associated with the holiday, the national day, the international day, the event, or the combination thereof (see Cama paragraph [0058], the music recommendation engine may be delivered to the user by the recommendation delivery manager 106 at pre-designated intervals of time, upon request by the user, following the completion or conclusion of a currently playing music selection or under context specific situations). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have executing a first query to determine holiday, a national day, an international day, an event. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 12, Cama discloses, wherein the instructions for deriving, via the model, the date, or the combination thereof, the music recommendation, the sound recommendation, or the combination thereof, for the selection of the photographic filter or the virtual lens, comprise instructions for deriving, via a media interrelationship recommendation query, the model, the date, or a combination thereof, the music recommendation, the sound recommendation, or the combination thereof, for the selection of the photographic filter or the virtual lens (see Cama paragraph [0030],The profile analyzer 120 refers to a module or program tool that is able to correlate and correspond the incoming user preferences and data source information to a specific user profile. The profile analyzer ensures that information relating to a single profile of the recommendation engine is being analyzed in conjunction with the user preferences, demographic information and other variables derived from a specific user to draw conclusions about that user and update the user's profile accordingly. The profile analyzer 120 , allows the analytics engine to retrieve profile attributes for a given profile or a profile associated with a given user name, in order to update the profile or previously generated models for predicting preferred musical parameters of a user). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have executing a first query to determine holiday, a national day, an international day, an event. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Regarding claim 13, Cama discloses, wherein the media interrelationship recommendation query is configured to determine music, sounds, or a combination thereof, associated with the photographic filter or with the virtual lens (see Cama paragraph [0020], web based and application based streaming services, including YouTube, Spotify, Pandora, Napster, Soundcloud, Amazon Music, Slacker Radio, and Shaazam can track, store and provide data surrounding the musical preferences of the user through the selections made by the user while the respective web or application service was running This data may include information relating to the songs, albums and artists listened to, the number of times specific songs were repeated or listened through the entire length, favorite or saved songs, favorite genre, as well as disliked or songs that were skipped when they were played on the streaming services). It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Cama into the method of White to have executing a first query to determine holiday, a national day, an international day, an event. Here, combining Cama with White , which are both related to content processing improves White by providing system for analytics and applications using analytical data to generate and present customized recommendations and offers (see Cama paragraph [0001]) . Remarks 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hwang et al. (US 20200045163 A1) discloses that the application recommendation result of the learning model may vary depending on the context information of each user used for training the learning model. However, when the application recommendation function is activated while using the camera application, a picture mode change function, a filter change function, a voice control function, a live streaming function of an SNS application, a TV connection application, and the like may be recommended . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DINKU W GEBRESENBET whose telephone number is (571)270-1636. The examiner can normally be reached between 8:00AM-5:00PM. 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, Amy Ng can be reached on 571- 270-1698. 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. /DINKU W GEBRESENBET/Primary Examiner, Art Unit 2164 Application/Control Number: 18/535,551 Page 2 Art Unit: 2164 Application/Control Number: 18/535,551 Page 3 Art Unit: 2164 Application/Control Number: 18/535,551 Page 4 Art Unit: 2164 Application/Control Number: 18/535,551 Page 5 Art Unit: 2164 Application/Control Number: 18/535,551 Page 6 Art Unit: 2164 Application/Control Number: 18/535,551 Page 7 Art Unit: 2164 Application/Control Number: 18/535,551 Page 8 Art Unit: 2164 Application/Control Number: 18/535,551 Page 9 Art Unit: 2164 Application/Control Number: 18/535,551 Page 10 Art Unit: 2164 Application/Control Number: 18/535,551 Page 11 Art Unit: 2164 Application/Control Number: 18/535,551 Page 12 Art Unit: 2164 Application/Control Number: 18/535,551 Page 13 Art Unit: 2164 Application/Control Number: 18/535,551 Page 14 Art Unit: 2164 Application/Control Number: 18/535,551 Page 15 Art Unit: 2164 Application/Control Number: 18/535,551 Page 16 Art Unit: 2164 Application/Control Number: 18/535,551 Page 17 Art Unit: 2164 Application/Control Number: 18/535,551 Page 18 Art Unit: 2164 Application/Control Number: 18/535,551 Page 19 Art Unit: 2164 Application/Control Number: 18/535,551 Page 20 Art Unit: 2164 Application/Control Number: 18/535,551 Page 21 Art Unit: 2164 Application/Control Number: 18/535,551 Page 22 Art Unit: 2164 Application/Control Number: 18/535,551 Page 23 Art Unit: 2164 Application/Control Number: 18/535,551 Page 24 Art Unit: 2164 Application/Control Number: 18/535,551 Page 25 Art Unit: 2164 Application/Control Number: 18/535,551 Page 26 Art Unit: 2164 Application/Control Number: 18/535,551 Page 27 Art Unit: 2164 Application/Control Number: 18/535,551 Page 28 Art Unit: 2164
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