DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/5/2026 has been entered.
Response to Arguments
Claims 1 – 20 are pending in this Office Action. After a further search and a thorough examination of the present application, claims 1 – 20 remain rejected under 35 U.S.C. §101 and under 35 U.S.C. §103.
Applicant's arguments filed with respect to claims 1 – 20 have been fully considered but they are not persuasive.
Applicant argues that the amended claims recite "generating, by one or more machine learning models of the artist platform, personalized collaboration recommendations for the artist, wherein the one or more machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist" and "filtering, by the artist platform, the personalized collaboration recommendations based on the one or more collaboration objectives and the relevancy scores to generate filtered collaboration recommendations." Training one or more machine learning models to "generate relevancy scores that predict a respective relevance of individual artists" cannot be practically performed in the human mind.
In response to Applicant’s argument, the Examiner submits that the amends claim 1 recites "generating, by one or more machine learning models of the artist platform, personalized collaboration recommendations for the artist, wherein the one or more machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist" and "filtering, by the artist platform, the personalized collaboration recommendations based on the one or more collaboration objectives and the relevancy scores to generate filtered collaboration recommendations". But in regards with training one or more machine learning models to "generate relevancy scores that predict a respective relevance of individual artists" cannot be practically performed in the human mind examiner disagrees and submits that a human can be mentally trained to process data according to a model and to make judgement on filtering out based on conditions can be done in the human mind. There is no indication that the claimed steps require any unconventional hardware or that they improve the basic operation of the computer itself. Under MPEP § 2106.05(a), an improvement to computer functionality must be a technological solution to a technological problem, such as improving the speed, security, or efficiency of the computer itself or its network. Here, the claimed workflow enforces business rules for licensing media content. The claims do not recite, for example, a new identity-verification algorithm, instead, they apply known components to a business process. The claim language does not describe any specifics of how the training is actually performed or how the actual model is updated that would be more than reciting linking this to the field of ML or applying ML as a generic computer component/tool. Reciting that the model is trained to generate relevancy scores is still no more than a generic invocation of a training function, the conceptual idea of training devoid of technological implementation details. All the inventive aspects as described in the specification reside in how the data are interpreted and the results desired, and not in some novel form of machine learning. The training is recited in a purely functional manner. The claim does not recite how the training occurs. The source of input data is insufficient to confer eligibility, similar to as was decided in SAP Am., Inc. v. InvestPic LLC, 898 F.3d 1161 (Fed. Cir. 2018).
Applicant argues that there is no teaching in Lee-Chan, Zhang or Sado alone or in combination of generating, by one or more machine learning models of the artist platform, personalized collaboration recommendations for the artist, wherein the one or more machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist or filtering, by the artist platform, the personalized collaboration recommendations based on the one or more collaboration objectives and the relevancy scores to generate filtered collaboration recommendations.
In response to Applicant’s argument, the Examiner submits that Lee-Chan in combination with Zhang and further in combination with Sado teach generating, by one or more machine learning models of the artist platform, personalized collaboration recommendations for the artist, wherein the one or more machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist and filtering, by the artist platform, the personalized collaboration recommendations based on the one or more collaboration objectives and the relevancy scores to generate filtered collaboration recommendations. Zhang teaches the machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist in paragraph 34 teaches scoring and ranking the candidates, paragraph 53 teaches the training of the model, paragraphs 70 and 77 teach the various criteria taken into consideration and the geographic consideration is taught in paragraphs 43, 49, 54 and 87 when selecting and filtering the candidates and profiles.
Remaining claims in instant application recite the same subject matter and for the same reasons as cited above the rejection is maintained. Hence, Applicant’s arguments do not distinguish the claimed invention over the prior art of record. In light of the foregoing arguments, the 103 rejections are maintained.
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 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:
In regards with independent claims 1, 11, 18, it recites receiving a request and the registration of an artist with sample media, request to generate collaboration recommendations for artist; generating collaboration recommendations, receiving from user a collaboration objective, filtering the recommendations and displaying the collaboration recommendations.
The limitation of generating the collaboration recommendations for the artist to include the subset of one or more artists, as drafted is a process that, under broadest reasonable interpretation, covers the process of organizing human activity but for the generic computer components. The limitation of generating recommendations could be something recommending two people/ students/ artist to work together based on determination of criteria, which is done by human activity. That is, other than reciting “computer-implemented method”, nothing in the claim elements precludes the step of organizing human activity.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements of “computer-implemented method”, “user interface” to perform steps. These computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitation of receiving a request to generate collaboration recommendations, processing artist data, filtering artists based on contextual attributes is a recitation of an insignificant extra-solution, a pre-solution activity which is a step of gathering data in a claimed method. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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. Furthermore the recitation of displaying the collaboration recommendations under broadest reasonable interpretation is not more than addition of insignificant extra-solution activity which does not amount to an inventive concept because adding a final step of displaying recommendation does not add a meaningful limitation to the method of recommending data. 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. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of computer-implemented method, user interface amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see MPEP 2106.05 (a) I (iii). The receiving and transmitting steps are directed to well-understood, routine, and conventional activities. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. 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); TLI MPEP 2106.05(d)(II). As recognized by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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). Storing content cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see "Storing and retrieving information in memory" MPEP 2106.05(d) 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. The claim is not patent eligible. 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. The claim is not patent eligible.
In regards with claims 2, 12 and 19 recites receiving selection, forming a communication channel and communication message to the selected collaborator.
The limitation of forming a communication channel between the artist and the collaborator is a recitation of an insignificant extra-solution activity. As recognized by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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). Storing content cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see "Storing and retrieving information in memory" MPEP 2106.05(d) 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. The claim is not patent eligible. 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 3, 4, 13, 14, recites receiving request to register.
The limitation of receiving request to register is a recitation of an insignificant extra-solution activity. As recognized by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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). Storing content cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see "Storing and retrieving information in memory" MPEP 2106.05(d) 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. The claim is not patent eligible. 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 5, 6, 15, 16,17 recites social graph with content consumer nodes.
The limitation of using social graphs is a recitation of an insignificant extra-solution activity. As recognized by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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). Storing content cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see "Storing and retrieving information in memory" MPEP 2106.05(d) 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. The claim is not patent eligible. 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 7, 14, 20 recites wherein the training data set that includes acceptable and unacceptable collaboration recommendations.
The limitation of training data is a recitation of an insignificant extra-solution activity. As recognized by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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). Storing content cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see "Storing and retrieving information in memory" MPEP 2106.05(d) 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. The claim is not patent eligible. 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 8 recites wherein the artist data includes audience data for an audience of the artist and relevancy scores.
The limitation of wherein the artist data includes audience data for an audience of the artist and relevancy scores is a recitation of an insignificant extra-solution activity. As recognized by the Supreme Court, the addition of insignificant extra-solution activity does not amount 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). Storing content cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see "Storing and retrieving information in memory" MPEP 2106.05(d) 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. The claim is not patent eligible. 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 9, 10 recite further comprising generating a collaboration recommendation for the artist/additional artist.
The limitation of generating a collaboration recommendation for the artist/additional artist as drafted, is a process that, under its broadest reasonable interpretation covers the process of organizing human activity, where one person is recommended to work with another group/team/person. 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
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.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Lee-Chan et al. (‘Lee-Chan’ herein after) (US 20180285933 A1) further in view of Zhang et al. (‘Zhang’ herein after) (US 20190236106 A1) further in view of Daniel J. Sado (‘Sado’ herein after) (US 20230169498 A1).
With respect to claim 1,
Lee-Chan discloses a computer-implemented method comprising: receiving, by an artist platform, a request from a user to register as an artist with the artist platform, the request including an indication of at least one sample of media content that the user has produced (figure 3, paragraph 28, 67 teaches a user/artist/Erin who had in the past requested an account/registered with the platform and has upload option of uploading her content on her channel, Lee-Chan); registering the artist with the artist platform (figure 3, paragraph 28, 67 teaches a user/artist/Erin who has created an account/registered with the platform and has upload option of uploading her content on her channel, Lee-Chan); generating, by the artist platform, personalized collaboration recommendations for the artist (figure 3, paragraphs 46, 65 – 68 teach the personalized recommendations for collaboration, Lee-Chan); receiving, via the user interface of the artist platform, user input specifying one or more collaboration objectives, the one or more collaboration objectives comprising a genre of media content produced by the collaborators and a location of the collaborators (figure 3, paragraphs 46, 66 – 69 teach where the criteria of different recommendations are based on different criteria like overlap of subscribers, cross-promotion using statistics, Lee-Chan); filtering, by the artist platform, the personalized collaboration recommendations based on the one or more collaboration objectives and the relevancy scores to generate filtered collaboration recommendations and displaying, in the user interface of the artist platform, the filtered collaboration recommendations (figure 3, paragraphs 42, 56 – 59, 66 – 69 teach where the criteria of different recommendations are based on different criteria like overlap of subscribers, cross-promotion using statistics and displaying a recommending the collaborations, Lee-Chan).
Lee-Chan teaches collaborating and collaboration but does not specifically teach the user requesting the collaboration request and the one or more machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist.
However, Zhang teaches receiving a request from the artist to collaborate with collaborators in figure 11, paragraph 34 – 37 and 206 teaching that a request is made by a new member or a member to connect with other members based on certain criteria. Zhang also teaches the machine learning models leverage a location of the artist and respective locations of the other artists to generate the personalized collaboration recommendations and wherein the one or more machine learning models are trained to generate relevancy scores that predict a respective relevance of individual artists registered with the artist platform as a collaborator with the artist in paragraph 34 teaches scoring and ranking the candidates, paragraph 53 teaches the training of the model, paragraphs 70 and 77 teach the various criteria taken into consideration and the geographic consideration is taught in paragraphs 43, 49, 54 and 87 when selecting and filtering the candidates and profiles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lee-Chan and Zhang because it incorporates the advantage to Lee-Chan of not only automatic recommended collaboration but also collaboration/ connection that is requested by a user.
Lee-Chan and Zhang teach the artist platform for collaboration and registration of the account holder but they do not specifically teach verifying whether the user is the artist based on the indication of the at least one sample of media content.
However, Sado teaches verifying whether the user is the artist based on the indication of the at least one sample of media content in paragraphs 28 and 157. Sado teaches in paragraph 157 registers creators/users; stores “registration data”; verifies information; maintains profiles. According to paragraph 28 Sado also publishes/listing gated on verification of rights; can require identity/right verification before allowing to publish. Distinct roles (creator vs. user) imply role-based permissions (only creators may list/publish).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of verifying an identity if an artist to grant or deny permissions (as disclosed by Sado) to the known method and system of collaboration for use (as disclosed by Lee-Chan and Zhang). One of ordinary skill in the art would have been motivated to apply the known technique of granting or denying permissions because it would allow for intellectual property protection (see Sado ¶ 28).
With respect to claim 2,
Lee-Chan as modified teaches the computer-implemented method of claim 1, further comprising: receiving, via the user interface of the artist platform, a selection of a collaborator from the filtered collaboration recommendations; forming, by the artist platform, a communication channel between the artist and the selected collaborator and communicating, via the communication channel, an electronic message from the artist to the selected collaborator (figure 3, paragraphs 66 – 71 teaching the collaboration and paragraphs 75 – 77 teach the communication of requesting the collaboration, Lee-Chan).
With respect to claim 3,
Lee-Chan as modified teaches the computer-implemented method of claim 1, wherein the user is a first user, and further comprising: receiving, by the artist platform, a request from a second user to register as the artist with the artist platform, the request including registration data; determining, based on the registration data, that the second user is not the artist and notifying the first user that the second user is an imposter (paragraphs 28, 157, Sado, paragraphs 73 – 74, Lee-Chan and paragraphs 34 – 36, 53 and 77, Zhang).
With respect to claim 4,
Lee-Chan as modified teaches the computer-implemented method of claim 1, further comprising dynamically updating the user interface of the artist platform based on a change in the one or more collaboration objectives (paragraphs 73 – 74, Lee-Chan and paragraphs 34 – 36, 53 and 77, Zhang).
With respect to claim 5,
Lee-Chan as modified teaches the computer-implemented method of claim 1, wherein the one or more machine learning models are further configured to leverage a social graph that is generated by the artist platform from artist data, and the social graph includes artist nodes that represent artists and content consumer nodes that represent content consumers, the social graph including edges that connect the content consumer nodes to one or more of the artist nodes based on interaction by the content consumers with the artists (paragraphs 73 – 74, Lee-Chan and figure 7, paragraphs 33 – 36 and 209 – 217, Zhang).
With respect to claim 6,
Lee-Chan as modified teaches the computer-implemented method of claim 5, wherein the edges are weighted based on one or more of a number of listens, amount of listening, or amount of digital interaction (paragraphs 73 – 74, Lee-Chan and figure 7, paragraphs 33 – 36 and 209 – 217, Zhang).
With respect to claim 7,
Lee-Chan as modified teaches the computer-implemented method of claim 1, wherein the personalized collaboration recommendations are generated using the one or more machine learning models that are further trained using training data that includes historical collaboration matches between artists, and wherein the one or more machine learning models are trained by generating outputs corresponding to collaboration matches and rewarding editorial matches by adjusting internal weights of the one or more machine learning models to encourage similar editorial outputs and discouraging dissimilar editorial collaboration matches by adjusting internal weights of the one or more machine learning models to discourage similar dissimilar editorial outputs (paragraphs 73 – 74, Lee-Chan and paragraphs 34 – 36, 53 and 77, Zhang).
With respect to claim 8,
Lee-Chan as modified teaches the computer-implemented method of claim 1, wherein the relevancy scores are based on an overlap between an audience of the artist and respective audiences of the other artist (figure 3, paragraphs 27, 52, 62 – 64, Lee-Chan).
With respect to claim 9,
Lee-Chan as modified teaches the computer-implemented method of claim 1, further comprising generating and surfacing a recommendation regarding one or more platforms to place a collaboration after the collaboration is produced by the artist and a particular collaborator from the collaborators, wherein the filtered collaboration recommendations include the particular collaborator (figure 3, paragraphs 41, 66 – 69, Lee-Chan).
With respect to claim 10,
Lee-Chan as modified teaches the computer-implemented method of claim 9, wherein the one or more platforms comprise one or more of television, a streaming application, a social networking platform, or a mobile application (figure 3, paragraphs 41, 66 – 69, Lee-Chan).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220067112 A1 teaches matching musical artists automatically. A request for musical matches is received from a first device. The request includes a first dataset of musical preference selections from a plurality of musical preference categories, with the first dataset being associated with a first user. A comparison of the first dataset is performed with a second dataset of musical preference selections from the plurality of musical preference categories, with the second dataset being associated with a second user. The first device is then prompted to display a name of the second user if at least a predetermined amount of the first dataset is the same as the second dataset.
US 20190130039 A1 teaches group or collaboration recommended from query server and where query server determines a relevancy factor between query requests, the relevancy factor relates to the similarity between the two search strings and learns from the feedback, preferably based on supervised machine learning algorithms, and automatically adjust the thresholds.
US 20190236106 A1 teaches members are selected from the identified members based on the connection scores, and a ranking score for each selected member is obtained utilizing a machine learning algorithm that utilizes similarity analysis of the attributes to calculate the ranking score. The selected members are presented to the member as the possible new connections based on the ranking scores.
US 20190066730 A1 teaches ranking the potential participants based on the number of followers in the social platform, determines an overlap of the followers between a potential participant and the first author or selects potential participants for the project based on the overlap or similar amount of followers, and searches for, during a social event, a plurality of users of the server to create a group video about the social event based on location and time information associated with electronic devices of the plurality of the users.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is 571-272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 9/2/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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