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 .
Status of Claims
2. This Office Action is in response to the initial filing of application #18/744295 on 06/14/2024.
3. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 12/11/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a system, which is a statutory class, comprising one or more processor and a non-transitory memory, first user device, second user device: the system, comprising:
receive entity data associated with a plurality of entities, the entity data comprising quantitative data and descriptive attribute data;
computing one or more vectors based on the entity data for each entity of the plurality of entities;
for each entity intersection of the plurality of entities, computing a vector distance;
receive, from a first user device associated with a first entity, a match request;
identify one or more second entities based on the match request and the computed vector distances associated with an intersection between the first entity and each complementary entity of the plurality of entities; and
transmit, to the first user device, an indication of the one or more second entities.
The steps of
receive entity data associated with a plurality of entities, the entity data comprising quantitative data and descriptive attribute data;
computing one or more vectors based on the entity data for each entity of the plurality of entities;
for each entity intersection of the plurality of entities, computing a vector distance;
receive, from a first user device associated with a first entity, a match request;
identify one or more second entities based on the match request and the computed vector distances associated with an intersection between the first entity and each complementary entity of the plurality of entities; and
transmit, to the first user device, an indication of the one or more second entities,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a system for optimally matching users of a media platform. The above identified steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to matching users of a media platform.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as tailored personalized marketing, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more processor and a non-transitory memory, first user device, second user device. The first and second user device is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions of receive entity data; computing one or more vectors; computing a vector distance; receive a match request; identify one or more second entities; and transmit an indication of the one or more second entities) such that they amount to no more than mere instructions to apply the exception using generic computer components. 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 is directed to an 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 elements of one or more processor and a non-transitory memory, first user device, second user device amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); providing a user with tailored information like advertisements based on information known about the user such as a location, address, or personal characteristics and a time of day is a fundamental practice long prevalent in our system); In re Morsa, 809 F. App’x 913, 917 (Fed. Cir. 2020). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is not already present when the steps are considered separately. That is, one or more processor and a non-transitory memory, first user device, second user device, performing commercial interactions including: receive entity data; computing one or more vectors; computing a vector distance; receive a match request; identify one or more second entities; and transmit an indication of the one or more second entities, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, claims 1 and 11 are not eligible.
As for dependent claims 2-10 and 12-20, these claims recite limitations that further define the same abstract idea noted in claims 1 and 11. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
7. 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.
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
9. Claims 1-7, 10-17 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Perelli-Minetti (U.S. Pub. No. 2024/00864480) (hereinafter ‘Perelli’).
Claim 1: Perelli discloses a system comprising: one or more processors; and a non-transitory memory coupled to the one or more processors, (See at least Figure 16), and storing instructions, that when executed by the one or more processors are configured to cause the system to:
receive entity data associated with a plurality of entities, the entity data comprising quantitative data and descriptive attribute data, Perelli teaches the platform may utilize machine-learning to identify the two or more entities in a manner that uses contextual attributes to match entities, where examples of such contextual attributes include social media status and/or behavior, locations of the entities, and media item profiles (e.g., musical similarity or musical dissimilarity), to name a few, (see at least figure1; Figure 2; figure 9; Figure 10 element 1002 and figure 11 element 1102 and paragraph 0019), furthermore, Perelli teaches the artist data may include both profile data of the artist as well as audience data (such as listener data) of the artist. Broadly, the profile data represents data specified by a respective artist ( or a manager of the artist), e.g., as part of setting up or subsequently modifying the artist profile. Examples of such information include, but are not limited to, artist name(s), title, genre, style, description, biography, geography, images, videos, sample media content (e.g., music), status, profile theme (e.g., colors), merchandise offered, territory (see at least paragraph 0033);
computing one or more vectors based on the entity data for each entity of the plurality of entities, Perelli teaches the correlation engine 130 may then determine a context-based artist fingerprint that corresponds to the request 140 and/or underlying data associated with the request, such as artist data, listener data, audio, and video data (see at least Figure 3 element 300, Figure 11 element 1104 and paragraph 0062), Pirelli further teaches the model input 208 is configured to be received as input by one or more of the model(s) 128. By way of example, the model input 208 may be configured as one or more feature vectors or context-based artist fingerprints generated in connection with the request 140 (see at least paragraph 0082), and Pirelli teaches the adaptive collaboration platform processes entity data, e.g., for the artist, with additional artist data for other artists in an artist population in order to generate collaboration recommendations for the artist (see at least paragraph 0032), and finally Pirelli teaches the collaboration system 126 may establish a proximity around locations of individual artist devices ( e.g., the computing device 111 of the artist 110 and/or the computing device 113 of the collaborator 112 (see at least paragraph 0092);
for each entity intersection of the plurality of entities, computing a vector distance, Perelli teaches ascertain whether the artist's audience overlaps with a collaborating artist (see at least paragraph 0025 and see Figures 2 and 3), furthermore, Perelli teaches as part of generating the collaboration recommendations, the one or more models process the artist data and the artist data associated with other artists to generate relevancy scores which predict how relevant each respective artist in the artist population would be as a collaborator with the artist. In one or more implementations, for example, the artist data includes audience data for the artist's audience, and the relevancy scores are based at least in part on the amount of overlap between the artist's audience and the respective audiences of the other artists in the artist population (computing a vector distance)(see at least paragraph 0035), and finally, Perelli teaches the proximities may be established using a geofence surrounding an individual artist 108's computing device. In an illustrative example, for instance, two artists 108 may both attend an event, and based on computing devices associated with the two artists 108 being within a venue of the event at a same or similar time, (computing a vector distance) (see at least paragraph 0092);
receive, from a first user device associated with a first entity, a match request, Perelli teaches a dedicated application or portal ( e.g., web page) configured for the collaboration platform receives a request to generate collaboration recommendations for an artist, e.g., recommendations for networking or collaboration on one more media items, creative objects, and so on. The request can be received when the artist joins the adaptive collaboration platform, e.g., through a dedicated resource identifier, such as URL, or interactive element, or responsive to a specific request from the artist to identify collaborators (see at least Figure 1 element 140, Figure 2, Figure 3 and Figure 9 element 902 and paragraph 0031), and Perelli further teaches the request processing module 204 processes the request 140 (and the specified collaboration objective(s) 202) and outputs model input 208 (see at least paragraph 0082);
identify one or more second entities based on the match request and the computed vector distances associated with an intersection between the first entity and each complementary entity of the plurality of entities, Perelli teaches the platform may utilize machine-learning to identify the two or more entities in a manner that uses contextual attributes to match entities, where examples of such contextual attributes include social media status and/or behavior, locations of the entities, and media item profiles (e.g., musical similarity or musical dissimilarity) (see at least Figures 1-4, Figure 9 element 908, Figure 11element 1104 and paragraph 0019), Perelli teaches This includes arranging collaboration recommendations in user interfaces in a way that is relevant to an artist seeking collaboration, such as based on contextual attributes of the artist, including for instance based on collaboration objectives specified by the artist via interactive elements of the platform's user interfaces. Based on contextual attributes and/or collaboration objectives, for instance, the platform exposes potential collaborators that best match a collaboration request in a more prominent position within a user interface and/or with an indication of how well the potential collaborators match relative to one another (based on the match request and the computer vector distances associated with an intersection between the first entity and each complimentary entities) (see at least paragraph 0030), and furthermore, Perelli teaches a subset of the artists in the artist population may then be surfaced to the artist based on the relevancy scores, e.g., by selecting the artists with the highest relevancy scores to surface as potential collaborators to the artist (see at least paragraph 0036), Perelli teaches other events may trigger the collaboration system 126 to deliver a notification to the device of the artist 110, such as a "match score" between the artist 110 and a collaborator 112 being above a threshold score, where the match score may correspond to one or more of a popularity of the collaborator, a genre of the collaborator (whether the same or different from the artist 110), a likelihood of the collaborator to collaborate with other artists (see at least paragraph 0061, and Perelli finally teach based on computing devices associated with the two artists 108 being within a venue of the event at a same or similar time, the collaboration system 126 may provide a collaboration recommendation 142, e.g., recommending to at least one of the two artists 108 that the two artists 108 collaborate (see at least paragraph 0092, and Perelli teaches each of the collaboration matches include a match between at least two artists of the plurality of artists registered with the collaboration system. By way of example, one or more models 128 of the collaboration system 126 processes the artist data 124 using machine learning in order to generate collaboration matches. In one or more implementations, the one or more models 128 generate collaboration match scores between artists of the plurality of artists registered with the collaboration system and compare the match scores to a threshold (based on the match request and the computed vector distances associated with an intersection between the first entity and each complementary entity of the plurality of entities) (see at least paragraph 0130); and
transmit, to the first user device, an indication of the one or more second entities, Perelli teaches a computing device, for instance, is configurable as a server, a desktop computer, a laptop computer, a mobile device (e.g., assuming a handheld configuration such as a tablet or mobile phone), an IoT device, a wearable device (e.g., a smart watch), an AR/VR device (see at least Figure 2, Figure 9 element 908, Figure 11 element 1110 and Figure 12 and paragraph 0050), Perelli also teaches the platform includes components to present (e.g., display) identified and filtered entities as collaboration recommendations via a user interface, e.g., of a computer application of the platform (see at least paragraph 0020), Perelli further teaches these recommendations are surfaced through a user interface of the adaptive collaboration platform which enables the artist to view and interact with potential collaborators (see at least paragraph 0036, and finally, Perelli teaches the collaboration system 126 may provide a notification recommending a collaboration in web browser or an application associated with the collaboration system 126 executing on the device of the artist 110, as a push notification (see at least paragraph 0061).
Claim 2: Perelli discloses the system according to claim 1, and Perelli further teaches wherein the non-transitory memory comprises further instructions, that when executed by the one or more processors, are configured to cause the system to:
based on the match request and the computed vector distances, transmit to a second user device associated with a second entity of the one or more second entities an indication comprising a preliminary match with the first entity, Perelli teaches a computing device, for instance, is configurable as a server, a desktop computer, a laptop computer, a mobile device (e.g., assuming a handheld configuration such as a tablet or mobile phone), an IoT device, a wearable device (e.g., a smart watch), an AR/VR device (see at least Figure 3, Figure 9 element 908, Figure 11 element 1110 and Figure 12 and paragraph 0050), and Perelli further teaches the user interface may also enable the artist to form a communication channel with the recommended collaborators (see at least paragraph 0036).
Claim 11: Perelli discloses a system comprising:
one or more processors, (see at least Figure 16), and
a non-transitory memory, (see at least Figure 16), coupled to the one or more processors, and storing
instructions, that when executed by the one or more processors are configured to cause the system to:
receive entity data associated with a plurality of entities, the entity data comprising quantitative data and descriptive attribute data, Perelli teaches the platform may utilize machine-learning to identify the two or more entities in a manner that uses contextual attributes to match entities, where examples of such contextual attributes include social media status and/or behavior, locations of the entities, and media item profiles (e.g., musical similarity or musical dissimilarity), to name a few, (see at least figure1; Figure 2; figure 9; Figure 10 element 1002 and figure 11 element 1102 and paragraph 0019), furthermore, Perelli teaches the artist data may include both profile data of the artist as well as audience data (such as listener data) of the artist. Broadly, the profile data represents data specified by a respective artist ( or a manager of the artist), e.g., as part of setting up or subsequently modifying the artist profile. Examples of such information include, but are not limited to, artist name(s), title, genre, style, description, biography, geography, images, videos, sample media content (e.g., music), status, profile theme (e.g., colors), merchandise offered, territory (see at least paragraph 0033);
computing one or more vectors based on the entity data for each entity of the plurality of entities, Perelli teaches the correlation engine 130 may then determine a context-based artist fingerprint that corresponds to the request 140 and/or underlying data associated with the request, such as artist data, listener data, audio, and video data (see at least Figure 3 element 300, Figure 11 element 1104 and paragraph 0062), Pirelli further teaches the model input 208 is configured to be received as input by one or more of the model(s) 128. By way of example, the model input 208 may be configured as one or more feature vectors or context-based artist fingerprints generated in connection with the request 140 (see at least paragraph 0082), and Pirelli teaches the adaptive collaboration platform processes entity data, e.g., for the artist, with additional artist data for other artists in an artist population in order to generate collaboration recommendations for the artist (see at least paragraph 0032), and finally Pirelli teaches the collaboration system 126 may establish a proximity around locations of individual artist devices ( e.g., the computing device 111 of the artist 110 and/or the computing device 113 of the collaborator 112 (see at least paragraph 0092);
for each entity intersection of the plurality of entities, computing a vector distance, Perelli teaches ascertain whether the artist's audience overlaps with a collaborating artist (see at least paragraph 0025 and see Figures 2 and 3), furthermore, Perelli teaches as part of generating the collaboration recommendations, the one or more models process the artist data and the artist data associated with other artists to generate relevancy scores which predict how relevant each respective artist in the artist population would be as a collaborator with the artist. In one or more implementations, for example, the artist data includes audience data for the artist's audience, and the relevancy scores are based at least in part on the amount of overlap between the artist's audience and the respective audiences of the other artists in the artist population (computing a vector distance)(see at least paragraph 0035), and finally, Perelli teaches the proximities may be established using a geofence surrounding an individual artist 108's computing device. In an illustrative example, for instance, two artists 108 may both attend an event, and based on computing devices associated with the two artists 108 being within a venue of the event at a same or similar time, (computing a vector distance) (see at least paragraph 0092);
receive, from a first user device associated with a first entity, a match request, Perelli teaches a dedicated application or portal ( e.g., web page) configured for the collaboration platform receives a request to generate collaboration recommendations for an artist, e.g., recommendations for networking or collaboration on one more media items, creative objects, and so on. The request can be received when the artist joins the adaptive collaboration platform, e.g., through a dedicated resource identifier, such as URL, or interactive element, or responsive to a specific request from the artist to identify collaborators (see at least Figure 1 element 140, Figure 2, Figure 3 and Figure 9 element 902 and paragraph 0031), and Perelli further teaches the request processing module 204 processes the request 140 (and the specified collaboration objective(s) 202) and outputs model input 208 (see at least paragraph 0082);
identify one or more second entities based on the match request and the computed vector distances associated with an intersection between the first entity and each complementary entity of the plurality of entities, Perelli teaches the platform may utilize machine-learning to identify the two or more entities in a manner that uses contextual attributes to match entities, where examples of such contextual attributes include social media status and/or behavior, locations of the entities, and media item profiles (e.g., musical similarity or musical dissimilarity) (see at least Figures 1-4, Figure 9 element 908, Figure 11element 1104 and paragraph 0019), Perelli teaches This includes arranging collaboration recommendations in user interfaces in a way that is relevant to an artist seeking collaboration, such as based on contextual attributes of the artist, including for instance based on collaboration objectives specified by the artist via interactive elements of the platform's user interfaces. Based on contextual attributes and/or collaboration objectives, for instance, the platform exposes potential collaborators that best match a collaboration request in a more prominent position within a user interface and/or with an indication of how well the potential collaborators match relative to one another (based on the match request and the computer vector distances associated with an intersection between the first entity and each complimentary entities) (see at least paragraph 0030), and furthermore, Perelli teaches a subset of the artists in the artist population may then be surfaced to the artist based on the relevancy scores, e.g., by selecting the artists with the highest relevancy scores to surface as potential collaborators to the artist (see at least paragraph 0036), Perelli teaches other events may trigger the collaboration system 126 to deliver a notification to the device of the artist 110, such as a "match score" between the artist 110 and a collaborator 112 being above a threshold score, where the match score may correspond to one or more of a popularity of the collaborator, a genre of the collaborator (whether the same or different from the artist 110), a likelihood of the collaborator to collaborate with other artists (see at least paragraph 0061, and Perelli finally teach based on computing devices associated with the two artists 108 being within a venue of the event at a same or similar time, the collaboration system 126 may provide a collaboration recommendation 142, e.g., recommending to at least one of the two artists 108 that the two artists 108 collaborate (see at least paragraph 0092, and Perelli teaches each of the collaboration matches include a match between at least two artists of the plurality of artists registered with the collaboration system. By way of example, one or more models 128 of the collaboration system 126 processes the artist data 124 using machine learning in order to generate collaboration matches. In one or more implementations, the one or more models 128 generate collaboration match scores between artists of the plurality of artists registered with the collaboration system and compare the match scores to a threshold (based on the match request and the computed vector distances associated with an intersection between the first entity and each complementary entity of the plurality of entities) (see at least paragraph 0130); and
based on the match request and the computed vector distances, transmit to a second user device associated with a second entity of the one or more second entities an indication comprising a preliminary match with the first entity, Perelli teaches a computing device, for instance, is configurable as a server, a desktop computer, a laptop computer, a mobile device (e.g., assuming a handheld configuration such as a tablet or mobile phone), an IoT device, a wearable device (e.g., a smart watch), an AR/VR device (see at least Figure 3, Figure 9 element 908, Figure 11 element 1110 and Figure 12 and paragraph 0050), Perelli also teaches the platform includes components to present (e.g., display) identified and filtered entities as collaboration recommendations via a user interface, e.g., of a computer application of the platform (see at least paragraph 0020), Perelli further teaches these recommendations are surfaced through a user interface of the adaptive collaboration platform which enables the artist to view and interact with potential collaborators (see at least paragraph 0036, and finally, Perelli teaches the collaboration system 126 may provide a notification recommending a collaboration in web browser or an application associated with the collaboration system 126 executing on the device of the artist 110, as a push notification (see at least paragraph 0061).
Claim 3 and 13: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein:
the match request comprises a selection of an intersection bias of a plurality of intersection biases, Perelli teaches by way of example, the collaboration system 126 generates a social graph that includes nodes that represent users, e.g., including nodes that represent listeners and/or nodes that represent artists. Further, the social graph includes edges that connect the nodes of users that are listeners to the nodes of users that are artists, whose music the listeners have listened to as or with whom the listeners have digitally interacted in some other way as indicated by the audience data 136. In one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares, etc. Thus, the node of a listener is connected to numerous nodes of artists to which the listener listens, and the node of an artist is connected to numerous nodes of listeners that have listened to the artist's music or otherwise interacted with the artist (see at least paragraph 0054);
each entity intersection comprises a plurality of dimensions each corresponding to a weight, Perelli teaches in one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares (see at least paragraph 0054); and
the weight is determined based on the selection of the intersection bias, Perelli teaches in one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares (see at least paragraph 0054), Perelli further teaches The collaborator selector 206 may weight artists 108 based on a variety of other factors (e.g., contextual such as same locations (e.g., city, state, zip code, etc.) at a same time) to include the artists 108 as recommended collaborators 112 in the collaboration recommendation 142 (see at least paragraph 0089), and finally Perelli teaches during training, the output of the model( s) 128 may be "rewarded" when the output corresponds to acceptable collaboration recommendations, such as by adjusting internal weights of the models to encourage such output and/or reinforce underlying
policies (e.g., of a reinforcement model) (see at least paragraph 0069).
Claims 4 and 14: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein each entity intersection comprises a plurality of dimensions each corresponding to a weight, Perelli teaches in one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares (see at least paragraph 0054), and the non-transitory memory comprises further instructions, that when executed by the one or more processors, arc configured to cause the system to:
select an intersection bias of a plurality of intersection biases for the match request based on entity data associated with the first entity, Perelli teaches which of the model(s) 128 are used to generate the collaboration recommendation 142 may depend at least in part on the request 140. For example, the artist 110 may specify one or more characteristics for the request, such as reasons the artist would like to collaborate. For instance, the artist 110 may specify that a reason a collaborator is requested is for artistic purposes (e.g., explore a new sound, make a sound that has not been made, etc.), is to enter into a different genre or medium, to improve or satisfy a performance metric ( e.g., number of listeners, number of streams, number of times song is used on a social media platform, popularity, amount of money on album or song sales, and so forth), is to connect with one or more particular other artists, and so forth (see at least paragraph 0065) and Perelli further teach by way of example, the collaboration system 126 generates a social graph that includes nodes that represent users, e.g., including nodes that represent listeners and/or nodes that represent artists. Further, the social graph includes edges that connect the nodes of users that are listeners to the nodes of users that are artists, whose music the listeners have listened to as or with whom the listeners have digitally interacted in some other way as indicated by the audience data 136. In one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares, etc. Thus, the node of a listener is connected to numerous nodes of artists to which the listener listens, and the node of an artist is connected to numerous nodes of listeners that have listened to the artist's music or otherwise interacted with the artist (see at least paragraph 0054); and
assign a weight to each dimension of the plurality of dimensions based on the selected intersection bias, Perelli teaches in one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares (see at least paragraph 0054), Perelli further teaches during training, the output of the model( s) 128 may be "rewarded" when the output corresponds to acceptable collaboration recommendations, such as by adjusting internal weights of the models to encourage such output and/or reinforce underlying policies (e.g., of a reinforcement model) (see at least paragraph 0069), Perelli teaches weight collaborations with other artists more heavily than one or more other considerations when making a collaboration recommendation, and finally Perelli teaches the collaborator selector 206 may weight artists 108 based on a variety of other factors (e.g., contextual such as same locations (e.g., city, state, zip code, etc.) at a same time) to include the artists 108 as recommended collaborators 112 in the collaboration recommendation 142 (see at least paragraph 0089).
Claims 5 and 15: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein:
each entity intersection comprises a plurality of dimensions each corresponding to a weight, Perelli teaches in one or more implementations, the edges are weighted, such as based on a number of listens, amount of listening, amount of digital interaction ( e.g., likes and/or comments), shares (see at least paragraph 0054); and
computing a component of the vector distance associated with the descriptive data comprises computing a co-occurrence of dimensions of the descriptive data between the first entity and each complementary entity of the plurality of entities, Perelli teaches ss part of generating the collaboration recommendations, the one or more models process the artist data and the artist data associated with other artists to generate relevancy scores which predict how relevant each respective artist in the artist population would be as a collaborator with the artist. In one or more implementations, for example, the artist data includes audience data for the artist's audience, and the relevancy scores are based at least in part on the amount of overlap between the artist's audience and the respective audiences of the other artists in the artist population (see at least paragraph 0035) and Perelli further teaches for instance, the user interface 402 may visually illustrate an overlap of listener demographics between the first artist 304 and the artist 110 (see at least paragraph 0098).
Claims 6 and 16: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein:
each vector of the one or more vectors comprises a plurality of dimensions
corresponding to components of the entity data, Perelli teaches the correlation
engine 130 may then determine a context-based artist fingerprint that corresponds to the request 140 and/or underlying data associated with the request, such as artist data,
listener data, audio, and video data (see at least paragraph 0062), and Perelli further teaches the model input 208 is configured to be received as input by one or more of the model(s) 128. By way of example, the model input 208 may be configured as one or more feature vectors or context-based artist fingerprints generated in connection
with the request 140 (see at least paragraph 0082); and
computing a component of the vector distance associated with the quantitative data comprises computing an ordinal rank for each dimension of the plurality of dimensions associated with the quantitative data, Perelli teaches the collaboration system 126 may also compare the metrics generated for at least two different artists 108, such that insights can be provided for recommending one artist 108 as a collaborator 112 instead of one or more other artists 108. In one or more implementations, the collaboration system 126 may also decide which insight about a recommended artist 108 to surface via the user interface 302 based on an availability of the insight and/or ranking criteria for insights (see at least paragraph 0096).
Claims 7 and 17: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein:
each vector of the one or more vectors comprises a plurality of dimensions
corresponding to components of the entity data, Perelli teaches the correlation
engine 130 may then determine a context-based artist fingerprint that corresponds to the request 140 and/or under lying data associated with the request, such as artist data,
listener data, audio, and video data, and Perelli further teaches the model input 208 is configured to be received as input by one or more of the model(s) 128. By way of example, the model input 208 may be configured as one or more feature vectors
or context-based artist fingerprints generated in connection with the request 140 (see at least paragraph 0082); and
computing a component of the vector distance associated with the quantitative data comprises normalizing the plurality of dimensions associated with the quantitative data, Perelli teaches the request processing module 204 obtains one or more of the media content 122 or the artist data 124 from the storage 118, and uses this data to generate the model input 208. For example, the request processing module 204 may generate an artist fingerprint indicative of one or more media content items ( or all the media content items) associated with the artist 110 (e.g., songs of the artist 110) (see at least paragraph 0082), and Perelli further teaches this artist fingerprint is then compared with one or more context-based artists fingerprints of other artists in a database ( e.g., in the storage 118) to identify a match, e.g., at the time when the artist fingerprint is taken. Matches need not be exact in various implementations, due to differences in levels, extraneous noise, imprecise or uncorrelated start times (see at least paragraph 0085).
Claims 10 and 20: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein identifying the one or more second entities further comprises selecting the one or more second entities associated with minimized vector distances, Perelli teaches a subset of the artists in the artist population may
then be surfaced to the artist based on the relevancy scores, e.g., by selecting the artists with the highest relevancy scores to surface as potential collaborators to the artist (see at least paragraph 0036, and Perelli further teaches the correlation engine 130 may generate match scores for the match of an artist fingerprint to other fingerprints of artists in the database, and identify as matching an artist fingerprint with a highest match score (see at least paragraph 0062).
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
13. Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Perelli-Minetti (U.S. Pub. No. 2024/00864480) (hereinafter ‘Perelli’) in view of Salkola (U.S. Patent no. 10,803,050) and further in view of Xie et al. (U.S. Patent No. 12,399,922) (hereinafter “Xie’).
Claims 8 and 18: Perelli discloses the system according to claims 7 and 17, and Perelli further teaches, wherein normalizing the plurality of dimensions associated with the quantitative data, Perelli teaches matches need not be exact in various implementations, due to differences in levels, extraneous noise, imprecise or uncorrelated start times, etc., but may be considered to be matching if two artist fingerprints correspond above a threshold. In other implementations, the correlation engine 130 may compare the received artist fingerprint to other artist fingerprints over a prior period, such as a five-minute period (see at least paragraph 0085). While Perelli teaches the limitation above, Perelli does not mexplicitly teach a method selected from min-max scaling and decimal scaling. However, Salkola teaches features used to calculate the vector may be based on information obtained from edge detection, corner detection, blob detection, ridge detection, scale-invariant feature transformation, edge direction, changing intensity, autocorrelation, motion detection, optical flow, thresholding, blob extraction, template matching (see at least column 46 lines 33-57). It would have been obvious for Perelli to modify to include the teaching of Salkola in order to map text to a vector representation.
Claims 9 and 19: Perelli discloses the system according to claims 1 and 11, and Perelli further teaches wherein computing the vector distance, Perelli teaches ascertain whether the artist's audience overlaps with a collaborating artist (see at least paragraph 0025). While Perelli teaches the limitation mentioned above, Perelli does not explicitly teach a method selected from computing a Euclidean distance, a Minkowski distance, a Manhattan distance, and a Chebyshev distance. However, Salkola teaches Euclidean distance and Minkowski distance (see at least column 47 lines 36-63). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Parelli to modify to include the teaching of Salkola in order to determine similarity metrics between two vectors in any suitable manner. While Salkola teaches Euclidean and Minkowski distance, Salkola does not explicitly teach Manhattan and Chebyshev distance. However, Xie teaches Manhattan and Chebyshev distance (see at least column 10 lines 46-55). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Parelli and Salkola to modify to include the teaching of Xie in order to determine similarity metrics between the similarity of the history query information to the query vector may be represented by the distance between the history query information and the query vector.
Conclusion
14. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
15. Pavletic et al. (U.S. Pub. No. 2019/0295114) discloses the Euclidean value for a distance may be determined using a Pythagorean or other approach, whereby a subset of the dimensions are used to determine a distance in N dimensional space. This distance can be normalized to establish a pseudo-correlation score representative of the system's estimation of a difference or other type of distance measure between users, which is then utilized to determine an effective “cost” of sending a referral to the other user. As noted in the example below, a user may be able to share 10 offers with similar users or only 2 with dissimilar users, the similarity estimated from the user feature vectors (see at least paragraph 0128).
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 12/26/2025