MACHINE-LEARNED MODEL SCORING TECHNIQUE FOR REDUCING MODEL INVOCATIONS

Application No. 17/346,196

• Filed: Jun 11, 2021
• Examiner: GORMLEY, AARON PATRICK
• Art Unit: 2148
• Tech Center: 2100 — Computer Architecture & Software
• Assignee: Microsoft Technology Licensing, LLC
• OA Round: 2 (Non-Final)

Office Action

DETAILED ACTION
This action is in response to the application filed 06/11/2021. Claims 1-20 are pending and have been examined.

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 .

Claim Interpretation
Claims 10-20 refer to “storage media”. Paragraph [0112] of the instant Specification states, “The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media”. Accordingly, storage media is not interpreted to include transitory signals per se.

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 inventions are directed to non-statutory subject matter without significantly more.

Claim 1
Step 1: The claim recites “A method”, and is therefore directed to the statutory category of process
Step 2A Prong 1: The claim recites the following judicial exception(s)
identifying first values for a set of non-entity pair-specific interaction features that are associated with a first entity, wherein the first entity comprises a source of an attribute, product, or service … wherein a non-entity pair-specific interaction feature pertains to an interaction involving a third entity different from the first entity and the second entity: This can be performed as a mental process. One can mentally construct features based on the average interactions of several salesperson selling to the same person.
identifying second values for a set of entity pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity, wherein an entity pair-specific interaction feature pertains to an interaction between the first entity and the second entity … and wherein the second entity comprises a target of the source of the attribute, product or service: This can be performed as a mental process. One can mentally construct features based on the interactions of a single salesperson selling to one target buyer.
generating … a first score based on the first values can be performed as a mental process. One could simply assign a score proportional to the effectiveness of the salespeople represented by the first values of non-entity pair-specific interaction features.
generating … a second score based on the first values and the second values, wherein the second score is different than the first score can be performed as a mental process. One could simply assign a score proportional to the effectiveness of the salesperson represented by the second values of entity pair-specific interaction features.
computing a final score for a first entity-second entity pair based on the first score and the second score, wherein the first entity-second entity pair comprises a pairing of the first entity and the second entity, and wherein the final score comprises a prediction of whether the target entity is likely to perform an action associated with the source entity can be performed as a mental process. One can simply sum the first and second scores.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity is mere instruction to use the exception to transmit data. Mere instruction to apply does not constitute practical integration (MPEP 2106.05(f)).
wherein the method is performed by one or more computing devices: Mere instruction to execute a judicial exception by a generic computing device does not constitute practical integration (MPEP 2106.05(f)).
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity is mere instruction to use the exception to transmit data. Mere instruction to apply does not amount to significantly more (MPEP 2106.05(f)).
wherein the method is performed by one or more computing devices: Mere instruction to execute a judicial exception by a generic computing device does not amount to significantly more (MPEP 2106.05(f)).

Claim 2
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
identifying third values that are associated with the first entity and the third entity that is different than the second entity can be performed as a mental process. A person can mentally construct features related to interactions between a pair of first and third entities they perceive and assign values to those features.
generating … a third score based on the first values and the third values, wherein the third score is different than the first score and the second score can be performed as a mental process. One could simply sum the first and third values as a score.
computing a final score for a first entity-third entity pair based on the first score and the third score, wherein the first entity-third entity pair comprises a pairing of the first entity and the third entity can be performed as a mental process. One could simply sum the first and third scores together for a final score.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
generating, by the machine-learned model, a third score based on the first values and the third values, wherein the third score is different than the first score and the second score: This is mere instruction to execute a judicial exception with a generic data structure (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
generating, by the machine-learned model, a third score based on the first values and the third values, wherein the third score is different than the first score and the second score: This is mere instruction to execute a judicial exception with a generic data structure (MPEP 2106.05(f)).

Claim 3
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the following judicial exception(s)
[W]herein computing the final score comprises computing a ratio of the second score to the first score: This can be performed as a mental process; one can easily compute a ratio of scores by hand. Consequently, the final score computation of claim 1 is still considered a mental process.
Step 2A Prong 2: The judicial exception is not related to any further additional elements. The analysis of claim 3 at this step mirrors that of claim 1.
Step 2B: The judicial exception is not related to any further additional elements. The analysis of claim 3 at this step mirrors that of claim 1.

Claim 4
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the following judicial exception(s)
[A]ssigning the final score to a scoring bucket, of the plurality of scoring buckets, that corresponds to a range of scores that includes the final score can be performed as a mental process. One could mentally assign a particular score to a particular bucket without any sort of computer interaction.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
[S]toring scoring bucket data about a plurality of scoring buckets, each scoring bucket corresponding to a different range of scores is mere data storage. This is insignificant extra-solution activity, and is not considered practical integration (MPEP 2106.05(g)).
[B]ased on the scoring bucket, performing one or more actions: Mere instruction to perform actions based on a judicial exception does not constitute practical integration (MPEP 2106.05(f)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
[S]toring bucket data about a plurality of scoring buckets, each scoring bucket corresponding to a different range of scores is considered storing information in memory, a process understood as a well-understood, routine, and conventional by the courts (MPEP 2106.05(d) II.iii., 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)
[B]ased on the scoring bucket, performing one or more actions constitutes mere instruction to apply an exception, and does not amount to significantly more (MPEP 2106.05(f)).

Claim 5
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the same judicial exception(s) as claim 4
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
[A]utomatically performing the one or more actions is tantamount to mere instruction to perform the one or more actions with a generic computing device or structure, anything with automatic functionality. Thus, this is not considered practical integration (MPEP 2106.05(f)).
[A]ssigning a target entity to a target audience of a content delivery operation, while further limiting the scope of the performable action(s), amounts to mere instruction to perform an action based on a scoring bucket and does not constitute practical integration (MPEP 2106.05(f)).
[S]ending an electronic message to a messaging account of the target entity, while further limiting the scope of the performable action(s), amounts to insignificant extra-solution activity of mere data transmission, and does not constitute practical integration (MPEP 2106.05(g)).
[S]ending, to an account of a source entity, a notification that identifies the target, while further limiting the scope of the performable action(s), amounts to mere instruction to perform an action based on a scoring bucket and does not constitute practical integration (MPEP 2106.05(f)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
[A]utomatically performing the one or more actions constitutes mere instruction to perform a judicial exception by a generic computing device, and does not amount to significantly more (MPEP 2106.05(f)).
[A]ssigning the target entity to a target audience of a content delivery operation based on the scoring bucket is mere instruction to apply, and does not amount to significantly more (MPEP 2106.05(f)).
[S]ending an electronic message to a messaging account of the target entity is considered data transmission, a process understood as a well-understood, routine, and conventional by the courts (MPEP 2106.05(d)).
[S]ending, to an account of the source entity, a notification that identifies the target entity based on the scoring bucket is mere instruction to apply, and does not amount to significantly more (MPEP 2106.05(f)).

Claim 6
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the following judicial exception(s)
[I]dentifying the first values comprises: for each user of the plurality of users, identifying a set of feature values that is associated with said each user. This can be performed as a mental process. For each user, one can mentally assign feature values to the effectiveness of some salesperson on them. Consequently, the identification of first values from claim 1 is still considered to be a mental process.
[I]dentifying the first values comprises ... aggregating the sets of feature values that are associated with the plurality of users to generate the first values. This can be performed as a mental process. One could simply add or average the corresponding feature values together. Consequently, the identification of first values from claim 1 is still considered to be a mental process.
[W]herein: the first entity comprises a plurality of users that have one or more attributes in common: This merely modifies the aforementioned mental processes.
Step 2A Prong 2: No further additional elements are introduced by this claim. Consequently, the analysis of claim 6 at this step mirrors that of claim 1.
Step 2B: No further additional elements are introduced by this claim. Consequently, the analysis of claim 6 at this step mirrors that of claim 1.

Claim 7
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the following judicial exception(s)
[I]dentifying the second value comprises: for each user of the plurality of users, identifying a set of feature values that is associated with said each user and the first entity. This can be performed as a mental process. For each user, one can mentally assign feature values proportional to the effectiveness of a salesperson on them. Consequently, the identification of first values from claim 1 is still considered to be a mental process.
[I]dentifying the second value comprises ... aggregating the sets of feature values that are associated with the plurality of users to generate the second values. This can be performed as a mental process. One could simply add or average the corresponding feature values together. Consequently, the identification of first values from claim 1 is still considered to be a mental process.
[W]herein: the second entity comprises a plurality of users that have one or more attributes in common: This merely modifies the aforementioned mental processes.
Step 2A Prong 2: No further additional elements are introduced by this claim. Consequently, the analysis of claim 7 at this step mirrors that of claim 1.
Step 2B: No further additional elements are introduced by this claim. Consequently, the analysis of claim 7 at this step mirrors that of claim 1.

Claim 8
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the same judicial exception(s) as claim 1
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
[W]herein the training data comprises a plurality of training instances, wherein each training instance includes a label that indicates whether the first entity performed one or more of the following: accepted an electronic message invitation, filled out a digital form, acquired a particular item, visited a particular webpage, or selected a particular content item: While this further limits the scope of what the “training data” of claim 1 can include, it merely restricts generating, by a machine-learned model ... a first score and a second score to the field of internet user-interactions. Generally linking a judicial exception to a particular field of use is not considered practical integration (MPEP 2106.05(h)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
The further limitations of claim 8 merely link generating, by a machine-learned model ... a first score and a second score to a particular field of use (internet user-interactions), which does not amount to significantly more (MPEP 2106.05(h)).

Claim 9
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the same judicial exceptions as claim 1
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
causing, to be transmitted over the computer network to be presented on the computing device, factor data that (1) identifies a plurality of factors that affect the final score and (2) includes an interest category of each factor of the plurality of factors based on interactions between the first entity and the second entity: This is mere transmission of data, and is thus considered to be insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
The transmission of data over a network is considered by the courts to be well-understood, routine, and conventional (MPEP 2106.05(d)(II)), (Intellectual Ventures v. Symantec 838, F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016)). Thus, causing to be transmitted over the computer network ... factor data is not considered to amount to significantly more than the recited judicial exceptions.

Claim 10
Step 1: The claim recites “One or more non-transitory storage media”, and is therefore directed to the statutory category of article of manufacture
Step 2A Prong 1: The claim recites the following judicial exception(s)
identifying first values for a set of non-entity pair-specific interaction features that are associated with a first entity, wherein the first entity comprises a source of an attribute, product, or service … wherein a non-entity pair-specific interaction feature pertains to an interaction involving a third entity different from the first entity and the second entity: This can be performed as a mental process. One can mentally construct features based on the average interactions of several salesperson selling to the same person.
identifying second values for a set of entity pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity, wherein an entity pair-specific interaction feature pertains to an interaction between the first entity and the second entity … and wherein the second entity comprises a target of the source of the attribute, product or service: This can be performed as a mental process. One can mentally construct features based on the interactions of a single salesperson selling to one target buyer.
generating … a first score based on the first values can be performed as a mental process. One could simply assign a score proportional to the effectiveness of the salespeople represented by the first values of non-entity pair-specific interaction features.
generating … a second score based on the first values and the second values, wherein the second score is different than the first score can be performed as a mental process. One could simply assign a score proportional to the effectiveness of the salesperson represented by the second values of entity pair-specific interaction features.
computing a final score for a first entity-second entity pair based on the first score and the second score, wherein the first entity-second entity pair comprises a pairing of the first entity and the second entity, and wherein the final score comprises a prediction of whether the target entity is likely to perform an action associated with the source entity can be performed as a mental process. One can simply sum the first and second scores.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause [operations]: This is mere instruction to execute the judicial exceptions with generic computer hardware (MPEP 2106.05(f)).
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity is mere instruction to use the exception to transmit data. Mere instruction to apply does not constitute practical integration (MPEP 2106.05(f)).
wherein the method is performed by one or more computing devices: Mere instruction to execute a judicial exception by a generic computing device does not constitute practical integration (MPEP 2106.05(f)).
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause [operations]: This is mere instruction to execute the judicial exceptions with generic computer hardware (MPEP 2106.05(f)).
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity is mere instruction to use the exception to transmit data. Mere instruction to apply does not amount to significantly more (MPEP 2106.05(f)).
wherein the method is performed by one or more computing devices: Mere instruction to execute a judicial exception by a generic computing device does not amount to significantly more (MPEP 2106.05(f)).

Claims 11-18
Step 1: Claims 11-18 recite “One or more storage media storing instructions”, and are directed to an article of manufacture.
Step 2A Prong 1: Claims 11-18 recite the same judicial exception(s) as claims 2-9, respectively.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through any additional elements. The analysis of claims 11-18 at this step mirrors that of claims 2-9, respectively, with the exception that claims 11-18 are directed to “One or more storage media storing instructions which, when executed by one or more processors, cause: [operations]”, said operations consisting of those from claims 2-9. As defined in the instant application, “The term ‘storage media’ as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media” (Instant application, [0112]). This is a mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s). The analysis of claims 11-18 at this step mirrors that of claims 2-9, with the exception that claims 10-18 are directed to “One or more storage media storing instructions which, when executed by one or more processors, cause: [operations]”, said operations mirroring those of claims 2-9. This is mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).

Claim 19
Step 1: The claim recites “A system”, and is therefore directed to the statutory category of article of manufacture
Step 2A Prong 1: The claim recites the following judicial exception(s)
identifying first values for a set of non-entity pair-specific interaction features that are associated with a first entity, wherein the first entity comprises a source of an attribute, product, or service … wherein a non-entity pair-specific interaction feature pertains to an interaction involving a third entity different from the first entity and the second entity: This can be performed as a mental process. One can mentally construct features based on the average interactions of several salesperson selling to the same person.
identifying second values for a set of entity pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity, wherein an entity pair-specific interaction feature pertains to an interaction between the first entity and the second entity … and wherein the second entity comprises a target of the source of the attribute, product or service: This can be performed as a mental process. One can mentally construct features based on the interactions of a single salesperson selling to one target buyer.
generating … a first score based on the first values can be performed as a mental process. One could simply assign a score proportional to the effectiveness of the salespeople represented by the first values of non-entity pair-specific interaction features.
generating … a second score based on the first values and the second values, wherein the second score is different than the first score can be performed as a mental process. One could simply assign a score proportional to the effectiveness of the salesperson represented by the second values of entity pair-specific interaction features.
computing a final score for a first entity-second entity pair based on the first score and the second score, wherein the first entity-second entity pair comprises a pairing of the first entity and the second entity, and wherein the final score comprises a prediction of whether the target entity is likely to perform an action associated with the source entity can be performed as a mental process. One can simply sum the first and second scores.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
A system comprising: one or more processors; one or more non-transitory storage media storing instructions which, when executed by the one or more processors, cause [operations]: This is mere instruction to execute the judicial exceptions with generic computer hardware (MPEP 2106.05(f)).
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity is mere instruction to use the exception to transmit data. Mere instruction to apply does not constitute practical integration (MPEP 2106.05(f)).
wherein the method is performed by one or more computing devices: Mere instruction to execute a judicial exception by a generic computing device does not constitute practical integration (MPEP 2106.05(f)).
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
A system comprising: one or more processors; one or more non-transitory storage media storing instructions which, when executed by the one or more processors, cause [operations]: This is mere instruction to execute the judicial exceptions with generic computer hardware (MPEP 2106.05(f)).
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity is mere instruction to use the exception to transmit data. Mere instruction to apply does not amount to significantly more (MPEP 2106.05(f)).
wherein the method is performed by one or more computing devices: Mere instruction to execute a judicial exception by a generic computing device does not amount to significantly more (MPEP 2106.05(f)).

Claim 20
Step 1: Claim 20 recites “A system comprising: one or more processors; one or more storage media storing instructions”, and is therefore directed to the statutory process of article of manufacture
Step 2A Prong 1: Claims 20 recites the same judicial exception(s) as claim 2
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through any additional elements. The analysis of claim 20 at this step mirrors that of claim 2, with the exception that claim 20 is directed to “A system comprising: one or more processors; one or more storage media storing instructions which, when executed by the one or more processors, cause: [operations]”, said operations mirroring those of claim 2. This is a mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s). The analysis of claims 20 at this step mirrors that of claim 2, with the exception that claim 20 is directed to “A system comprising: one or more processors; one or more storage media storing instructions which, when executed by the one or more processors, cause: [operations]”, said operations mirroring those of claim 2. This is mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).

Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.

The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.

Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.

Claim 1, limitation 4, describes “generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score;”. As described in limitations 1 and 2, the first values are for a set of non-entity pair-specific interaction features associated with a source entity and the second values are for a set of entity pair-specific interaction features associated with a target entity. While paragraph [0015] of the instant specification describes generating a score “based on values of non-interaction features of a target entity” and another score “based on the values of the non-interaction features and values of interaction features pertaining to the target entity and a source entity”, it does not disclose the generation of a nonfinal score based on the non-entity pair-specific interaction features of a source entity and the entity pair-specific interaction features of a target entity, and therefore this limitation appears to be new matter. Claims 10 and 19, which recite substantially similar limitations as claim 1, are rejected under the same rationale. Claims 2-9, 11-18, and 20 inherit this deficiency.

Claim 1, limitation 6, describes “causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity”, the first and second entities comprising a source and target, respectively. While paragraph [0069] the instant specification describes “The final score may be used to determine whether to present, on a computing device, an identity of the target entity (e.g., a name) to the source entity (or a representative thereof)”, the instant specification does not disclose presenting information about a source entity to a target entity’s computing device. Claims 10 and 19, which recite substantially similar limitations as claim 1, are rejected under the same rationale. Claims 2-9, 11-18, and 20 inherit this deficiency.

Claim 8, limitation 1, describes a method, “wherein each training instance includes a label that indicates whether the first entity performed one or more of the following: accepted an electronic message invitation, filled out a digital form, acquired a particular item, visited a particular webpage, or selected a particular content item”. Amended claim 1, the parent of claim 8, discloses that “the first entity comprises a source of an attribute, product, or service”. In light of this interpretation of the first entity, claim 8 discloses a source entity performing the aforementioned actions. While paragraph [0059] of the instant specification describes “Examples of actions that a target entity ( or an associated user) performs relative to a source entity (or an associated user or entity, such as a product) include filling out an electronic (e.g., web) form that is associated with (e.g., originated from) the source entity, responding to an electronic message from the source entity, selecting a content item associated with (e.g., provided/generated by, mentions) the source entity, scheduling a meeting with the source entity, meeting with the source entity, signing a contract with the source entity, watching at least three seconds of a video associated with (e.g., that mentions) the source entity, acquiring (e.g., purchasing) a product/service manufactured/hosted by the source entity, selecting a notification about (e.g., mentions) the source entity, and accepting an invitation to connect with the source entity”, the specification does not disclose the ability of a source entity to perform these actions. Claim 17, which recites substantially similar limitations as claim 8, is rejected under the same rationale.

The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.

The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.

Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.

Claim 1 recites the limitations "the target entity" and “the source entity” in limitation 5. There is insufficient antecedent basis for these limitations in the claim. This rejection is inherited by dependent claims 2-9. These limitations are interpreted as referring to the second entity and the first entity, respectively.
Claim 10 recites the limitations "the target entity" and “the source entity” in limitation 5. There is insufficient antecedent basis for these limitations in the claim. This rejection is inherited by dependent claims 11-18. These limitations are interpreted as referring to the second entity and the first entity, respectively.
Claim 19 recites the limitations "the target entity" and “the source entity” in limitation 7. There is insufficient antecedent basis for these limitations in the claim. This rejection is inherited by dependent claim 20. These limitations are interpreted as referring to the second entity and the first entity, respectively.

Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.

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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.

Claim(s) 1-2, 6-7, 9-11, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fei et al. (US 2019/0130444 A1) in view of Iannaccone et al. (US 2019/0018897 A1).

Regarding claim 1, Fei teaches [a] method, comprising:
identifying first values for a set of non-entity pair-specific interaction features that are associated with a first entity: “The action logger 215 receives communications about user actions internal to and/or external to the online system 110, populating the action log 220 with information about user actions … a number of actions may involve an object and one or more particular users (multiple first entit[ies]), so these actions are associated with those users as well and stored in the action log 220” (Fei, [0030]); “The action logger is used by the online system to track user actions on the online system, as well as actions on third-party systems that communicate information to the online system (first values for a set of non-entity pair-specific interaction features). Users may interact with various objects on the online system, and information describing these interactions is stored in the action log” (Fei, [0031])
wherein the first entity comprises a source of an attribute, product, or service: “Each user (first entity) of the online system 110 is associated with a user profile, which is stored in the user database 205 … In one embodiment, a user profile includes multiple data fields, each describing one or more attributes of the corresponding online system user” (Fei, [0022]).
… wherein a non-entity pair-specific interaction feature pertains to an interaction involving a third entity different from the first entity and the second entity: “The action logger is used by the online system to track user actions on the online system, as well as actions on third-party systems that communicate information to the online system. Users may interact with various objects (third entit[ies]) on the online system, and information describing these interactions (first values for a set of non-entity pair-specific interaction features) is stored in the action log” (Fei, [0031]).
identifying second values for a set of entity pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity, wherein an entity pair-specific interaction feature pertains to an interaction between the first entity and the second entity: “The edge store stores information describing connections between users and other objects of the online system as edges. Some edges may be defined by users, allowing users (first entit[ies]) to specify their relationships with other users (second entit[ies]). Other edges are generated when users interact with objects in the online system” (Fei, [0033]); “An edge includes various features representing characteristics of interactions between users (pair-specific interaction features), interactions between users and objects, or interactions between objects. For example, features included in an edge describe the rate of interaction between two users, how recently two users have interacted with each other” (Fei, [0034]). A pair-specific interaction feature covers an interaction between two different users.
… wherein the second entity comprises a target of the source of the attribute, product or service: “Various content items may include an objective identifying an interaction that a user (source entity) associated with a content item desires other users (target entity) to perform when presented” (Fei, [0027]). Users can be both sources of attributes and targets of other users.
generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-entity pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values:
”The user interaction score (first score) ... may be determined based on a predictive model according to various user characteristics and/or content item characteristics … In some embodiments, the user interaction score is affected by a value (e.g., a bid amount) associated with the user action associated with the content item (first values)” (Fei, [0037]).
“The ratings module uses machine learning techniques to generate and train the ratings model to output predicted user values (second scores)” (Fei, [0049]). The ratings model is explicitly trained with machine learning.
“[T]he scoring module calculates a content score by combining the predicted user value and a user interaction score” (Fei, [0059]). The outputs of the ratings model and the predictive model are used as input for the scoring model. As such, these three models can collectively be considered an ensemble machine learning model.
generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score: “The ratings module uses machine learning techniques to generate and train the ratings model to output predicted user values (second scores) that indicate whether a content item is of high or low quality” (Fei, [0049]); “Once trained, the ratings model uses as input embeddings for a first user and an online system page and edge factors for the first user to generate a predicted quality for a content item” (Fei, [0043]); “An edge includes various features representing characteristics of interactions between users (second values), interactions between users and objects (first values), or interactions between objects” (Fei, [0034]).
computing a final score for a first entity-second entity pair based on the first score and the second score, wherein the first entity-second entity pair comprises a pairing of the first entity and the second entity: “[T]he scoring module calculates a content score (final score) by combining the predicted user value (second score) and a user interaction score (first score)” (Fei, [0059]).
wherein the final score comprises a prediction of whether the target entity is likely to perform an action associated with the source entity: “The scoring module calculates the content score for the content item based on the predicted user value and a user interaction score indicating a likelihood that users will interact with the content item” (Fei, [0008]).
based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity: “If the content score (final score) exceeds a content score threshold, a content selection module selects the content item for display to the user” (Fei, [0008])
wherein the method is performed by one or more computing devices: “These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs” (Fei, [0061]).
While Fei fails to disclose the further limitations of the claim, Iannaccone discloses a method of generating a machine-learned model that has been trained: “[T]he feature generation module may utilize a predictor model (machine-learned model) to generate the weights. To train the predictor, the feature generation module 230 may use as training data the historical data for the third party system” (Iannaccone, [0037]); “The predictor model, after training, assigns higher weights to those features that are most predictive of the resulting value for a user” (Iannaccone, [0038]).
Iannaccone and Fei both relate to machine learning techniques for recommender systems and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to use a trained machine learning model to generate a first score, as disclosed by Iannaccone. A trained model assigns higher weights to those features most predictive of the resulting value, as disclosed by Iannaccone [0038].

Regarding claim 2, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Fei further teaches a method, comprising:
identifying third values that are associated with the first entity and the third entity that is different than the second entity: “The action logger is used by the online system to track user actions on the online system, as well as actions on third-party systems that communicate information to the online system. Users may interact with various objects (third entit[ies]) on the online system, and information describing these interactions (third values) is stored in the action log” (Fei, [0031]).
generating, by the machine-learned model, a third score based on the first values and the third values, wherein the third score is different than the first score and the second score: “In one embodiment, affinity scores (third score[s]), or "affinities," are computed by the online system 110 over time to approximate a user's interest in an object or another user in the online system 110 based on the actions performed by the user (third values). A user's affinity may be computed by the online system 110 over time to approximate a user's affinity for an object, interest, and other users in the online system 110 based on the actions performed by the user” (Fei, [0035]).
computing a final score for a first entity-third entity pair based on the first score and the third score, wherein the first entity-third entity pair comprises a pairing of the first entity and the third entity: “[I]nput to the ratings model includes the user's affinity (third score) for the content provider” (Fei, [0052]); “The ratings model uses the input data to calculate a predicted user value for the content item representing the predicted rating that the first user would assign to the content item and sends the predicted user value to the scoring module for calculation a content score” (Fei, [0053]); “[T]he scoring module calculates a content score (final score) by combining the predicted user value and a user interaction score (first score)” (Fei, [0059]).

Regarding claim 6, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Iannaccone further teaches a method wherein:
[T]he first entity comprises a plurality of users that have one or more attributes in common: “Embodiments include an online system that identifies seed users (first entity) with a high value score to a third party system” (Iannaccone, [0005]).
[I]dentifying the first values comprises: for each user of the plurality of users, identifying a set of feature values that is associated with said each user: “[A]n edge store stores information describing connections between users and other objects on the online system as edges ... Other edges are generated when users interact with objects in the online system (non-entity pair-specific interaction features)” (Iannaccone, [0027]); “[T]he feature generation module identifies features for these seed users (first entity). A feature is data that is or describes some information related to the seed user ... A feature may include information about the user in the ... edge store” (Iannaccone, [0033])
[I]dentifying the first values comprises ... aggregating the sets of feature values that are associated with the plurality of users to generate the first values: “The identified features (sets of feature values associated with the plurality of users) are divided into a plurality of buckets or groups, each bucket indicating a property associated with one or more of the identified features, the plurality of buckets having non-overlapping identified features” (Iannaccone, [0006]); “To weight the buckets, in one embodiment, the bucket ranking module adds the weights of the features within each bucket, and determines a cumulative score for each bucket (first values) that includes the sums of all the weights of all the features within each bucket” (Iannaccone, [0056]).
Iannaccone and Fei both relate to machine learning methods for recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to identify features for each user, as disclosed by Iannaccone. Doing so would enable them to identify users with high value relative to a particular interest group, which may be useful to a related third-party. See Iannaccone [0036]. It would also have been obvious to have modified Fei to aggregate the sets of features to generate the first values. Doing so would make the features easier to parse semantically and easier to present as factors. See Iannaccone, [0055].

Regarding claim 7, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Iannaccone further teaches a method wherein:
[T]he second entity comprises a plurality of users that have one or more attributes in common: “The online system identifies one or more additional users (second entity) that have a threshold measure of similarity to the seed users” (Iannaccone, [0007]).
[I]dentifying the second values comprises: for each user of the plurality of users, identifying a set of feature values that is associated with said each user and the first entity: “The feature generation module generates features and weights (set of feature values) used to match additional users (plurality of users / second entity) with a group of seed users (first entity) provided by a third party system based on a set of similar features shared between the additional users and the group of seed users” (Iannaccone, [0031]); “[A]n edge store stores information describing connections between users and other objects on the online system as edges. Some edges may be defined by users, allowing users to specify their relationships with other users (entity pair-specific interaction features)” (Iannaccone, [0027]); “A feature is data that is or describes some information related to the seed user ... A feature may include information about the user in the ... edge store” (Iannaccone, [0033]).
[I]dentifying the second values comprises ... aggregating the sets of feature values that are associated with the plurality of users to generate the second values: “The identified features (sets of feature values associated with the plurality of users) are divided into a plurality of buckets or groups, each bucket indicating a property associated with one or more of the identified features, the plurality of buckets having non-overlapping identified features” (Iannaccone, [0006]); “To weight the buckets, in one embodiment, the bucket ranking module adds the weights of the features within each bucket, and determines a cumulative score for each bucket (second values) that includes the sums of all the weights of all the features within each bucket” (Iannaccone, [0056]).
Iannaccone and Fei both relate to machine learning methods for recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to identify features for each user, as disclosed by Iannaccone. Doing so would enable them to identify users with high value relative to a particular interest group, which may be useful to a related third-party. See Iannaccone [0036]. It would also have been obvious to have modified Fei to aggregate the sets of features to generate the second values. Doing so would make the features easier to parse semantically and easier to present as factors. See Iannaccone, [0055].

Regarding claim 9, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Iannaccone further teaches a method comprising:
[C]ausing, to be transmitted over the computer network to be presented on the computing device, factor data: “[T]he primary factor presentation module presents a primary factor (or multiple factors) (factor data) to the client device associated with an additional user” (Iannaccone, [0083]); “The client devices are one or more computing devices capable of receiving user input as well as transmitting and/or receiving data via the network” (Iannaccone, [0016]).
... factor data that (1) identifies a plurality of factors that affect the final score: “The identified features (factors that affect the final score) are divided into a plurality of buckets or groups, each bucket indicating a property associated with one or more of the identified features” (Iannaccone, [0006]); “Additionally, in some embodiments, the online system transmits one or more third party-presentable factors (factor data) based on the bucket having the highest rank for the third party system” (Iannaccone, [0008]).
... factor data that ... (2) includes an interest category of each factor of the plurality of factors based on interactions between the first entity and the second entity: “The primary factor presentation module presents a user-presentable (i.e., user-friendly) factor to a user and/or a third party-presentable factor to a third party system, with the presentable factor indicating to either the user or the third party system a primary reason (interest category) for why the user was selected for distribution and presentation of a particular content item from the third party system” (Iannaccone, [0058]); “The user-presentable factor and/or the third party presentable factor may be based on 1) features (interaction [features] between the first entity and the second entity) in the highest ranked bucket in the feature bucket list” (Iannaccone, [0059]).
Iannaccone and the instant application both relate to machine learning methods for recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to transmit data of factors that affected the final score, as disclosed by Iannaccone. Doing so would assist the recipient in estimating which other users would likely receive the same recommendations, and inform them why they were selected. See Iannaccone [0048]. It would also have been obvious to have modified Fei to transmit interest categories for the factors. Doing so would make the data more user-friendly. See Iannaccone [0062].

Regarding claims 10-11, 15-16, and 18, Fei teaches a structure of [o]ne or more storage media storing instructions which, when executed by one or more processors, cause [a method to execute]: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium (storage media) containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” (Fei, [0061]). The methods stored in the instructions of claims 10-11, 15-16, and 18 mirror those taught by claims 1-2, 6-7, and 9, respectively. Therefore, claims 10-11, 15-16, and 18 are rejected in view of Fei and Iannaccone for the same reasons given above for claims 1-2, 6-7, and 9, respectively.

Regarding claims 19-20, Fei teaches [a] system comprising: one or more processors; one or more storage media storing instructions which, when executed by the one or more processors, cause [a method to execute]: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium (storage media) containing computer program code (instructions), which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” (Fei, [0061]). The methods stored in the instructions of claims 19-20 mirror those taught by claims 1-2, respectively. Therefore, claims 19-20 are rejected in view of Fei and Iannaccone for the same reasons given for claims 1-2, respectively.

Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Fei et al. (US 2019/0130444 A1) in view of Iannaccone et al. (US 2019/0018897 A1), and further in view of Palmert et al. (US 2014/0019544 A1).

Regarding claim 3, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Fei and Iannaccone fail to disclose the further limitations of claim 3, but Palmert teaches a method, wherein computing the final score comprises computing a ratio of the second score to the first score: “A ratio of the adjusted, i.e. normalized affinity score (second score) to a measure of the amount of information in the user's feed regarding the entity (first score) can be calculated” (Palmert, [0041]). The affinity score describes the relationship between a user and an entity, which can be another user: “[T]he data entries including: an identification of an entity, an identification of a user having a social networking relationship with the entity, and an affinity score (second score) indicating an amount of interaction by the user with the entity” (Palmert, Claim 1); “[T]he disclosed techniques can be implemented to optimize a first user's news feed by ferreting out entities such as groups, records, posts and second users” (Palmert, [0041]). The measure of information quantifies the presence of an entity in a user’s feed, the entity possibly being a non-user object such as a record or post.
Palmert, Fei and Iannaccone relate to computer recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to compute a final score with a ratio of the second to the first score, as disclosed by Palmert. Doing so would enable response actions to be selected based on a dynamic threshold for individual users, conditionally sending a recommendation depending on whether the score is above or below it. See Palmert, [0279-0280].

Regarding claim 12 Fei teaches a structure of [o]ne or more storage media storing instructions which, when executed by one or more processors, cause [a method to execute]: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium (storage media) containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” (Fei, [0061]). The method stored in the instructions of claim 12 mirrors that of claim 3. Therefore, claim 12 is rejected in view Fei, Iannaccone, and Palmert for the same reasons given for claim 3.

Claims 4-5 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Fei et al. (US 2019/0130444 A1) in view of Iannaccone et al. (US 2019/0018897 A1), and further in view of Makashir et al. (US 11,195,522 B1).

Regarding claim 4, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Fei and Iannaccone fail to teach the further limitations of claim 4, but Makashir teaches a method comprising:
[S]toring scoring bucket data about a plurality of scoring buckets, each scoring bucket corresponding to a different range of scores: “In various examples, the scores may be separated into two or more bins” (Makashir, Col. 5, lines 46-47); “Each bin may represent a continuous range of confidence scores” (Makashir, Col. 19, lines 47-48).
[A]ssigning the final score to a scoring bucket, of the plurality of scoring buckets, that corresponds to a range of scores that includes the final score: “Various machine learning algorithms may be used to implement the FIRM service” (Makashir, Col. 21, lines 2-3); “In various examples, the confidence scores output (final scores) by the FIRM service 295 may be separated into confidence score bins” (Makashir, Col. 9, lines 25-27).
[B]ased on the scoring bucket, performing one or more actions: “Applications may be referred to herein as ‘skills’” (Makashir, Col. 3, lines 53-54); “[P]rior to executing the determined action, the skill may send a request to FIRM service computing device implementing a FIRM service” (Makashir, Col. 8, lines 15-16); “The FIRM service may output a confidence score. At action the skill computing device(s) may determine the bin that is associated with the confidence score. At action the skill may determine the appropriate action to take in response to the bin identifier” (Makashir, Col. 11, lines 30-35).
Makashir, Fei and Iannaccone relate to machine learning methods for recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to place scores into buckets and perform an action accordingly, as disclosed by Makashir. Doing so would allow the system to execute more relevant actions corresponding to the bucket destination of the score or a threshold relative to it. See Makashir Col. 9 lines 54-64, Col. 10 lines 11-17.

Regarding claim 5, the rejection of claim 4 in view of Fei, Iannaccone, and Makashir is incorporated. While Fei and Iannaccone fail to teach the further limitations of claim 5, Makashir further teaches a method wherein automatically performing the one or more actions comprises one or more of: assigning a target entity to a target audience of a content delivery operation; sending an electronic message to a messaging account of the target entity; or sending, to an account of a source entity, a notification that identifies the target entity: “At action, the skill (target entity) may send the determined action to speech processing computing device(s) (source entity) as a directive” (Makashir, Col. 10, lines 38-39); “[A] directive may be a signal (notification) sent from a skill (e.g., from skill computing device(s)) to speech processing computing device(s)” (Makashir, Col. 10, lines 45-47); While the target entity isn’t explicitly identified by the signal transmitted, the packet carrying it will contain a sending address that identifies the computer or application of the target entity sender.
Makashir, Fei and Iannaccone relate to machine learning methods for recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to send a notification identifying the target entity to the source entity. Doing so would allow the source entity and other devices in communication with it to perform relevant actions. See Makashir Col. 10 lines 48-67 & Col. 11 lines 1-17.

Regarding claims 13-14, Fei teaches a structure of [o]ne or more storage media storing instructions which, when executed by one or more processors, cause [a method to execute]: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium (storage media) containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” (Fei, [0061]). The methods stored in the instructions of claims 13-14 mirror those of claims 4-5, respectively. Therefore, claims 13-14 are rejected in view of Fei, Iannaccone, and Makashir for the same reasons given for claims 4-5, respectively.

Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Fei et al. (US 2019/0130444 A1) in view of Iannaccone et al. (US 2019/0018897 A1), and further in view of Chordia et al. (US 20180293611 A1).

Regarding claim 8, the rejection of claim 1 in view of Fei and Iannaccone is incorporated. Fei and Iannaccone fail to disclose the further limitations of claim 8, but Chordia teaches a method wherein the training data comprises a plurality of training instances, wherein each training instance includes a label that indicates whether the first entity performed one or more of the following: accepted an electronic message invitation, filled out a digital form, acquired a particular item, visited a particular webpage, or selected a particular content item: “The training is done via machine learning techniques ... associating features of the training group with the target interest ... these features can include ... views or visits to a profile or page by the user (visited a particular webpage)” (Chordia, [0046]).
Chordia, Fei and Iannaccone relate to machine learning methods for recommender systems and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Fei to train the machine learning model with labels indicating whether a user visited a particular webpage, as disclosed by Chordia. Doing so would train the model around features potentially relevant to whether or not users have target interests. See Chordia [0034].

Regarding claim 17, Fei teaches a structure of [o]ne or more storage media storing instructions which, when executed by one or more processors, cause [a method to execute]: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium (storage media) containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” (Fei, [0061]). The method stored in the instructions of claim 17 mirrors that of claim 8. Therefore, claim 17 is rejected in view of Fei, Iannaccone, and Chordia for the same reasons given for claim 8.

Response to Arguments
The following responses address arguments and remarks made in the instant remarks dated 06/05/2025.

Objections
Objections to the specification have been withdrawn in light of the instant amendments.
Objections to the drawings have been withdrawn in light of the replacement sheet.
Objections to the claims have been withdrawn in light of the instant amendments.

112 Rejections
In light of the instant amendments to the claims, the previous 112(b) rejections of claim(s) 1-20 are withdrawn. However, the Examiner notes the 112(a) and 112(b) rejections for claims 1-20 in light of the instant amendments.

101 Rejections
On page 12 of the instant remarks, the Applicant argues that the 101 rejection of claim 1, as amended, should be withdrawn, due to some steps of the claim not being practically performable in the human mind:
“ Here, claim 1 as amended is not a mental process at least because the steps of the claim cannot practically be performed entirely in the human mind. The human mind is simply not equipped to perform the detailed steps of claim 1, including ‘generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values; generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score; computing a final score for a first entity-second entity pair based on the first score and the second score, wherein the first entity-second entity pair comprises a pairing of the first entity and the second entity, and wherein the final score comprises a prediction of whether the target entity is likely to perform an action associated with the source entity; based on the final score, causing data about the first entity to be transmitted over a computer network to be presented on a computing device associated with the second entity.’
For at least these reasons, the rejection should be withdrawn.”
In regards to the Applicant’s arguments above, the Examiner respectfully disagrees that claim 1, as amended, recites no mental processes. As stated in MPEP 2106.04(a)(2)(III), The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 … Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Claim 1 recites limitations amounting to mental processes performed on generic data structures. Generic data structures amount to generic computer components and are insufficient to render a mentally performable task non-abstract. For example, claim 1 recites the limitation “generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score”, reciting a mental process of “generating a second score based on the first value sand the second values” performed by a machine-learned model, a generic data structure insufficient to render the limitation non-abstract.
The Examiner asserts that claim 1, as amended, recites mental processes, and maintains its rejection on the basis of the Alice/Mayo tests performed above.

On pages 12-15 of the instant remarks, the Applicant argues that claims 1, 10, and 19 practically integrate their recited judicial exceptions by improving on the technical field of invoking a machine learning model to generate accurate scores at scale, and their 101 rejections should be withdrawn:
“As shown above, claim 1 contains limitations related to at least the aforementioned
improvements described in Applicant's specification. Thus, claim 1 reflects at least the disclosed
improvements to a technology or technical field.

Therefore, when considered in combination, the limitations of claim 1 integrate any
alleged abstract idea into a practical application because the claim improves the functioning of a
computer or technical field. See MPEP 2106.04(d)(l) and 2106.05(a). The claimed invention
reflects this improvement in the technical field of invoking a machine learning model to generate
accurate scores at scale. Thus, the claim as a whole integrates the judicial exception into a
practical application (Step 2A, Prong Two: YES), such that the claim is not directed to the
judicial exception. (Step 2A: NO). Therefore, claim 1 is eligible. For at least these reasons, the
rejection should be withdrawn.”
In regards to the arguments above, the Examiner respectfully disagrees that the limitations of claim 1 are sufficient to integrate recited abstract ideas into a practical application. As stated in MPEP 2106.05(I), An inventive concept ‘cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.’ Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, ‘we then ask, ‘[w]hat else is there in the claims before us?’) (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”). Instead, an “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)
The aspects of the Applicant’s specification argued to improve over previously described methods in the instant remarks are present solely in the abstract ideas of claim 1, while the additional elements are generic and not representative of an inventive concept. Consequently, the amendments to claim 1 are insufficient to overcome current 101 rejections over the recitations of mental processes. Moreover, there are significant differences between the argued improvements as recited in the instant specification and as claimed in amended claim 1.
As stated in MPEP 2106.05(a), while improvements were evaluated in Alice Corp. as relevant to the search for an inventive concept (Step 2B), several decisions of the Federal Circuit have also evaluated this consideration when determining whether a claim was directed to an abstract idea (Step 2A). See, e.g., Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-16, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016); Visual Memory, LLC v. NVIDIA Corp., 867 F.3d 1253, 1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017). Thus, an examiner should evaluate whether a claim contains an improvement to the functioning of a computer or to any other technology or technical field at Step 2A Prong Two and Step 2B, as well as when considering whether the claim has such self-evident eligibility that it qualifies for the streamlined analysis … If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art..
The claimed invention of claim 1 generates one model score based on non-entity pair-specific interaction features, and a second model score based on both non-entity pair-specific interaction features and entity pair-specific interaction features. Paragraphs [0051-0053] and [0055] of the instant specification specify that both “non-entity pair-specific interaction features” and “entity pair-specific interaction features” are types of “interaction features”, which differ from “non-interaction features”. Hence, the claimed invention generates two scores solely using interaction features.
The alleged improvements described by paragraphs [0006], [0014], and [0015] of the instant specification generate one model score based on non-interaction features and a second model score based on interaction features. This is in contrast to the claimed invention of claim 1, which only uses interaction features and does not disclose the usage of non-interaction features. Thus, claim 1 does not recite the argued improvements of the instant specification.

On page 15 of the instant remarks, the Applicant argues that the previous arguments apply to independent claims 10 and 19, and consequently requests that 101 rejections for all independent and dependent claims are withdrawn:
“The above arguments are applicable to claims 10 and 19, and to the dependent claims. Therefore, claims 1-20 are eligible and the rejection should be withdrawn. Withdrawal of the rejection under 35 U.S.C. § 101 is respectfully requested”
As detailed in the previous paragraphs, the Examiner respectfully disagrees that the 101 rejection of claim 1 should be withdrawn. Consequently, the Examiner’s arguments apply to the 101 rejections of similar independent claims 10 and 19, and withdrawal of the dependent claims on this basis is not accepted.

103 Rejections
On pages 16-17 of the instant remarks, the Applicant argues that regarding claim 1, Fei does not multiple invocations of the same machine learning model to generate a score, and Iannaccone does not remedy the deficiencies of Fei:
“As discussed in the interview, the cited combination does not describe multiple invocations of the same machine learning model to generate a score. Instead, Fei uses two models: a ratings model and a scoring module (see, e.g., Fig. 2 and paras. 37 and 47 of Fei). Fei para. 37 describes a user interaction score, which is generated based on a predictive model, while "user values" are output by a ratings model. Thus, Fei's alleged "first score" is the user interaction score, a likelihood of the user interacting with a content item, which is "determined based on a predictive model" (Fei para. 37) while Fei's alleged "second score" is a "predicted quality for a content item" generated by the trained ratings model. See also Fei, Abstract. Iannaccone generally describes a method of using a machine learning model that has been
trained to assign higher weights to features that are most predictive of a resulting value for a
user. Thus, Iannocone does not remedy the deficiencies of Fei.”
In regards to the Applicant’s arguments above, the Examiner respectfully disagrees. While the Applicant’s assertion that Fei uses a predictive model to generate a user interaction score, and uses a trained ratings model to generate a user value is correct, these two models are components of a larger model, one that combines their outputs into a final content score result with a scoring model (Fei, [0059]). As the trained ratings model uses machine learning (Fei, [0049]), these models form a single ensemble machine-learning model. Thus, Fei lacks additional deficiencies that require remediation by Iannaccone or other references.

On page 17 of the instant remarks, the Applicant argues that the combination of Fei and Iannaccone does not disclose, teach, or suggest all the limitations of amended claim 1:
“Additionally, the combination of Fei and Iannocone does not disclose, teach or suggest all of the limitations particularly recited in amended claim 1, including "identifying first values for a set of non-entity pair-specific interaction features that are associated with a first entity, wherein the first entity comprises a source of an attribute, product, or service;" identifying second values for a set of entity pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity, wherein an entity pair-specific interaction feature pertains to an interaction between the first entity and the second entity, wherein a non-entity pair-specific interaction feature pertains to an interaction involving a third entity different from the first entity and the second entity, and wherein the second entity comprises a target of the source of the attribute, product or service;" "generating, by inputting the first values into a machine-learned model that has been trained based on the set of non-pair-specific interaction features and the set of entity pair-specific interaction features, a first score based on the first values;" "generating, by inputting the second values into the machine-learned model, a second score based on the first values and the second values, wherein the second score is different than the first score;" and "computing a final score for a first entity-second entity pair based on the first score and the second score, wherein the first entity-second entity pair comprises a pairing of the first entity and the second entity, and wherein the final score comprises a prediction of whether the target entity is likely to perform an action associated with the source entity;" alone or in combination with the other limitations of claim 1.

As such, there is at least one limitation of claim 1 that is not disclosed, taught, or suggested by the combination of Fei and Iannoccone. Thus, the rejection lacks the articulated reasoning and rational underpinning required to support the legal conclusion of obviousness. Therefore, claim 1 would not have been obvious to a person of ordinary skill in the art at the effective filing date of the invention.”
In light of the amended claims, the claimed invention of claim 1 is found to be obvious over the combination of Fei and Iannaccone. Fei discloses a method of finding a first set of non-entity pair-specific interaction features pertaining to source entity-third entity interactions (Fei, [0022], [0030-0031]), as well as finding a second set of entity pair-specific interaction features associated with source-target entity interactions (Fei, [0027], [0033-0034]). Fei also discloses a method of generating a first score based on the first values (Fei, [0037], [0049], [0059]) and a second score based on the first and second values from a machine learning model. Fei further discloses the computation of a final score from the first and second scores, which comprises a prediction of whether the target entity will perform some action associated with the source entity (Fei, [0008], [0059]). Iannaccone discloses a method of training a predictive scoring model for a recommender machine learning system (Iannaccone, [0037-0038]). For more details on the obviousness of claim 1 over prior art, refer to the 103 rejections above.

On page 17 of the instant remarks, the Applicant argues that the previous arguments apply to independent claims 10 and 19, and additionally requests that 103 rejections for dependent claims 6-7, 9, 11, 15-16, and 20 are withdrawn:
“The above arguments apply to independent claims 10 and 19, and to dependent claims 2, 6-7, 9, 11, 15-16, and 20. Accordingly, Applicant requests withdrawal of the rejections under 35 U.S.C. § 103”
As detailed in the previous paragraph, the Examiner respectfully disagrees that the 103 rejection of claim 1 should be withdrawn. Consequently, the Examiner’s argument applies to the 103 rejections of similar independent claims 10 and 19, and withdrawal of the dependent claims on this basis is not accepted.

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Modarresi et al. (US 20180137522 A1) – Teaches a method of generating factor data for factors that affect a final score, with interest categories for factors based on interactions between two entities
Halecky et al. (US 20180365710 A1) – Teaches a method of generating a final score for specific user-hostname website interactions
Singh et al. (US 20190034827 A1) – Teaches a method of automatically sending an electronic message and / or executing a digital content campaign based on bucket selection
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.

Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaron P Gormley whose telephone number is [phone]. The examiner can normally be reached Monday - Friday 12:00 PM - 8:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T Bechtold can be reached at [phone]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AG/Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148

Prosecution Timeline

• Jun 11, 2021: Application Filed
• Mar 05, 2025: Non-Final Rejection mailed — §101, §103, §112
• Apr 14, 2025: Interview Requested
• May 06, 2025: Applicant Interview (Telephonic)
• May 06, 2025: Examiner Interview Summary
• Jun 05, 2025: Response Filed
• Jul 16, 2025: Final Rejection mailed — §101, §103, §112
• Sep 15, 2025: Interview Requested
• Oct 08, 2025: Applicant Interview (Telephonic)
• Oct 08, 2025: Examiner Interview Summary
• Nov 17, 2025: Response after Non-Final Action