DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant’s submission filed 03 June 2026 [hereinafter Response] is entered, where:
Claims 1, 4, 11-14 and 20 have been amended.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Drawings
3. The objection to the drawings as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description has been WITHDRAWN in view of the Applicant’s amendments to the disclosure.
Claim Rejection - 35 U.S.C. § 112
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1, lines 31-34, recites “generating, by the at least one processor, via at least one random value generator module, a new event feedback for at least one new event, wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event, . . . .”
Claim 11, lines 33-36, recites “generate, via at least one random value generator module, a new event feedback for at least one new event, wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event, . . . .”
Claim 20, lines 25-28, recites “generating, by the at least one processor, via at least one random value generator module, a new event feedback for at least one new event, wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event, based . . . .”
This limitation is considered to be unclear and indefinite because the use of the word “only if” creates a rebuttable presumption that this step is only executed “only if the at least one new event attribute indicates that the at least one new event is a qualifying event;” however, if this condition does not occur in the respective claim, then none of the further steps/limitations in the claim are executed. For purpose of examination, Examiner will interpret the limitation “the at least one new event attribute indicates that the at least one new event is a qualifying event” as though the condition occurs. Clarification is required.
Claims 2-10 depend directly or indirectly from claim 1. Claims 12-19 depend directly or indirectly from claim 11. Claims 2-10 and 12-19 are rejected as depending from a rejected claim; further, the claims fails to cure the deficiencies of claims 1 and 11, respectively.
Claim Rejections - 35 U.S.C. § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “utilizing, by the at least one processor, a feedback machine learning model to predict an average feedback attribute and an average feedback variability attribute based at least in part on the event data,” “generating, by the at least one processor, a feedback probability distribution object in the at least one database, the feedback probability distribution object storing a feedback probability distribution parameterized,” and “generating, by the at least one processor, via at least one random value generator module, a new event feedback for at least one new event.”
These activities of “utilizing” and “generating” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim recites more specifics or details to the abstract idea of “utilizing . . . to predict,” “wherein the average feedback attribute comprises an average event feedback percentage,” and “wherein the average feedback variability attribute comprises an average event feedback variability percentage,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more specifics or details to the abstract idea of “generating . . . a feedback probability distribution,” “based at least in part on: i) the at least one new event attribute, ii) the average feedback attribute, iii) the average feedback variability attribute of the feedback data entry,” and “iv) the target variability attribute specifying the target variability of the average feedback attribute,” and accordingly, is merely more specific to the abstract idea.
The claim further recites more specifics or details to the abstract idea of “generating . . . a new event feedback,” “wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event, based at least in part on: i) the at least one new event attribute, and ii) at least one random selection, using the at least one random value generator module, sampled from the feedback probability distribution of the feedback probability distribution object,” and “wherein the at least one random selection is within a predetermined variability of a predetermined average target,” and accordingly is merely more specific to the abstract idea. Accordingly, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “at least one processor,” “at least one database,” and a “computing device,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application.
The claim also recites a “feedback machine learning model,” which is recited at a high level of generality, and accordingly, a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim recites additional elements of “receiving, by at least one processor, event data comprising at least one event data entry that represents at least one event,” and “receiving, by the at least one processor, at least one new event indication associated with the user profile.”
The activities of “receiving” are insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application.
The claim also recites “generating, by the at least one processor, a feedback data entry in a user profile to store the average feedback attribute and the average feedback variability attribute in association with the user profile,” the activity of “generate . . . to store” is the insignificant extra-solution activity of providing for data gathering and storage, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites “updating, by the at least one processor, automatically and without any human intervention, the user profile in the at least one databased with a feedback score indicative of the new event feedback,” which is a post-processing insignificant extra-solution of data storage, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
Further, the claim recites “instructing, by the at least one processor, to display the new event feedback for the at least one new event indication on a computing device associated with the user profile.” The activity of “instructing . . . to display” is a post-solution insignificant extra-solution activity of outputting a data display instruction, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim recites more details or specifics of the additional element of “receiving,” “wherein the at least one event data entry comprises at least one event attribute,” and “receiving,” “wherein the at least one new event indication indicates at least one new event and at least one new event attribute of the at least one new event,” which are merely more specific to the respective additional element. Accordingly, claim 1 is directed to an abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “at least one processor,” “at least one database,” and a “computing device,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea.
The claim also recites a “feedback machine learning model,” which is recited at a high level of generality, and accordingly, a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim recites additional elements of “receiving, by at least one processor, event data comprising at least one event data entry that represents at least one event,” and “receiving, by the at least one processor, at least one new event indication associated with the user profile.” The activities of “receiving” are well-understood, routine, and conventional activities of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that do not amount to significantly more than the abstract idea.
The claim also recites “generating, by the at least one processor, a feedback data entry in a user profile to store the average feedback attribute and the average feedback variability attribute in association with the user profile,” the activity of “generate . . . to store” is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites “updating, by the at least one processor, automatically and without any human intervention, the user profile in the at least one databased with a feedback score indicative of the new event feedback,” which is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv)), that does not amount to significantly more than the abstract idea.
Further, the claim recites “instructing, by the at least one processor, to display the new event feedback for the at least one new event indication on a computing device associated with the user profile.” The activity of “instructing . . . to display” is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim recites more details or specifics of the additional element of “receiving,” “wherein the at least one event data entry comprises at least one event attribute,” and “receiving,” “wherein the at least one new event indication indicates at least one new event and at least one new event attribute of the at least one new event,” which are merely more specific to the respective additional element. Accordingly, claim 1 is subject-matter ineligible.
Claim 11 recites a system, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “utilize a feedback machine learning model to predict an average feedback attribute and an average feedback variability attribute based at least in part on the event data,” “generate a feedback probability distribution object in the at least one database, the feedback probability distribution object storing a feedback probability distribution parameterized,” and “generate, via at least one random value generator module, a new event feedback for at least one new event.” These activities of “utilize” and “generate” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim recites more specifics or details to the abstract idea of “utilize . . . to predict,” “wherein the average feedback attribute comprises an average event feedback percentage,” and “wherein the average feedback variability attribute comprises an average event feedback variability percentage,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more specifics or details to the abstract idea of “generate a feedback probability distribution,” “based at least in part on: iv) the at least one new event attribute, v) the average feedback attribute, vi) the average feedback variability attribute of the feedback data entry, and viii) the target variability attribute specifying the target variability of the average feedback attribute,” and accordingly, is merely more specific to the abstract idea.
The claim further recites more specifics or details to the abstract idea of “generate . . . a new event feedback,” “wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event based at least in part on: iii) the at least one new event attribute, iv) at least one random selection, using the at least one random value generator module, sampled from the feedback probability distribution of the feedback probability distribution object; wherein the at least one random selection is within a predetermined variability of a predetermined average target,” and accordingly is merely more specific to the abstract idea. Accordingly, claim 11 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “at least one processor configured to execute software instructions, wherein upon execution the software instructions cause the at least one processor,” “at least one database,” and a “computing device,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application.
The claim also recites a “feedback machine learning model,” which is recited at a high level of generality, and accordingly, a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim recites additional elements of “receive event data comprising at least one event data entry that represents at least one event,” and “receive at least one new event indication associated with the user profile.” The activities of “receive,” which are insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application.
The claim also recites “generate a feedback data entry in a user profile to store the average feedback attribute and the average feedback variability attribute in association with the user profile,” the activity of “generate . . . to store” is the insignificant extra-solution activity of providing for data gathering and storage, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites “update, automatically and without any human intervention, the user profile in the at least one database with a feedback score indicative of the new event feedback;” which is a post-processing insignificant extra-solution of data storage, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
Further, the claim recites “instruct to display the new event feedback for the at least one new event indication on a computing device associated with the user profile.” The activity of “instruct to display” is a post-solution insignificant extra-solution activity of outputting a data display instruction, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim recites more details or specifics of the additional element of “receive,” “wherein the at least one event data entry comprises at least one event attribute,” and “receive,” “wherein the at least one new event indication indicates at least one new event and at least one new event attribute of the at least one new event,” which are merely more specific to the respective additional element. Accordingly, claim 11 is directed to an abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “at least one processor configured to execute software instructions, wherein upon execution the software instructions cause the at least one processor,” “at least one database,” and a “computing device,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea.
The claim also recites a “feedback machine learning model,” which is recited at a high level of generality, and accordingly, a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim recites additional elements of “receive event data comprising at least one event data entry that represents at least one event,” and “receive at least one new event indication associated with the user profile.” The activities of “receive,” which are well-understood, routine, and conventional activities of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that do not amount to significantly more than the abstract idea.
The claim also recites “generate a feedback data entry in a user profile to store the average feedback attribute and the average feedback variability attribute in association with the user profile,” the activity of “generate . . . to store” is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites “update, automatically and without any human intervention, the user profile in the at least one database with a feedback score indicative of the new event feedback;,” which is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv)), that does not amount to significantly more than the abstract idea.
Further, the claim recites “instruct to display the new event feedback for the at least one new event indication on a computing device associated with the user profile.” The activity of “instruct to display” is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim recites more details or specifics of the additional element of “receive,” “wherein the at least one event data entry comprises at least one event attribute,” and “receive,” “wherein the at least one new event indication indicates at least one new event and at least one new event attribute of the at least one new event,” which are merely more specific to the respective additional element. Accordingly, claim 11 is subject-matter ineligible.
Claim 2 depends from claim 1. Claim 12 depends from claim 11. The claims further recite “determining, by the at least one processor, a target variability.” The activity of “determining” is a limitation that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The claims recite more details or specifics of the abstract idea of “determining,” where “the average feedback attribute comprising at least one of: a maximum feedback rate, a minimum feedback rate, or a number of standard deviations,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 2 and 12 are subject-matter ineligible.
Claim 3 depends directly or indirectly from claim 1. Claim 13 depends directly or indirectly from claim 11. The claims further recite “generating, by the at least one processor, a user-specific variable rate feedback record linked to the user profile.” The activity of "generating,” is a limitation that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The claims recite more details or specifics of the abstract idea of “generating,” “wherein the user-specific variable rate feedback record comprises the average feedback attribute and a target variability attribute specifying the target variability,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 3 and 13 are subject-matter ineligible.
Claim 4 depends from claim 1. Claim 14 depends from claim 11. The claims recite more details or specifics to the abstract idea of “b)] utilizing . . . to predict,” “wherein the average feedback attribute comprises at least one: a target frequency comprising an average frequency of applying the new event feedback in response to the at least one new event, or a target feedback quantity an average quantity of the new event feedback in response to the at least one new event,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 4 and 14 are subject-matter ineligible.
Claim 5 depends from claim 1. Claim 15 depends from claim 11. The claims further recite the limitations of “determining, by the at least one processor, at least one engagement metric measuring user engagement based at least in part on the at least one new event,” “comparing, by the at least one processor, the at least one engagement metric with at least one threshold engagement value,” and “determining, by the at least one processor, a modification to the average feedback attribute based at least in part on comparing the at least one engagement metric with at least one threshold engagement value.” The activities of "determining” and “comparing” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 5 and 15 are subject-matter ineligible.
Claim 6 depends directly or indirectly from claim 1. Claim 16 depends directly or indirectly from claim 11. The claims further recite the limitation “utilizing, by the at least one processor, the feedback machine learning model to determine the modification to the average feedback attribute based at least in part on model parameters and the at least one engagement metric.” The activity of “utilizing . . . to determine the modification” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 6 and 16 are subject-matter ineligible.
Claim 7 depends directly or indirectly from claim 1. Claim 17 depends directly or indirectly from claim 11. The claims recite more details or specifics to the additional element of “the feedback machine learning model,” “wherein the feedback machine learning model comprises at least one reinforcement model,” and accordingly, is merely more specific to the additional element. Thus, claims 7 and 17 are subject-matter ineligible.
Claim 8 depends directly or indirectly from claim 1. Claim 18 depends directly or indirectly from claim 11. The claims further recite the limitation of “producing, by the at least one processor, a training dataset that correlates the event data with previous modifications to the average feedback attribute.” The plain meaning of the term “producing” is making or creating something actively, such as selecting data for a training use. Thus, the broadest reasonable interpretation of the term “producing” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claims also further recite the limitation “training, by the at least one processor, the feedback machine learning model based at least in part on the training dataset,” which is the use of a generic computer component (feedback machine learning model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application under Step 2A Prong Two, nor amounts to significantly more than the abstract idea under Step 2B. Thus, claims 8 and 18 are subject-matter ineligible.
Claim 9 depends from claim 1. Claim 19 depends from claim 11. The claims recite more details or specifics to the abstract idea of “generating . . . a feedback probability distribution” “wherein the feedback probability distribution comprises a normal distribution,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 9 and 19 are subject-matter ineligible.
Claim 10 depends from claim 1. The claim recites more details or specifics to the abstract idea of “generating . . . a feedback probability distribution” “wherein the feedback probability distribution comprises a gamma distribution,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claim 10 is subject-matter ineligible.
Claim 20 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, the claim recites the limitations of
“generating, by the at least one processor, a feedback probability distribution object in the at least one database, the feedback probability distribution object storing a feedback probability distribution parameterized,” and
“generating, by the at least one processor, via at least one random value generator module, a new event feedback for at least one new event.” The activities of “generating” are limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim recites more details or specifics of the abstract idea of “generating . . . a feedback a feedback probability distribution object,” that is “parameterized based at least in part on: i) the at least one new event attribute, ii) the average feedback attribute, and iii) the average feedback variability attribute of the feedback data entry, and iv) the target variability attribute specifying the target variability of the average feedback attribute,” and the abstract idea of generating, by the at least one processor, a new event feedback” that is “wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event, based at least in part on: v) the at least one new event attribute, and vi) at least one random selection from the feedback probability distribution, and wherein the at least one random selection is within a predetermined variability of a predetermined average target” and accordingly, are respectively merely more specific to the abstract idea. Accordingly, claim 20 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “at least one processor,” and “at least one database,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application.
The claim also recites the limitation of “receiving, by the at least one processor, at least one new event indication associated with a user profile,” which is a pre-solution insignificant extra-solution activity of mere data gathering, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites the limitation of “accessing, by the at least one processor, an average feedback attribute and an average feedback variability attribute in a feedback data entry linked to associated with the user profile in at least one database;” which is an insignificant extra-solution activity of mere data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites more details or specifics to the additional element of “accessing,” “wherein the average feedback attribute comprises an average event feedback percentage;” “wherein the average feedback variability attribute comprises an average event feedback variability percentage,” “wherein the user profile comprises a user-specific variable rate feedback record linked to the user profile,” and “wherein the user-specific variable rate feedback record comprises the average feedback attribute and a target variability attribute specifying a target variability of the average feedback attribute,” and accordingly, are merely more specific to the additional element.
The claim also recites the limitation of “updating, by the at least one processor, automatically and without any human intervention, the user profile, in the at least one database, with a feedback score indicative of the new event feedback,” which is a post-solution insignificant extra-solution activity of outputting data, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites more details or specifics of the additional element of “receiving . . . at least one new event indication,” “wherein the at least one new event indication indicates at least one new event and at least one new event attribute of the at least one new event,” and accordingly, is merely more specific to the additional element. Accordingly, claim 20 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “at least one processor,” and “at least one database,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim also recites the limitation of “receiving, by the at least one processor, at least one new event indication associated with a user profile,” in which the activity of “receiving” is a well-understood, routine, and conventional activity of receiving data over a network, (MPEP §2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim also recites the limitation of “accessing, by the at least one processor, an average feedback attribute and an average feedback variability attribute in a feedback data entry linked to associated with the user profile in at least one database;” which is a well-understood, routine, and conventional activity of retrieving information from memory, (MPEP § 2106.05(d) sub III.v), that does not amount to significantly more than the abstract idea.
The claim also recites more details or specifics to the additional element of “accessing,” “wherein the average feedback attribute comprises an average event feedback percentage;” “wherein the average feedback variability attribute comprises an average event feedback variability percentage,” “wherein the user profile comprises a user-specific variable rate feedback record linked to the user profile,” and “wherein the user-specific variable rate feedback record comprises the average feedback attribute and a target variability attribute specifying a target variability of the average feedback attribute,” and accordingly, are merely more specific to the additional element.
The claim also recites the limitation of “updating, by the at least one processor, automatically and without any human intervention, the user profile, in the at least one database, with a feedback score indicative of the new event feedback,” in which the activity of “updating” is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites more details or specifics of the additional element of “receiving . . . at least one new event indication,” “wherein the at least one new event indication indicates at least one new event and at least one new event attribute of the at least one new event,” and accordingly, is merely more specific to the additional element. Accordingly, claim 20 is subject-matter ineligible.
Response to Arguments
8. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Section 101
9. Under Step 2A Prong Two, “Applicant's specification identifies a particular problem in the technical field of computer-based feedback generation and content recommendation to achieve dynamic, bounded, and automatically applied feedback that avoids overfitting and static outputs.” (Response at p. 16 (quoting Specification ¶ 0039)).
“Thus, the Application as filed identifies a particular technical problem in computer-based feedback and recommendation systems that can overfit to a particular feedback value or content item, resulting in static content. Further, the Application as filed identifies a particular solution of using confined randomization, tied to user-profile data and bounded by predetermined average and variability parameters, to generate dynamic variable feedback.” (Response at p. 17).
Under Leg 2 of MPEP § 2106.04(d)(1), Applicant submits that the instant claims reflect the improvement set out in the Specification. (Response at p. 16 (quoting claim 1)). Particularly, Applicant submits the “recited steps represent a specific ordered process that uses the claimed ''feedback probability distribution object" as a system-generated data structure to store a parameterized feedback probability distribution and control sampling of a bounded randomized feedback value. The sampled value is not an unrestricted random value or a mental choice, but is generated from a database-stored probability distribution object parameterized by stored feedback attributes, a stored feedback variability attribute, and a target variability attribute linked to the user profile. The selected feedback is then used to automatically update the user profile in the database with a feedback score indicative of the new event feedback, without human intervention.
Therefore, the method of amended claim 1 further improves the computer-based technology of dynamic feedback generation and content recommendation by using a linked chain of computer data structures, event data entries, a database-stored user profile, a feedback data entry, a user-specific variable rate feedback record, a database-stored feedback probability distribution object, a sampled feedback value, and an automatically updated profile-state record, to provide bounded randomized feedback while avoiding overfitting and static content. The Specification further supports this ordered architecture by describing a user-specific variable rate feedback record linked to the user profile and including a target variability attribute specifying the target variability, and by explaining that the feedback probability distribution provides probabilities of different feedback values within the parameters of the predetermined average event feedback percentage and average event feedback variability percentage. See Specification at [0073] and [0117].” (Response at p. 18).
In regards to Desjardin, Applicant submits that “the claims utilize a "feedback probability distribution object" as a system-generated data structure to store a feedback probability distribution parameterized by database-stored feedback attributes, including an average feedback attribute, an average feedback variability attribute, and a target variability attribute linked to a user profile. The claimed process then uses at least one random selection sampled from that parameterized feedback probability distribution object to generate a bounded randomized feedback value within a predetermined variability of a predetermined average target, and automatically updates the user profile in the database with a feedback score indicative of the generated feedback.” (Response at pp. 19-20).
“As in Ex Parte Desjardins, the present claims are more than mere generic computer components with an unpatentable ’algorithm.’ Rather, amended claim 1 provides a particular computer-implemented solution for controlling dynamic feedback generation using linked database structures, including event data entries, a feedback data entry in a database-stored user profile, a user-specific variable rate feedback record, a parameterized feedback probability distribution object, a bounded selected feedback value, and an automatically updated user-profile state. This ordered architecture confines randomization within stored variability constraints and applies the resulting feedback automatically to the database-stored user profile, thereby improving computer-based feedback generation and avoiding static or overfit feedback outputs.” (Response at p. 20).
Examiner’s Response:
Examiner respectfully disagrees. Under Step 2A Prong Two, the rejection identifies any additional elements (specifically point to claim features/limitations/steps) recited in the claim beyond the identified judicial exception; and evaluates the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)- (c) and (e)- (h). Moreover, all of claimed additional elements are given weight, even when these elements represent well-understood, routine, conventional activity. (see MPEP § 2106.07(a)).
“Integration” may be based on the improvements in the functioning of a computer or an improvement to any other technology or technical field. (MPEP § 2106.04(d)(1)). The evaluation requires, [i]n sum, that (1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Next, (2) if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
By way of example to Desjardins, the MPEP provides under Step 2A Prong Two that “the [Desjardins] specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems. Importantly, the [appeals review panel (ARP)] evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task’ reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).” (MPEP § 2106.04(d) sub III; see “Advance Notice of Change to the MPEP in light of Ex Parte Desjardins” (05 December 2025) at p. 2)).
To be a patent-eligible improvement to computer functionality, the claims need to be directed to an improvement in the functionality of the computer or network platform itself.
As discussed above, claim 1 is directed to the concept of using machine learning to learn a target average feedback level and a target variability level from event data, then applies those values to future events by sampling from a controlled probability distribution. In effect, the system delivers randomized feedback that is still bounded around a desired average. It can also update the target feedback level based on user engagement metrics. This creates dynamic, profile-specific feedback that changes over time while staying within programmed limits. The claim does not recite an improvement to the functioning of a computer or technical field (that is, at least one processor, at least one database, a computing device, feedback machine learning model). Any purported improvement that is, to the feedback for user-engagement systems that otherwise experience overfitting of a reward pattern and reduced user interest -- relates to the abstract idea itself, and does not improve a computer, technology, or a technical field.
Accordingly, the pending claims are subject-matter ineligible as set out above in detail.
Section 102 / 103
10. Applicant submits that “Amended claim 1, which is representative of claims 11 and 20, recites:
generating, by the at least one processor, a feedback probability distribution object in the at least one database, the feedback probability distribution object storing a feedback probability distribution parameterized based at least in part on:
i) the at least one new event attribute,
ii) the average feedback attribute, and
iii) the average feedback variability attribute of the feedback data entry;
iv) the target variability attribute specifying the target variability of the average feedback attribute;
Amended claim 1 further recites:
generating, by the at least one processor, via at least one random value generator module, a new event feedback for at least one new event, wherein generating the new event feedback is performed only if the at least one new event attribute indicates that the at least one new event is a qualifying event, based at least in part on:
i) the at least one new event attribute, and
ii) at least one random selection, using the at least one random value generator module, sampled from the feedback probability distribution of the feedback probability distribution object; and
wherein the at least one random selection is within a predetermined variability of a predetermined average target;
[(claim 1, lines 23-40 (emphasis added by Examiner showing amended language))]. (Response at p. 21).
In view thereof, Applicant submits “[t]the Office Action's mapping of Sivaraman's score, aggregate score, and customer-rating output to the quoted claim language is not an anticipation showing. The cited passages do not disclose
‘a feedback probability distribution object in the database,’ [(claim 1, lines 23-24),
‘at least one feedback probability distribution parameterized based at least in part on’ the recited attributes, [(claim 1, lines 24-29)],
‘at least one random selection . . . sampled from the feedback probability distribution of the feedback probability distribution object,’ [(claim 1, lines 36-38)], or
‘wherein the at least one random selection is within a predetermined variability of a predetermined average target.’[(claim 1, lines 39-40)]”
(Response at p. 22 (emphasis added by Examiner showing claim language)).
11. “Claims 5-8 and 15-18 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Sivaraman in view of US Published Application 20190205402 to Semau et al. [hereinafter Semau]. Applicant respectfully traverses.
As discussed above, Sivaraman does not disclose the independent-claim limitations requiring
"generating . . . a feedback probability distribution" [(claim 1, line 23)] and
"at least one random selection from the feedback probability distribution." [(claim 1, lines 36-37)].
Semau does not remedy these deficiencies. The Office Action relies on Semau for engagement metrics, ranking scores, thresholds, and affinity-based ranking, but does not identify any disclosure in Semau that supplies the missing feedback-probability-distribution and random-selection limitations absent from Sivaraman. Accordingly, the combination of Sivaraman and Semau does not render claims 5-8 and 15-18 obvious.
Claims 9 and 19 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Sivaraman in view of US Published Application 20140207718 to Bento Ayres Pereira et al. [hereinafter Pereira]. Applicant respectfully traverses. Pereira is relied upon only for a normal distribution. Even if Pereira discloses a normal distribution in a recommendation context, that does not cure Sivaraman' s failure to disclose the claimed ordered process of generating the claimed ''feedback probability distribution" and then generating feedback based on "at least one random selection from the feedback probability distribution." Merely identifying a normal distribution in Pereira does not supply the missing arrangement of the independent claims.
Claim 10 stands rejected under 35 U.S.C. § 103 as allegedly unpatentable over Sivaraman in view of Gharibshah et al., "User Response Prediction in Online Advertising," arXiv (2021) [hereinafter Gharibshah]. Applicant respectfully traverses for the same reasons. Gharibshah is relied upon only for an alleged gamma distribution or maximum-entropy model in user-response prediction. That disclosure does not remedy Sivaraman' s failure to disclose the claimed ''feedback probability distribution" and "at least one random selection from the feedback probability distribution," as recited in claim 1. Thus, Gharibshah does not cure the deficiencies of Sivaraman.
Accordingly, the cited secondary references do not supply the missing limitations of the independent claims, and the obviousness rejections rely on Sivaraman for subject matter that Sivaraman does not disclose. Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. § 103. Thus, claims 1, 11, and 20 are patentable and non-obvious over the reference of record. Claims 5-10 depend, either directly or indirectly, from claim 1, and thus include all features of claim 1. Claims 15-19 depend, either directly or indirectly, from claim 11, and thus include all features of claim 11. Accordingly, claims 5-10 and 15-19 are patentable and non-obvious over the references of record due at least to their respective dependencies from claims 1 and 11.” (Response at pp. 22-23).
Examiner’s Response:
Examiner finds Applicant’s amendments and arguments thereto persuasive. Accordingly, the rejections under Sections 102 and 103 are WITHDRAWN.
Conclusion
12. 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.
13 The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20120054040 to Bagherjeiran et al.) teaches an adaptive display of internet advertisements to look-alike users using a desired user profile dataset as a seed to machine learning modules. Upon availability of a desired user profile, that user profile is mapped other look-alike users (from a larger database of users). The method proceeds to normalize the desired user profile object, proceeds to normalize known user profile objects, then seeding a machine-learning training model with the normalized desired user profile object. A scoring engine uses the normalized user profiles for matching based on extracted features (i.e. extracted from the normalized user profile objects). Once look-alike users have been identified, the internet display system may serve advertisements to the look-alike users, and analyze look-alike users' behaviors for storing the predicted similar user profile objects into the desired user profile object dataset, thus adapting to changing user behavior.
(US Published Application 20140316934 to Nunez et al.) teaches a method where content is previously rated by a group of users via computing devices, and it is characterised in that it comprises generating adaptive catalogues for a user by means of Item Response Theory models applied to said content and generating a user profile for said user by at least presenting items of said adaptive catalogues to said user, through a user computing device, and analysing scores given by said user to said items via said user computing device.
(Yu et al., "Personalized Adaptive Meta Learning for Cold-start User Preference Prediction," arXiv (2020)) teaches a novel personalized adaptive meta learning approach to consider both the major and the minor users with three key contributions: 1) We are the first to present a personalized adaptive learning rate meta-learning approach to improve the performance of MAML by focusing on both the major and minor users. 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users’ features for fast search. 3) To reduce the memory usage, we design a memory agnostic regularizer to further reduce the space complexity to constant while maintain the performance.
14. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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