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 .
Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 February 2026 [hereinafter Response] has been entered, where:
Claims 1, 3, 5, 8, 11, 14, 15, 16, 18, and 14 have been amended.
Claims 6, 10, and 17 have been cancelled.
New claims 21-23 are presented for examination.
Claims 1-5, 7-9, 11-16, and 18-23 are pending.
Claims 1-5, 7-9, 11-16, and 18-23 are rejected.
Claim Rejections – 35 U.S.C. § 112
3. 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.
4. Claims 1-5, 7-9, 11-16, and 18-23 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, line 7-10, recites “determining . . . probabilities of the binary state change associated with a client computing device included in the group of client computing devices based on the posterior probability distribution, the probabilities corresponding to a plurality of future periods of time.” Claim 1, lines 11-12 recites “identifying, by the processing device, a future period of time of the plurality of future periods of time,” and claim 1, lines 16-21, recites “causing . . . the binary state change associated with the client computing device at the determined future period of time . . . by transmitting, via a network, a communication . . . at the future period of time.”
The claim is indefinite because it is not clear whether “the determined future period of time” is intended to be an additional “future period of time,” or is intended to draw antecedence from “a future period of time” or perhaps intended to draw antecedence from the limitation of “determining . . . probabilities . . . corresponding to a plurality of future periods of time.”
Claim 8, line 5-7, recites “computing probabilities of a binary state change associated with a group of client computing devices using a machine learning model, the probabilities corresponding to a plurality of future periods of time,” claim 8, lines 14, recites “determining a future period of time of the plurality of future periods,” and claim 8, lines 20-25 recites “causing . . . the binary state change associated with the client computing device at the determined future period of time . . . by transmitting, via a network, a communication . . . at the future period of time.”
The claim is indefinite because it is not clear whether “the determined future period of time” is intended to be an additional “future period of time,” or is intended to draw antecedence from “a future period of time.”
Claim 14, line 9-12, recites “determining . . . probabilities of the binary state change associated with a client computing device included in the group of client computing devices based on the posterior probability distribution, the probabilities corresponding to a plurality of future periods of time.” Claim 14, lines 13-14 recites “identifying, by the processing device, a future period of time of the plurality of future periods of time,” and claim 14, lines 18-23, recites “causing . . . the binary state change associated with the client computing device at the determined future period of time . . . by transmitting, via a network, a communication . . . at the future period of time.”
The claim is indefinite because it is not clear whether “the determined future period of time” is intended to be an additional “future period of time,” or is intended to draw antecedence from “a future period of time” or perhaps intended to draw antecedence from the limitation of “determining . . . probabilities . . . corresponding to a plurality of future periods of time.”
Claims 2-5, 7, and 21-23 depend directly or indirectly from claim 1. Claims 9 and 11-13 depend directly or indirectly from claim 8. Claims 15, 16, and 18-20 depend directly or indirectly from claim 14. Claims 2-5, 7, 9, 11-13, 15, 16, and 18-23 are rejected as depending from a rejected claim; further, the claims fail to cure the deficiencies of claims 1, 8, and 14.
Claim Rejections - 35 U.S.C. § 101
5. 35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 1-5, 7-9, 11-16, and 18-23 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 “[(a)]1 computing, by a processing device using a machine learning model, a posterior probability distribution for temporal occurrences of binary state changes associated with client computing devices included in a group of client computing devices,” “[(b)] determining, by the processing device using the machine learning model, probabilities of a binary state change associated with a client computing device included in the group of client computing devices based on the posterior probability distribution,” and “[(c)] identifying, by the processing device, a future period of time having a highest cumulative propensity score computed by the machine learning model based on a probability of the binary state change associated with the client computing device.” The activities of “[(a)] computing a posterior probability distribution,” “[(b)] determining probabilities,” and “[(c)] identifying a future period of time,” 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. Still further, the limitation of “[(a)] computing, by a processing device using a machine learning model, a posterior probability distribution for temporal occurrences of binary state changes associated with a group of client computing devices,” where “[(a)] computing . . . a posterior probability distribution” is directed to mathematical relationships, mathematical formulas or equations, and mathematical calculations, and accordingly, is also a mathematical concept, (MPEP § 2106.04(a)(2) sub I; see also Specification ¶¶ 0041-42), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim also recites more details or specifics to the abstract idea of “[(a)] computing . . . binary state changes,” where “[(a.1)] the binary state changes involving exceeding an amount of cloud -based resources allocated for client computing device usage during a defined period of time,” and accordingly, is merely more specific to the abstract idea.
The claim also recites more details or specifics to the abstract idea of “[(b)] determining,” where “[(b.1)] the probabilities corresponding to future periods of time,” and accordingly, are merely more specific to the abstract idea. Thus, 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 a “processing device” and a “network,” 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 “machine learning model,” which is recited at a high-level of generality, and accordingly, is 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 also recites that “binary state changes associated with client computing devices included in a group of client computing devices,” which is generally linking the use of the abstract idea to a particular technological environment or field of use," (MPEP § 2106.05(h)), that does not serve to integrate the abstract idea into a practical application.
Further, the claim recites “[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device the future period of time,” which is a post-solution, insignificant extra-solution activity of outputting a result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Thus, claim 1 recites 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 limitations include a “processing device” and a “network,” 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 “machine learning model,” which is recited at a high-level of generality, and accordingly, is 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 also recites that “binary state changes associated with client computing devices included in a group of client computing devices,” which is generally linking the use of the abstract idea to a particular technological environment or field of use," (MPEP § 2106.05(h)), that does not amount to significantly more than the abstract idea.
The claim also recites “[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time,” which is a well-understood, routine, and conventional activity of sending a result of the abstract idea, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 1 is subject-matter ineligible.
Claim 8 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 “[(a)] computing probabilities of a binary state change associated with a group of client computing devices using a machine learning model,” “[(b)] identifying a client computing device included in the group of client computing devices using the machine learning model based on a group membership probability,” and “[(c)] determining a future period of time of the plurality of future periods of time having a highest cumulative propensity score computed by the machine learning model based on a probability of the binary state change associated with the group of client computing devices as not exceeding the amount of cloud-based resources.” The activities of “[(a)] computing probabilities of a binary state change,” “[(b)] identifying a client computing device,” and “[(c)] determining a future period of time,” 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. The claim also recites more details or specifics to the abstract idea of “[(a)] computing . . . binary state changes,” where “[(a.1)] the binary state changes involving exceeding an amount of cloud -based resources allocated for client computing device usage during a defined period of time,” and accordingly, is merely more specific to the abstract idea. The claim also recites more details or specifics to the abstract idea of “[(a)] computing,” where “[(a.1)] the probabilities corresponding to future periods of time,” and accordingly, are merely more specific to the abstract idea. Thus, claim 8 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 a “processing device coupled to the memory component, the processing device to perform operations comprising” and a “network,” 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 “machine learning model,” which is recited at a high-level of generality, and accordingly, is 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 also recites that “binary state changes associated with client computing devices included in a group of client computing devices,” which is generally linking the use of the abstract idea to a particular technological environment or field of use," (MPEP § 2106.05(h)), that does not serve to integrate the abstract idea into a practical application.
Further, the claim recites “[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device the future period of time,” which is a post-solution, insignificant extra-solution activity of outputting a result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Thus, claim 8 recites 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 limitations include a “processing device coupled to the memory component, the processing device to perform operations comprising” and a “network,” 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 “machine learning model,” which is recited at a high-level of generality, and accordingly, is 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 also recites that “binary state changes associated with client computing devices included in a group of client computing devices,” which is generally linking the use of the abstract idea to a particular technological environment or field of use," (MPEP § 2106.05(h)), that does not amount to significantly more than the abstract idea.
The claim also recites “[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time,” which is a well-understood, routine, and conventional activity of sending a result of the abstract idea, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 8 is subject-matter ineligible.
Claim 14 recites a non-transitory computer-readable storage medium, 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 “[(a)] computing a posterior probability distribution for temporal occurrences of binary state changes associated with client computing devices included in a group of client computing devices,” “[(b)] determining probabilities of a binary state change associated with a client computing device included in the group of client computing devices based on the posterior probability distribution,” and “[(c)] identifying a future period of time having a highest cumulative propensity score computed by the machine learning model based on the probability of the binary state change associated with the client computing device as not exceeding the amount of cloud-based resources.” The activities of “[(a)] computing a posterior probability distribution,” “[(b)] determining probabilities,” and “[(c)] identifying a future period of time,” 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.
The claim also recites more details or specifics to the abstract idea of “[(a)] computing . . . binary state changes,” where “[(a.1)] the binary state changes involving exceeding an amount of cloud -based resources allocated for client computing device usage during a defined period of time,” and accordingly, is merely more specific to the abstract idea. The claim also recites more details or specifics to the abstract idea of “[(b)] determining,” where “[(b.1)] the probabilities corresponding to future periods of time,” and accordingly, are merely more specific to the abstract idea. Thus, claim 14 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 a “non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device” and a “network,” 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 “machine learning model,” which is recited at a high-level of generality, and accordingly, is 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 also recites that “binary state changes associated with client computing devices included in a group of client computing devices,” which is generally linking the use of the abstract idea to a particular technological environment or field of use," (MPEP § 2106.05(h)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites “[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time,” which is a post-processing insignificant extra-solution activity of transmitting data over a network, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Thus, claim 14 recites 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 limitations include a “non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device” and a “network,” 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 “machine learning model,” which is recited at a high-level of generality, and accordingly, is 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 also recites that “binary state changes associated with client computing devices included in a group of client computing devices,” which is generally linking the use of the abstract idea to a particular technological environment or field of use," (MPEP § 2106.05(h)), that does not amount to significantly more than the abstract idea. The claim also recites “[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time,” which is a well-understood, routine, and conventional activity of sending a result of the abstract idea, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 14 is subject-matter ineligible.
Claims 2, 3, and 4 depend directly or indirectly from claim 1. Claims 11, 12, and 13 depend directly or indirectly from claim 8. Claims 15 and 16 depend directly or indirectly from claim 14. The claims recite more details or specifics to the additional element of the “machine learning model,” (claim 2: “wherein the machine learning model uses Bayesian Model-Agnostic Meta-Learning”; claim 15: “[(a.1)] wherein the machine learning model uses Bayesian Model-Agnostic Meta-Learning, [(a.2)] wherein the machine learning model meta-learns the posterior probability distribution for temporal occurrences of binary state changes associated with each client device included in the group of client computing devices followed by transferring the posterior probability distribution to individual client devices based on individualized data”; claims 3 and 16: “[(a.1)] wherein the machine learning model is trained on training data describing historic temporal occurrences of the binary state changes, [(a.2)] wherein the training data is generated under a survival regime to represent a time decaying effect on probabilities of occurrence of the binary state change”; claims 4 and 13: “wherein the machine learning model is trained on a training objective based on a conditional log-likelihood and a prior distribution”; claim 11: “[(a.2)] wherein the machine learning model includes a Bayesian mixture multi-armed bandit model, [(a.3)] wherein the machine learning model is trained using a nested hierarchy having an outer loop that optimizes over each client device included in the group of client computing devices and an inner loop that fits individualized data for the client device initializing from the outer loop;” claim 12: “[(a.2)] wherein a mixture distribution of the machine learning model defines the group of client computing devices”), and accordingly, are merely more specific to the additional element. Therefore, claims 2-4, 11-13, 15 and 16 are subject-matter ineligible.
Claims 5 and 7 depend directly or indirectly from claim 1. Claims 18 and 20 depend directly or indirectly from claim 14. The claims recite more details or specifics to the abstract idea of “[(a)] computing a posterior probability distribution,” (claims 5 and 18: “[(b.2)] wherein a temporal decaying factor is applied to the probabilities of the binary state change, [(b.3)] the temporal decaying factor representing probabilities of the binary state change decreasing as new communications are received that suppress an initial communication transmitted at an initial period of time”; claims 7 and 20: “[(b.2)] wherein the probabilities of the binary state change are determined using Stein Variational Gradient Descent”), and accordingly, are merely more specific to the abstract idea. Moreover, the limitation of “[(b.2)] wherein the probabilities of the binary state change are determined using Stein Variational Gradient Descent” of claims 7 and 20 is a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 5, 7, 18, and 20 are subject-matter ineligible.
Claim 9 depends directly or indirectly from claim 8. The claim recites more details or specifics to the abstract idea of “[(a)] computing probabilities,” where “[(a.2)] wherein the probabilities are computed using an expectation-maximization algorithm,” and accordingly, is merely more specific to the abstract idea. Moreover, the limitation recites a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 9 is subject-matter ineligible.
Claim 19 depends directly or indirectly from claim 14. The claim recites more details or specifics to the abstract idea of “[(b)] determining probabilities of a binary state change,” “[(b.2)] wherein the binary state change is a Bernoulli event with a constant success rate,” and accordingly, is merely more specific to the abstract idea. Moreover, the limitation recites a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 19 is subject-matter ineligible.
Claim 21 depends directly or indirectly from claim 1. The claim recites more details or specifics of the abstract idea of [(c)] identifying . . . a future periods of time having a highest cumulative propensity score,” “[(c.1) wherein the cumulative propensity score is computed for each hour of day based on the probabilities of temporal occurrence of the binary state changes associated with each client device include in the group of client computing devices,” 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. Therefore, claim 21 is subject-matter ineligible.
Claim 22 depends directly or indirectly from claim 1. The claim recites more details or specifics to the additional element of “[(d)] causing, by the processing device, the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication,” “[(d.1)] wherein the communication is one of: a communication describing an available update to software of the client computing device, a communication describing an invitation to join a virtual meeting, or a communication describing an electronic document to be reviewed,” and accordingly, is merely more specific to the additional element. Therefore, claim 22 is subject-matter ineligible.
Claim 23 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “[(b)] determining . . . probabilities of the binary state change,” “[(b.2)] wherein the binary state change corresponds to one of: updated or not updated, joined or not joined, or reviewed or not reviewed,” 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. Therefore, claim 23 is subject-matter ineligible.
Response to Arguments
7. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
35 U.S.C. § 101
8. Applicant submits that “Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly directed to an abstract idea without significantly more. Applicant respectfully disagrees. Nevertheless, Applicant submits that the amendments to the independent claims overcome this rejection.
Under Step 2A Prong Two, the amended claims integrate an alleged abstract idea into a practical application. Specifically, the independent claims now recite
‘[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time.’
This is not a mental process, but rather a concrete action (transmitting a communication at a specifically determined time) that produces a real-world technical effect (causing the binary state change to not exceed cloud-based resources). The claims further recite that the future period of time is identified as ‘having a highest cumulative propensity score computed by the machine learning model.’ This cumulative propensity score is a specific technical feature, which is also not a mental process, that determines when to transmit the communication. Together, the cumulative propensity score identifies the optimal transmission time, and the causing/transmitting step applies that determination to produce a concrete technical result for preventing cloud resource exhaustion. (Application, [0015], [0019]).
The "causing .. by transmitting" claimed feature is not post-solution, insignificant extra-solution activity of merely outputting a result as characterized in the prior Office Action with respect to claim 8. (MPEP § 2106.05(g)). Rather, the transmitting step is integral to achieving the claimed technical result where the system causes the binary state change to not exceed cloud-based resources. The transmitting step imposes meaningful limits on the claim because the timing of the transmission (at the future period of time having the highest cumulative propensity score) directly determines whether the binary state change exceeds the allocated cloud-based resources. This is not merely transmitting data over a network. It is a specifically timed intervention that produces a concrete technical effect.
Similarly, the machine learning model recited in the amended claims is not a generic computer component used to implement the abstract idea. (MPEP § 2106.05(£)). The machine learning model computes a specific cumulative propensity score for each future period of time, a particular technical computation that is not recited at a high level of generality. Nor is the recitation of "binary state changes associated with client computing devices" merely linking the abstract idea to a particular technological environment. (MPEP § 2106.05(h)). The amended claims recite causing the binary state change to not exceed cloud-based resources, which achieves a concrete technical effect.
Under the two-part analysis of MPEP § 2106.05(a), the Specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing such an improvement. The Specification describes that
‘by determining when to transmit the communication to the client computing device, the occurrence module 110 is capable of increasing or decreasing a probability of causing the binary state change associated with the client computing device.’
(Application, [0030]). The Specification further verifies this improvement through performance evaluations demonstrating significantly lower per period regret than conventional systems. (Application, [0019]). The claims reflect this by describing the features that provide the improvement, such as computing a cumulative propensity score for each future period of time using the machine learning model, identifying the period having the highest score, and causing the binary state change to not exceed cloud-based resources by transmitting a communication at that time, the combined features of the claims provide the improvement.” (Response at pp. 9-11).
Examiner’s Response:
Examiner submits that for Step 2A Prong Two, the rejection above identifies any additional elements (pointing to claim features/limitations/steps) recited in the claim beyond the identified judicial exception (that is, abstract idea), 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).
With regard to the limitation of
‘[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time.’
(see claim 1, lines 16-22). The limitation is based on the “transmitting . . . a communication,” which is a post-processing insignificant extra-solution activity of sending a result of the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The “causing the binary state change” appears as a prefatory clause, that sets a context for the operatory clause of “transmitting.”
Applicant submits that the claim limitation “is not merely transmitting data over a network. It is a specifically timed intervention that produces a concrete technical effect.” (Response at p. 10 (citing Specification ¶¶ 0015, 0019). The Specification recites the possibility of “intervening” to affect whether a binary state change occurs:
Binary state changes associated with client computing devices are types of changes which either occur or do not occur during a defined period of time such as a day, a week, a month, etc. During the defined period of time, for example, an amount of cloud-based resources allocated for use by a client computing device is either exceeded or not exceeded. By estimating temporal occurrence probabilities of the binary state change associated with the client computing device, it is possible to intervene in a manner which increases or decreases a likelihood that the binary state change actually occurs, e.g., by increasing the allocated amount of the cloud-based resources before a period of time associated with a highest probability of exceeding the allocated amount. Conventional systems for estimating temporal occurrence of a binary state change such as Thompson sampling are associated with relatively high per period regret. Because of this, interventions based on probabilities estimated using these conventional systems are unlikely to increase or decrease a likelihood that the binary state change actually occurs.
(Specification ¶ 0015 (emphasis added by Examiner); see also Specification ¶ 0019 (“capable of intervention in a manner”)). The Specification also recites the “desirability” and/or “undesirability” of a binary state change being indicated by a “communications protocol’:
In an example in which the communications protocol described by protocol data 112 indicates that it is undesirable for the binary state change associated with the client computing device to occur, the occurrence module 110 identifies the period of time during the day as having a relatively low probability of occurrence for the binary state change (e.g., an hour in a range of 0 to 5 hours or an hour in a range of 20 to 23 hours). In another example in which the communications protocol indicates that it is desirable for the binary state change associated with the client computing device to occur, the occurrence module 110 identifies the period of time during the day as having a relatively high probability of occurrence for the binary state change (e.g., hour 15). Accordingly, in these examples, by determining when to transmit the communication to the client computing device, the occurrence module 110 is capable of increasing or decreasing a probability of causing the binary state change associated with the client computing device.
(Specification ¶ 0030 (emphasis added by Examiner)). However, neither the Specification nor the claims describe how such an indication occurs through the communication protocol. In this respect, it appears the disclosure does not provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement under the guidance of MPEP § 2106.04(d)(1).
With regard to a “propensity score,” the claims recite:[(c)] identifying, by the processing device, a future period of time of the plurality of future periods of time having a highest cumulative propensity score computed by the machine learning model based on the probability of the binary state change associated with the client computing device as not exceeding the amount of cloud-based resources; and
The limitation, however, 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, as set out above in detail.
With regard to the additional element of the claimed “machine learning model,” Applicant submits that this is not a generic computer component being used to implement the abstract idea. (see MPEP § 2106.05(f)). However, the claims recite “a processing device using a machine learning model,” without specificity or details to the model framework, which in turn has a broadest reasonable interpretation of covering a “generic computer component,” which is not inconsistent with the Applicant’s specification. (MPEP § 2111; see Specification ¶ 0024).
Accordingly, under Step 2A Prong Two, a claim to integrate a judicial exception (that is, abstract idea) into a practical application of the exception will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize or preempt the judicial exception. In this instance, it appears the instant claims do not impose a meaningful limit on the abstract idea. (see 2024 SME Guidance, 89 Fed. Reg. 137 at p. 58136 (17 July 2024)).
9. Applicant also submits that “[u]nder Step 2B, the cumulative propensity score computation by the machine learning model combined with the identification of the highest-scoring time period and causing the binary state change to not exceed cloud-based resources by transmitting communications at that identified time represents significantly more than an abstract idea. The specific combination of computing probabilities for temporal occurrences of binary state changes, computing cumulative propensity scores for each future period using the machine learning model, identifying the period with the highest score, and causing the binary state change to not exceed cloud-based resources by transmitting a communication at that identified period is not well-understood, routine, or conventional. This ordered combination of elements provides a technical improvement over conventional approaches.
While transmitting data over a network may be conventional in isolation (MPEP § 2106.05(d)(II)(i)), the claims do not recite mere data transmission. The claims recite transmitting a communication at a specific time identified by a highest cumulative propensity score computed by the machine learning model to cause a binary state change to not exceed cloud-based resources. This specific combination of elements, the cumulative propensity score computation, the identification of the highest-scoring time period, and the causing of the technical effect by transmitting at that time, is not well-understood, routine, or conventional activity.” (Response at pp. 11-12).
Examiner’s Response:
Examiner respectfully submits that for Step 2B, the rejections explain why the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception. (see MPEP § 2106.07(a)).
With regard to the limitation of
‘[(d)] causing the binary state change associated with the client computing device at the determined future period of time to not exceed the amount of cloud-based resources by transmitting, via a network, a communication based on a communications protocol to the client computing device at the future period of time.’
(see claim 1, lines 16-22). The limitation is based on the “transmitting . . . a communication,” which 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. Also, the “causing the binary state change” appears as a prefatory clause, that simply sets a context for the operatory clause of “transmitting.”
10. Applicant submits that the instant “amendments transform the claims to recite a specific technological solution addressing cloud resource management. By estimating temporal occurrence probabilities of the binary state change associated with the client computing device, it is possible to intervene in a manner which increases or decreases a likelihood that the binary state change actually occurs, e.g., by increasing the allocated amount of the cloud-based resources before a period of time associated with a highest probability of exceeding the allocated amount. Application, [0015]. The amended claims also specify that the communication is generated "using the cloud-based resources" based on a communications protocol for transmission to the client computing device via a network.” (Response at p. 8).
Under Step 2A Prong Two, Applicant submits that “these Claims integrate the abstract idea into a practical application of cloud resource management. Similar to the examples in MPEP § 2106.05(a), the Claims as amended apply the machine learning model to solve a specific technological problem - preventing cloud resource exhaustion through predictive intervention. The Claims are analogous to the statutory examples in MPEP § 2106.05(a) where ‘applying a mathematical formula to improve an existing technological process’ is considered a practical application. Here, the machine learning model is applied to improve cloud resource allocation by predicting when client devices will exceed allocated resources and proactively generating communications using those same cloud resources.” (Response at p. 8).
Examiner’s Response:
Examiner respectfully disagrees because the amended limitation pertains to “binary state changes,” which provides more details or specifics to the “computing . . . a posterior probability distribution” as set out in detail above. Accordingly, the amended language is merely more specific to the abstract idea.
Applicant submits that the “claims provide a technological improvement. By estimating temporal occurrence of a binary state change in this way, the described systems are capable of intervention in a manner that increases or decreases a likelihood that the binary state change actually occurs. This is not possible using conventional systems which are associated with relatively high per period regret.
This improvement is verified in results of performance evaluations which indicate that the described systems achieve significantly lower per period regret than conventional systems, Application, [0019]. Additionally, the claims use specific machine learning techniques. The Claims further specify particular machine learning approaches including Bayesian Model-Agnostic Meta-Learning model, and Bayesian mixture multi-armed bandit model, that are specifically adapted for this technological application. Application, [0017]-[0018].” (Response at p. 9).
Though under Step 2A Prong Two, “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.
Varying forms of machine learning models, however, does not appear to provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.
Accordingly, the claims are subject-matter ineligible, as set out above in detail
35 U.S.C. § 103
11. Applicant submits that “Claim 1 stands rejected under § 103 as unpatentable over the combination of Sarraf, Yoon, and Mauer. In the interest of advancing prosecution and without conceding the propriety of the rejection, claim 1 is amended to recite
"[(c)] identifying . . . a future period of time of the plurality of future periods of time having a highest cumulative propensity score computed by the machine learning model based on the probability of the binary state change associated with the client computing device as not exceeding the amount of cloud-based resources."
[(claim 1, lines 11-15 (emphasis added by Examiner showing amended language))]. Support for this amendment can be found throughout Applicant's specification and at least at paragraphs [0042], [0043], and [0046]. Applicant submits that the asserted references of record do not disclose, teach, or suggest the subject matter of this amendment.
The Office relies on Sarraf as allegedly describing computing probabilities of binary state changes and identifying future periods of time based on those probabilities. (See Office Action at pp. 4-7). However, Sarraf is directed to resource allocation predictions and does not disclose or suggest identifying a future period of time "having a highest cumulative propensity score computed by the machine learning model based on the probability of the binary state change associated with the client computing device as not exceeding the amount of cloud-based resources," as now recited in amended claim 1.
The Office further relies on Yoon for teaching Bayesian Model-Agnostic Meta-Learning. (See Office Action at pp. 7-8). While Yoon describes meta-learning techniques, Yoon does not disclose computing a cumulative propensity score by the machine learning model for each future period of time or identifying the period having the highest such score to determine when cloud-based resources will not be exceeded. (See Yoon at pp. 1-10).
The Office additionally relies on Mauer for teaching communication generation and transmission. (See Office Action at pp. 8-9). However, Mauer is directed to communication protocols and does not address the computation of cumulative propensity scores by a machine learning model based on probabilities of binary state changes, nor does Mauer teach identifying a future period of time having a highest cumulative propensity score. (See Mauer at cols. 3-7).
In contrast, Applicant's specification describes that
"the cumulative propensity score is computed for each future period of time based on the probabilities of the binary state change"
and that
"the future period of time having the highest cumulative propensity score is identified as the optimal time for transmission."
(Application at ¶¶ [0042]-[0043]). The specification further explains that this approach enables "identifying when cloud-based resources are predicted to not be exceeded based on the highest cumulative propensity score." (Id. at ¶ [0046]).
Accordingly, Applicant submits that Sarraf, Yoon, and Mauer, whether considered alone or in combination, do not disclose, teach, or suggest the subject matter of claim 1, particularly as amended. As such, Applicant requests that the § 103 rejection of claim 1 be withdrawn.” (Response at pp. 13-15).
Examiner’s Response:
Examiner finds Applicant’s arguments and amendments persuasive, and accordingly, WITHDRAWS the rejection under Section 103.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(US Published Application 20230106369 to Flynn) teaches a task scheduler may ignore or drop tasks (e.g., not assign tasks) or assign a low priority to tasks that would be processed by misbehaving plugins. In the case of an indexer, a misbehaving plugin associated with a particular file type may be no longer loaded and content or property extraction may be avoided for data items or documents of a particular type, but basic properties and metadata may still be extracted by other mechanisms (e.g., other, non-misbehaving plugins associated with metadata extraction rather than extracting the content of the document) and these basic properties and metadata may still be added to the indexer database.
(Xiong et al., "Estimating Device Availability in Pervasive Peer-to-Peer Environment," IEEE (2004)) teaches predicting the availability of other devices using historic availability data of those devices obtained in routine usage. In this scheme, each device separately maintains data about the past communication with the other devices, and predicts current and future availability using the statistics of those data.
(Hummaida et al., “Adaptation in Cloud Resource Configuration: A Survey,” Journal of Cloud Computing (2016)) teaches that with increased demand for computing resources at a lower cost by end-users, cloud infrastructure providers need to find ways to protect their revenue. To achieve this, infrastructure providers aim to increase revenue and lower operational costs. A promising approach to addressing these challenges is to modify the assignment of resources to workloads.
13. 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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, 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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 References to the limitations are provided for the limited purpose of aiding in the subject-matter eligibility evaluation under the Office guidance and not for the purpose of oversimplifying the claims.
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