DETAILED ACTION
This action is made FINAL in response to the amendments filed on 4/27/2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 4, 6 - 11, 13 - 17, and 19 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
Claims 1 – 4, 6 - 7, and 15 - 20 are directed to a method (claims 1-7) and system (claims 15 - 20 ). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claims 8 - 11 and 13 - 14 recites a “machine-readable medium” that has information recorded for performing a function (estimating quantiles). The Specification fails to expressly limit the recited “medium’ to a statutory embodiment. Thus, the plain and ordinary meaning of the recited "medium" includes signals, electromagnetic waves, carrier waves, etc. Accordingly, the recited “machine-readable medium” are not a process, a machine, a manufacture or a composition of matter, and claims 8 - 14 fail to recite statutory subject matter as defined in 35 U.S.C. 101.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
generating the one or more quantile estimates from the sample with a probability estimated to represent a confidence in that the one or more quantile estimates are indicative of corresponding quantiles by rank in the full data set within the accuracy range;
if the confidence does not meet a desired confidence level, iteratively performing the following steps:
increasing the first size,
obtaining an updated sample with the increased first size,
generating updated one or more quantile estimates based on the updated sample, and
estimating the confidence of the updated one or more quantile estimates;
producing a decision based on at least one of the one or more quantile estimates, the accuracy range, and the confidence.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, generating an estimate is a mental process that involves various cognitive functions like reasoning, memory, prediction, and using prior experiences to form a reasonable approximation when exact data isn't available. Additionally, producing a confident decision is a mental process that involves simply making best informed choice.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving a sample of a first size with a plurality of items sampled from a full data set of a second size
receiving an input specifying one or more quantile estimates to be determined from the sample, wherein the one or more quantile estimates from the sample are indicative of corresponding quantiles by rank in the full data set within an accuracy range;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites at least one processor, a memory and a communication platform which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of quantile estimates amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving a sample of a first size with a plurality of items sampled from a full data set of a second size
receiving an input specifying one or more quantile estimates to be determined from the sample, wherein the one or more quantile estimates from the sample are indicative of corresponding quantiles by rank in the full data set within an accuracy range;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The at least one processor, a memory and a communication platform which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of quantile estimates amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites in part:
sorting the plurality of items in the sample to create an ordered list of items;
generating paired conditions for each quantile of the one or more quantile estimates, wherein the paired conditions are directed to subranges of samples by rank in the full data set contributing to subranges of items in the ordered list of items;
computing the probability that the one or more quantile estimates satisfy the accuracy range based on the paired conditions of the one or more quantile estimates,
wherein the subranges of the samples by rank in the full data set in the paired conditions for each quantile are determined based on the quantile, the second size, and the accuracy range, and
the subranges of items in the ordered list in the paired conditions for each quantile are determined based on the first size, the quantile, and the accuracy range.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, sorting and pairing are fundamental mental processes that involve observing, comparing, identifying attributes, and categorizing information to organized data. Additionally, computing the probability refers to how the brain makes estimations about uncertain outcomes.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Sorting” and “computing” can be performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
the probability is determined based on a plurality of successive hypergeometric probabilities; and
the hypergeometric probabilities are computed via recurrence obtained based on the paired conditions of the one or more quantile estimates.
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. Specifically, the recited “determined” involves a mathematical formula which simply performs a calculation on values, which is considered a data processing step that can be performed with a generic computer. Further the claim does not include additional elements that integrate this abstract idea into a practical application. The recited steps are performed on a generic processor and do not improve the functioning of a computer or any other technology. The claim merely uses a computer as a tool to perform the abstract mathematical operations involved in computing the hypergeometric probabilities.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determined” and “computed” can be performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
The recitation of hypergeometric probabilities and recurrence amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
The recitation of hypergeometric probabilities and recurrence amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
wherein the recurrence is determined based on dynamic programming
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Dynamic Programming (DP) is mental process for problem-solving, involving breaking down complex problems into simpler subproblems, solving each once, to build to the final answer.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determined” can be performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
The recitation of dynamic programming amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
The recitation of dynamic programming amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
wherein the iteratively performed steps further comprise:
determining the increased first size as a current sample size;
sampling the full data set to generate a current sample of the current sample size;
estimating the one or more quantile estimates from the current sample with a probability representing a current estimated confidence in that the one or more quantile estimates from the current sample are indicative of corresponding quantiles by rank in the full data set within the accuracy range; and
comparing the desired confidence level and the current estimated confidence to determine
whether the current sample size corresponds to an optimized sample size in accordance with a pre-determined sample size search scheme.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Humans have been determining, estimating, sampling, and comparing before computers where even invented.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determining,” “sampling,” “estimating,” and “comparing.” can be performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
The recitation of sample size search scheme to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
The recitation of sample size search scheme amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
outputting the current sample size as the first size if the current sample size corresponds to an optimized sample size according to some criterion associated with the pre-determined sample size search scheme;
updating the current sample size according to the sample size search scheme if the current
sample size does not correspond to the optimal sample size;
repeating the steps of sampling, estimating, comparing, outputting, and updating until the
pre-determined sample search scheme yields the optimal sample size.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Humans have been determining, estimating, sampling, and comparing before computers where even invented.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determining,” “sampling,” “estimating,” and “comparing.” can be performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites a machine-readable medium which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 9 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 10 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 11 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The claim further recites a system, a quantile estimate generator, a processor, and a quantile-based decision determiner which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 16 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 8, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hosking et al (US 2010/0114526) in view of Greifeneder et al (US 2015/0032752) and in further view of THOMAIDOU et al (US 2019/0258983).
As to claim 1, Hosking et al teaches a method implemented on at least one processor, a memory, and a communication platform for estimating quantiles (paragraph [0061]the CPUs 1611 are interconnected via a system bus 1612 to a random access memory (RAM) 1614, read-only memory (ROM) 1616,), comprising:
receiving a sample of a first size with a plurality of items sampled from a full data set of a second size (paragraph [0010]…receiving a complete data set ; paragraph [0034]…a subsample refers to a subset of a complete data sample ; Examiner’s Note: “subsample” reads on “sample of a first size” ; “complete data sample” reads on “a full data set of a second size”);
receiving an input specifying one or more quantile estimates to be determined from the sample, wherein the one or more quantile estimates from the sample are indicative of corresponding quantiles by rank in the full data set within an accuracy range (paragraph [0010]…receiving a specific exceedance probability ; paragraph [0017]… computing a quantile estimate based on the optimal subsample and the specified exceedance probability, the quantile estimate indicating the magnitude of the extremely rare events ; paragraph [0034]… fitting a distribution to "m" largest historical events, for a range of values of m, and designating one of these sets of m historical events as "optimal". In one approach, there is considered a bias-variance tradeoff occurring when choosing the optimal value. ; Examiner’s Note: “a specific exceedance probability” reads on “input specifying” ; “m largest historical events” reads on “rank” ; “range of values” reads on “accuracy range”);
generating the one or more quantile estimates from the sample with a probability in that the one or more quantile estimates are indicative of corresponding quantiles by rank in the full data set within the accuracy range (paragraph [0052]…the system computes a quantile estimate for the optimal subsample. In this invention, the quantile estimate is equivalent to a frequency of rare events in the optimal subsample or the distribution. In one embodiment, the system computes the quantile estimate based on the optimal subsample and a specific exceedance probability, which is provided as an input or is predetermined before executing the steps 100-160 in FIG. 1.; Examiner’s Note: “frequency of rare events in the optimal subsample or the distribution” reads on “probability” ; “confidence intervals” reads on “confidence” );
Hosking et al fails to explicitly show/teach the one or more quantile estimates from the sample with a probability estimated to represent a confidence; and producing a decision based on at least some of the one or more quantile estimates, the accuracy range, and the confidence.
However, Greifeneder et al teaches one or more quantile estimates from the sample with a probability estimated to represent a confidence (paragraph [0142]… calculating a confidence interval for the estimated current quantile, defining a range in which the real current quantile lies with a specific, defined probability (e.g. 99%). The size of the confidence interval depends on the number of samples 805 of current and baseline distribution, and the desired confidence (probability that the real value lies within the interval). In case the expected baseline quantile value lies outside of the confidence interval of the current quantile value, a significant deviation was detected and the process continues with step 1707.); and producing a decision based on at least some of the one or more quantile estimates, the accuracy range, and the confidence (paragraph [0143]…Step 1707 creates a statistical statement record indicating a deviation between baseline and current distribution. In case the higher bound of the confidence interval of the current quantile is lower than the baseline quantile with applied tolerance, the current quantile value is below the baseline with applied tolerance with required statistical certainty, and a statistical statement 901 indicating a normal performance behavior is created. In case the lower bound of the confidence interval of the current quantile is higher than the baseline quantile value with applied tolerance, the current quantile is higher than the baseline with required statistical certainty, and a statistical statement record indicating abnormal, degraded performance behavior is created ; Examiner’s Note: “a statistical statement record indicating abnormal, degraded performance behavior” reads on “producing a decision”).
Therefore, it would have been obvious for one having ordinary skill in the are as claimed for Hosking et al to have the one or more quantile estimates from the sample with a probability estimated to represent a confidence; and producing a decision based on at least some of the one or more quantile estimates, the accuracy range, and the confidence, as in Greifeneder et al, for the purpose of indicating abnormal, degraded performance behavior is created with precise accuracy.
Hosking et al and Greifeneder et al both fail to explicitly show/teach if the confidence does not meet a desired confidence level, iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates.
However, THOMAIDOU et al teaches if the confidence does not meet a desired confidence level, iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates (paragraph [0055]…At block 220, manager system 110 can run determining process 114. Manager system 110 can run processes 114 iteratively until process 114-116 is terminated at block 222. In a machine learning aspect of manager system 110 the iterative updating of service support database 2121 as well as database 140 database 150 and database 160 by other processes can refine the cognitive AI decision making decision-making by manager system 110 which can provide determination of increased confidence level as sample sizes of sampled data increase)(Examiner’s Note: “the iterative updating of service support database 2121 as well as database 140 database 150 and database 160 by other processes can refine the cognitive AI decision making decision-making by manager system 110 which can provide determination of increased confidence level as sample sizes of sampled data increase” reads on “iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates”).
Therefore, it would have been obvious for one having ordinary skill in the are as claimed for Hosking et al for if the confidence does not meet a desired confidence level, iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates, as in THOMAIDOU et al, for the purpose of improved computer system operation e.g. in terms of algorithm efficiency, memory usage efficiency, maintainability, and reliability.
Claim 8 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 4/27/2206 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 112
The newly added amendments overcome the 112 Rejection and the 112 Rejection has been withdrawn.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
The Office Action alleges that the claims fall under the "Mental Processes" grouping of abstract ideas. See, Office Action at page 4. Applicant respectfully disagrees for at least the following reasons.
Initially, claim 1 as a whole is related to "estimating quantiles in data analytics" (e.g.,data set of 1 billion items, see para. [0046]), which does not fall under the "Mental Processes” grouping of abstract ideas because human mind cannot estimate quantiles in big data an
The examiner disagrees, the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitation of “estimating quantiles in data analytics" (e.g.,data set of 1 billion items, see para. [0046]).” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. Without clear support in the claim language the examiner cannot give weight to arguments premised on these alleged limitations.
The mental process depends on the nature of the claimed analysis, not the quantity of data analyzed. Processing a large number of data items does not transform and otherwise mental process into a non-abstract concept.
The applicant argues:
Further, the recited machine learning features extend far beyond fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following
rules or instructions), and thus do not fall within the enumerated group of Certain methods of organizing human activity.
Also, these claim features are not directed to mathematical concepts.
Accordingly, the claims do not fall into any of the abstract ideas exceptions provided by the Guidance, and thus the claims are patent eligible under Prong One of the Step 2A Analysis of the Guidance.
The examiner disagrees, the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitation of “machine learning features.” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. Without clear support in the claim language the examiner cannot give weight to arguments premised on these alleged limitations.
The claims recite analyzing information to generate predictions and classifications, which constitutes a mental process. Just arguing (or reciting) “machine learning features” does not remove the claims from the mental process category.
The applicant argues:
Moreover, claim 1 is patent eligible because the claimed concepts are integrated into a practical application. MPEP 2106.04(d) states: "after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two." MPEP 2106.04(d) also states "Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).'
Initially, as mentioned previously, the Office Action has improperly analyzed claim 1 when determining whether claim 1 recites a judicial exception because claim 1 does not fall into any of the abstract idea exceptions - mathematical concepts, certain methods of organizing human activity, or mental processes.
Even assuming, for the sake of argument, that claim 1 does recite an abstract idea (which the Applicant disagrees), Applicant respectfully submits that claim 1 is patent eligible under Prong Two of the Step 2A Analysis.
The recited features of claim 1 are clearly tied to a practical application, i.e., big data analytics (e.g., data set of 1 billion items, see para. [0046]). The claims provide an improvement to known technical problems of traditional approaches. As an example of the known technical problems, "[t]raditional approaches determine such a probability for each quantile estimate
separately, requiring larger sample size." Para. [0004] as filed. In Applicant's claimed concept of estimating quantiles in big-data analytics, an optimized sample size is determined via an iterative process. Initially, a sample of a certain size is used to perform quantile estimation to produce quantile estimates as well as confidence. If the confidence does not satisfy the desired confidence level, the sample size is increased and repeats generating quartile estimates with updated sample of the increased sample size. The iterative process continues until the sample size is just big enough to reach the desired confidence level (para. [0031]). That is, the iterative process continues until an optimized sample size is found, which is adequately large to enable
quantile estimation at the desired confidence level but not too large to waste resources (para.[0057]).
Applicant's claimed concept overcomes such technical problems by using the recited machine learning technology to provide fraudulent protection.
Thus, Applicant respectfully submits that, under the Prong Two of the Step 2A Analysis from the Guidance, the claimed concept is integrated into a practical application and therefore is not directed to a judicial exception. Therefore, Applicant respectfully submits that the claims are directed to patent eligible subject matter.
The examiner disagrees. The additional elements do not integrate the abstract idea into a practical application. The claims merely use generic machine learning techniques (and machine learning features) to analyze data and generate results. Any improvement identified by the applicant relates to the abstract idea itself (e.g. estimating quantiles, determining sample sizes, or improving analytical accuracy), rather than an improvement to computer functionality. Accordingly the claims do not integrated the judicial exception into a practical application.
The applicant argues:
Further, claim 1 amounts to significantly more than the judicial exception. The Berkheimer v. HP Inc, No. 2017-1437 (Fed. Cir. Feb. 8, 2018)¹ ("Berkheimer") decision re-emphasized that, "[a]t step two, we consider the elements of each claim both individually and 'as an ordered combination' to determine whether the additional elements 'transform the nature of the claim' into a patent eligible application." Berkheimer, pages 11 and 12. Berkheimer resolved
that the "inventive concept" is not restricted to only the additional elements, but may include one or more allegedly abstract elements that, in combination with the additional elements, form the claim's inventive concept. See Id., page 12 (stating, without reference to an "additional" element, that "[t]he question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact"). For example, while the Berkheimer Court held independent claim 1 to be directed to "the abstract idea of parsing and comparing data with conventional computer components, the Berkheimer Court nevertheless concluded that dependent claim 4 (which depended on claim 1) was potentially patent-eligible. In concluding that claim 4 could be patent-eligible, the Berkheimer Court did not narrow the inventive concept to merely the additional limitation of "storing a reconciled object structure in the archive without substantial redundancy." Indeed, the general operation of storing data/object structures in some archive without substantial
redundancy by itself would clearly have been found to be well-understood, routine, and conventional. Despite this, however, the Berkheimer Court concluded that the claimed invention of claim 4 could be patent-eligible.
Berkheimer showed that it is not merely the additional elements that are to be viewed for eligibility, but the claimed concept described by the additional elements in conjunction with the non-additional elements. Furthermore, even assuming arguendo that each of the claim limitations individually is abstract, or is performed by or is a generic computer, so too are the
BASCOM claim limitations (e.g., BASCOM Global Internet V. AT&T Mobility LLC,
No. 2015-1763 (Fed. Cir. Jun. 27, 2016)² ("BASCOM")).
In the instant application, the Office Action appears to allege that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees with the contentions, and further submits that claim 1 is patent eligible under Step 2B Analysis from the Guidance.
Accordingly, Applicant respectfully submits that claim 1 is patent eligible under the Step 2B Analysis of the Guidance.
The examiner disagrees. The applicant mentions “Berkheimer” as a reference but the Applicant does not explain how the cited reference is relevant to the presently claimed invention. The example is not tied to the claimed features, nor is any comparison provided demonstrating how it supports patent eligibility. It is unclear why the Applicant relies on this refence.
The claims and arguments cited in “Berkheimer” and the claims in the current application are not similar and therefore the reason why the claims in the cited case were found eligible are not relevant to the current set of claims.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Claim Rejections - 35 USC § 103
Hosking et al and Greifeneder et al both fail to explicitly show/teach if the confidence does not meet a desired confidence level, iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates.
However, THOMAIDOU et al teaches if the confidence does not meet a desired confidence level, iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates (paragraph [0055]…At block 220, manager system 110 can run determining process 114. Manager system 110 can run processes 114 iteratively until process 114-116 is terminated at block 222. In a machine learning aspect of manager system 110 the iterative updating of service support database 2121 as well as database 140 database 150 and database 160 by other processes can refine the cognitive AI decision making decision-making by manager system 110 which can provide determination of increased confidence level as sample sizes of sampled data increase)(Examiner’s Note: “the iterative updating of service support database 2121 as well as database 140 database 150 and database 160 by other processes can refine the cognitive AI decision making decision-making by manager system 110 which can provide determination of increased confidence level as sample sizes of sampled data increase” reads on “iteratively performing the following steps: increasing the first size, obtaining an updated sample with the increased first size, generating updated one or more quantile estimates based on the updated sample, and estimating the confidence of the updated one or more quantile estimates”).
Therefore, Hosking et al in view of Greifeneder and THOMAIDOU et al clearly shows all the limitations as claimed.
Conclusion
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.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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