DETAILED ACTION
This Office Action is in response to Amendments and Arguments submitted on June 03, 2026 for Application # 18/455,332 filed on August 24, 2023 in which claims 1-19 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1-19 are pending, of which claims 1-19 are rejected under 35 U.S.C. 103 and also claims 1-19 are rejected under 35 U.S.C. 101.
Claims 1, 16, 18 and 19 are amended.
No claims are canceled.
No newly added claims.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The Provisional Application# 63/400,481 with filing date August 24, 2022.
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 1-19 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-19 recite a method, device and readable medium respectively.
The analysis of claims 1 and 19 is as follows:
Step 2A, prong one: Does claims 1 and 19 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “monitoring a data source;
during the monitoring, detecting an arrival of each of one or more sets of one or more features at the data source; and
in response to detecting the arrival at the data source of at least a first set of one or more features of the one or more sets of one or more features:
extracting data from the first set of one or more features;
estimating data for at least a second set of one or more features of the one or more sets of one or more features, wherein the second set of one or more features has not yet arrived at the data source” as drafted, are mental steps based on various processes can be performed in a human mind of building a prediction model based on features in the data (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “computer” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “based on the extracted data and the estimated data, predicting the data quality metric” are mere gathering data and applying process steps (i.e., predicting the data quality metric); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “based on the extracted data and the estimated data, predicting the data quality metric “, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., prediction model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating cubes from collection of queries is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and generating are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1 and 19 are rejected as being directed to non-patentable subject matter under §101.
The analysis of claims 2-18 are as follows:
Step 2A, prong one: Does claims 2-18 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “Claim 2, in response to detecting the arrival at the data source of the second set of one or more features: extracting data from the second set of one or more features; and based on the data extracted from the second set of one or more features, updating the prediction of the data quality metric.
Claim 3, wherein predicting the data quality metric comprises inputting the extracted data to a trained machine learning model.
Claim 4, wherein the trained machine learning model is a gradient-boosted tree model.
Claim 5, wherein estimating the data for the second set of one or more features comprises estimating the data for the second set of one or more features using one or more of: a statistical method based on historical data; a regression model based on historical data; and a machine learning model based on historical data.
Claim 6, wherein the one or more sets of one or more features comprise one or more of: one or more end-of-day contract features; one or more number of records features; and one or more market-related features.
Claim 7, wherein detecting the arrival of at least the first set of one or more features comprises: sequentially detecting the arrival of the one or more end-of-day contract features, the arrival of the one or more number of records features, and the arrival of the one or more market-related features.
Claim 8, in response to detecting the arrival at the data source of the second set of one or more features: estimating data for at least a third set of one or more features of the one or more sets of one or more features, wherein the third set of one or more features has not yet arrived at the data source; and based on the estimated data for the third set of one or more features, updating the prediction of the data quality metric; and in response to detecting the arrival at the data source of the third set of one or more features: extracting data from the third set of one or more features; and based on the data extracted from the third set of one or more features, further updating the data quality metric.
Claim 9, wherein: the first set of one or more features comprises one or more end-of-day contract features; the second set of one or more features comprises one or more number of records features; and the third set of one or more features comprise one or more market-related features.
Claim 10, wherein extracting the data from the first set of one or more features comprises extracting one or more of: a source system associated with an end-of-day contract; a source region associated with the end-of-day contract; an arrival time associated with the end-of-day contract; a business date associated with the end-of-day contract; and a business day associated with the end-of-day contract.
Claim 11, wherein extracting the data from the second set of one or more features comprises extracting a number of records associated with a number of records feature.
Claim 12, wherein extracting the data from the third set of one or more features comprises extracting one or both of: a request time associated with a market-related feature; and a response time associated with the market-related feature.
Claim 13, wherein estimating the data for the second set of one or more features comprises estimating a number of records associated with a number of records feature.
Claim 14, wherein estimating the data for the third set of one or more features comprises estimating one or both of: a request time associated with a market-related feature; and a response time associated with the market-related feature.
Claim 15, further comprising transmitting to one or more users a notification indicative of the prediction.
Claim 16, wherein the data quality metric comprises a metric indicative of a delay in data processing, and wherein the method further comprises: for each feature in each of the one or more sets of one or more features, identifying a Shapley value; comparing each Shapley value to a threshold; and based on the comparison, identifying a reason associated with the delay in the data processing.
Claim 18, wherein the data quality metric comprises one or more: a metric indicative of a delay in data processing; a metric indicative of a completeness of data; and a metric indicative of an accuracy of data” as drafted, are mental steps based on various processes can be performed in a human mind of building a prediction model based on features in the data (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “computer” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “Claim 17, wherein identifying the Shapley value comprises, for each feature: inputting the feature to a trained regression or classification model; and identifying, using the trained regression or classification model, the Shapley value” are mere gathering data and applying process steps (i.e., predicting the data quality metric); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “based on the extracted data and the estimated data, predicting the data quality metric “, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., prediction model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating cubes from collection of queries is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and generating are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1-18 are rejected as being directed to non-patentable subject matter under §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharifi Sedeh et al. US 2020/0074313 A1 (hereinafter ‘Sharifi’) in view of Cella et al. US 2023/0123322 A1 (hereinafter ‘Cella’).
As per claim 1, Sharifi disclose, A method of predicting (Sharifi: paragraph 0018: disclose a predictive model) a data quality metric (Sharifi: paragraph 0024: disclose calculate a measure according to a quality metric for the output of the predictive model), comprising performing, by one or more computer processors (Sharifi: paragraph 0024: disclose the processor):
monitoring a data source (Sharifi: paragraph 0052: disclose predictive model suitable for analyzing ‘monitoring’ a dataset ‘data source’);
during the monitoring (Sharifi: paragraph 0052: disclose analyzing ‘monitoring’ a dataset ‘data source’), detecting an arrival of each of one or more sets of one or more features at the data source (Sharifi: paragraph 0055: disclose one or more features of the plurality of features that are most relevant. Examiner concedes that the prior art is silent on arrival of each of one or more sets and examiner would discuss about this limitation in secondary art below); and
in response to detecting the arrival at the data source of at least a first set of one or more features of the one or more sets of one or more features (Sharifi: paragraph 0055: disclose one or more features of the plurality of features that are most relevant. Examiner concedes that the prior art is silent on arrival of each of one or more sets and examiner would discuss about this limitation in secondary art below):
extracting data from the first set of one or more features (Sharifi: paragraph 0056: disclose total of five features should be included in the risk assessment instrument);
based on the extracted data and the estimated data, predicting the data quality metric (Sharifi: paragraph 0086: disclose determine a revised quality measure according to the quality metric for the output of the predictive model and one or more features of the revised feature ‘estimated data’ set that are most relevant).
It is noted, however, Sharifi did not specifically detail the aspects of
arrival of each of one or more sets;
estimating data correspond at least a second set of one or more features of the one or more sets of one or more features, wherein the second set of one or more features has not yet arrived at the data source;
wherein the data quality metric comprises a metric indicative of a delay in data processing as recited in claim 1.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
arrival of each of one or more sets (Cella: paragraph 0009: disclose a plurality of data values of a data stream ‘arrival’);
estimating data correspond at least a second set of one or more features of the one or more sets of one or more features, wherein the second set of one or more features has not yet arrived at the data source (Cella: paragraph 0009: disclose predictive model for predicting future data values ‘second set’ of the data stream based on the received plurality of data values).
wherein the data quality metric comprises a metric indicative of a delay in data processing (Cella: paragraph 2276: disclose various metrics includes timing performance metrics, financial performance metrics, physical performance metrics, cargo damage metrics, data regarding resource usage, or some other type of reporting data. Examiner equates timing performance metrics to metric indicative of a delay in data processing).
Sharifi and Cella are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Predicting Model Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Sharifi and Cella because they are both directed to predicting model systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Cella with the method described by Sharifi in order to solve the problem posed.
The motivation for doing so would have been to convert data into insights and to translate insights into well-informed decisions and timely execution of efficient operations (Cella: paragraph 0006).
Therefore, it would have been obvious to combine Cella with Sharifi to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Sharifi disclose, in response to detecting the arrival at the data source of the second set of one or more features: extracting data from the second set of one or more features (Sharifi: paragraph 0056: disclose total of five features should be included in the risk assessment instrument); and based on the data extracted from the second set of one or more features, updating the prediction of the data quality metric (Sharifi: paragraph 0086: disclose determine a revised ‘updated’ quality measure according to the quality metric for the output of the predictive model and one or more features of the revised feature ‘estimated data’ set that are most relevant).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Sharifi disclose, wherein predicting the data quality metric comprises inputting the extracted data to a trained machine learning model (Sharifi: paragraph 0017: disclose a predictive model such as a machine learning model).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claims 1 and 3 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein the trained machine learning model is a gradient-boosted tree model as recited in claim 4.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein the trained machine learning model is a gradient-boosted tree model (Cella: Paragraph 1230: disclose gradient-boosted trees model).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein estimating the data for the second set of one or more features comprises estimating the data for the second set of one or more features using one or more of: a statistical method based on historical data; a regression model based on historical data; and a machine learning model based on historical data as recited in claim 5.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein estimating the data for the second set of one or more features comprises estimating the data for the second set of one or more features using one or more of: a statistical method based on historical data; a regression model based on historical data; and a machine learning model based on historical data (Cella: Paragraph 1930: disclose perform regression to provide output in the form of a continuous numeric values and paragraph 2296: disclose using historical data that is collected).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein the one or more sets of one or more features comprise one or more of: one or more end-of-day contract features; one or more number of records features; and one or more market-related features as recited in claim 6.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein the one or more sets of one or more features comprise one or more of: one or more end-of-day contract features; one or more number of records features; and one or more market-related features (Cella: Paragraph 0164: disclose storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claims 1 and 6 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein detecting the arrival of at least the first set of one or more features comprises: sequentially detecting the arrival of the one or more end-of-day contract features, the arrival of the one or more number of records features, and the arrival of the one or more market-related features as recited in claim 7.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein detecting the arrival of at least the first set of one or more features comprises: sequentially detecting the arrival of the one or more end-of-day contract features (Cella: paragraph 0009: disclose a plurality of data values of a data stream ‘arrival’), the arrival of the one or more number of records features, and the arrival of the one or more market-related features (Cella: Paragraph 0164: disclose storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, Sharifi disclose, based on the estimated data for the third set of one or more features, updating the prediction of the data quality metric; and in response to detecting the arrival at the data source of the third set of one or more features (Sharifi: paragraph 0055: disclose one or more features of the plurality of features that are most relevant. Examiner concedes that the prior art is silent on arrival of each of one or more sets and examiner would discuss about this limitation in secondary art below): extracting data from the third set of one or more features (Sharifi: paragraph 0056: disclose total of five features should be included in the risk assessment instrument); and based on the data extracted from the third set of one or more features, further updating the data quality metric (Sharifi: paragraph 0086: disclose determine a revised quality measure according to the quality metric for the output of the predictive model and one or more features of the revised feature ‘estimated data’ set that are most relevant).
It is noted, however, Sharifi did not specifically detail the aspects of
in response to detecting the arrival at the data source of the second set of one or more features: estimating data for at least a third set of one or more features of the one or more sets of one or more features, wherein the third set of one or more features has not yet arrived at the data source as recited in claim 8.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
in response to detecting the arrival at the data source of the second set of one or more features (Cella: paragraph 0009: disclose a plurality of data values of a data stream ‘arrival’): estimating data for at least a third set of one or more features of the one or more sets of one or more features, wherein the third set of one or more features has not yet arrived at the data source (Cella: paragraph 0009: disclose predictive model for predicting future data values ‘second set’ of the data stream based on the received plurality of data values).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 8 above.
It is noted, however, Sharifi did not specifically detail the aspects of
the first set of one or more features comprises one or more end-of-day contract features; the second set of one or more features comprises one or more number of records features; and the third set of one or more features comprise one or more market-related features as recited in claim 9.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
the first set of one or more features comprises one or more end-of-day contract features (Cella: paragraph 0009: disclose a plurality of data values of a data stream ‘arrival’); the second set of one or more features comprises one or more number of records features; and the third set of one or more features comprise one or more market-related features (Cella: Paragraph 0164: disclose storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities).
As per claim 10, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 8 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein extracting the data from the first set of one or more features comprises extracting one or more of: a source system associated with an end-of-day contract; a source region associated with the end-of-day contract; an arrival time associated with the end-of-day contract; a business date associated with the end-of-day contract; and a business day associated with the end-of-day contract as recited in claim 10.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein extracting the data from the first set of one or more features comprises extracting one or more of: a source system associated with an end-of-day contract; a source region associated with the end-of-day contract; an arrival time associated with the end-of-day contract; a business date associated with the end-of-day contract; and a business day associated with the end-of-day contract (Cella: Paragraph 0164: disclose storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities. Examiner argues that the end-of-day contract is part of the management activities).
As per claim 11, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 8 above. In addition, Sharifi disclose, wherein extracting the data from the second set of one or more features comprises extracting a number of records associated with a number of records feature (Sharifi: paragraph 0056: disclose total of five features should be included in the risk assessment instrument).
As per claim 12, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 8 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein extracting the data from the third set of one or more features comprises extracting one or both of: a request time associated with a market-related feature; and a response time associated with the market-related feature as recited in claim 12.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein extracting the data from the third set of one or more features comprises extracting one or both of: a request time associated with a market-related feature; and a response time associated with the market-related feature (Cella: Paragraph 0164: disclose storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities).
As per claim 13, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 8 above. In addition, Sharifi disclose, wherein estimating the data for the second set of one or more features comprises estimating a number of records associated with a number of records feature (Sharifi: paragraph 0056: disclose total of five features should be included in the risk assessment instrument).
As per claim 14, most of the limitations of this claim have been noted in the rejection of claims 1, 2 and 8 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein estimating the data for the third set of one or more features comprises estimating one or both of: a request time associated with a market-related feature; and a response time associated with the market-related feature as recited in claim 14.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein estimating the data for the third set of one or more features comprises estimating one or both of: a request time associated with a market-related feature; and a response time associated with the market-related feature (Cella: Paragraph 0164: disclose storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities).
As per claim 15, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Sharifi disclose, comprising transmitting to one or more users a notification indicative of the prediction (Sharifi: paragraph 0083: disclose user to know the accuracy of the output of the predictive model).
As per claim 16, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein the data quality metric comprises a metric indicative of a delay in data processing, and wherein the method further comprises: for each feature in each of the one or more sets of one or more features, identifying a Shapley value; comparing each Shapley value to a threshold; and based on the comparison, identifying a reason associated with the delay in the data processing as recited in claim 16.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein the data quality metric comprises a metric indicative of a delay in data processing, and wherein the method further comprises: for each feature in each of the one or more sets of one or more features, identifying a Shapley value (Cella: paragraph 0366: disclose Shapley values); comparing each Shapley value to a threshold; and based on the comparison, identifying a reason associated with the delay in the data processing (Cella: paragraph 0999: disclose to account for time shifts in data, delays are added to one or more inputs, or between one or more nodes, so that multiple data points (from distinct points in time) are analyzed together).
As per claim 17, most of the limitations of this claim have been noted in the rejection of claims 1 and 16 above.
It is noted, however, Sharifi did not specifically detail the aspects of
wherein identifying the Shapley value comprises, for each feature: inputting the feature to a trained regression or classification model; and identifying, using the trained regression or classification model, the Shapley value as recited in claim 17.
On the other hand, Cella achieved the aforementioned limitations by providing mechanisms of
wherein identifying the Shapley value comprises, for each feature: inputting the feature to a trained regression or classification model; and identifying, using the trained regression or classification model, the Shapley value (Cella: paragraph 0994: disclose complex pattern classification of one or more items, phenomena, modes, states).
As per claim 18, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Sharifi disclose, wherein the data quality metric comprises one or more: a metric indicative of a delay in data processing; a metric indicative of a completeness of data; and a metric indicative of an accuracy of data (Sharifi: paragraph 0024: disclose calculate a measure according to a quality metric for the output of the predictive model and paragraph 0085: disclose accurate prediction of the likelihood may be achieved if a greater number of features).
As per claim 19, Sharifi disclose, A computer-readable medium (Sharifi: paragraph 0106: disclose computer-readable medium) having stored thereon computer program code configured, when executed by one or more processors (Sharifi: paragraph 0049: disclose store a set of instructions which, when executed by the processor), to cause the one or more processors to perform a method comprising (Sharifi: paragraph 0085: disclose processor may be configured): remaining limitations in this claim 19 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
Response to Arguments
Examiner withdraws 35 U.S.C. 112(a) due to applicant identifies sections of specification in the response that provides enablement.
Applicant's arguments filed on June 03, 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Claims 1 and 19 are directed to the abstract idea of predicting a data quality metric, comprising performing, by one or more computer processors:
monitoring a data source;
during the monitoring, detecting an arrival of each of one or more sets of one or more features at the data source; and
in response to detecting the arrival at the data source of at least a first set of one or more features of the one or more sets of one or more features;
estimating data corresponding at least a second set of one or more features of the one or more sets of one or more features, wherein the second set of one or more features has not yet arrived at the data source; and
based on the extracted data and the estimated data, predicting the data quality metric; wherein the data quality metric comprises a metric indicative of a delay in data processing. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements such as estimating data and predicting the data quality metric, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Estimating data and predicting data quality metric of the second set that is yet to arrive at the data source based on the first set of data, based on the broadest reasonable interpretation in view of the specification.
Mathematical relationships and algorithms have been found by the courts (e.g. Benson, Flook, Diehr, Grams) to be abstract ideas. For example, in Benson, a mathematical procedure for converting one form of numerical representation to another was found to be an exception, as was an algorithm for calculating parameters indicating an abnormal condition in Grams.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Additional elements recited in the claim include the limitations: based on the extracted data and the estimated data, predicting the data quality metrics wherein the data quality metric comprises a metric indicative of a delay in data processing. These limitations are directed to realizing the mathematical algorithm in a computer system. Executing the using a model to estimating and predicting data quality of the yet to arrive data is little more than a broad recitation of generic use of a computer (i.e., executing). Providing the predicting of the quality metrics is at most insignificant post solution activity of estimating the data quality. The preamble's recitation of a "computer-readable medium" and a "computer" are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Further, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because merely providing classification and executing the algorithm by a computer is akin to adding the words “apply it” with a computer in conjunction with the abstract idea. Such limitations are not enough to add significantly more to the method of business rules of predicting data quality metrics, which represent mathematical relationships and algorithms.
Considering all the limitations in combination, the claimed additional computer elements do not show any inventive concept in applying the mathematical operations, such as improving the performance of a computer or any other technology. The steps describe nothing more than a computer’s basic function of estimating and predicting the data quality metrics of the delayed data, and do not meaningfully limit the performance of the calculation. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Applicant's arguments filed on June 03, 2026 have been fully considered but they are not persuasive.
Examiner’s response to applicant’s argument related to claims 1 and 19.
Applicant argues neither Sharifi nor Cella teach ‘predicting the data quality metric; wherein the data quality metric comprises a metric indicative of a delay in data processing’. Examiner respectfully disagrees with applicant’s argument. Examiner cited Cella in paragraph 0354 disclose that the artificial intelligence system is trained to predict delay based on a training data set that includes a set of images of value chain network activities and a set of value chain network timing outcomes to enhance the argument that teaches the limitation that applicant’s argued where the metrics is equated to value chain network activities ‘processing data’. Examiner understand the applicant arguments in view of specification as disclosed in page 3 lines 0013-0016. However, examiner did not see the limitations as argued in the claim language specifically related to predicting whether the processing of data be delayed and believe the prior-art teaches the limitations as claimed. Since examiner can’t import limitations unnecessarily into the claim language under BRI guidelines. Examiner is not persuaded by the applicant’s argument that Sharifi and Cella are not analogous art as Sharifi defines in abstract as a predictive model to a dataset representing the ground truth and Cella defines in abstract as prioritizing predictive model data streams . The motivation for doing so would have been to convert data into insights and to translate insights into well-informed decisions and timely execution of efficient operations.
Examiner interprets these claim limitations under BRI (Broadest Reasonable Interpretation) guidelines where examiner without importing limitations from the specification into the claims unnecessarily.
MPEP 2106.II.C
USPTO personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim should not be read into the claim. E-Pass Techs., Inc. v. 3Com Corp., 343 F.3d 1364, 1369, 67 USPQ2d 1947, 1950 (Fed. Cir. 2003) (claims must be interpreted “in view of the specification” without importing limitations from the specification into the claims unnecessarily). In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow.... The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed.... An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2010/0114638 A1 disclose “Method and Software for the Measurement of Quality of Process”
US Pub. US 2023/0281505 A1 disclose “AUTOMATIC DATA QUALITY MONITORING USING MACHINE LEARNING”
THIS ACTION IS MADE FINAL. 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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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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