DETAILED ACTION
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 .
DETAILED ACTION
This action is responsive to the Amendment filed on 3/27/2026. Claims 1, 9 and 15 have been amended. Claims 2, 5, 10, 13, 16 and 19 have been cancelled. Claims 1-20 are pending in the case. Claims 1, 9, and 15 are independent claims.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If itis determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claims 1-8 are drawn to a method, claims 9-14 are drawn to a system and claims 15-20 are drawn to a non-transitory computer readable storage medium, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 9 and 15 are non-verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
As to claim 1:
Claim 1 recites “A method for providing automated data science as a service, comprising: receiving, by a Data Science as a Service (DSaaS) computer program, training data; receiving, by the DSaaS computer program, a type of machine learning engine to train, wherein the machine learning engine is trained to predict an output for input data; performing, by the DSaaS computer program, a high-level data analysis on the training data and returning descriptive statistics, inferential statistics, mean, median, mode, dispersion, skewness, correlation coefficients and data size/frequency for the training data; identifying, by the DSaaS computer program, a plurality of essential variables to perform prediction in order of importance, wherein the plurality of essential variables comprise variables that are not being predicted from the training data; receiving, by the DSaaS computer program, a selection of one or more of the essential variables; training, by the DSaaS computer program, the machine learning engine with the training data using the type of machine learning engine to train and the selected one or more essential variables based on the type of machine learning engine; receiving, by the DSaaS computer program, production data from one or more production systems; applying, by the DSaaS computer program, the production data to the trained machine learning engine; and outputting, by the DSaaS computer program, an output of the trained machine learning engine to the one or more production systems and/or a data consumer, wherein the one or more production systems and/or the data consumer is configured to consume the output of the trained machine learning engine.“
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “performing… a high-level data analysis on the training data; returning, identifying… a plurality of essential variables to perform prediction in order of importance, , wherein the plurality of essential variables comprise variables that are not being predicted from the training data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mentally analyze data and figuring out important variables to predict the order of importance of data, which is an observation or evaluation that is practically capable of being performed in the human mind with the assistance of pen and paper. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, this limitation “machine learning”, “… based on the type of machine learning engine” are an additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “Data Science as a Service (DSaaS)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, this limitation “returning descriptive statistics, inferential statistics, mean, median, mode, dispersion, skewness, correlation coefficients and data size/frequency for the training data” “receiving, by the DSaaS computer program, a type of machine learning engine to train, wherein the machine learning engine is trained to predict an output for input data”, “receiving, by the DSaaS computer program, production data from one or more production systems” and “outputting, by the DSaaS computer program, an output of the trained machine learning engine to the one or more production systems and/or a data consumer, wherein the one or more production systems and/or the data consumer is configured to consume the output of the trained machine learning engine” amount to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform the steps. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g).
No, This limitation “applying, by the DSaaS computer program, the production data to the trained machine learning engine” is merely a post-solution step and as such is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
This limitation “training, by the DSaaS computer program, the machine learning engine with the training data using the type of machine learning engine to train and the selected one or more essential variables” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
No, this limitation “machine learning”, “… based on the type of machine learning engine” are an additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
No, this limitation “Data Science as a Service (DSaaS)” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, this limitation “returning descriptive statistics, inferential statistics, mean, median, mode, dispersion, skewness, correlation coefficients and data size/frequency for the training data” “receiving, by the DSaaS computer program, a type of machine learning engine to train, wherein the machine learning engine is trained to predict an output for input data”, “receiving, by the DSaaS computer program, production data from one or more production systems” and “outputting, by the DSaaS computer program, an output of the trained machine learning engine to the one or more production systems and/or a data consumer, wherein the one or more production systems and/or the data consumer is configured to consume the output of the trained machine learning engine” amount to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform the steps. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, This limitation “applying, by the DSaaS computer program, the production data to the trained machine learning engine” is merely a post-solution step and as such is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
This limitation “training, by the DSaaS computer program, the machine learning engine with the training data using the type of machine learning engine to train and the selected one or more essential variables” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-6 which are dependent on claim 1, claims 8-12 which are dependent on claim 7, and claims 14-18 which are dependent on claim 3, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Dependent claims 2, 10 and 16.
Incorporates the rejection of independent claims 1, 9 and 15.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “returning… insights into data types, distributions, frequencies, and classifications of the data.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claims 3, 11 and 17.
Incorporates the rejection of independent claims 1, 9 and 15.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “identifying… a subset of the training data to exclude” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claims 4, 12 and 18.
Incorporates the rejection of independent claims 1, 9 and 15.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, abstract idea of the independent claims.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). This limitation “a Naïve Bayes model, an XGBoost model, or a Logistic Regression model” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? This limitation “a Naïve Bayes model, an XGBoost model, or a Logistic Regression model” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Dependent claims 5, 13 and 19.
Incorporates the rejection of independent claims 1, 9 and 15.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “the essential variables are identified from the training data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 6.
Incorporates the rejection of independent claim 1.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). The abstract idea of independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claim 7.
Incorporates the rejection of independent claim 1.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “the output data is formatted in the same format as the production data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No.
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No.
Dependent claims 8, 14 and 20.
Incorporates the rejection of independent claims 1, 9 and 15.
Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). The abstract idea of the independent claim.
Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). This limitation “the production data and the output data have a format selected from the group consisting of xls, xlsx, json, and csv” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? This limitation “the production data and the output data have a format selected from the group consisting of xls, xlsx, json, and csv” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h).
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what the courts have identified as “significantly more”, see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible.
As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole the dependent claims do not recite what the courts have identified as “significantly more” than the recited judicial exception.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as “significantly more” than the recited judicial exception.
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 (i.e., changing from AIA to pre-AIA ) 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.
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 1-3, 5-9, 11, 14-15, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cotton (US 20190066133 A1) in view of Wang et al (US 20230081472 A1).
Referring to claims 1, 9 and 15, Cotton discloses a method for providing automated data science as a service, comprising:
receiving, by a Data Science as a Service (DSaaS) computer program, training data; receiving, by the DSaaS computer program, a type of machine learning engine to train, wherein the machine learning engine is trained to predict an output for input data; (The current Specification does not describe what is the “machine learning engine” and how it works with the data, hence under BRI, it is interpreted as a software module within the DSaaS system that does something with the data. [0009] of Cotton, DSaaS receives data to be organized and analyzed within the system, where the input data is inputted into the DSaaS system and having a predicted output based on the DSaaS system)
performing, by the DSaaS computer program, a high-level data analysis on the training data; (The current Specification does not describe what is the “high-level data analysis” and how it works with the data, hence under BRI, it is interpreted as a software module within the DSaaS system that does something with the data. [0009] of Cotton, DSaaS receives data to be organized and analyzed within the system)
identifying, by the DSaaS computer program, a plurality of essential variables to perform prediction in order of importance, wherein the plurality of essential variable comprise variables that are not being predicted from the training data; (Fig. 9 and [0073] of Cotton, “The leaderboard can be configured to allow participants to sort the leaderboard by any of the foregoing variables. In the FIG. 9 example, the contests are ranked by share, hence ranked by order of importance. Further, [0040] of Cotton, “The DSaaS system can create a new, efficient workflow that can be exploited by leading companies and organizations and their customers. In the short time frame (e.g., seconds) that may be allotted for a response, participants' bots (e.g., web-based software robots) can scrape company filings, utilize any number of resources on the programmable Web, search for correlated variables in public databases, or even poll for data from their drone” hence data used to predict output can be imported from public database or provided within the DSaaS system)
receiving, by the DSaaS computer program, a selection of one or more of the essential variables; (Fig. 9 and [0073] of Cotton, “The leaderboard can be configured to allow participants to sort the leaderboard by any of the foregoing variables. In the FIG. 9 example, the contests are ranked by share”.)
training, by the DSaaS computer program, the machine learning engine with the training data using the type of machine learning engine to train and the selected one or more essential variables based on the type of machine learning engine; (Fig. 9 and Summary and [0073] of Cotton, variables within the DSaaS system can be selected and organized into predictive of certain data fields.)
receiving, by the DSaaS computer program, production data from one or more production systems; ([0046] of Cotton, receiving by the DSaaS system, products and services as input data/training data)
applying, by the DSaaS computer program, the production data to the trained machine learning engine; ([0046] of Cotton, receiving by the DSaaS system, products and services as input data/training data to be analyzed and outputted) and
outputting, by the DSaaS computer program, an output of the trained machine learning engine to the one or more production systems and/or a data consumer, wherein the one or more production systems and/or the data consumer is configured to consume the output of the trained machine learning engine. (Figs. 4-5 and [0046] of Cotton, receiving by the DSaaS system, products and services as input data/training data to be analyzed and outputted)
Cotton does not specifically disclose “returning descriptive statistics, inferential statistics, mean, median, mode, dispersion, skewness, correlation coefficients and data size/frequency for the training data”.
However, Wang discloses returning descriptive statistics, inferential statistics, mean, median, mode, dispersion, skewness, correlation coefficients and data size/frequency for the training data ([0473] of Wang, different variables are being used in the software system as listed).
Cotton and Wang are analogous art because both references concern data analysis within the Dsaas system. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Cotton’s input data format with the different data variables can be used in the prediction as taught by Wang. The motivation for doing so would have been to use more methods to process and analyze data input.
Referring to claims 3, 11 and 17, Cotton in view of Wang disclose the method of claim 1, further comprising: identifying, by the DSaaS computer program, a subset of the training data to exclude. ([0073] of Cotton, “exclude (a check-box the sponsor of the competition can use to ignore certain participants)”, here a subset of the participants can be excluded)
Referring to claim 6, Cotton in view of Wang disclose the method of claim 1, wherein the production data is received in real time. ([0073] of Cotton, “ the data consumer can submit this real-time question by identifying the website (subject data source) and data field where this data is posted using the consumer interface…”)
Referring to claims 7, Cotton in view of Wang disclose the method of claim 1, wherein the output data is formatted in the same format as the production data. ([0289] of Cotton, “That is because any source of updating data, such as might be available on a table in a web page or a public JSON page, is already close to being a specification of a real-time forecasting contest.” Here, the data used is outputted to a webpage or JSON page, which is the same as input data format)
Referring to claims 8, 14 and 20, Cotton in view of Wang disclose the method of claim 1, wherein the production data and the output data have a format selected from the group consisting of xls, xlsx, json, and csv. ([0289] of Cotton, “That is because any source of updating data, such as might be available on a table in a web page or a public JSON page, is already close to being a specification of a real-time forecasting contest.” Here, the data used is outputted to a webpage or JSON page, which is the same as input data format)
Claims 4, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cotton (US 20190066133 A1) in view of Dietrich et al (US 9710767 B1).
Referring to claims 4, 12 and 18, Cotton in view of Wang disclose the method of claim 1. Cotton in view of Wang do not specifically disclose wherein the type of machine learning engine comprises a Naïve Bayes model, an XGBoost model, or a Logistic Regression model.
However, Dietrich discloses wherein the type of machine learning engine comprises a Naïve Bayes model, an XGBoost model, or a Logistic Regression model (col. 13, lines 29-46 of Dietrich, DsaaS system using Naïve Bayes model)
Cotton and Wang and Dietrich are analogous art because both references concern data analysis within the Dsaas system. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Cotton and Wang’s input data format with the data formatting using Naïve Bayes model as taught by Dietrich. The motivation for doing so would have been to use more methods to process and analyze data input.
Response to Arguments
Applicant's arguments filed on 3/27/2026 have been fully considered but they are not persuasive.
Applicant argues 1) amended claims is legible under 101 rejection; 2) Cotton does not disclose the amended limitations.
Examiner respectfully disagrees in part.
Applicant did not specifically point out why the elements of the claims recite subject matter beyond general linking the use of judicial exception.
Please see detailed 101 rejection above.
First, Applicant argues that Cotton fails to disclose different types of machine learning engine to train input data to predict output data (emphasize added). “Machine Learning engine” in the broadest reasonable interpretations, without the claims specify how and what is such “machine learning engine”, it is just a software module, same as the DSaaS which is a software system that processes data with input and output. So selecting “types of machine learning engine” to train input data is Cotton’s DSaaS system where the DSaaS receives data to be organized and analyzed within the system, where the input data is inputted into the DSaaS system and having a predicted output based on the DSaaS system, where “wherein the consumer interface allows a data consumer to (a) identify a subject data source having data fields that can be predicted,” and depending on what constraints are selected for predicted data, different types of software module is used for that purpose.
Second, Applicant argues that Cotton does not disclose “returning descriptive statistics, inferential statistics, mean, median, mode, dispersion, skewness, correlation coefficients and data size/frequency for the training data”. A new references is used to teach this limitation.
Third, Applicant argues that Cotton does not disclose “wherein the plurality of essential variables comprise variables that are not being predicted from the training data.” Here, [0040] of Cotton, “The DSaaS system can create a new, efficient workflow that can be exploited by leading companies and organizations and their customers. In the short time frame (e.g., seconds) that may be allotted for a response, participants' bots (e.g., web-based software robots) can scrape company filings, utilize any number of resources on the programmable Web, search for correlated variables in public databases, or even poll for data from their drone” hence data used to predict output can be imported from public database or provided within the DSaaS system.
Therefore, the cited arts disclose the recited limitations.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Adjaoute (US 20160071017 A1): A method of improving the training and performance of predictive models. A first method of operating an artificial intelligence machine produces predictive model language documents describing improved predictive models that generate better business decisions from raw data record inputs. A second method of operating an artificial intelligence machine including processors for predictive model algorithms produces and outputs better business decisions from raw data record inputs. Both methods enrich the raw data records their processors are fed by deleting data fields with data values that have little benefit in decision making, and that derive and add new data fields from information sources then available that do benefit in the decision making of the artificial intelligence machine through improved accuracies of prediction.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAIMEI JIANG whose telephone number is (571)270-1590. The examiner can normally be reached M-F 9-5pm.
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, Mariela D Reyes can be reached at 571-270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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