DETAILED ACTION
Election/Restrictions
Applicant elected Claims 35-44 without traverse
Claim Rejections - 35 USC § 101
Claims 35-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. This judicial exception is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons that follow;
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Step 1
The claims are directed to method and product. Claim 11 the method is the representative claim. The abstract idea is identifying products posted online that are infringing intellectual property rights.
Step 2A
Eligibility Analysis, Step 1
Regarding claims 1-20 the claims are each directed to one a statutory categories of invention, a system and a method. As such, the analysis proceeds to Step 2. The 2019 Patent Subject Matter Eligibility Guidance (“2019 PEG”) sets forth a revised Step 2A analysis which includes a two-prong inquiry.
Eligibility Analysis, Step 2A Prong One
Prong one consists of determining if the claims recite a judicial exception, which includes abstract ideas, laws of nature, and natural phenomenon. Groupings of abstract ideas may include mathematical concepts, mental processes, and certain methods of organizing human activity. Here, representative independent claim 35 recites limitations relating actions to choose a domain, pick data sources, and run analytics through a machine-learning. The claim is directed at is core to longstanding fundamental economic/commercial practice.
The steps comprise the following:
1)receiving data from heterogeneous agnostic sources with appropriate access permissions and security protocol clearance;
sequentially processing the received data by:
selecting a user domain;
selecting a data source;
extracting the data;
evaluating the data’s suitability for cognitive intelligence operations;
storing data informatics in a cloud environment;
cleansing non-suitable data; and
performing extract, transform, and load (ETL) operations before storing the data in a Big Data repository for further processing;
2) analyzing data models to initiate machine learning (ML) using:
key performance indicator (KPI) analytics, ML tools, and compliance-based analytics for learning processes; and
canned analytics, custom analytics, and compliance analytics for core data analysis;
testing models to determine model accuracy level (MAL), and advancing the models through an evaluation pipeline if MAL exceeds 50%;
evaluating a scope for improvement ratio (SIR) and determining output results if SIR is less than 5%; and
publishing and visualizing the output results,
wherein the infrastructure comprises multiple functional and domain areas, including but not limited to smart cities, data centers, urban and rural infrastructure, campuses, seaports, airports, rail networks, energy and utility systems, and industrial facilities.
Steps 1-3 above describe a fundamental economic practice and therefore a certain method of organizing human activity. Further, the limitations, as drafted, describe a process that, under its broadest reasonable interpretation, covers performance of the limitation by a human analog but for the recitation of generic computer components.
That is, other than the recitation of the high level technology, nothing in the claims precludes the steps from practically being performed by a human analog. Performing the above features via generic computer technology amounts to a method with no recited specific technological solution. Here, the mere nominal recitation of the generic computer components does not take the claim limitation out of the “certain methods of organizing human activity” grouping. As such, the claims recite an abstract idea under prong one. The analysis proceeds to Step 2A Prong Two.
Eligibility Analysis, Step 2A Prong Two
Prong two consists of determining whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim recites the following additional elements:
Big Data Repsitory
ML tools
processors
The additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. They do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As such, the claims are directed to the abstract idea. The analysis proceeds to Step 2B.
Eligibility Analysis, Step 2B
Step 2B consists of determining whether the claim provides an inventive concept by considering whether the additional elements go beyond what is well-understood, routine, and conventional activity.
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106; see also USPTO: July 2015 Update: Subject Matter Eligibility):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”);
iii. Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
In the case of the instant claims, the generic application of the computing devices similarly does not make the invention patent-eligible. Note that the disclosure recites general computer products which are suitable to perform the claimed method (see eg. para. 0023-0028). Moreover, the specification does not contribute any technically-specific computer algorithm or code, but rather merely states that the claimed steps may be performed by the generic modules with the expectation that one of ordinary skill in the art would be capable of implementation without further instruction. The use of computing devices in this manner is merely what computers do, ie. training algorithms, performing repetitive calculations, receiving, processing, and storing data, and automating mental tasks, and does not change the analysis. Whilst the implementation of such a solution may include the use of generic technical features, these merely serve their well-understood functions as would be recognized by one of ordinary skill in the art in the technical field under consideration. As such, the claims' invocation of the computer merely amounts to the limiting of the use of the abstract idea to a particular technological environment.
Here, the involvement of the generic computer products does not amount to significantly more than the abstract idea because the mere recitation of a generic computer cannot transform a patent-eligible abstract idea into a patent-eligible invention. The claims do 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. The computer components 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. The use of generic computer components in this manner does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the 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.
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are recited at a high level of generality, as discussed above. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claim 36, 41 and 42 additional components but these component fail to render the claims patent eligible for the reasons cited for the independent claim’s additional components. Claims 37-40 , 43-44 are rejected for the reasons of the independent claims.
Closes prior art is Doddi Pub. No. 2019/0258947
Doddi teaches a computer-implemented method (para. 0011) performed by at least one processor, the method comprising: presenting, by the at least one processor, in a user interface (UI), a plurality of UI elements (see fig. 3) associated with a plurality of operators (nodes are operators, para. 0033) including at least one data preparation operator (workflow manager node 312 prepares data, para. 0035) and a first machine learning (ML) operator for training a model (trainer node 324 trains a machine learning model, para. 0038); determining, by the at least one processor, a model generation workflow that describes at least one input data source (collector node 308 is input data, para. 0034) and an execution order for the plurality of operators (workflow defines order, para. 0033); presenting, by the at least one processor, a visual depiction of the model generation workflow in the UI (see fig. 3); and executing, by the at least one processor, the model generation workflow, including executing the at least one data preparation operator and the first ML operator in the execution order to process data that is included in the at least one input data source, wherein executing the model generation workflow trains the model (executing workflow causes model to be trained using input data source, data prep, and ML operator, para. 0033-38); determining, by the at least one processor, a prediction workflow that describes the at least one input data source and a second ML operator that includes the trained model, wherein the second ML operator is different than the first ML operator; and executing, by the at least one processor, the prediction workflow to generate the at least one predictive output from the trained model (predictions can be output using the trained model at predictor node 328 and publisher node 332, para. 0033-38; these nodes are different operators than the first trainer node).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD C WEISBERGER whose telephone number is (571)272-6753. The examiner can normally be reached Monday - Thursday 10AM-8PM PCT.
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RICHARD C. WEISBERGER
Examiner
Art Unit 3693
/RICHARD C WEISBERGER/Primary Examiner, Art Unit 3693
