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 .
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 1-20 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 it is 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
According to the first part of the analysis, in the instant case, claims 1-9, 10-14, 15-20 are directed to a method, system and computer product program of workload distribution to minimizing exposure risk from VOC. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 10 and 15
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0016]-[0030], Fig. 1, etc. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a practical application of the exception (Step 2A, Prong 2). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that integrate the exception into a practical application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 10 and 15 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 10 and 15: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). Further the claimed invention appears to be something that can be performed by head and hand (Gottschalk v. Benson). The claimed solution is not necessarily rooted in computer technology in order to overcome a problem (DDR v. Hotels.com). 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 a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites “obtaining…”, “identifying…”, “determining…”, and “implementing…” etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 11, 16
Claim 2, 11, 16 merely recite other additional elements that define training the learning model which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 12, 17
Claim 3, 12, 17 merely recite other additional elements that define obtaining computing space and a set of conditions and generate vectors which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 13, 18
Claim 4, 13, 18 merely recite other additional elements that define determining the VOC exposure risk which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5-6, 19
Claim 5-6, 19 merely recite other additional elements that define the set of conditions of the computing space which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7
Claim 7 merely recite other additional elements that define updating the model which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 14, 20
Claim 8, 14, 20 merely recite other additional elements that define selecting the distribution option which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9
Claim 9 merely recite other additional elements that define selecting the distribution option based on a condition which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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 1-2, 8-11, 14-16, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aurongzeb et al. (Aurongzeb) US 2024/0354172 in view of Pastore et al. (Pastore) US 2022/0130491
In regard to claim 1, Aurongzeb disclose A computer-implemented method comprising: ([0014]-[0018])
obtaining a workload for a set of servers in a computing space, wherein the set of servers off-gas a gas during operation; ([0013]-[0019] [0051][0098] collect a workload for a plurality of data storage systems (server farms) within an enterprise computing node in various locations within one or more geographic regions which emit C02) during operation)
identifying, for the workload, a set of distributions options to process the workload with the set of servers; ([0013]-[0019] [0051][0069]-[0073] [0083]-[0090] [0098] recommend workload adjustment recommendations for the plurality of data storage systems (server farms) to conduct the workload)
determining, by a learning model, a emission level for each distribution option; ([0013]-[0019] [0051][0069]-[0073] [0083]-[0090] [0098] determine CO2 emission for the recommendations by a model) and
implementing, based on the determining, a first distribution option of the set of distribution options to process the workload. ([0018][0048]-[0050][0069]-[0075] [0083]-[0090] [0098] execute the workload redistribution based on the selected recommendation. Note: please further define the first distribution option’s relationship with the exposure risk to help move forward the prosecution, call to discuss if necessary.)
But Aurongzeb fail to explicitly disclose “off-gas a volatile organic compound (VOC), determining, a VOC exposure risk,”
Pastore disclose off-gas a volatile organic compound (VOC), determining, a VOC exposure risk, ([0003]-[0004][0012]-[0014] [0024]-[0028] [0041]-[0047] detect VOC and determine a VOC anomaly degree)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Pastore‘s VOC detection and classification into Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Pastore‘s VOC detection and classification would help to provide more risk substance identification into Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing VOC risk substance identification would help to expand applications with ML model training.
In regard to claim 2, Aurongzeb and Pastore disclose The computer-implemented method of claim 1,
Aurongzeb disclose training the clustering model to determine the emission level for a distribution option. ([0013]-[0019] [0051][0060]-[0073] [0083]-[0090] [0098] training the model to determine CO2 emission for the recommendation)
But Aurongzeb fail to explicitly disclose “wherein the learning model is a clustering model, the method further comprising: training the clustering model to determine the VOC exposure risk.”
Pastore disclose wherein the learning model is a clustering model, the method further comprising: training the clustering model to determine the VOC exposure risk ([0003]-[0004][0012]-[0014] [0021]-[0028] [0041]-[0047] a classification model and train the model to determine a VOC anomaly degree)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Pastore‘s VOC detection and classification into Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Pastore‘s VOC detection and classification would help to provide more risk substance identification into Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing VOC risk substance identification would help to expand applications with ML model training.
In regard to claim 8, Aurongzeb and Pastore disclose The computer-implemented method of claim 1, further comprising:
Aurongzeb disclose selecting the first distribution option, wherein the selecting the first distribution option is based on an emission level for the first distribution option being below a risk threshold. ([0017]-[0019] [0057][0065]-[0073] [0083]-[0090] [0098] selecting one of the suggested recommendations, based on the CO2 emission level below a threshold)
But Aurongzeb fail to explicitly disclose “a first exposure risk.”
Pastore disclose based on a first exposure risk. ([0003]-[0004][0012]-[0014] [0021]-[0028] [0041]-[0047] determine a VOC anomaly degree)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Pastore‘s VOC detection and classification into Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Pastore‘s VOC detection and classification would help to provide more risk substance identification into Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing VOC risk substance identification would help to expand applications with ML model training.
In regard to claim 9, Aurongzeb and Pastore disclose The computer-implemented method of claim 8, further comprising:
Aurongzeb disclose selecting the first distribution option, wherein the selecting the first distribution option is based on the first distribution option having a lowest determined risk of the set of distribution options. ([0017]-[0019] [0030]-[0031] [0057][0065]-[0073] [0083]-[0090] [0098] selecting one of the suggested recommendations, based on the CO2 emission level below a threshold such as to minimize CO2 emissions)
But Aurongzeb fail to explicitly disclose “a determined VOC exposure risk.”
Pastore disclose a determined VOC exposure risk. ([0003]-[0004][0012]-[0014] [0021]-[0028] [0041]-[0047] determine a VOC anomaly degree)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Pastore‘s VOC detection and classification into Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Pastore‘s VOC detection and classification would help to provide more risk substance identification into Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing VOC risk substance identification would help to expand applications with ML model training.
In regard to claims 10-11, 14, claims 10-11, 14 are system claims corresponding to the method claims 1-2, 8, above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2, 8.
In regard to claims 15-16, 20, claims 15-16, 20 are computer program product claims corresponding to the method claims 1-2, 8, above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2, 8.
Claims 3-7, 12-13, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Aurongzeb et al. (Aurongzeb) US 2024/0354172 in view of Pastore et al. (Pastore) US 2022/0130491 and Salman et al. (Salman) US 2024/0200991
In regard to claim 3, Aurongzeb and Pastore disclose The computer-implemented method of claim 2, further comprising:
Aurongzeb disclose determining a set of conditions of the computing space; ([0013]-[0020] [0031]-[0032] [0048]-[0064] [0069]-[0073] [0090]-[0103] determine telemetry measurements for the region)
But Aurongzeb fail to explicitly disclose “generating vectors to represent the set of conditions, wherein the training the clustering model is based on the vectors.”
Pastore disclose generating vectors to represent the set of conditions, wherein the training the clustering model is based on the vectors. ([0003]-[0004][0012]-[0028] [0041]-[0047] generating vectors to represent the features of the VOC and training the model based on the vectors)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Pastore‘s VOC detection and classification into Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Pastore‘s VOC detection and classification would help to provide more risk substance identification into Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing VOC risk substance identification would help to expand applications with ML model training.
But Aurongzeb and Pastore fail to explicitly disclose “obtaining a layout of the computing space;”
Salman disclose obtaining a layout of the computing space; ([0013] [0075] [0145]-[0151] obtain a layout of the sensors at the site)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Salman‘s ML based emission monitoring into Pastore and Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Salman‘s ML based emission monitoring with sensor layout would help to provide more location related information into Pastore and Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing location related information would help to improve accuracy of the ML model training.
In regard to claim 4, Aurongzeb and Pastore, Salman disclose The computer-implemented method of claim 3,
Aurongzeb disclose wherein the determining the VOC exposure risk further comprises:
analyzing an input of a current set of conditions; ([0013]-[0020] [0031]-[0032] [0048]-[0064] [0069]-[0073] [0090]-[0105] input the training data with CO2 emission values and telemetric measurements, etc. to the model and identifying the relationships between the training data) and
obtaining an output that includes the predicted emission level in the computing space. ([0013]-[0020] [0031]-[0032] [0048]-[0066] [0069]-[0073] [0090]-[0105] obtain an prediction with predicted CO2 level in the region)
But Aurongzeb and Pastore fail to explicitly disclose “a predicted VOC concentration level.”
Salman disclose obtaining a predicted VOC concentration level; ([0075] [0103]-[0107] [0145]-[0151] output predicted a concentration level)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Salman‘s ML based emission monitoring into Pastore and Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Salman‘s ML based emission monitoring with sensor layout would help to provide more location related information into Pastore and Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing location related information would help to improve accuracy of the ML model training.
In regard to claim 5, Aurongzeb and Pastore, Salman disclose The computer-implemented method of claim 3,
Aurongzeb disclose wherein the set of conditions of the computing space comprises workload distribution data, environmental data, ([0013]-[0020] [0031]-[0032] [0048]-[0064] [0069]-[0073] [0090]-[0105] CO2 emission values and env. telemetries, and monitored workload from servers)
But Aurongzeb and Pastore fail to explicitly disclose “and VOC concentrations.”
Salman disclose and VOC concentrations; ([0013] [0023] [0075] [0103]-[0107] [0145]-[0151] measured concentrations)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Salman‘s ML based emission monitoring into Pastore and Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Salman‘s ML based emission monitoring with sensor layout would help to provide more location related information into Pastore and Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing location related information would help to improve accuracy of the ML model training.
In regard to claim 6, Aurongzeb and Pastore, Salman disclose The computer-implemented method of claim 3,
wherein the set of conditions is recorded at a predetermined interval, and the set of conditions is stored. (0013]-[0020] [0031]-[0035] [0048]-[0064] [0069]-[0073] [0090]-[0105] the emission values and env. telemetries, and monitored workload from servers are recorded with time window data and stored)
In regard to claim 7, Aurongzeb and Pastore, Salman disclose The computer-implemented method of claim 6,
But Aurongzeb and Pastore fail to explicitly disclose “further comprising: updating the cluster model based on the generated vectors.”
Salman disclose updating the cluster model based on the generated vectors. ([0013] [0023] [0033]-[0035] [0067]-[0075] [0103]-[0107][0117] [0143]-[0151] updating the model based on the generated vectors)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Salman‘s ML based emission monitoring into Pastore and Aurongzeb’s invention as they are related to the same field endeavor of model training and learning related to system risk. The motivation to combine these arts, as proposed above, at least because Salman‘s ML based emission monitoring with sensor layout would help to provide more location related information into Pastore and Aurongzeb’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing location related information would help to improve accuracy of the ML model training.
In regard to claims 12-13, claims 13-13 are system claims corresponding to the method claims 3-4, above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3-4.
In regard to claims 17-19, claims 17-19 are computer program product claims corresponding to the method claims 3-5, above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3-5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20150248318 A1 2015-09-03 Farinacci et al.
Z/OS WORKLOAD MAPPER FOR POTENTIAL PROBLEM AREAS USING MODULES AND DEFECT DATA
Farinacci et al. disclose methods for improving the efficiency at which problem areas are identified and prioritized for an existing large, multi-module software system. In some embodiments, a workload mapper generates workload maps that identify the intersection between defect risk scores accumulated for various modules and a log of the modules that are accesses by a given workload. A graphical user interface (GUI) provides the ability to sort, search, compare and display the workload maps against various sort, search and/or compare criteria… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights