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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/25/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
The present application is being examined under the claims filed on 1/25/2024.
Claims 1-20 are rejected.
Claims 1-20 are pending.
Specification
The specification filed on 1/25/2024 is acceptable for examination purposes.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “615” has been used to designate the decision on reverse logistics (Figure 7) wherein the specification says the decision on reverse logistics “715” (Page 15, Lines 17-18). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Regarding Claim 1,
Step 1: Claim 1 is an apparatus claim. Therefore, Claims 1-14 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
to generate a first data structure characterizing one or more issues encountered on one or more information technology assets (mental process - to generate a first data structure characterizing one or more issues encountered on one or more information technology assets may be performed manually by a user with the aid of pen and paper by observing/analyzing one or more issues on one or more information technology assets. See MPEP 2106.04(a)(2)(III)(C).)
to generate a second data structure by parsing the first data structure [utilizing a first machine learning model], the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets (mental process - to generate a second data structure by parsing the first data structure utilizing a first machine learning model, the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets may be performed manually by a user with the aid of pen and paper by analyzing/parsing the first data structure. See MPEP 2106.04(a)(2)(III)(C).)
to generate a third data structure [utilizing a second machine learning model], the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets (mental process - to generate a third data structure utilizing a second machine learning model, the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets may be performed manually by a user with the aid of pen and paper by analyzing/parsing the second data structure. See MPEP 2106.04(a)(2)(III)(C).)
to control at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure (mental process - to control at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure may be performed manually by a user with the aid of pen and paper by observing/analyzing the generated third data structure and controlling a portion of a routing of one or more information technology assets. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a first machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a second machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a first machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a second machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-14. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2,
Step 2A Prong 1:
[wherein the first machine learning model comprises one or more natural language understanding machine learning models configured] to determine sentiment of the text data (mental process – to determine sentiment of the text data may be performed manually by a user with the aid of pen and paper by observing/analyzing the text data. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the first data structure comprises text data (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
wherein the first machine learning model comprises one or more natural language understanding machine learning models configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the first data structure comprises text data (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
wherein the first machine learning model comprises one or more natural language understanding machine learning models configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 3,
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 3 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the first machine learning model comprises a bi-directional recurrent neural network with long short-term memory (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the first machine learning model comprises a bi-directional recurrent neural network with long short-term memory (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 4,
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 4 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the text data comprises user-generated descriptions of the one or more issues encountered on the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the text data comprises user-generated descriptions of the one or more issues encountered on the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 5,
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 5 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the text data comprises support engineer feedback related to the one or more issues encountered on the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the text data comprises support engineer feedback related to the one or more issues encountered on the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 6,
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 6 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the text data comprises a description of a physical condition of the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the text data comprises a description of a physical condition of the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 7,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the context of the one or more issues encountered on the one or more information technology assets characterizes a cause of the one or more issues encountered on the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the context of the one or more issues encountered on the one or more information technology assets characterizes a cause of the one or more issues encountered on the one or more information technology assets (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 8,
Step 2A Prong 1:
[wherein the second machine learning model comprises a multi-class classifier configured] to predict a given class from among a set of two or more classes, [the set of two or more classes comprising two or more different reverse logistics fulfilment options] (mental process – to predict a given class from among a set of two or more classes may be performed manually by a user with the aid of pen and paper by observing/analyzing a set of two or more classes and predicting a given class. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the second machine learning model comprises a multi-class classifier configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the set of two or more classes comprising two or more different reverse logistics fulfilment options (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the second machine learning model comprises a multi-class classifier configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the set of two or more classes comprising two or more different reverse logistics fulfilment options (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 9,
Step 2A Prong 1:
See the rejection of Claim 8 above, which Claim 9 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the two or more different reverse logistics fulfilment options comprises at least two of repair, refurbish, recycle, repacking, remanufacturing, deconstruction and salvage, and disposal (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the two or more different reverse logistics fulfilment options comprises at least two of repair, refurbish, recycle, repacking, remanufacturing, deconstruction and salvage, and disposal (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 10,
Step 2A Prong 1:
[wherein the second machine learning model comprises a random forest classifier comprising a plurality of decision trees trained on at least one of different data samples and different data features, the random forest classifier being configured] to perform aggregation of class predictions from the plurality of decision trees to generate the third data structure (mental process – to perform aggregation of class predictions from the plurality of decision trees to generate the third data structure may be performed manually by a user with the aid of pen and paper by observing/analyzing the plurality of decision trees, performing aggregation of class predictions and generating the third data structure. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the second machine learning model comprises a random forest classifier comprising a plurality of decision trees trained on at least one of different data samples and different data features, the random forest classifier being configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the second machine learning model comprises a random forest classifier comprising a plurality of decision trees trained on at least one of different data samples and different data features, the random forest classifier being configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 11,
Step 2A Prong 1:
See the rejection of Claim 8 above, which Claim 11 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the second machine learning model comprises a multi-layer neural network comprising an input layer, one or more hidden layers and an output layer (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the second machine learning model comprises a multi-layer neural network comprising an input layer, one or more hidden layers and an output layer (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 12,
Step 2A Prong 1:
See the rejection of Claim 11 above, which Claim 12 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the input layer comprises a first set of neurons which take as input a set of independent variables from the second data structure, and wherein the output layer comprises a second set of neurons corresponding to the two or more classes (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the input layer comprises a first set of neurons which take as input a set of independent variables from the second data structure, and wherein the output layer comprises a second set of neurons corresponding to the two or more classes (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 13,
Step 2A Prong 1:
[wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises] selecting the second location based at least in part on a given type of reverse logistics processing recommended for the given information technology asset (mental process – selecting the second location based at least in part on a given type of reverse logistics processing recommended for the given information technology asset may be performed manually by a user with the aid of pen and paper by analyzing/selecting the second location based at least in part on a given type of reverse logistics processing recommended. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 14,
Step 2A Prong 1:
[wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises] selecting the second location based at least in part on determining a geographical demand for the given information technology asset processing utilizing a given type of reverse logistics processing recommended for the given information technology asset (mental process – selecting the second location based at least in part on determining a geographical demand for the given information technology asset processing utilizing a given type of reverse logistics processing recommended for the given information technology asset may be performed manually by a user with the aid of pen and paper by analyzing/selecting the second location based at least in part on determining a geographical demand and processing utilizing a given type of reverse logistics processing recommended for the given information technology asset. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 15,
Step 1: Claim 15 is a computer program product claim. Therefore, Claims 15-17 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
to generate a first data structure characterizing one or more issues encountered on one or more information technology assets (mental process - to generate a first data structure characterizing one or more issues encountered on one or more information technology assets may be performed manually by a user with the aid of pen and paper by observing/analyzing one or more issues on one or more information technology assets. See MPEP 2106.04(a)(2)(III)(C).)
to generate a second data structure by parsing the first data structure [utilizing a first machine learning model], the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets (mental process - to generate a second data structure by parsing the first data structure utilizing a first machine learning model, the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets may be performed manually by a user with the aid of pen and paper by analyzing/parsing the first data structure. See MPEP 2106.04(a)(2)(III)(C).)
to generate a third data structure [utilizing a second machine learning model], the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets (mental process - to generate a third data structure utilizing a second machine learning model, the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets may be performed manually by a user with the aid of pen and paper by analyzing/parsing the second data structure. See MPEP 2106.04(a)(2)(III)(C).)
to control at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure (mental process - to control at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure may be performed manually by a user with the aid of pen and paper by observing/analyzing the generated third data structure and controlling a portion of a routing of one or more information technology assets. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a first machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a second machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a first machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a second machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 16-17. The additional limitations of the dependent claims are addressed below.
Regarding Claim 16,
Step 2A Prong 1:
[wherein the first machine learning model comprises one or more natural language understanding machine learning models configured] to determine sentiment of the text data (mental process – to determine sentiment of the text data may be performed manually by a user with the aid of pen and paper by observing/analyzing the text data. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the first data structure comprises text data (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
wherein the first machine learning model comprises one or more natural language understanding machine learning models configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the first data structure comprises text data (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
wherein the first machine learning model comprises one or more natural language understanding machine learning models configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 17,
Step 2A Prong 1:
[wherein the second machine learning model comprises a multi-class classifier configured] to predict a given class from among a set of two or more classes, [the set of two or more classes comprising two or more different reverse logistics fulfilment options] (mental process – to predict a given class from among a set of two or more classes may be performed manually by a user with the aid of pen and paper by observing/analyzing a set of two or more classes and predicting a given class. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the second machine learning model comprises a multi-class classifier configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the set of two or more classes comprising two or more different reverse logistics fulfilment options (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the second machine learning model comprises a multi-class classifier configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the set of two or more classes comprising two or more different reverse logistics fulfilment options (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 18,
Step 1: Claim 18 is a method claim. Therefore, Claims 18-20 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
generating a first data structure characterizing one or more issues encountered on one or more information technology assets (mental process - generating a first data structure characterizing one or more issues encountered on one or more information technology assets may be performed manually by a user with the aid of pen and paper by observing/analyzing one or more issues on one or more information technology assets. See MPEP 2106.04(a)(2)(III)(C).)
generating a second data structure by parsing the first data structure [utilizing a first machine learning model], the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets (mental process - generating a second data structure by parsing the first data structure utilizing a first machine learning model, the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets may be performed manually by a user with the aid of pen and paper by analyzing/parsing the first data structure. See MPEP 2106.04(a)(2)(III)(C).)
generating a third data structure [utilizing a second machine learning model], the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets (mental process - generating a third data structure utilizing a second machine learning model, the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets may be performed manually by a user with the aid of pen and paper by analyzing/parsing the second data structure. See MPEP 2106.04(a)(2)(III)(C).)
controlling at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure (mental process - controlling at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure may be performed manually by a user with the aid of pen and paper by observing/analyzing the generated third data structure and controlling a portion of a routing of one or more information technology assets. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
utilizing a first machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a second machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
utilizing a first machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
utilizing a second machine learning model (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 18 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 19-20. The additional limitations of the dependent claims are addressed below.
Regarding Claim 19,
Step 2A Prong 1:
[wherein the first machine learning model comprises one or more natural language understanding machine learning models configured] to determine sentiment of the text data (mental process – to determine sentiment of the text data may be performed manually by a user with the aid of pen and paper by observing/analyzing the text data. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the first data structure comprises text data (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
wherein the first machine learning model comprises one or more natural language understanding machine learning models configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the first data structure comprises text data (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
wherein the first machine learning model comprises one or more natural language understanding machine learning models configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 20,
Step 2A Prong 1:
[wherein the second machine learning model comprises a multi-class classifier configured] to predict a given class from among a set of two or more classes, [the set of two or more classes comprising two or more different reverse logistics fulfilment options] (mental process – to predict a given class from among a set of two or more classes may be performed manually by a user with the aid of pen and paper by observing/analyzing a set of two or more classes and predicting a given class. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the second machine learning model comprises a multi-class classifier configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the set of two or more classes comprising two or more different reverse logistics fulfilment options (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the second machine learning model comprises a multi-class classifier configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
the set of two or more classes comprising two or more different reverse logistics fulfilment options (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7-9, 13-15, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mahajan et al. (US 20200126027 A1) (hereinafter Mahajan), in view of Rao et al. (US 11314552 B1) (hereinafter Rao).
Regarding Claim 1,
Mahajan teaches:
“An apparatus comprising:” (preamble)
“at least one processing device comprising a processor coupled to a memory” (Mahajan, Paragraph [0050], “[…] a computing device 304 with processor 308 and memory 310 with data storage 312 and modules and applications 314 stored in memory, which when executed by the processor communicate with various modules 314 […]”)
“to generate a first data structure characterizing one or more issues encountered on one or more information technology assets” (Mahajan, Paragraph [0032], “An inspection and cosmetic grading process 116 may evaluate the physical condition of the item for damage, defects and imperfections that might reduce its value. Results of these processes and services are collected and are added to the item profile 118.”; Examiner’s note: to generate a first data structure (i.e. results of an inspection and cosmetic grading process) characterizing one or more issues encountered on one or more information technology assets (i.e. the physical condition of the item for damage, defects and imperfections) is taught.)
“to generate a second data structure by parsing the first data structure utilizing a first machine learning model, the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets” (Mahajan, Paragraph [0054], “Many industries, such as the mobile device industry, publish cosmetic grading criteria which define the number and type of defects that may be associated with a particular cosmetic grade. This classification system and criteria may be programmed into the machine to provide the standard grade associated with the condition determined by the cosmetic grading machine.”; Examiner’s note: to generate a second data structure (i.e. the standard grade associated with the condition) by parsing the first data structure (i.e. the number and type of defects associated with a particular cosmetic grade) utilizing a first machine learning model (i.e. the cosmetic grading machine), the second data structure characterizing a context of the one or more issues (i.e. the classification and criteria) encountered on the one or more information technology assets (i.e. the mobile device) is taught.)
“to control at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure” (Mahajan, Paragraphs [0027] and [0033], “The disclosed subject matter may be practiced in receiving locations such as a centralized warehouse, at a retail store, or at receiving station kiosks or vending machines […] The system may calculate the optimal value disposition for the item and finally direct or convey 124 the item to the appropriate location […]”; Examiner’s note: to control at least a portion of a routing of at least a given one of the one or more information technology assets (i.e. directing or conveying the item) from a first location (i.e. receiving locations) to a second location (i.e. appropriate location) based at least in part on the generated third data structure (i.e. the optimal value disposition for the item) is taught.)
Mahajan does not explicitly teach:
“to generate a third data structure utilizing a second machine learning model, the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets”
Rao teaches:
“to generate a third data structure utilizing a second machine learning model, the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets” (Rao, Col. 3, Lines 8-9, 15-22 and 29-32, “[…] the program 104 improves the efficiency of reverse logistics systems […] dynamically determining a sequence of a plurality of sub-processes associated with the received data based on the return process; dynamically prioritizing an optimal sequence of a plurality of variations associated with each sub-process within the plurality of sub-processes of the received data; and transmitting a result of the dynamic prioritization of the optimal sequence of the plurality of variations to a user interface of another computing device […] For example, the program 104 identifies an eligibility for refund of an item, eligibility of repair of an item, and eligibility of return of an item as sub-processes within the return process.”; Rao, Col. 4, Lines 8-11, “the program 104 retrieves information associated with the identified item by performing a query on the identified item using a machine learning algorithm and artificial intelligence algorithm.”; Examiner’s note: to generate a third data structure (i.e. a result of the dynamic prioritization of the optimal sequence of the plurality of variations) utilizing a second machine learning model (i.e. the program using a machine learning algorithm and artificial intelligence algorithm), the second machine learning model taking as input the second data structure (i.e. a plurality of sub-processes associated with the received data), the third data structure characterizing one or more recommendations (i.e. the dynamic prioritization of the optimal sequence of the plurality of variations) for one or more types of reverse logistics processing (i.e. eligibility for refund, eligibility of repair and eligibility of return) to be utilized for the one or more information technology assets (i.e. an item) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the intelligent disposition of returned assets in Mahajan, and the dynamic determination of reverse logistics as taught in Rao. Mahajan teaches the intelligent disposition of returned assets. Rao teaches the data structure characterizing one or more recommendations for one or more types of reverse logistics processing. One of ordinary skill would have motivation to combine Mahajan and Rao to “improve the efficiency associated with soiled process due to lack of information by dynamically sequencing the eligible sub-process and re-evaluating contextualized return processes at predetermined milestones within the return process using the artificial intelligence algorithm engine” (Rao, Col. 2, Lines 39-43).
Regarding Claim 7,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 1” (preamble)
“wherein the context of the one or more issues encountered on the one or more information technology assets characterizes a cause of the one or more issues encountered on the one or more information technology assets” (Mahajan, Paragraph [0053], “a cosmetic grading machine 216 is an automated device comprising a machine vision system (image capture and image processing) with the ability to identify, count and/or measure the number and configuration of defects such as scratches, breaks, etc., or other anomalies on any surface of the device and to distinguish defects from dust, reflection, and other artifacts.”; Examiner’s note: wherein the context of the one or more issues encountered (i.e. defects) on the one or more information technology assets (i.e. the mobile device) characterizes a cause of the one or more issues encountered (i.e. configuration of defects such as scratches, breaks, etc., or other anomalies) on the one or more information technology assets (i.e. the mobile device) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 8,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 1” (preamble)
“wherein the second machine learning model comprises a multi-class classifier configured to predict a given class from among a set of two or more classes, the set of two or more classes comprising two or more different reverse logistics fulfilment options” (Mahajan, Paragraphs [0048] and [0067], “[…] perform a value-based process to identify and direct the optimal disposition of new or used items. Devices (i.e. items) may be dispositioned along multiple categories, for example, an item may go to (1) a forward channel, such as an insurance channel or re-shelved for resale as-is, (2) auction (may be sold to resellers as is or with minor repairs), (3) remanufacture (may be repaired by an outside vendor and resold), (4) refurbish (minor cosmetic improvements required by an outside vendor) or (5) salvage (scrapped), and more […] To determine forecast demand and pricing, a machine learning server 614 may connect via a network 616 […] A machine learning server 614, comprising history for these attributes over time, may collect periodic data from the markets in order to train its model to determine the forecast demand and pricing […]”; Examiner’s note: wherein the second machine learning model (i.e. a machine learning server training its model) comprises a multi-class classifier (i.e. model determining the forecast demand and pricing) configured to predict a given class from among a set of two or more classes (i.e. the forecast demand and pricing), the set of two or more classes comprising two or more different reverse logistics fulfilment options (i.e. a forward channel or re-shelved for resale, auction, remanufacture, refurbish or salvage) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 9,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 8” (preamble)
“wherein the two or more different reverse logistics fulfilment options comprises at least two of repair, refurbish, recycle, repacking, remanufacturing, deconstruction and salvage, and disposal” (Mahajan, Paragraph [0048], “Devices (i.e. items) may be dispositioned along multiple categories, for example, an item may go to (1) a forward channel, such as an insurance channel or re-shelved for resale as-is, (2) auction (may be sold to resellers as is or with minor repairs), (3) remanufacture (may be repaired by an outside vendor and resold), (4) refurbish (minor cosmetic improvements required by an outside vendor) or (5) salvage (scrapped), and more.”; Examiner’s note: wherein the two or more different reverse logistics fulfilment options (i.e. a forward channel or re-shelved for resale, auction, remanufacture, refurbish or salvage, and more) comprises at least two of repair, refurbish, recycle, repacking, remanufacturing, deconstruction and salvage, and disposal is taught.)
The reasons of obviousness have been noted in the rejection of Claim 8 above and applicable herein.
Regarding Claim 13,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 1” (preamble)
“wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises selecting the second location based at least in part on a given type of reverse logistics processing recommended for the given information technology asset” (Mahajan, Paragraphs [0027], [0033] and [0068], “The disclosed subject matter may be practiced in receiving locations such as a centralized warehouse, at a retail store, or at receiving station kiosks or vending machines […] The system may calculate the optimal value disposition for the item and finally direct or convey 124 the item to the appropriate location […] Mobile device disposition categories may be re-manufacture 620, salvage 622, channel (such as insurance channel demand) 624 and auction 626.”; Examiner’s note: wherein controlling at least a portion of the routing of the given information technology asset (i.e. directing or conveying the item) from the first location (i.e. receiving locations) to the second location (i.e. appropriate location) based at least in part on the generated third data structure comprises selecting the second location based at least in part on a given type of reverse logistics processing recommended for the given information technology asset (i.e. the optimal value disposition for the item and disposition categories including re-manufacture, salvate, channel and auction) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 14,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 1” (preamble)
“wherein controlling at least a portion of the routing of the given information technology asset from the first location to the second location based at least in part on the generated third data structure comprises selecting the second location based at least in part on determining a geographical demand for the given information technology asset processing utilizing a given type of reverse logistics processing recommended for the given information technology asset” (Mahajan, Paragraphs [0027], [0033] and [0068], “The disclosed subject matter may be practiced in receiving locations such as a centralized warehouse, at a retail store, or at receiving station kiosks or vending machines […] The optimal disposition 122 process comprises an evaluation of the item profile, the available disposition paths based on the item profile, the demand and pricing in secondary markets, business concerns (such as tax effects), and the demand, costs and capacity of secondary processing vendors defined for various disposition categories. The system may calculate the optimal value disposition for the item and finally direct or convey 124 the item to the appropriate location […] Mobile device disposition categories may be re-manufacture 620, salvage 622, channel (such as insurance channel demand) 624 and auction 626.”; Examiner’s note: wherein controlling at least a portion of the routing of the given information technology asset (i.e. directing or conveying the item) from the first location (i.e. receiving locations) to the second location (i.e. appropriate location) based at least in part on the generated third data structure comprises selecting the second location based at least in part on determining a geographical demand (i.e. business concerns (such as tax effects), and the demand) for the given information technology asset processing utilizing a given type of reverse logistics processing recommended for the given information technology asset (i.e. the optimal value disposition for the item and disposition categories including re-manufacture, salvage, channel and auction) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 15,
The combination of Mahajan and Rao teaches:
“A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:” (Mahajan, Paragraph [0070], “Software applications, comprised of computer-executable instructions stored in computer-usable or computer-readable, non-transitory memory or non-transitory secondary storage for execution by a processor are operatively configured to perform the operations as described in the various embodiments. Any suitable computer-usable or computer-readable medium may be utilized […] Generally, software modules are program code or instructions for controlling a computer processor to perform a particular method to implement the features or operations of the system. The modules may also be implemented using program products or a combination of software and specialized hardware components.”)
“to generate a first data structure characterizing one or more issues encountered on one or more information technology assets” (Mahajan – see supra claim 1)
“to generate a second data structure by parsing the first data structure utilizing a first machine learning model, the second data structure characterizing a context of the one or more issues encountered on the one or more information technology assets” (Mahajan – see supra claim 1)
“to generate a third data structure utilizing a second machine learning model, the second machine learning model taking as input the second data structure, the third data structure characterizing one or more recommendations for one or more types of reverse logistics processing to be utilized for the one or more information technology assets” (Rao – see supra claim 1)
“to control at least a portion of a routing of at least a given one of the one or more information technology assets from a first location to a second location based at least in part on the generated third data structure” (Mahajan – see supra claim 1)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 17,
The combination of Mahajan and Rao teaches:
“The computer program product of claim 15” (preamble)
“wherein the second machine learning model comprises a multi-class classifier configured to predict a given class from among a set of two or more classes, the set of two or more classes comprising two or more different reverse logistics fulfilment options” (Mahajan – see supra claim 8)
The reasons of obviousness have been noted in the rejection of Claim 15 above and applicable herein.
Regarding Claim 18,
Claim 18 recites substantially the same limitations as Claim 1, in the form of a method, therefore
it is rejected under the same rationale.
Regarding Claim 20,
Claim 20 recites substantially the same limitations as Claim 8, in the form of a method, therefore
it is rejected under the same rationale.
Claims 2-6, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mahajan in view of Rao as applied in claim 1, and further in view of Ma et al. (“End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF”) (hereinafter Ma).
Regarding Claim 2,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 1” (preamble)
“wherein the first data structure comprises text data” (Mahajan, Paragraph [0007], “The item profile may include attributes such as those of the item itself (e.g. serial number), the type of product (e.g. make, model, item number) […] As the item moves through the process it may be functionally and cosmetically evaluated with the results added to the item profile”; Examiner’s note: wherein the first data structure comprises text data (i.e. item profile including the type of product (make, model, item number) and evaluated results added to the item profile) is taught.)
The combination of Mahajan and Rao does not explicitly teach:
“wherein the first machine learning model comprises one or more natural language understanding machine learning models configured to determine sentiment of the text data”
Ma teaches:
“wherein the first machine learning model comprises one or more natural language understanding machine learning models configured to determine sentiment of the text data” (Ma, Section 1, “We first use convolutional neural networks (CNNs) (LeCun et al., 1989) to encode character-level information of a word into its character-level representation. Then we combine character- and word-level representations and feed them into bi-directional LSTM (BLSTM) to model context information of each word. On top of BLSTM, we use a sequential CRF to jointly decode labels for the whole sentence. We evaluate our model on two linguistic sequence labeling tasks – POS tagging […] and NER […]”; Examiner’s note: wherein the first machine learning model (i.e. convolutional neural networks) comprises one or more natural language understanding machine learning models (i.e. LSTM) configured to determine sentiment of the text data (i.e. BLSTM) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mahajan, Rao and the end-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF as taught in Ma. The combination of Mahajan and Rao teaches the intelligent disposition of returned assets and the data structure characterizing one or more recommendations for one or more types of reverse logistics processing. Ma teaches the natural language understanding machine learning models configured to determine sentiment of the text data. One of ordinary skill would have motivation to combine Mahajan, Rao and Ma to “achieve[] state-of-the-art performance on two linguistic sequence labeling tasks” (Ma, Section 6).
Regarding Claim 3,
The combination of Mahajan, Rao and Ma teaches:
“The apparatus of claim 2” (preamble)
“wherein the first machine learning model comprises a bi-directional recurrent neural network with long short-term memory” (Ma, Section 1, “We first use convolutional neural networks (CNNs) (LeCun et al., 1989) to encode character-level information of a word into its character-level representation. Then we combine character- and word-level representations and feed them into bi-directional LSTM (BLSTM) to model context information of each word. On top of BLSTM, we use a sequential CRF to jointly decode labels for the whole sentence.”; Examiner’s note: to generate a second data structure (i.e. wherein the first machine learning model (i.e. convolutional neural networks) comprises a bi-directional recurrent neural network with long short-term memory (i.e. BLSTM) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 2 above and applicable herein.
Regarding Claim 4,
The combination of Mahajan, Rao and Ma teaches:
“The apparatus of claim 2” (preamble)
“wherein the text data comprises user-generated descriptions of the one or more issues encountered on the one or more information technology assets” (Mahajan, Paragraph [0007], “The item profile may include attributes such as those of the item itself (e.g. serial number), the type of product (e.g. make, model, item number) […] As the item moves through the process it may be functionally and cosmetically evaluated with the results added to the item profile”; Examiner’s note: wherein the text data (i.e. item profile including the type of product (make, model, item number)) comprises user-generated descriptions of the one or more issues encountered (i.e. evaluated results added to the item profile) on the one or more information technology assets (i.e. the item) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 2 above and applicable herein.
Regarding Claim 5,
The combination of Mahajan, Rao and Ma teaches:
“The apparatus of claim 2” (preamble)
“wherein the text data comprises support engineer feedback related to the one or more issues encountered on the one or more information technology assets” (Mahajan, Paragraphs [0010] and [0011], “[…] reports and analytics to provide key performance metrics for operational throughput, exceptions and disposition value […] Highly graded product may be tagged for refurbishment sale and less desirable products tagged for scrap or liquidation.”; Examiner’s note: wherein the text data comprises support engineer feedback (i.e. reports and analytics, exceptions and disposition value, highly graded and less desirable) related to the one or more issues encountered on the one or more information technology assets (i.e. products) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 2 above and applicable herein.
Regarding Claim 6,
The combination of Mahajan, Rao and Ma teaches:
“The apparatus of claim 2” (preamble)
“wherein the text data comprises a description of a physical condition of the one or more information technology assets” (Mahajan, Paragraph [0032], “An inspection and cosmetic grading process 116 may evaluate the physical condition of the item for damage, defects and imperfections that might reduce its value. Results of these processes and services are collected and are added to the item profile 118.”; Examiner’s note: wherein the text data comprises a description of a physical condition of the one or more information technology assets (i.e. results of evaluating the physical condition of the item for damage, defects and imperfections) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 2 above and applicable herein.
Regarding Claim 16,
The combination of Mahajan, Rao and Ma teaches:
“The computer program product of claim 15” (preamble)
“wherein the first data structure comprises text data” (Mahajan – see supra claim 2)
“wherein the first machine learning model comprises one or more natural language understanding machine learning models configured to determine sentiment of the text data” (Ma – see supra claim 2)
The reasons of obviousness have been noted in the rejection of Claim 2 above and applicable herein.
Regarding Claim 19,
Claim 19 recites substantially the same limitations as Claim 2, in the form of a method, therefore
it is rejected under the same rationale.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Mahajan in view of Rao as applied in claim 1, and further in view of Breiman (“Random Forests”).
Regarding Claim 10,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 8” (preamble)
The combination of Mahajan and Rao does not explicitly teach:
“wherein the second machine learning model comprises a random forest classifier comprising a plurality of decision trees trained on at least one of different data samples and different data features, the random forest classifier being configured to perform aggregation of class predictions from the plurality of decision trees to generate the third data structure”
Breiman teaches:
“wherein the second machine learning model comprises a random forest classifier comprising a plurality of decision trees trained on at least one of different data samples and different data features, the random forest classifier being configured to perform aggregation of class predictions from the plurality of decision trees to generate the third data structure” (Breiman, Section 1.1 and Section 3.1, “A random forest is a classifier consisting of a collection of tree-structured classifiers {h(x, ϴk), k = 1, . . .} where the { ϴk } are independent identically distributed random vectors and each tree casts a unit vote for the most popular class at input x […] bagging is used in tandem with random feature selection. Each new training set is drawn, with replacement, from the original training set. Then a tree is grown on the new training set using random feature selection […] Given a specific training set T, form boostrap training sets Tk, construct classifiers h(x, Tk) and let these vote to form the bagged predictor. For each y, x in the training set, aggregate the votes only over those classifiers for which Tk does not containing y, x. Call this the out-of-bag classifier. Then the out-of-bag estimate for the generalization error is the error rate of the out-of-bag classifier on the training set.”; Examiner’s note: wherein the second machine learning model comprises a random forest classifier comprising a plurality of decision trees trained (i.e. a classifier consisting a collection of tree-structured classifiers) on at least one of different data samples (i.e. bagging) and different data features (i.e. random feature selection), the random forest classifier being configured to perform aggregation of class predictions from the plurality of decision trees (i.e. aggregating the votes casted by each tree) to generate the third data structure (i.e. out-of-bag estimate) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mahajan, Rao and the random forest classifier as taught in Breiman. The combination of Mahajan and Rao teaches the intelligent disposition of returned assets and the data structure characterizing one or more recommendations for one or more types of reverse logistics processing. Breiman teaches the random forest classifier being configured to perform aggregation of class predictions from the plurality of decision trees to generate the third data structure. One of ordinary skill would have motivation to combine Mahajan, Rao and Breiman to “produce improved accuracy” (Breiman, Section 1.2).
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Mahajan in view of Rao as applied in claim 1, and further in view of Gardner et al. (“Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks”) (hereinafter Gardner).
Regarding Claim 11,
The combination of Mahajan and Rao teaches:
“The apparatus of claim 8” (preamble)
The combination of Mahajan and Rao does not explicitly teach:
“wherein the second machine learning model comprises a multi-layer neural network comprising an input layer, one or more hidden layers and an output layer”
Gardner teaches:
“wherein the second machine learning model comprises a multi-layer neural network comprising an input layer, one or more hidden layers and an output layer” (Gardner, Section 2.4, “[…] we considered fully-connected, feedforward SNNs […] Data samples presented to a network were encoded by the collective firing activity of input layer neurons, according to one of the temporal encoding strategies described above; hidden layer neurons were free to perform computations on these input patterns, and learn features useful for downstream processing. Neurons in the last, or output, layer of a network were tasked with forming class predictions on these data samples according to a first-to-spike mechanism, where the predicted class label was determined according to which one of Nl=3 = c output neurons was the first to respond with an output spike.”; Examiner’s note: wherein the second machine learning model (i.e. full-connected, feedforward SNNs) comprises a multi-layer neural network comprising an input layer (i.e. the input layer neurons teach the input layer), one or more hidden layers (i.e. hidden layer neurons teach the hidden layer) and an output layer (i.e. the last, or output, layer) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mahajan, Rao and the multilayer spiking neural networks as taught in Gardner. The combination of Mahajan and Rao teaches the intelligent disposition of returned assets and the data structure characterizing one or more recommendations for one or more types of reverse logistics processing. Gardner teaches the multilayer spiking neural networks comprising an input layer, one or more hidden layers and an output layer. One of ordinary skill would have motivation to combine Mahajan, Rao and Gardner to “place[] much less of a constraint on the network’s parameters during training, thereby avoid[] overfitting of the data” (Gardner, Section 4).
Regarding Claim 12,
The combination of Mahajan, Rao and Gardner teaches:
“The apparatus of claim 11” (preamble)
“wherein the input layer comprises a first set of neurons which take as input a set of independent variables from the second data structure, and wherein the output layer comprises a second set of neurons corresponding to the two or more classes” (Gardner, Sections 2.3 and 2.4, “[…] first convert input features into spike-based representations: to be conveyed by the input layer of an SNN for downstream processing […] Neurons in the last, or output, layer of a network were tasked with forming class predictions on these data samples according to a first-to-spike mechanism, where the predicted class label was determined according to which one of Nl=3 = c output neurons was the first to respond with an output spike.”; Examiner’s note: wherein the input layer comprises a first set of neurons which take as input a set of independent variables (i.e. converting input features into spike-based representations) from the second data structure (i.e. data samples), and wherein the output layer comprises a second set of neurons corresponding to the two or more classes (i.e. neurons in the output layer tasked with forming class predictions on the data samples) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 11 above and applicable herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rumelhart et al. teaches Learning representations by back-propagating errors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YONG D RHO whose telephone number is (571)270-0194. The examiner can normally be reached 8am-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, Viker Lamardo can be reached at 5712705871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YONG DOO RHO/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
Prosecution Insights