DETAILED ACTION
1. This communication is in response to the Application No. 18/045,765 filed on October 11, 2022 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
3. Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Rejections - 35 USC § 101
4. 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.
5. 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 a method type claim. Therefore, Claims 1-10 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
determining a first interpretability score for each data field of the plurality of data fields used to train the first neural network, wherein each first interpretability score is indicative of a relevance for each data field of the plurality of data fields used to train the first neural network (mental process – determining a first interpretability score for each data field may be performed manually by a user observing/analyzing the plurality of data fields and accordingly using judgement/evaluation to determine relevance of the fields and a respective interpretability score)
selecting a subset of data fields from the plurality of data fields used to train the first neural network, the selection based at least in part on the first interpretability score for each data field exceeding a first relevance threshold (mental process – selecting a subset of data fields may be performed manually by a user observing/analyzing the plurality of fields and interpretability scores and accordingly using judgement/evaluation to select a subset of data fields based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving data from a database, wherein the data is indicative of a first instance of the database, and wherein the data comprises a plurality of data fields (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
training a first neural network for a first data field, the training based at least in part on utilizing the plurality of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
training a second neural network using the selected subset of data fields, the second neural network trained to provide predictions related to the first data field (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving data from a database, wherein the data is indicative of a first instance of the database, and wherein the data comprises a plurality of data fields (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training a first neural network for a first data field, the training based at least in part on utilizing the plurality of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
training a second neural network using the selected subset of data fields, the second neural network trained to provide predictions related to the first data field (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
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 2-10. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2 & Step 2B:
wherein training the first neural network for the first data field comprises iteratively training the first neural network for the first data field (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner's note: high level recitation of iteratively training a machine learning model with previously determined data)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 3 depends on.
determining a second interpretability score for each combination of the plurality of combinations of the subset of data fields, wherein each second interpretability score is indicative of a relevance for each combination of the plurality of combinations of the subset of data fields (mental process – determining a second interpretability score for each combination of data fields may be performed manually by a user observing/analyzing the plurality of combinations of data fields and accordingly using judgement/evaluation to determine relevance of the fields and a respective interpretability score)
Step 2A Prong 2 & Step 2B:
based at least in part on the selected subset of data fields, iteratively training the first neural network, wherein the training comprises iterating over a plurality of combinations of the subset of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner's note: high level recitation of iteratively training a machine learning model with previously determined data)
wherein the second neural network is trained by iteratively training the second neural network utilizing a selected combination of the plurality of combinations, the selection based at least in part on the determined second interpretability scores (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner's note: high level recitation of iteratively training a machine learning model with previously determined data)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
iteratively training the second neural network further based at least based at least in part on utilizing parameters, hyperparameters, or combinations thereof (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
receiving at the first instance of the database, an input query regarding the plurality of data fields, the query written using Boolean logic (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
based at least in part on receiving the query, utilizing the second trained neural network to determine and transmit an output answer (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner's note: high level recitation of utilizing an already trained machine learning model without significantly more)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 3 above, which Claim 6 depends on.
Step 2A Prong 2 & Step 2B:
wherein each second interpretability score determined for combination of the plurality of combinations of the subset of data is indicative of the relevance for each combination of the plurality of combinations of the subset of data fields (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the second interpretability is indicative of relevance for each combination of data fields does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2 & Step 2B:
wherein the data fields comprise one or more row fields, one or more column fields, or combinations thereof (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that data fields comprise row fields, column fields, or a combination does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 8 depends on.
determining the first interpretability score for each data field of the plurality of data fields used to train the first neural network is based at least in part on utilizing an interpretability algorithm (mathematical process – determining a first interpretability score may be performed by mathematical process utilizing an interpretability algorithm such as SHAP or LIME (as supported by Applicant’s specification Par. [0042]))
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 9 depends on.
Step 2A Prong 2 & Step 2B:
wherein each first interpretability score determined for each data field of the plurality of data fields used to train the first neural network is indicative of the relevance for each data field of the plurality of data fields used to train the first neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the first interpretability is indicative of relevance for each combination of data fields does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 10:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 10 depends on.
determining the second interpretability score for each combination of the plurality of combinations of the subset of data is based at least in part on utilizing an interpretability algorithm (mathematical process – determining a second interpretability score may be performed by mathematical process utilizing an interpretability algorithm such as SHAP or LIME (as supported by Applicant’s specification Par. [0042]))
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 11:
Step 1: Claim 11 is a computer-readable storage medium type claim. Therefore, Claims 11-17 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
locating matching data between the first set of data and the second set of data by comparing the corresponding data fields of the first set of data and the second set of data (mental process – locating matching data may be performed manually by a user observing/analyzing the first and second sets of data and accordingly using judgement/evaluation to compare the data fields and locate matching data)
creating a combined data set from the first set of data and the second set of data by appending rows of data from the second set of data corresponding to the matching data of the first set of data, wherein the combined data set includes a plurality of data fields (mental process – creating a combined data set may be performed manually by a user observing/analyzing the sets of data and accordingly using judgement/evaluation to append rows of data corresponding to the matching data)
selecting a subset of data fields from the plurality of data fields used to train the first neural network, based at least in part on determining a first interpretability score for each data field exceeding a first relevance threshold, wherein each first interpretability score is indicative of a relevance for each data field of the plurality of data fields used to train the first neural network (mental process – selecting a subset of data fields may be performed manually by a user observing/analyzing the plurality of fields and interpretability scores and accordingly using judgement/evaluation to select a subset of data fields based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a non-transitory computer readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method […] (recited at a high-level of generality (i.e., as a generic computer readable storage medium) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
receiving a first set of data from a first database and a second set of data from a second database, wherein the first set of data and the second set of data include at least one corresponding data field (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
training a first neural network for a first data field of a plurality of data fields of the combined set of data, the training based at least in part on utilizing the plurality of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
training a second neural network using the selected subset of data fields, the second neural network trained to provide predictions related to the first data field (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
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 computer readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
receiving a first set of data from a first database and a second set of data from a second database, wherein the first set of data and the second set of data include at least one corresponding data field (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training a first neural network for a first data field of a plurality of data fields of the combined set of data, the training based at least in part on utilizing the plurality of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
training a second neural network using the selected subset of data fields, the second neural network trained to provide predictions related to the first data field (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
For the reasons above, Claim 11 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 12-17. The additional limitations of the dependent claims are addressed below.
Claim 12 recites substantially the same limitations as Claim 3, in the form of a non-transitory computer readable storage medium, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 4, in the form of a non-transitory computer readable storage medium, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 5, in the form of a non-transitory computer readable storage medium, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 15 recites substantially the same limitations as Claim 10, in the form of a non-transitory computer readable storage medium, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 16 recites substantially the same limitations as Claim 8, in the form of a non-transitory computer readable storage medium, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claim 9, in the form of a non-transitory computer readable storage medium, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Regarding Claim 18:
Step 1: Claim 18 is a system type claim. Therefore, Claims 18-20 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
determine a first interpretability score for each data field of the one or more of data fields used to train the first neural network, wherein each first interpretability score is indicative of a relevance for each data field of the one or more data fields used to train the first neural network (mental process – determining a first interpretability score for each data field may be performed manually by a user observing/analyzing the plurality of data fields and accordingly using judgement/evaluation to determine relevance of the fields and a respective interpretability score)
select a subset of data fields from the one or more data fields used to train the first neural network, the selection based at least in part on the first interpretability score for each data field exceeding a first relevance threshold (mental process – selecting a subset of data fields may be performed manually by a user observing/analyzing the plurality of fields and interpretability scores and accordingly using judgement/evaluation to select a subset of data fields based on said analysis)
determine a second interpretability score for each combination of the one or more of combinations of the subset of data fields, wherein each second interpretability score is indicative of a relevance for each combination of the one or more of combinations of the subset of data fields (mental process – determining a second interpretability score for each combination of the subset of data fields may be performed manually by a user observing/analyzing the plurality of combinations of data fields and accordingly using judgement/evaluation to determine relevance of the combinations and a respective interpretability score)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a system comprising: a datastore comprising one or more instances of a database, wherein a first instance of the database is included in the one or more instances of the database, and wherein the first instance of the database comprises one or more data fields including row fields, column fields, or combinations thereof; a computing device, in communication with the datastore, and configured to: (recited at a high-level of generality (i.e., as a generic system comprising generic computer components) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
receive the first instance of the database (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
train a first neural network for a first data field, the training based at least in part on utilizing the one or more of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
based at least in part on the selected subset of data fields, train the first neural network, wherein the training comprises iterating over one or more combinations of the subset of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
train a second neural network utilizing a selected combination of the one or more of combinations, the selection based at least in part on the determined second interpretability scores (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
display, communicatively coupled to the computing device, and configured to cause presentation of the first interpretability score, the second interpretability score, or combinations thereof (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a system comprising: a datastore comprising one or more instances of a database, wherein a first instance of the database is included in the one or more instances of the database, and wherein the first instance of the database comprises one or more data fields including row fields, column fields, or combinations thereof; a computing device, in communication with the datastore, and configured to: (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
receive the first instance of the database (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
train a first neural network for a first data field, the training based at least in part on utilizing the one or more of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
based at least in part on the selected subset of data fields, train the first neural network, wherein the training comprises iterating over one or more combinations of the subset of data fields (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
train a second neural network utilizing a selected combination of the one or more of combinations, the selection based at least in part on the determined second interpretability scores (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
display, communicatively coupled to the computing device, and configured to cause presentation of the first interpretability score, the second interpretability score, or combinations thereof (MPEP 2106.05(d)(II) indicates that merely “Presenting offers and gathering statistics” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
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.
Claim 19 recites substantially the same limitations as Claim 5, in the form of a system, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 8, in the form of a system, including generic computer components. The claim is also directed to performing mental processes/mathematical calculations without significantly more, therefore it is rejected under the same rationale.
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Banville et al. (hereinafter Banville) (US PG-PUB 20240273361), in view of Yu et al. (hereinafter Yu) (US PG-PUB 20180329951), in view of Stergioudis et al. (hereinafter Stergioudis) (US PG-PUB 20210136098).
Regarding Claim 1, Banville teaches a method (Banville, Abstract, “Systems and methods disclosed herein are directed at the dynamic filtering of channels based on relevance of a channel to a learning task or channel corruption.”, thus, a method is disclosed) comprising:
receiving data from a database (See introduction of Yu reference below for teaching of receiving data from a database), wherein the data is indicative of a first instance of the database, and wherein the data comprises a plurality of data fields (Banville, Claim 1, “receiving a dataset from a plurality of channels, each channel of the plurality of channels comprising data;” & Par. [0234], “Plurality of channels 502 may provide a dataset stored in a memory rather than a dataset arising from optional plurality of sensors 500.” therefore, a dataset is received (data stored in memory) wherein the data is indicative of the plurality of channels. Further, the data may comprise a plurality of data fields (See Par. [0069] which mentions how data may be aggregated from multiple channels in order to generate a complete dataset);
training a first neural network for a first data field, the training based at least in part on utilizing the plurality of data fields (Banville, Par. [0211], “In some embodiments, the system can use the results of the learning task to adjust at least one trainable parameter of at least one of neural network 214 or neural network 216. In such embodiments, it may be possible that the system can engage in further learning in order to adapt to particular use contexts.”, thus, a first neural network is trained for a first data field (trainable parameter), the training based at least in part on utilizing the plurality of data/dataset);
determining a first interpretability score (See introduction of Stergioudis reference below for explicit disclosure of an interpretability score determined by an interpretability algorithm) for each data field of the plurality of data fields used to train the first neural network, wherein each first interpretability score is indicative of a relevance for each data field of the plurality of data fields used to train the first neural network (Banville, Par. [0213], “In some embodiments, the system is further configured to selectively transmit at least one channel of the plurality of channels based in part on the dynamic spatial filter. In some embodiments, a dynamic spatial filter component can modify the rate of data sampling from a channel of the plurality of channels based in part on the relevance to a learning task or channel corruption or noise of that channel. In some embodiments, a sensor associated with a channel of the plurality of the channels can be selectively activated or deactivated based in part on the relevance to a learning task or channel corruption or noise of that channel.”, therefore, the relevance of a channel (comprising a plurality of data fields as supported by Banville Claim 1) is determined and may be used to adjust training for the first neural network);
selecting a subset of data fields from the plurality of data fields used to train the first neural network, the selection based at least in part on the first interpretability score for each data field exceeding a first relevance threshold (Banville, Par. [0216], “In another aspect, embodiments described herein provide a system for dynamically reweighing a plurality of channels according to relevance given a learning task or channel corruption using a neural network. The system includes a memory, a processor coupled to the memory programmed with executable instructions, and a monitor device. The instructions including an interface for receiving a dataset from a plurality of channels. When executing the instructions the processor extracts a representation of the dataset or the plurality of channels, predicts a dynamic spatial filter from the representation of the dataset or the plurality of channels using a neural network, applies the dynamic spatial filter to dynamically reweigh each of the channels of the plurality channels, and performs a learning task using the reweighed channels and a second neural network.”, thus, based on assessing the relevance of each of a plurality of channels (comprising a plurality of data fields) and determining if the relevance exceeds a certain threshold (See Par. [0209] for recitation of such a threshold), only certain channels are selected and reweighted); and
training a second neural network using the selected subset of data fields, the second neural network trained to provide predictions related to the first data field (Banville, Par. [0212], “In some embodiments, applying the dynamic spatial filter involves adjusting the channels to a form acceptable by the second neural network in the performing a learning task. In some embodiments, the second neural network is trained with input having a specific structure. In some embodiments, a dynamic spatial filter component can reweigh the plurality of channels such that their structure matches a specific structure required by second neural network. This can involve, for example, reweighing the channels such that multiple channels are integrated into one (e.g., dimensionality reduction or channel compression). New ‘channels’ can also be generated if required as well (e.g., dimensionality expansion.”, thus, a second neural network is trained based on the reweighted channels. The network is trained to provide predictions according to the reweighted channels (comprising a plurality of data fields)).
Banville does not explicitly disclose receiving data from a database
However, Yu teaches receiving data from a database (Yu, Par. [0028], “According to embodiments of the disclosure, the proposed methodology provides for estimating a number of samples that satisfies a database query. Subsets from a sample dataset of a collection of all data are randomly drawn. Once drawn, the subsets are queried to determine a number of cardinalities. The number of cardinalities may then be used as training data to train a prediction model using machine learning or statistical methods. The trained prediction model may then be used to estimate a sample size satisfying the database query of the collection of all data.”, thus, data is received from a database)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 1, as disclosed by Banville to include receiving data from a database, as disclosed by Yu. One of ordinary skill in the art would have been motivated to make this modification to enable database utilization, hence allowing the network to receive and efficiently handle high-cardinality data, in turn improving model performance and insights (Yu, Par. [0028], “According to embodiments of the disclosure, the proposed methodology provides for estimating a number of samples that satisfies a database query. Subsets from a sample dataset of a collection of all data are randomly drawn. Once drawn, the subsets are queried to determine a number of cardinalities. The number of cardinalities may then be used as training data to train a prediction model using machine learning or statistical methods. The trained prediction model may then be used to estimate a sample size satisfying the database query of the collection of all data.”).
While Banville discloses the determination of a relevance of a plurality of data fields as shown by the rejection above, Banville in view of Yu does not explicitly disclose determining a first interpretability score
However, Stergioudis discloses determining a first interpretability score (Stergioudis, Par. [0050], “As shown in FIG. 5, and in some embodiments, the root cause analyzer 206 may be configured to compute contribution scores for each of the features for a particular data sample. The contribution scores may be indicative of a likelihood that the corresponding feature contributed to or otherwise caused the data sample to be predicted as anomalous. The root cause analyzer 206 may be configured to compute the contribution scores using a shapley additive explanations (SHAP) algorithm. The SHAP algorithm may be or include an algorithm which computes SHAP values corresponding to relative importance of features in a data sample.”, thus, a first interpretability/contribution score is determined utilizing an interpretability algorithm (SHAP algorithm – as supported by Applicant’s specification Par. [0042]))
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 1, as disclosed by Banville in view of Yu to include determining a first interpretability score utilizing an interpretability algorithm, as disclosed by Stergioudis. One of ordinary skill in the art would have been motivated to make this modification to enable enhanced feature learning and selection, which may improve model fairness and reliability (Stergioudis, Par. [0050], “As shown in FIG. 5, and in some embodiments, the root cause analyzer 206 may be configured to compute contribution scores for each of the features for a particular data sample. The contribution scores may be indicative of a likelihood that the corresponding feature contributed to or otherwise caused the data sample to be predicted as anomalous. The root cause analyzer 206 may be configured to compute the contribution scores using a shapley additive explanations (SHAP) algorithm. The SHAP algorithm may be or include an algorithm which computes SHAP values corresponding to relative importance of features in a data sample.”).
Regarding Claim 2, Banville in view of Yu in view of Stergioudis teaches the method of claim 1, wherein training the first neural network for the first data field comprises iteratively training the first neural network for the first data field (Banville, Par. [0321], “Training was repeated on different training-validation splits (two for PC18, three for TUAB and ISD). Neural networks and random forests were trained three times per split on TUAB and ISD (two times on PC18) with different parameter initializations. Training ran for at most 40 epochs or until the validation loss stopped decreasing for a period of a least 7 epochs on TUAB and PC18 (a maximum of 150 epochs and patience of 30 for ISD, given the smaller size of the dataset).”, thus, training may be repeated iteratively for the neural networks).
Regarding Claim 3, Banville in view of Yu in view of Stergioudis teaches the method of claim 1, further comprising:
based at least in part on the selected subset of data fields, iteratively training the first neural network, wherein the training comprises iterating over a plurality of combinations of the subset of data fields (Banville, Abstract, “In one aspect. a system is disclosed herein for dynamically reweighing a plurality of channels according to relevance given a learning task or channel corruption using a neural network. The system comprising a plurality of channels. each channel of the plurality of channels comprising data and a computing device. The computing device can be configured to receive a dataset from a plurality of channels. extract a representation of the dataset or the plurality of channels. predict a dynamic spatial filter from the representation of the dataset or the plurality of channels using a neural network. apply the dynamic spatial filter to dynamically reweigh each of the channels of plurality channels. and perform a learning task using the reweighed channels and a second neural network.”, thus, the training of the first neural network may comprise iterating over a plurality of combinations of channels);
determining a second interpretability score for each combination of the plurality of combinations of the subset of data fields (Stergioudis, Par. [0050], “The SHAP algorithm may be or include an algorithm which computes SHAP values corresponding to relative importance of features in a data sample. The root cause analyzer 206 may apply the SHAP algorithm to the data sample(s) for computing the contribution scores. In other words, the contribution scores may be, include, or otherwise correspond to SHAP values. The SHAP algorithm may determine which features (e.g., which individual feature(s), which group of features individually, and/or which combination of features) contributed to the prediction that the data sample is anomalous.”, thus, a second interpretability score for a combination of data fields may be determined), wherein each second interpretability score is indicative of a relevance for each combination of the plurality of combinations of the subset of data fields (Banville, Par. [0007], “In one aspect, embodiments described herein provide a method of using a neural network to dynamically reweigh a plurality of channels according to relevance given a learning task or channel corruption. The method involves receiving a dataset from a plurality of channels, each channel of the plurality of channels having data.”, thus, an interpretability score may be assigned to a combination of a plurality of channels); and
wherein the second neural network is trained by iteratively training the second neural network utilizing a selected combination of the plurality of combinations, the selection based at least in part on the determined second interpretability scores (Banville, Par. [0212], “In some embodiments, applying the dynamic spatial filter involves adjusting the channels to a form acceptable by the second neural network in the performing a learning task. In some embodiments, the second neural network is trained with input having a specific structure. In some embodiments, a dynamic spatial filter component can reweigh the plurality of channels such that their structure matches a specific structure required by second neural network. This can involve, for example, reweighing the channels such that multiple channels are integrated into one (e.g., dimensionality red