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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 11th, 2026 has been entered.
This action is in response to the amendment and remarks filed June 11th, 2026. In the
amendment, claims 1, 3, 11, 13, 15, 17-20 were amended, claims 2 and 12 were cancelled, and claims 21-22 were added. As such, claims 1, 3-11, and 13-22 are pending.
Response to Arguments
Applicant’s arguments with respect to the rejections of claims 1, 3-11, and 13-22 under 35 U.S.C § 101 and 103 are not persuasive for the following reasons:
35 U.S.C 101:
Applicant argues that the amended claims recite a specific improvement to how machine learning models are trained and therefore conform to example (xiv) of Desjardins (Pages 8 to 11 of Remarks). The Examiner respectfully disagrees. Desjardins and its associated guidance require that the claim itself, and not merely the specification, reflect an improvement to how the model operates rather than an improvement to the underlying mathematical calculation. The body of claim 1 recites generating a sharing loss value, a prediction loss value, and an aggregated loss value, all of which are mathematical operations, followed by a generic updating step that applies those calculated values. The asserted improvement is produced by the loss calculations themselves and is claimed only through the result of improved accuracy. The claim is directed to an improvement in the abstract idea rather than a technical improvement to the model, which is the distinction Desjardins draws. Example (xiv) is not satisfied because the claim recites no specific parameter adjustment mechanism that shows how the updating improves system performance of the machine learning model apart from performing and applying the recited mathematics.
Step 2A Prong 1:
Applicant argues that claim 1 does not recite a mathematical concept and merely involves or is based on mathematics under MPEP 2106.04(a)(2) (Pages 11-12 of Remarks). The Examiner respectfully disagrees. As set forth in the rejection, generating the sharing loss value requires computing a measured dissimilarity using a distance function such as L1, L2, or Euclidean distance. Generating the prediction loss value requires applying a loss function such as binary cross entropy, and generating the aggregated loss value requires computing a weighted sum. Each of these limitations recite a mathematical calculation rather than limitations that is merely based on mathematics. Paragraphs 31, 36, and 37 of the instant specification confirm that these steps are defined in terms of mathematical operations. Labeling these calculations as training steps does not remove them from the mathematical concepts grouping, because the claim still recites the underlying calculations.
Step 2A Prong 2:
Applicant argues that an improvement to machine learning training, including a closed loop that updates both the model and the similarity matrix, integrates any abstract idea into a practical application under Ex Parte Desjardins (Pages 13-15 of Remarks). The Examiner respectfully disagrees. While Desjardins recognizes that an improvement to how a model operates can integrate a mathematical concept into a practical application, it also makes clear that an assertion of improvement in the specification is insufficient unless the claim itself reflects that improvement and the improvement is to the technology rather than to the mathematical calculation. The updating step recites only that the model and the similarity matrix are updated based on the aggregated loss value, which is a generic application of the previously calculated values and is characterized solely by the intended result of improved accuracy. Unlike the claims in Desjardins, which recited a specific selective parameter adjustment that improved performance on a prior task, claim 1 recites no particular training mechanism that improves model operation independent of the loss calculations. The recitation of improved accuracy is an intended result that does not impose a meaningful limit on the judicial exception under MPEP 2106.05(a) and 2106.04(d).
Step 2B:
Applicant argues that the combination of operations and data structures is unconventional and supplies an inventive concept (Page 15 of Remarks). The Examiner respectfully disagrees. The additional elements beyond the abstract idea consist of receiving a similarity matrix, which is mere data gathering, generic processors, and machine learning models recited at a high level of generality as the environment for the calculations.
35 U.S.C 103:
Applicant argues that none of the cited references teach updating the first machine learning model and the similarity matrix based on the aggregated loss value, and that Li's style transfer network is not a similarity matrix (Pages 15-17 of Remarks). The Examiner respectfully disagrees. Li is relied upon only for aggregating multiple loss components into a single cumulative loss and iteratively updating a model based on that loss, as Li teaches that the losses are aggregated to form a cumulative loss 118 and then are used to update the model during the training process. The updating of the similarity values is supplied by Malakouti whose goal is to adapt model parameters from parent and child diagnostic tasks while simultaneously learning the importance of the set of auxiliary models where the relatedness coefficient quantifies inter model similarity. Because Malakouti minimizes a single objective while also regularizing both the auxiliary task weights and model parameters, it teaches updating both the model and the similarity values based on the same aggregated loss, with Duan supplying the similarity matrix that stores those values.
Applicant argues that the rejection relies on five references with only a conclusory same field of endeavor statement (Pages 16-17 of Remarks). The Examiner respectfully disagrees. In response to applicant's argument that the examiner has combined an excessive number of references, reliance on a large number of references in a rejection does not, without more, weigh against the obviousness of the claimed invention. See In re Gorman, 933 F.2d 982, 18 USPQ2d 1885 (Fed. Cir. 1991). Each reference is analogous art because each is reasonably pertinent to the problem addressed by the claimed invention which is improving the accuracy of multiple related machine learning models trained on limited data by leveraging and refining inter model similarities during training. Duan supplies the similarity matrix, Sun supplies the measured dissimilarity between models, Li supplies the aggregation of multiple losses into one cumulative loss used to update a model, and Malakouti supplies the joint updating of the model and the relatedness values to prevent negative transfer and improve accuracy. A skilled artisan would have been motivated to combine these teachings to build a multi task training framework that adapts inter model relationships during training in order to achieve that benefit.
Applicant argues that Duan is directed to post training model selection and never uses its similarity during training (Page 17 of Remarks). The Examiner respectfully disagrees and clarifies that Duan is relied upon only for the data structure, namely a matrix in which each entry is a pairwise comparison of latent representations. The training time application of using and updating those values is supplied by Malakouti, which learns inter model relatedness during optimization. A skilled artisan would recognize Duan’s similarity matrix as the substitute for Malakouti’s relatedness coefficients (similarity values between models) and would store and update those values in such a matrix during training.
Applicant argues that Sun’s federated learning aggregation is not related to model training and teaches away from using similarity as a training signal because Sun uses it to detect malicious client updates (Pages 17-18 of Remarks). The Examiner respectfully disagrees. Sun teaches a specific computation, the improved Hamming distance comparison of model update matrices that uses a numerical dissimilarity value. The fact that Sun applies that value to detect malicious updates does not dispose its use as a sharing loss in a cooperative multi task setting. Under MPEP 2145, a reference does not teach away merely because it can be used for a different purpose, so Sun remains a valid source for the measured dissimilarity underlying the sharing loss.
Applicant argues that there is no objective reason to combine Li’s aggregation mechanism with the non-training disclosures of Duan and Sun (Page 18 of Remarks). The Examiner respectfully disagrees because Li is combined for the specific operation of aggregating several distinct loss terms into one cumulative loss and using that loss to iteratively update a model’s parameters which is the mechanism recited in claim 1. A skilled artisan training multiple related models with both a prediction loss and a similarity-based sharing loss would have looked to Li’s demonstrated approach of forming a single objective from multiple components. This is reinforced by Malakouti which independently optimizes model parameters and inter model relatedness within a single objective. The combination of Li for how to aggregate and update, Malakouti for what to update, and Duan and Sun for where to store and how to measure similarity enables a person in the ordinary skill of the art to derive to the claimed invention.
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, 3-11, and 13-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a
process.
Step2A Prong 1: The claim recites, inter alia:
[G]enerating… a sharing loss value for the first machine learning model and the second machine learning model on a measured dissimilarity between the first machine learning model and the second machine learning model: This limitation recites a mathematical concept because it involves using math functions to calculate the dissimilarity between the model values.
[G]enerating… and using a loss function and a training dataset, a prediction loss value for the first machine learning model: This limitation is a mathematical concept since it involves using a previously calculated loss to apply it to create the prediction loss value using mathematical operations.
[G]enerating…an aggregated loss value for the first machine learning model based on the code similarity value, the sharing loss value, and the prediction loss value: This limitation recites a mathematical concept because it involves using mathematical operations to generate the aggregated loss value. See Paragraph 37 which states, “…the aggregated loss value may be the weighted sum of prediction loss matrix (e.g., the predictive performance of the machine learning models) and the sharing-similarity loss matrix (e.g., the sharing loss matrix, D, scaled through multiplication by similarity matrix, S).”
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[R]eceiving… a similarity matrix corresponding to a plurality of machine learning models, wherein: a first machine learning model of the plurality of machine learning model… a second machine learning model of the plurality of machine learning models… and the similarity matrix comprises a code similarity value corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code: Mere data gathering recited at a high level of generality, and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).
…is trained to generate a first class-specific predictive output based on a first code… is trained to generate a second class-specific predictive output based on a second code that is different from the first code: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
…by one or more processors…and updating… the first machine learning model and the similarity matrix based on the aggregated loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model: 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 (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 because the additional elements are as follows:
[R]eceiving… a similarity matrix corresponding to a plurality of machine learning models, wherein: a first machine learning model of the plurality of machine learning model… a second machine learning model of the plurality of machine learning models… and the similarity matrix comprises a code similarity value corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
…is trained to generate a first class-specific predictive output based on a first code… is trained to generate a second class-specific predictive output based on a second code that is different from the first code: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
…by one or more processors… and updating… the first machine learning model and the similarity matrix based on the aggregated loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of mental processes and mathematical concepts for calculating similarity values, generating loss values, and aggregating those values for model updating). The claim merely describes a process of applying known mathematical operations (computing dissimilarity measures, generating sharing and prediction loss values, and calculating a weighted aggregated loss value) to data associated with machine learning models, and performing standard data processing steps (receiving a similarity matrix, training models and their produced outputs, and updating the model based on the calculated loss). The recitation of one or more processors and machine learning models to generate class-specific predictive outputs merely indicates a technological environment in which the abstract ideas are applied, without improving the functioning of a computer or machine learning model itself.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility
analysis.
Claim 3
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
The first code is a first medical code associated with a first textual description, and the second code is a second medical code associated with a second textual description: This limitation is a mental process which involves associating features to medical codes, which can be performed in the human mind.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the measured dissimilarity comprises at least one of: (i) a distance between one or more coefficients of the first machine learning model and the second machine learning model or (ii) an output difference between one or more outputs of the first machine learning model and the second machine learning model: This limitation recites a mathematical concept because it involves using math functions to calculate the dissimilarity between the output values of the models.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the sharing loss value is represented by a sharing loss matrix comprising a respective sharing loss value…: This limitation recites a mathematical concept because it represents sharing loss values in a matrix.
Step 2A Prong Two: This judicial exception is not integrated into a practical application because the additional elements are as follows:
…for each pair of machine learning models of the plurality of machine learning models: 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 (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 because the additional elements are as follows:
…for each pair of machine learning models of the plurality of machine learning models: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
generating the aggregated loss value comprises: generating… a sharing-similarity loss matrix based on the sharing loss matrix and the similarity matrix, wherein (a) the sharing-similarity loss matrix comprises a sharing-similarity loss value for the first machine learning model and the second machine learning model and (b) the sharing-similarity loss value comprises the sharing loss value scaled by the code similarity value: This recites a mathematical concept because it incorporates using a math formula to calculate the sharing-similarity loss value to get the aggregative loss.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the aggregated loss value for the particular first machine learning model is representative of a joint loss for each of the plurality of machine learning models, and wherein the aggregated loss value comprises a weighted sum of (a) a prediction loss matrix comprising a respective prediction loss value for each machine learning model of the plurality of machine learning models and (b) the sharing-similarity loss matrix: This limitation recites a mathematical concept because it involves adding the other losses in order to attain the aggregated loss value.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the prediction loss value for the first machine learning model and the second machine learning model is represented by a prediction loss matrix comprising a respective prediction loss value for each machine learning model of the plurality of machine learning models: This limitation is a mathematical concept because it involves mathematical representations of the models using a matrix.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the plurality of… models are represented by a model matrix: This limitation is a mathematical concept since it deals with representing models using matrices.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
and wherein updating the first machine learning model comprises: updating… the model matrix for the plurality of machine learning models to optimize the aggregated loss value: 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 (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 because the additional elements are as follows:
and wherein updating the first machine learning model comprises: updating… the model matrix for the plurality of machine learning models to optimize the aggregated loss value: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
the model matrix is indicative of a set of coefficients…: This limitation is a mathematical concept because it represents model parameters as a matrix of coefficients.
Step 2A Prong Two: This judicial exception is not integrated into a practical application because the additional elements are as follows:
…for each of the plurality of machine learning models: 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 (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 because the additional elements are as follows:
…for each of the plurality of machine learning models: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 11
Step 1: The claim recites a system; therefore, it is directed to the statutory category of machine.
Step2A Prong 1: The claim recites, inter alia:
[G]enerating… a sharing loss value for the first machine learning model and the second machine learning model on a measured dissimilarity between the first machine learning model and the second machine learning model: This limitation recites a mathematical concept because it involves using math functions to calculate the dissimilarity between the model values.
[G]enerating… and using a loss function and a training dataset, a prediction loss value for the first machine learning model: This limitation is a mathematical concept since it involves using a previously calculated loss to apply it to create the prediction loss value using mathematical operations.
[G]enerating…an aggregated loss value for the first machine learning model based on the code similarity value, the sharing loss value, and the prediction loss value: This limitation recites a mathematical concept because it involves using mathematical operations to generate the aggregated loss value. See Paragraph 37 which states, “…the aggregated loss value may be the weighted sum of prediction loss matrix (e.g., the predictive performance of the machine learning models) and the sharing-similarity loss matrix (e.g., the sharing loss matrix, D, scaled through multiplication by similarity matrix, S).”
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations: 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 (MPEP 2106.05(f)).
[R]eceiving… a similarity matrix corresponding to a plurality of machine learning models, wherein: a first machine learning model of the plurality of machine learning model… a second machine learning model of the plurality of machine learning models… and the similarity matrix comprises a code similarity value corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code: Mere data gathering recited at a high level of generality, and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).
…is trained to generate a first class-specific predictive output based on a first code… is trained to generate a second class-specific predictive output based on a second code that is different from the first code: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
…by one or more processors…and updating… the first machine learning model and the similarity matrix based on the aggregated loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model: 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 (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 because the additional elements are as follows:
A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations: 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 (MPEP 2106.05(f)).
[R]eceiving… a similarity matrix corresponding to a plurality of machine learning models, wherein: a first machine learning model of the plurality of machine learning model… a second machine learning model of the plurality of machine learning models… and the similarity matrix comprises a code similarity value corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
…is trained to generate a first class-specific predictive output based on a first code… is trained to generate a second class-specific predictive output based on a second code that is different from the first code: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
…by one or more processors… and updating… the first machine learning model and the similarity matrix based on the aggregated loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 13 recites similar limitations to claim 3. Therefore, claims 13 is rejected using the same rationale as claims 3.
Claim 14 recites similar limitations to claim 4. Therefore, claims 14 is rejected using the same rationale as claims 4.
Claim 15
Step 1: The claim recites a non-transitory computer medium; therefore, it is directed to the statutory
category of article of manufacture.
Step2A Prong 1: The claim recites, inter alia:
[G]enerating… a sharing loss value for the first machine learning model and the second machine learning model on a measured dissimilarity between the first machine learning model and the second machine learning model: This limitation recites a mathematical concept because it involves using math functions to calculate the dissimilarity between the model values.
[G]enerating… and using a loss function and a training dataset, a prediction loss value for the first machine learning model: This limitation is a mathematical concept since it involves using a previously calculated loss to apply it to create the prediction loss value using mathematical operations.
[G]enerating…an aggregated loss value for the first machine learning model based on the code similarity value, the sharing loss value, and the prediction loss value: This limitation recites a mathematical concept because it involves using mathematical operations to generate the aggregated loss value. See Paragraph 37 which states, “…the aggregated loss value may be the weighted sum of prediction loss matrix (e.g., the predictive performance of the machine learning models) and the sharing-similarity loss matrix (e.g., the sharing loss matrix, D, scaled through multiplication by similarity matrix, S).”
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
[R]eceiving… a similarity matrix corresponding to a plurality of machine learning models, wherein: a first machine learning model of the plurality of machine learning model… a second machine learning model of the plurality of machine learning models… and the similarity matrix comprises a code similarity value corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code: Mere data gathering recited at a high level of generality, and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).
…is trained to generate a first class-specific predictive output based on a first code… is trained to generate a second class-specific predictive output based on a second code that is different from the first code: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
…by one or more processors…and updating… the first machine learning model and the similarity matrix based on the aggregated loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model: 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 (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 because the additional elements are as follows:
One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
[R]eceiving… a similarity matrix corresponding to a plurality of machine learning models, wherein: a first machine learning model of the plurality of machine learning model… a second machine learning model of the plurality of machine learning models… and the similarity matrix comprises a code similarity value corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code: The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
…is trained to generate a first class-specific predictive output based on a first code… is trained to generate a second class-specific predictive output based on a second code that is different from the first code: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
…by one or more processors… and updating… the first machine learning model and the similarity matrix based on the aggregated loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model: 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 and cannot provide inventive concept (MPEP 2106.05(f)).
Claim 16 recites similar limitations to claim 5. Therefore, claims 16 is rejected using the same rationale as claims 5.
Claim 17 recites similar limitations to claim 6. Therefore, claims 17 is rejected using the same rationale as claims 6.
Claim 18 recites similar limitations to claim 7. Therefore, claims 18 is rejected using the same rationale as claims 7.
Claim 19 recites similar limitations to claim 8. Therefore, claims 19 is rejected using the same rationale as claims 8.
Claim 20 recites similar limitations to claim 9. Therefore, claims 20 is rejected using the same rationale as claims 9.
Claim 21
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on
on claim 1, which recites an abstract idea.
Step 2A Prong Two: This judicial exception is not integrated into a practical application because the additional elements are as follows:
the textual similarity is a Jaccard similarity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
the textual similarity is a Jaccard similarity: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 22
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
generating a heatmap representation of the similarity matrix, wherein the code similarity value is represented by a color intensity: This limitation encompasses a mental process of evaluation/judgement/opinion to generate a heatmap representing the similarity matrix/ code similarity, which can be performed by pen and paper.
Step 2A Prong Two and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4-6, 8-11, 14-16, and 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 20220121934 A1) in view of Sun (CN 113553582 A), in view of Li (US 20210233239 A1), in view of Malakouti (“Not All Samples Are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification”, 2020), and in further view of Wu (CN 111680136 A).
Regarding claim 1,
Duan teaches [a] computer-implemented method comprising: receiving, by one or more processors, a similarity matrix corresponding to a plurality of machine learning models, wherein… (Paragraph 0003, “This specification describes a method and corresponding system for automatically, that is without supervision, identifying a computer-implemented neural network which is able to generate a disentangled latent variable representation of an input data item”, Paragraph 0004, “In implementations determining the measure of similarity between the sets of latent representations of the trained neural networks is performed in parallel between pairs or groups of the trained neural networks that is the determining is performed as a set of parallel tasks, optionally on a distributed computing system”, See also Para 0019, “That is the latent representation of the (each) first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations.”, Paragraph 95 of Duan, “code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.”
Duan teaches generating a similarity matrix containing pairwise similarity values between latent representations of different neural networks where each entry represents a similarity value corresponding to the models, which corresponds to the code similarity value.)
Duan does not teach generating, by the one or more processors, a sharing loss value for the at least two machine learning models, wherein the sharing loss value is based at least in part on a measured dissimilarity between the at least two machine learning models.
Sun, in the same field of endeavor, teaches generating… a sharing loss value for the first machine learning model and the second machine learning model based on a measured dissimilarity between the first machine learning model and the second machine learning model (Page 4 of Sun, “…using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity.”
Sun teaches generating a sharing loss value based on a measured dissimilarity between machine learning models by applying a Hamming-distance metric to compare the model update matrices of different models.)
generating… using a loss function and a training dataset, a prediction loss value for the first machine learning model (Page 5 of Sun, “wherein dk is the local data set of the k-th client, fi (w) = α (xi, yi, w) is a loss function…the loss function is inversely proportional to the model precision; therefore, the
optimization of the target function of the machine learning generally is to make the loss function reach
the minimum value.”
Sun teaches generating a prediction loss value using a loss function computed on a training dataset where the loss function measures the difference between the predicted outputs and training data and is minimized to improve the model accuracy.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan’s teaching with Sun’s distance metric between coefficients between two machine learning models in order to improve predictive accuracy and control over the prediction result to improve the model performance (Sun’s Background).
Duan in view of Sun does not teach generating, by the one or more processors, an aggregated loss value for the particular machine learning model based at least in part on the similarity [value], the … loss value, and the prediction loss value; and updating, by the one or more processors, the particular machine learning model based on the aggregated loss value.
Li, in the same field of endeavor, teaches generating… an aggregated loss value for the first machine learning model based on the… similarity value, the… loss value, and the… loss value; and updating… the first machine learning model … based on the aggregated loss value to generate an updated machine learning model … (Paragraph 20 of Li, “The style similarity estimator 110, the clinical quality estimator 112, and the image content regularizer 120, determine a similarity loss 114, a clinical quality loss 116, and a content loss 122, respectively, which are aggregated to form a cumulative loss 118. The parameters of style transfer network 106 are then iteratively updated based on the cumulative loss 118 during the training process”, Paragraph 29 of Li, “Image content regularizer may comprise one or more differentiable functions, which take medical images 102 and style transferred medical images 108 as input, and produce content loss 122 as output.”
Li teaches aggregating multiple loss components including a similarity loss and other loss values into a cumulative loss to update the parameters of a machine learning model during training. Li’s content loss corresponds to the prediction loss value because it measures the error between the model output and the desired output characteristics during training.)
Therefore, it would have been obvious to one of ordinary skill in the art to combine Duan in view of Sun’s teaching with Li’s aggregated loss in order to improve model performance through iterative optimization (Abstract of Li).
Duan in view of Sun in further view of Li does not teach a first machine learning model of the plurality of machine learning models is trained to generate a first class-specific predictive output based on a first code, a second machine learning model of the plurality of machine learning models is trained to generate a second class-specific predictive output based on a second code that is different from the first code… to generate an updated machine learning model with improved accuracy relative to the first machine learning model.
Malakouti, in the same field of endeavor, teaches a first machine learning model of the plurality of machine learning models is trained to generate a first class-specific predictive output based on a first code, a second machine learning model of the plurality of machine learning models is trained to generate a second class-specific predictive output based on a second code that is different from the first code (Page 2 Methodology, "Assume we have T diagnoses and diagnostic categories, each covered by a separate binary classification task... Our objective is to learn T discriminant functions f1,f2,...,fT in which ft : RD → R. Hence, the predicted score of the discriminant function ft can be mapped to one of the binary labels 0,1 using a task specific threshold..."
Malakouti teaches learning multiple task-specific functions (f_1,f_2…f_T) where each function operates on a patient representation to generate its own classification score using a task specific threshold, which corresponds to separate machine learning models that produce different class-specific outputs.),
and updating… the first machine learning model and the similarity [value] based on the … loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model (Page 2 Introduction of Malakouti, “Finally, we show our method can learn models with improved classification performance and analyze the difference between model adaptation from parent diagnostic categories for positive and negative classes.”, Page 1 Introduction, “This motivates the use of multi-task learning methods that can take advantage of available data for similar diagnostic tasks. This is achieved by imposing similarities between behaviour of related models.”, Page 3 Hierarchical Adaptive Multi-task Learning Section, “HA-MTL’s goal is to adapt model parameters from parent and child diagnostic tasks while simultaneously learning the importance of the set of auxiliary models… In order to perform the model parameter adaptation and simultaneously learn the importance of auxiliary task they propose Regularized Adaptive SVM (RA SVM) as show in equation 1.
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Where vt corresponds to the model parameters for Δft = ft − aux(t) a τafa and τa refers to the relatedness or usefulness coefficient of auxiliary task a for target task t… The optimization problem in Equation 1 is minimizing the hinge loss while also regularizing both the auxiliary task weights and model parameters of Δft.”
Malakouti teaches that its optimization objective (Equation 1) minimizes a single loss that includes both the model-parameter difference term and the auxiliary-task weight regularizer. The auxiliary weights quantify how related each model is to the target model (inter-model similarity values). The model parameters and the relatedness coefficients are being simultaneously updated which corresponds to updating the model and its similarity values based on the hinge loss value.).
Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Duan in view of Sun in view of Li’s teaching with Malakouti’s task specific multi-model training framework in order to enable Duan’s similarity-based relationships between models to be applied in a multi-task learning environment to improve classification performance of multiple machine learning models (Introduction of Malakouti).
Duan in view of Sun in view of Li and in further view of Malakouti does not teach a code similarity… corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code.
Wu, in the same field of endeavor, teaches a code similarity… corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code (Page 5 Paragraph 5 of Wu, “…the first preset model is different from the second preset model; using different preset models to judge the similarity of the text to be matched and the matching text in different ways. a first preset model or a second preset model; it can adopt depth semantic model DSSM, neural network depth semantic model CNN-DSSM or long-term memory network depth semantic LSTM-DSSM… mask weight reuse extracting the similarity maximum value in each data dimension of the first model code, and the similarity maximum value in each data dimension of the second model code. the first similar dimension refers to the similarity of the text to be matched and the matching text; the second similar dimension refers to the similarity of the text to be matched and the matching text… if the first model code is (3.89, 9.23)”
Wu teaches generating a first model and second model code using different preset models (e.g. DSSM, CNN-DSSM, LSTM-DSSM), where each model code contains dimensions representing the semantic similarity between a text to be matched and a matching text. Wu further teaches extracting similarity dimensions from the respective model codes, where the extracted dimensions represent textual similarity between texts. The model codes are vector representations generated and are compared to determine textual similarity.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan in view of Sun in view of Li in further view of Malakouti’s teaching with Wu’s textual code similarity in order to enable more accurate inter relationships between codes (Paragraphs 1-2 Page 2 of Wu).
Regarding claim 4,
Duan does not teach the measured dissimilarity comprises at least one of: (i) a distance between one or more coefficients… the first machine learning model and the second machine learning model.
Sun, in the same field of endeavor, teaches the measured dissimilarity comprises at least one of: (i) a distance between one or more coefficients between the first machine learning model and the second machine learning model
(Page 4 of Sun, “…using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity”, Claim 4 of Sun, “determining whether sign marks of corresponding bit weight values in the different mathematical matrixes are the same to obtain a similarity matrix…”, Page 7 Paragraph 4, “…if the server compares the positive sign similarity percentage between two model updates satisfies the condition (e.g., reaches the threshold value); Well, it is believed that these two models update the attack from the sybil.” Claim 1, “…obtaining different mathematical matrixes corresponding to respectively different local models uploaded by the client”
Sun teaches computing a distance between parameters (coefficients) between two machine learning models by comparing the weight values in the model update matrices using the Hamming distance algorithm.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan’s teaching with Sun’s distance metric between coefficients between two machine learning models in order to improve predictive accuracy and control over the prediction result to improve the model performance (Sun’s Background).
Regarding claim 5,
Duan does not disclose the sharing loss value is represented by a sharing loss matrix comprising a respective sharing loss value for each pair of machine learning models of the plurality of machine learning models.
Sun, in the same field of endeavor, teaches the sharing loss value is represented by a sharing loss matrix comprising a respective sharing loss value for each pair of machine learning models of the plurality of machine learning models [Pg. 4, “Wherein, the improved hamming distance algorithm comprises: judging the number of the sign of the sign of the corresponding bit between the two matrixes.” See also Pg. 8, Para 4, “if the comparison discovery of the positive and negative similarity percentage between two model updating satisfies the condition (e.g., to reach the threshold value), then it is considered that the two models update from malicious (e.g., sybil attack)”] to identify updates similar to each between two different models to further enhance the generation of distance similarities between models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the claimed invention of Duan, with the calculation of distance similarity between two models of Sun, because such a modification would allow to compare output similarities between two different machine learning models to improve the model’s performance (See Sun’s Background, See Para 0084 in Duan).
Regarding claim 6, Duan discloses the similarity matrix [Para 0019, That is the latent representation of the (each first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations, See also Para 0019, That is the latent representation of the (each) first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations. See also Para 0095, The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers]
Duan does not teach generating the aggregated loss value comprises: generating, by the one or more processors, a sharing -similarity loss matrix based on the sharing loss matrix and the similarity matrix, wherein (a) the sharing- similarity loss matrix comprises a sharing-similarity loss value for the first machine learning model and the second machine learning model, and (b) the sharing-similarity loss value comprises the sharing loss value scaled by the code similarity value.
Sun, in the same field of endeavor, teaches (a) the sharing- similarity loss matrix comprises a sharing-similarity loss value for the first machine learning model and the second machine learning model [Pg. 4, “Wherein, the improved hamming distance algorithm comprises: judging the number of the sign of the sign of the corresponding bit between the two matrixes.” See also Pg. 8, Para 4, “if the comparison discovery of the positive and negative similarity percentage between two model updating satisfies the condition (e.g., to reach the threshold value), then it is considered that the two models update from malicious (e.g., sybil attack)”]
(b) the sharing-similarity loss value scaled by the code similarity value [Pg. 4, using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity] to generate a similarity matrix between pairs or groups of models where each entry of the similarity matrix corresponds to the claimed code similarity value.
Therefore, it would have been obvious to one of ordinary skill in the art to generate and improve the performance of similarities between multiple machine learning models using a sharing loss value.
Duan in view of Sun do not teach an aggregated loss value comprising the sharing loss value and
sharing matrix.
Li, in the same field of endeavor, teaches the aggregated loss value [Para 0020, The
style similarity estimator 110, the clinical quality estimator 112, and the image content regularizer 120, determine a similarity loss 114, a clinical quality loss 116, and a content loss 122, respectively, which are aggregated to form a cumulative loss 118. The parameters of style transfer network 106 are then iteratively updated based on the cumulative loss 118 during the training process] to further enhance the evaluation of medical images (Abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan in view of Sun’s teachings of similarity matrix and sharing loss value scaled by the code similarity value to generate the aggregated loss value of Li to improve model optimization between multiple machine learning models (See Sun’s Background, See Para 0084 in Duan, and See the Abstract of Li). Combining Duan’s generation of a similarity matrix, Sun’s distance similarity between multiple machine learning models would both generate the aggregated loss value taught from Li. Such combination would predictably enhance model coordination learning by jointly considering similarity and dissimilarity losses.
Regarding claim 8,
Duan teaches for the first machine learning model and the second machine learning model (Paragraph 75 of Duan, “In some implementations making a pairwise comparison between two sets of latent representations involves comparing each individual dimension or component of one latent variable representation to each individual dimension or component of another latent variable representation.”, Paragraph 81 of Duan, “To compare a pair of trained neural networks i,j the set of evaluation data items is processed by first and second trained neural networks of the pair”)
Duan does not teach the prediction loss value for the at least two machine learning models is represented by a prediction loss matrix comprising a respective prediction loss value for each machine learning model of the plurality of machine learning models.
Sun, in the same field of endeavor, teaches the prediction loss value… is represented by a prediction loss matrix comprising a respective prediction loss value for each machine learning model of the plurality of machine learning models [Pg. 4, using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity; See also Pg. 5, the loss function is inversely proportional to the model precision; therefore, the optimization of the target function of the machine learning generally is to make the loss function reach the minimum value]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the claimed invention of Duan, with the prediction loss value generated for different machine learning models of Sun, because such a modification would allow to improve predictive accuracy and control over the prediction result to improve the model performance (See Sun’s Background, See Para 0084 in Duan, and See the Abstract of Li).
Regarding claim 9,
Duan teaches the plurality of machine learning models are represented by a model matrix the plurality of machine learning models are represented by a model matrix [Para 0004, In implementations determining the measure of similarity between the sets of latent representations of the trained neural networks is performed in parallel between pairs or groups of the trained neural networks that is the determining is performed as a set of parallel tasks, optionally on a distributed computing system; See also Para 0019, That is the latent representation of the (each) first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations. See also Para 0095, The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.]
Duan in view of Sun does not teach updating the first machine learning model comprises: updating, by the one or more processors, the model matrix for the plurality of machine learning models to optimize the aggregated loss value.
Li, in the same field of endeavor, teaches updating the first machine learning model comprises: updating, by the one or more processors, the model matrix for the plurality of machine learning models to optimize the aggregated loss value [Para 0020, The style similarity estimator 110, the clinical quality estimator 112, and the image content regularizer 120, determine a similarity loss 114, a clinical quality loss 116, and a content loss 122, respectively, which are aggregated to form a cumulative loss 118. The parameters of style transfer network 106 are then iteratively updated based on the cumulative loss 118 during the training process]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the claimed invention of the similarity matrix from Duan, with the updating of a machine learning model using the aggregated loss from Sun, because such a modification would allow to improve prediction result of the machine learning model and improve model performance (See Sun’s Background, See Para 0084 in Duan, and See the Abstract of Li).
Regarding claim 10,
Duan teaches the model matrix is indicative of a set of coefficients for each of the plurality of machine learning models [Para 0004, In implementations determining the measure of similarity between the sets of latent representations of the trained neural networks is performed in parallel between pairs or groups of the trained neural networks that is the determining is performed as a set of parallel tasks, optionally on a distributed computing system; See also Para 0019, That is the latent representation of the (each) first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations. See also Para 0095, The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers] to generate a similarity matrix using multiple machine learning models.]
Regarding claim 11,
Duan teaches [a] system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Paragraph 93, “For a system of one or more computers to be configured to perform particular operations or actions means that the system has installed on it software, firmware, hardware, or a combination of them that in operation cause the system to perform the operations or actions.”, Paragraph 95, “The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. ”)
[a] computer-implemented method comprising: receiving, by one or more processors, a similarity matrix corresponding to a plurality of machine learning models, wherein… (Paragraph 0003, “This specification describes a method and corresponding system for automatically, that is without supervision, identifying a computer-implemented neural network which is able to generate a disentangled latent variable representation of an input data item”, Paragraph 0004, “In implementations determining the measure of similarity between the sets of latent representations of the trained neural networks is performed in parallel between pairs or groups of the trained neural networks that is the determining is performed as a set of parallel tasks, optionally on a distributed computing system”, See also Para 0019, “That is the latent representation of the (each) first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations.”, Paragraph 95 of Duan, “code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.”
Duan teaches generating a similarity matrix containing pairwise similarity values between latent representations of different neural networks where each entry represents a similarity value corresponding to the models, which corresponds to the code similarity value.)
Duan does not teach generating, by the one or more processors, a sharing loss value for the at least two machine learning models, wherein the sharing loss value is based at least in part on a measured dissimilarity between the at least two machine learning models.
Sun, in the same field of endeavor, teaches generating… a sharing loss value for the first machine learning model and the second machine learning model based on a measured dissimilarity between the first machine learning model and the second machine learning model (Page 4 of Sun, “…using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity.”
Sun teaches generating a sharing loss value based on a measured dissimilarity between machine learning models by applying a Hamming-distance metric to compare the model update matrices of different models.)
generating… using a loss function and a training dataset, a prediction loss value for the first machine learning model (Page 5 of Sun, “wherein dk is the local data set of the k-th client, fi (w) = α (xi, yi, w) is a loss function…the loss function is inversely proportional to the model precision; therefore, the
optimization of the target function of the machine learning generally is to make the loss function reach
the minimum value.”
Sun teaches generating a prediction loss value using a loss function computed on a training dataset where the loss function measures the difference between the predicted outputs and training data and is minimized to improve the model accuracy.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan’s teaching with Sun’s distance metric between coefficients between two machine learning models in order to improve predictive accuracy and control over the prediction result to improve the model performance (Sun’s Background).
Duan in view of Sun does not teach generating, by the one or more processors, an aggregated loss value for the particular machine learning model based at least in part on the similarity [value], the … loss value, and the prediction loss value; and updating, by the one or more processors, the particular machine learning model based on the aggregated loss value.
Li, in the same field of endeavor, teaches generating… an aggregated loss value for the first machine learning model based on the… similarity value, the… loss value, and the… loss value; and updating… the first machine learning model … based on the aggregated loss value to generate an updated machine learning model … (Paragraph 20 of Li, “The style similarity estimator 110, the clinical quality estimator 112, and the image content regularizer 120, determine a similarity loss 114, a clinical quality loss 116, and a content loss 122, respectively, which are aggregated to form a cumulative loss 118. The parameters of style transfer network 106 are then iteratively updated based on the cumulative loss 118 during the training process”, Paragraph 29 of Li, “Image content regularizer may comprise one or more differentiable functions, which take medical images 102 and style transferred medical images 108 as input, and produce content loss 122 as output.”
Li teaches aggregating multiple loss components including a similarity loss and other loss values into a cumulative loss to update the parameters of a machine learning model during training. Li’s content loss corresponds to the prediction loss value because it measures the error between the model output and the desired output characteristics during training.)
Therefore, it would have been obvious to one of ordinary skill in the art to combine Duan in view of Sun’s teaching with Li’s aggregated loss in order to improve model performance through iterative optimization (Abstract of Li).
Duan in view of Sun in further view of Li does not teach a first machine learning model of the plurality of machine learning models is trained to generate a first class-specific predictive output based on a first code, a second machine learning model of the plurality of machine learning models is trained to generate a second class-specific predictive output based on a second code that is different from the first code… to generate an updated machine learning model with improved accuracy relative to the first machine learning model.
Malakouti, in the same field of endeavor, teaches a first machine learning model of the plurality of machine learning models is trained to generate a first class-specific predictive output based on a first code, a second machine learning model of the plurality of machine learning models is trained to generate a second class-specific predictive output based on a second code that is different from the first code (Page 2 Methodology, "Assume we have T diagnoses and diagnostic categories, each covered by a separate binary classification task... Our objective is to learn T discriminant functions f1,f2,...,fT in which ft : RD → R. Hence, the predicted score of the discriminant function ft can be mapped to one of the binary labels 0,1 using a task specific threshold..."
Malakouti teaches learning multiple task-specific functions (f_1,f_2…f_T) where each function operates on a patient representation to generate its own classification score using a task specific threshold, which corresponds to separate machine learning models that produce different class-specific outputs.),
and updating… the first machine learning model and the similarity [values] based on the … loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model (Page 2 Introduction of Malakouti, “Finally, we show our method can learn models with improved classification performance and analyze the difference between model adaptation from parent diagnostic categories for positive and negative classes.”, Page 1 Introduction, “This motivates the use of multi-task learning methods that can take advantage of available data for similar diagnostic tasks. This is achieved by imposing similarities between behaviour of related models.”, Page 3 Hierarchical Adaptive Multi-task Learning Section, “HA-MTL’s goal is to adapt model parameters from parent and child diagnostic tasks while simultaneously learning the importance of the set of auxiliary models… In order to perform the model parameter adaptation and simultaneously learn the importance of auxiliary task they propose Regularized Adaptive SVM (RA SVM) as show in equation 1.
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Where vt corresponds to the model parameters for Δft = ft − aux(t) a τafa and τa refers to the relatedness or usefulness coefficient of auxiliary task a for target task t… The optimization problem in Equation 1 is minimizing the hinge loss while also regularizing both the auxiliary task weights and model parameters of Δft.”
Malakouti teaches that its optimization objective (Equation 1) minimizes a single loss that includes both the model-parameter difference term and the auxiliary-task weight regularizer. The auxiliary weights quantify how related each model is to the target model (inter-model similarity values). The model parameters and the relatedness coefficients are being simultaneously updated which corresponds to updating the model and its similarity values based on the hinge loss value.).
Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Duan in view of Sun in view of Li’s teaching with Malakouti’s task specific multi-model training framework in order to enable Duan’s similarity-based relationships between models to be applied in a multi-task learning environment to improve classification performance of multiple machine learning models (Introduction of Malakouti).
Duan in view of Sun in view of Li and in further view of Malakouti does not teach a code similarity… corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code.
Wu, in the same field of endeavor, teaches a code similarity… corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code (Page 5 Paragraph 5 of Wu, “…the first preset model is different from the second preset model; using different preset models to judge the similarity of the text to be matched and the matching text in different ways. a first preset model or a second preset model; it can adopt depth semantic model DSSM, neural network depth semantic model CNN-DSSM or long-term memory network depth semantic LSTM-DSSM… mask weight reuse extracting the similarity maximum value in each data dimension of the first model code, and the similarity maximum value in each data dimension of the second model code. the first similar dimension refers to the similarity of the text to be matched and the matching text; the second similar dimension refers to the similarity of the text to be matched and the matching text… if the first model code is (3.89, 9.23)”
Wu teaches generating a first model and second model code using different preset models (e.g. DSSM, CNN-DSSM, LSTM-DSSM), where each model code contains dimensions representing the semantic similarity between a text to be matched and a matching text. Wu further teaches extracting similarity dimensions from the respective model codes, where the extracted dimensions represent textual similarity between texts. The model codes are vector representations generated and are compared to determine textual similarity.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan in view of Sun in view of Li in further view of Malakouti’s teaching with Wu’s textual code similarity in order to enable more accurate inter relationships between codes (Paragraphs 1-2 Page 2 of Wu).
Claim 14 recites similar limitations to claim 4. Therefore, claims 14 is rejected using the same rationale as claims 4.
Regarding claim 15,
Duan teaches [o]ne or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: (Paragraph 19, “One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations”).
[a] computer-implemented method comprising: receiving, by one or more processors, a similarity matrix corresponding to a plurality of machine learning models, wherein… (Paragraph 0003, “This specification describes a method and corresponding system for automatically, that is without supervision, identifying a computer-implemented neural network which is able to generate a disentangled latent variable representation of an input data item”, Paragraph 0004, “In implementations determining the measure of similarity between the sets of latent representations of the trained neural networks is performed in parallel between pairs or groups of the trained neural networks that is the determining is performed as a set of parallel tasks, optionally on a distributed computing system”, See also Para 0019, “That is the latent representation of the (each) first neural network may be compared with the latent representation of each second neural network. The comparison may employ a similarity matrix in which each entry is a pairwise comparison of latent representations.”, Paragraph 95 of Duan, “code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.”
Duan teaches generating a similarity matrix containing pairwise similarity values between latent representations of different neural networks where each entry represents a similarity value corresponding to the models, which corresponds to the code similarity value.)
Duan does not teach generating, by the one or more processors, a sharing loss value for the at least two machine learning models, wherein the sharing loss value is based at least in part on a measured dissimilarity between the at least two machine learning models.
Sun, in the same field of endeavor, teaches generating… a sharing loss value for the first machine learning model and the second machine learning model based on a measured dissimilarity between the first machine learning model and the second machine learning model (Page 4 of Sun, “…using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity.”
Sun teaches generating a sharing loss value based on a measured dissimilarity between machine learning models by applying a Hamming-distance metric to compare the model update matrices of different models.)
generating… using a loss function and a training dataset, a prediction loss value for the first machine learning model (Page 5 of Sun, “wherein dk is the local data set of the k-th client, fi (w) = α (xi, yi, w) is a loss function…the loss function is inversely proportional to the model precision; therefore, the
optimization of the target function of the machine learning generally is to make the loss function reach
the minimum value.”
Sun teaches generating a prediction loss value using a loss function computed on a training dataset where the loss function measures the difference between the predicted outputs and training data and is minimized to improve the model accuracy.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan’s teaching with Sun’s distance metric between coefficients between two machine learning models in order to improve predictive accuracy and control over the prediction result to improve the model performance (Sun’s Background).
Duan in view of Sun does not teach generating, by the one or more processors, an aggregated loss value for the particular machine learning model based at least in part on the similarity [value], the … loss value, and the prediction loss value; and updating, by the one or more processors, the particular machine learning model based on the aggregated loss value.
Li, in the same field of endeavor, teaches generating… an aggregated loss value for the first machine learning model based on the… similarity value, the… loss value, and the… loss value; and updating… the first machine learning model … based on the aggregated loss value to generate an updated machine learning model … (Paragraph 20 of Li, “The style similarity estimator 110, the clinical quality estimator 112, and the image content regularizer 120, determine a similarity loss 114, a clinical quality loss 116, and a content loss 122, respectively, which are aggregated to form a cumulative loss 118. The parameters of style transfer network 106 are then iteratively updated based on the cumulative loss 118 during the training process”, Paragraph 29 of Li, “Image content regularizer may comprise one or more differentiable functions, which take medical images 102 and style transferred medical images 108 as input, and produce content loss 122 as output.”
Li teaches aggregating multiple loss components including a similarity loss and other loss values into a cumulative loss to update the parameters of a machine learning model during training. Li’s content loss corresponds to the prediction loss value because it measures the error between the model output and the desired output characteristics during training.)
Therefore, it would have been obvious to one of ordinary skill in the art to combine Duan in view of Sun’s teaching with Li’s aggregated loss in order to improve model performance through iterative optimization (Abstract of Li).
Duan in view of Sun in further view of Li does not teach a first machine learning model of the plurality of machine learning models is trained to generate a first class-specific predictive output based on a first code, a second machine learning model of the plurality of machine learning models is trained to generate a second class-specific predictive output based on a second code that is different from the first code… to generate an updated machine learning model with improved accuracy relative to the first machine learning model.
Malakouti, in the same field of endeavor, teaches a first machine learning model of the plurality of machine learning models is trained to generate a first class-specific predictive output based on a first code, a second machine learning model of the plurality of machine learning models is trained to generate a second class-specific predictive output based on a second code that is different from the first code (Page 2 Methodology, "Assume we have T diagnoses and diagnostic categories, each covered by a separate binary classification task... Our objective is to learn T discriminant functions f1,f2,...,fT in which ft : RD → R. Hence, the predicted score of the discriminant function ft can be mapped to one of the binary labels 0,1 using a task specific threshold..."
Malakouti teaches learning multiple task-specific functions (f_1,f_2…f_T) where each function operates on a patient representation to generate its own classification score using a task specific threshold, which corresponds to separate machine learning models that produce different class-specific outputs.),
and updating… the first machine learning model and the similarity [values] based on the … loss value to generate an updated machine learning model with improved accuracy relative to the first machine learning model (Page 2 Introduction of Malakouti, “Finally, we show our method can learn models with improved classification performance and analyze the difference between model adaptation from parent diagnostic categories for positive and negative classes.”, Page 1 Introduction, “This motivates the use of multi-task learning methods that can take advantage of available data for similar diagnostic tasks. This is achieved by imposing similarities between behaviour of related models.”, Page 3 Hierarchical Adaptive Multi-task Learning Section, “HA-MTL’s goal is to adapt model parameters from parent and child diagnostic tasks while simultaneously learning the importance of the set of auxiliary models… In order to perform the model parameter adaptation and simultaneously learn the importance of auxiliary task they propose Regularized Adaptive SVM (RA SVM) as show in equation 1.
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Where vt corresponds to the model parameters for Δft = ft − aux(t) a τafa and τa refers to the relatedness or usefulness coefficient of auxiliary task a for target task t… The optimization problem in Equation 1 is minimizing the hinge loss while also regularizing both the auxiliary task weights and model parameters of Δft.”
Malakouti teaches that its optimization objective (Equation 1) minimizes a single loss that includes both the model-parameter difference term and the auxiliary-task weight regularizer. The auxiliary weights quantify how related each model is to the target model (inter-model similarity values). The model parameters and the relatedness coefficients are being simultaneously updated which corresponds to updating the model and its similarity values based on the hinge loss value.).
Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Duan in view of Sun in view of Li’s teaching with Malakouti’s task specific multi-model training framework in order to enable Duan’s similarity-based relationships between models to be applied in a multi-task learning environment to improve classification performance of multiple machine learning models (Introduction of Malakouti).
Duan in view of Sun in view of Li and in further view of Malakouti does not teach a code similarity… corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code.
Wu, in the same field of endeavor, teaches a code similarity… corresponding to the first machine learning model and the second machine learning model that is based on a textual similarity between the first code and the second code (Page 5 Paragraph 5 of Wu, “…the first preset model is different from the second preset model; using different preset models to judge the similarity of the text to be matched and the matching text in different ways. a first preset model or a second preset model; it can adopt depth semantic model DSSM, neural network depth semantic model CNN-DSSM or long-term memory network depth semantic LSTM-DSSM… mask weight reuse extracting the similarity maximum value in each data dimension of the first model code, and the similarity maximum value in each data dimension of the second model code. the first similar dimension refers to the similarity of the text to be matched and the matching text; the second similar dimension refers to the similarity of the text to be matched and the matching text… if the first model code is (3.89, 9.23)”
Wu teaches generating a first model and second model code using different preset models (e.g. DSSM, CNN-DSSM, LSTM-DSSM), where each model code contains dimensions representing the semantic similarity between a text to be matched and a matching text. Wu further teaches extracting similarity dimensions from the respective model codes, where the extracted dimensions represent textual similarity between texts. The model codes are vector representations generated and are compared to determine textual similarity.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan in view of Sun in view of Li in further view of Malakouti’s teaching with Wu’s textual code similarity in order to enable more accurate inter relationships between codes (Paragraphs 1-2 Page 2 of Wu).
Claim 16 recites similar limitations to claim 5. Therefore, claims 16 is rejected using the same rationale as claims 5.
Claim 17 recites similar limitations to claim 6. Therefore, claims 17 is rejected using the same rationale as claims 6.
Claim 18 recites similar limitations to claim 8. Therefore, claims 18 is rejected using the same rationale as claims 8.
Claim 20 recites similar limitations to claim 9. Therefore, claims 20 is rejected using the same rationale as claims 9.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 20220121934 A1) in view of Sun (CN 113553582 A), in view of Li (US 20210233239 A1), in view of Malakouti (“Not All Samples Are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification”, 2020), in view of Wu (CN 111680136 A), and in further view of Xu (“Multimodal Machine Learning for Automated ICD Coding”).
Regarding claim 3,
Duan in view of Sun in view of Li in view of Malakouti and in further view of Wu does not teach first code is a first medical code associated with a first textual description, and the second code is a second medical code associated with a second textual description.
Wu, in the same field of endeavor, teaches first code is… associated with a first textual description, and the second code is… associated with a second textual description (Page 5 Paragraph 5, “…the first preset model is different from the second preset model; using different preset models to judge the similarity of the text to be matched and the matching text in different ways. a first preset model or a second preset model; it can adopt depth semantic model DSSM, neural network depth semantic model CNN-DSSM or long-term memory network depth semantic LSTM-DSSM… mask weight reuse extracting the similarity maximum value in each data dimension of the first model code, and the similarity maximum value in each data dimension of the second model code. the first similar dimension refers to the similarity of the text to be matched and the matching text; the second similar dimension refers to the similarity of the text to be matched and the matching text… if the first model code is (3.89, 9.23)”
Wu teaches generating a first model and second model code using different preset models (e.g. DSSM, CNN-DSSM, LSTM-DSSM), where each model code contains dimensions representing the semantic similarity between a text to be matched and a matching text. Wu also teaches extracting similarity dimensions from model codes where the extracted dimensions represent textual similarity corresponding to a code similarity between the first code and second code based on the textual similarity generated)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan and Malakouti’s teaching with Wu’s textual code similarity in order to enable more accurate inter relationships between codes (Paragraphs 1-2 Page 2 of Wu).
Duan and Wu do not teach first medical code and second medical code.
Xu, in the same field of endeavor, teaches first medical code… second medical code… (Pg.9 Para 3, “measure the overlap between two sets, which are our extracted evidence (ICD Code Prediction) and physicians’ annotations] to compare results between physician’s medical code descriptions and their model’s predictive output of medical code description.”
Here, the first medical code corresponds to the physician’s medical code and the second medical code corresponds to the model’s output of medical code.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan and Wu’s teaching with Xu’s textual similarity between medical code descriptions in order to accurately compare medical codes to improve the model performance (Introduction of Xu).
Claim 13 recites similar limitations to claim 3. Therefore, claims 13 is rejected using the same rationale as claims 3.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 20220121934 A1) in view of Sun (CN 113553582 A), in view of Li (US 20210233239 A1), in view of Malakouti (“Not All Samples Are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification”, 2020), in view of Wu (CN 111680136 A), and in further view of Gao (US 20210357687 A1).
Regarding claim 7,
Duan in view of Sun in view of Li in view of Malakouti and in further view of Wu does not teach the aggregated loss value for the particular machine learning model is representative of a joint loss for each of the plurality of machine learning models, and wherein the aggregated loss value comprises a weighted sum of (a) a prediction loss matrix comprising a respective prediction loss value for each machine learning model of the plurality of machine learning models and (b) the sharing-similarity loss matrix.
Gao, in the same field of endeavor, teaches the aggregated loss value for the first machine learning model is representative of a joint loss for each of the plurality of machine learning models, and wherein the aggregated loss value comprises a weighted sum [Claim 9, wherein the loss metric is computed by a weighted sum of an online action recognizer loss and a temporal proposal generator loss, wherein the online action recognizer loss includes a sum of the frame loss and the start loss, and wherein the temporal proposal generator loss includes a multiple instance learning loss and a pair-wise co-activity similarity loss. See also Para 0019, As used herein, the term “network” may comprise any hardware or software-based framework that includes any artificial intelligence network or system, neural network or system and/or any training or learning models implemented thereon or therewith].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the claimed invention of Duan, with the aggregated loss computed by a weighted sum of Gao, because such a modification would allow updating the model based on the loss values calculated to improve the training process (See Abstract in Duan and Abstract of Gao).
Duan and Gao do not teach the sharing-similarity loss matrix/ sharing loss value.
Sun, in the same field of endeavor, teaches (a) a prediction loss matrix comprising a respective prediction loss value for each machine learning model of the plurality of machine learning models and (Pg. 4, using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity; See also Pg. 5, the loss function is inversely proportional to the model precision; therefore, the optimization of the target function of the machine learning generally is to make the loss function reach the minimum value)
(b) the sharing-similarity loss matrix [Pg. 4, using the improved hamming distance algorithm, judging the similarity of different mathematical matrix corresponding to different local model updating respectively as the gradient descending direction similarity] to calculate the similarity between different models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the claimed invention of Duan and Gao, with the calculation of distance similarity between two models of Sun, because such a modification would allow updating the model based on the loss values calculated to improve model performance (See Sun’s Background, See Para 0084 in Duan, and Abstract of Gao).
Claim 19 recites similar limitations to claim 7. Therefore, claim 19 is rejected using the same rationale as claim 7.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 20220121934 A1) in view of Sun (CN 113553582 A), in view of Li (US 20210233239 A1), in view of Malakouti (“Not All Samples Are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification”, 2020), in view of Wu (CN 111680136 A), and in further view of Parker (US 20130013603 A1).
Regarding claim 21,
Duan in view of Sun in view of Li in view of Malakouti and in further view of Wu does not teach the textual similarity is a Jaccard similarity.
Parker, in the same field of endeavor, teaches the textual similarity is a Jaccard similarity (Paragraph 66 of Parker, “The Jaccard coefficient may be used in creating similarity matrices, as in the exemplary embodiments disclosed herein; nevertheless, in various embodiments of the invention other coefficients and methodologies known to those skilled in the art could (additionally or alternatively) be used to determine similarity, dissimilarity, correlation, or geometric distance.” Paragraph 86, “The first and second frequency arrays can then be converted to a similarity matrix, for example using the Jaccard coefficient (see FIG. 4, Tables 423 and 413). These similarity matrices can in turn be combined in an entrywise manner using a continuous function, for example multiplication (see FIG. 4, Table 430).”, Paragraphs 92-93, “Each of the frequency matrices was converted to a simple binary matrix (FIG. 4, Table 421) in which the names or patent classifications were scored as present or absent. Similarity matrices were then produced using the Jaccard coefficient (FIG. 4, Table 413, and FIG. 4, Table 423) and then examined by various well-known methods to determine the possible relationships among the patents... Simple associations (e.g., names vs. patent, names vs. classification, classifications vs. patent) could be directly derived from the captured data.”
Parker teaches converting frequency arrays derived from document terms and patent classification codes into a similarity matrix using the Jaccard coefficient where the Jaccard coefficient measures the overlap between the term and code sets of the documents.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan in view of Sun in view of Li in view of Malakouti in further view of Wu’s teaching with Parker’s Jaccard coefficient in order to compute the textual similarity between codes using a simple and effective overlap measure to improve the accuracy of the inter code similarity values (Paragraphs 31 and 86 of Parker).
Regarding claim 22,
Duan in view of Sun in view of Li in view of Malakouti and in further view of Wu does not teach generating a heatmap representation of the similarity matrix, wherein the code similarity value is represented by a color intensity.
Parker, in the same field of endeavor, teaches generating a heatmap representation of the similarity matrix, wherein the code similarity value is represented by a color intensity (Paragraph 68 of Parker, “A heatmap includes grayscale, color gradient or color-mapped matrices in which the magnitude of the underlying data is reflected in the shading or color scheme. Unlike other graphical techniques that are used to visualize high-dimensional data, heatmaps scale well and introduce no distortion into the underlying data.”, Paragraph 86, “To facilitate visualization and analysis of the relationships between the various documents, the clustered results can be displayed in various ways including heatmaps (e.g., a matrix such as that shown in FIG. 4, Table 430 would be translated to a heatmap by depicting the values as colors or grayscales).”, Paragraph 95, “Similarity was estimated by the Jaccard coefficient and the matrix was reordered by single-linkage clustering along both axes prior to visualization. The matrix is symmetric with nearest neighbors appearing close to the diagonal. Shading is based on similarity, as shown in the key to the right side. Clusters of similar patents appear as light colored blocks.”
Parker teaches rendering the Jaccard similarity matrix as a heatmap in which the magnitude of each similarity value is reflected in the shading or color so that the cell color encodes the similarity between the corresponding documents and their codes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Duan in view of Sun in view of Li in view of Malakouti in view of Wu in further view of Parker’s heatmap representation in order to visually depict the code similarity values as color intensity so that inter code similarities and clusters are interpretable (Paragraphs 68 and 95 of Parker).
Conclusion
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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