DETAILED ACTION
This non-final office action is in response to applicant’s response to restriction filed May 11, 20216. Claims 15-36 were canceled. Claims 1-14 are being examined and are pending.
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 .
Election/Restrictions
Applicant’s election without traverse of Group 1 (claims 1-14) in the reply filed on 05/11/2026 is acknowledged.
Information Disclosure Statement
The information disclosure statement filed 08/09/2023 and 01/12/2024 has been placed in the application file and the information referred to therein has been considered as to the merits.
Drawings
The drawings filed on 085/09/2023 have been accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter, which the applicant regards as his invention.
Claims 1-9 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 14 recite,
“a device configured to..., a nanosecond optimized configured to…, a tree flattener configured to…, a tree merger configured to…, a score normalizer configured to…, a tree remover configured to…, a cut eraser configured to…, a converter configured to…, a forest merger configured to…”
Reason for rejection:
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification, as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a device configured to..., a nanosecond optimized configured to…, a tree flattener configured to…, a tree merger configured to…, a score normalizer configured to…, a tree remover configured to…, a cut eraser configured to…, a converter configured to…, a forest merger configured to…” in claims 1 and 14. Figure 2 shows Tree flattener, Forest Merger, Score Finder, Score Normalizer, Tree Remover, Cut Eraser. The disclosure recites “The system includes a device 10…the device may be a software (e.g., fwXmachina) but the claim did not limit the claimed device software only and nowhere in specification clearly mentioned corresponding hardware structure or hardware with algorithm for the above phrases that invoke 112(f).
“A general-purpose computer is usually only sufficient as the corresponding structure for performing a general computing function (e.g., “means for storing data”), but the corresponding structure for performing a specific function is required to be more than simply a general-purpose computer or microprocessor.” (MPEP 2181)
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Dependent claims 2-9 do not cure the deficiencies set forth above.
Examiner’s Notes:
**Applicant can amend the claim limitations in such a way so they do not invoke §112 (f) or keep the same limitations but point out the corresponding structure in the specification to overcome the §112 (b) rejection. Corresponding structure must be more than simply a general-purpose computer. Algorithm is needed to transform a general-purpose computer or microprocessor to a specific computer for performing the claimed function. “A general-purpose computer is usually only sufficient as the corresponding structure for performing a general computing function (e.g., “means for storing data”), but the corresponding structure for performing a specific function is required to be more than simply a general-purpose computer or microprocessor. For example, mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239; Finisar, 523 F.3d at 1340-41, 86 USPQ2d at 1623. In addition, merely referencing a specialized computer (e.g., a "bank computer"), some undefined component of a computer system (e.g., "access control manager"), "logic," "code," or elements that are essentially a black box designed to perform the recited function, will not be sufficient because there must be some explanation of how the computer or the computer component performs the claimed function. Blackboard, Inc. v. Desire2Learn, Inc., 574 F.3d 1371, 1383-85, 91 USPQ2d 1481, 1491-93 (Fed. Cir. 2009); Net MoneyIN, Inc. v. VeriSign, Inc., 545 F.3d 1359, 1366-67, 88 USPQ2d 1751, 1756-57 (Fed. Cir. 2008); Rodriguez, 92 USPQ2d at 1405-06.” (MPEP 2181).
** Applicant is reminded to include hardware, or a combination of hardware and software, to avoid a possible rejection under 35 U.S.C. § 101.
If all claim language invoking § 112(f) is interpreted as software-only, a system/machine/apparatus/device claim lacking sufficient hardware structure may be considered directed to non-statutory subject matter under 35 U.S.C. § 101 because the recited functions would not be tied to concrete hardware implementation.
Allowable Subject Matter
Claims 1-14 are allowed over prior arts.
Claims 1-9 and 14 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Prior Art US 11176471 B1 (DeCaprio et al.) has been found to teach “(31) Generally, the machine learning model 108 can be any model having a set of trainable parameters that can be trained on a set of training examples to optimize an objective function. A training example can include: (i) a feature representation, and (ii) a target output that should be generated by the machine learning model by processing the feature representation. The objective function can measure, e.g., an error between predictions generated by the machine learning model and corresponding target outputs, e.g., a cross-entropy error, a squared-error, or any other appropriate error. The objective function can optionally include one or more “regularization” terms to stabilize and enhance the training of the machine learning model, e.g., a regularization term that measures an L.sub.1 or L.sub.2 norm of the parameter values of the machine learning model. Training the machine learning model on the training examples can encourage the machine learning model to generate predictions that match the target outputs specified by the training examples. To provide a few non-limiting examples, the machine learning model could include one or more of: a linear model, a gradient boosted decision tree model, a neural network model, random forest model, a support vector machine model, or any other appropriate model.”
Prior Art WO 2017/096358 A1 (Cromwell et al.) has been found to teach “a new decision tree is generated to optimize classification of the outcome based on the new case weights. The mis-classified cases again have their weights boosted, and a new decision tree is generated. This approach is repeated iteratively, typically hundreds or thousands of times, until an optimal boosted tree is identified. This boosted decision tree is then applied to the validation data set, and cases in the validation data set are classified as responders or non-responders. Many other known approaches can be used, such as [0108] It should be noted that the boosted tree machine learning approach as well as any of the more sophisticated tree generating approaches, may produce very complex algorithms (containing many if-then conditions), as has been previously described. Instead, the selection of variables used as inputs into any of the regression and classification tree techniques to generate an algorithm and/or the relative importance of the variables also uniquely identify the algorithm.”
Prior Art WO 2024107412 A1 (Nair et al.) has been found to teach “[0025] Use of machine learning (ML) in developing risk scores for HF mortality seems to have an edge over conventional methods. For instance, The MARKER-HF score has a c-statistic of 0.88 and has been validated in 2 external study cohorts. This model used a boosted decision tree algorithm to derive a model based on automated training using two well defined cohorts - the low and high groups. In another study, telemetry data analyses from a wearable monitor used a general machine learning similarity-based modeling to predict HF hospitalization. Receiver operating characteristic curves showed a c-statistic of 0.86-89 using the analytics platform. The alert from such prediction models could help clinicians intervene before a HF hospitalization occurs. Prediction of mortality post LVAD implantation in general has been attempted using Bayesian network analysis with a c-statistic of 0.7 for 1-, 3- and 12-month mortality. [0026] Applications of machine learning algorithms to assess tricuspid annulus excursion on 2- dimensional (2D) and 3-dimensional (3D) echocardiography have been attempted with considerable success in assessment of RV function. Application of an automated segmented model based on neural network architecture was used in a 2D echo image analysis. An ML algorithm was trained and tested in a 6-fold cross validation approach. Tricuspid annular- displacement measurements using manual and automated ML segmentation showed that the automated approach was comparable to MRI data. The ROC curves used to test the model showed a c-statistic of 0.69- 0.73 in a small population studied. The ML driven assessment used a deep learning framework and was time efficient with a processing time of less than 1 second. In another study, ML based algorithms using 3D echocardiographic images, RV volumes, and ejection fraction measurements were made with optimal reproducibility, suggesting that automated analysis of data may be more efficient.”
Independent claim 1 identifies the following combination of features:
Providing a boosted decision tree (BDT) for use on an electronic device to provide an event score based on a user input event, comprising:
a device configured to optimize nanosecond execution of a machine learning algorithm, wherein the device comprises:
a. a machine learning trainer configured to create a trained BDT from an untrained BDT by determining parameters for the untrained BDT;
b. a nanosecond optimizer configured to optimize the trained BDT to create an optimized BDT, the nanosecond optimizer comprising at least one of:
i. a tree flattener configured to flatten a plurality of vertical layers of a decision three into one layer,
ii. a tree merger configured to merge a plurality of flattened decision trees into one tree,
iii. a score normalizer configured to normalize an event score of a bin of a flattened tree,
iv. a tree remover configured to remove one or more flattened decision trees in accordance with a user specification, or
v. a cut eraser configured to erase a cut between bins within a flattened decision tree in accordance with the user specification; and
c. a converter coupled to the nanosecond optimizer and configured to receive the optimized BDT from the nanosecond optimizer and convert the optimized BDT to a language for high-level-synthesis to produce a hardware description language representation of the optimized BDT, wherein the hardware description language representation of the optimized BDT is structured and configured to be implemented in firmware provided on the electronic device to enable the electronic device to determinate and output an event score based on a user input event.
Independent claims 9 and 15, although different recite similar limitations to those found in claim 1.
None of the prior arts taken alone or in combination teach the above limitations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAWNCHOY RAHMAN whose telephone number is (571)270-7471. The examiner can normally be reached Monday - Friday 8:30A-5P ET.
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/Shawnchoy Rahman/Primary Examiner, Art Unit 2438
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