DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 limitation(s) is/are: network unit in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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.
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 1-13 are rejected under 35 U.S.C. 101 because of the following analysis:
1 – statutory category: Claim 1-11 recite a series of steps and therefore, falls under the statutory category of being a process. See MPEP 2106.03. Claims 12 and 13 recite a system, and therefore, falls under the statutory category of being a thing or products. See MPEP 2106.03.
2A – Prong 1: The independent claims 1, 12-13 recite a judicial exception by reciting the limitations of “obtaining heart data including a heart signal measured within a first reference time; and generating heart rate variability for a second reference time, which is a value larger than the first reference time, based on the heart data”. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in mind or by a person using a pen and paper. Therefore, an abstract idea is involved.
It is noted that the act of using a pre-trained (first, second, third, etc.) deep learning model is equivalent to using an equation and falls under the judicial exception of mathematical calculations.
Additionally and/or alternatively, using a pre-trained model, under its broadest reasonable interpretation, is understood to be the same as using a generic model. In other words, using a generic model is simply applying an abstract idea on a computer. See 2106.05(f).
2A – Prong 2: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The independent claims 1, 12-13 recite the additional limitations of “computing device”, “processor”, “model”, etc. The mentioned limitations are recited at a high level of generality and are considered to be data gathering/processing which are mere extra-solution activity. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
2B: The emphasized elements cited above do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’I, 110 USPQ2d 1976 (2014)).
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Claims 2-11 depend on claim 1. The mentioned dependent claims recite the same abstract idea as the independent claims. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). For example, the dependent claim recites the limitations “copmuting a mean”, “predicting a probability distribution”, “computing values”, “computing the values included in the heart rate variability for the second reference time”, “computing the values included in the heart rate variability for the second reference time by performing random sampling []”, “determining whether the predicted probability distribution corresponds to a normal distribution; selecting one of a plurality of sampling techniques for extracting the heart rate variability for the second reference time based on a result of the determination; and computing values included in the heart rate variability for the second reference time by performing []”, “rearranging an order of the values []”, “generating an attention map indicating correlations []”, etc., are recited at a high level of generality and are mere extra-solution activity, and recited as performing generic computer functions. i.e., data processing. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)).
The additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Thus, claims 1-13 are directed to an abstract idea and are therefore rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The examiner contends that applicant fails to provide adequate written description for the breadth of the claim.
Specifically, the limitation “pre-trained model” in claims 1-13 are broad enough to encompass any way imaginable that an artificial intelligent algorithm could work. Additionally, the specification does not provide any algorithm regarding how the pre-trained model was trained or works. The specification generally recites “a neural network "model" may refer to an overall system implemented as a neural network that is provided with problem-solving capabilities through training”. However, the specification does not shed any light on any algorithms or training. Therefore, since the “pre-trained” model is claimed to perform the steps, the model itself becomes of importance.
Claim 2 states “the heart data further includes at least one of biological information, disease information, or physical activity information of a person from whom the heart signal was measured” which is broad enough to encompass any/all biological information, any/all disease information and any/all physical activity information. The specification does not provide sufficient details regarding this limitation.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 1-2, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over single reference to US20110224565A1 to Ong et al. (hereinafter “Ong”).
Regarding [Claim 1, 12 and 13] Ong discloses a method, a computer program stored in a computer- readable storage medium, the computer program performing operations, a computing device of generating long-term heart rate variability based on a short-term measured heart signal (figs 6-8, para 0172-0217), the method being performed by a computing device including at least one processor (para 0052, 0059 0153, “device”, “processing unit”, “memory”, etc. fig. 4), the method comprising: obtaining heart data including a heart signal measured within a first reference time (para 0172 “raw ECG data”, para 0173-0174, ); and generating heart rate variability for a second reference time, which is a value larger than the first reference time, based on the heart data by using (para 0177 “de-noised ECG signal is obtained which is used for further processing to detect QRS and calculate HRV measures”; it is noted that the claim does not provide any details regarding the specific windows, length of windows, and/or how the reference times are shorter/longer. Under its broadest reasonable interpretation, any signal that is filtered to remove outliers could be argued to be shorter than the window that is represented. Here, Ong provides de-noising the signal acquired (withing an arbitrary window) to calculate HRV, which is HRV of the whole window)) a pre- trained deep learning model (para 0085, 0118-0136, etc.).
Regarding [Claim 2] Ong discloses the method of claim 1, wherein the heart data further includes at least one of biological information, disease information, or physical activity information of a person from whom the heart signal was measured (para 0059, 0074-0075, etc.).
Claim(s) 3-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ong as applied to claims above, and further in view of US 20190038148 to Valys et a. (hereinafter “Valys”).
Regarding [Claim 3] Ong discloses the method of claim 1, wherein generating the heart rate variability for the second reference time, which is the value larger than the first reference time, based on the heart data by using the pre-trained deep learning model comprises: computing a mean and a standard deviation for the heart rate variability for the second reference time (para 0069) by inputting the heart data to a pre-trained first model (para 0085, 0118-0136, etc.); but fails to disclose predicting a probability distribution for the heart rate variability for the second reference time based on the mean and the standard deviation; and computing values included in the heart rate variability for the second reference time performing sampling based on the predicted probability distribution.
Valys from a similar field of endeavor teaches the trained model to provide a probability distribution for the predicted health-indicator (para 0029, 0036, 0038, 0056, etc.) and wherein the probability distribution function is sampled to select a predicted health-indicator value (para 0072). 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 disclosure of Ong with the known teachings of Valys to provide the predictable result of sampling the probability distribution function to predict the health indicator value.
Regarding [Claim 4] Ong as modified by Valys renders obvious the method of claim 3, wherein computing the values included in the heart rate variability for the second reference time by performing the sampling based on the predicted probability distribution comprises: when the predicted probability distribution corresponds to a normal distribution, computing the values included in the heart rate variability for the second reference time by performing random sampling on the predicted probability distribution (para 0029, 0036, 0038, 0056, etc.).
Regarding [Claim 5] The method of claim 3, wherein computing the values included in the heart rate variability for the second reference time by performing the sampling based on the predicted probability distribution comprises: when the predicted probability distribution does not correspond to a normal distribution, performing modeling on the predicted probability distribution; and computing the values included in the heart rate variability for the second reference time by performing random sampling on a probability distribution generated through the modeling (para 0036).
Regarding [Claim 6] The method of claim 5, wherein the modeling performed on the predicted probability distribution includes a Gaussian mixture model or a normalizing flow (para 0036).
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ong as applied to claims above, and further in view of US 20230200664 to Peters. (hereinafter “Peters”).
Regarding [Claim 10] The method of claim 1, wherein generating the heart rate variability for the second reference time, which is the value larger than the first reference time, based on the heart data by using the pre-trained deep learning model comprises: computing the values included in the heart rate variability for the second reference time by inputting the heart data to a third model, (para 0085, 0118-0136, etc.) but fails to explicitly disclose the model is a pre-trained generative model.
Peters, from a similar field of endeavor teaches that it is known for the neural networks to be used interchangeably and to use generative networks (para 0207). 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 disclosure of Ong with the known teachings of Peters to provide the predictable result of using various neural networks as desired.
Regarding [Claim 11] The method of claim 1, wherein generating the heart rate variability for the second reference time, which is the value larger than the first reference time, based on the heart data by using the pre-trained deep learning model comprises: computing the values included in the heart rate variability for the second reference time by inputting the heart data to a fourth model (para 0085, 0118-0136, etc.) but fails to explicitly disclose the model is a pre-trained sequence-to-sequence model.
Peters, from a similar field of endeavor teaches that it is known for the neural networks to be used interchangeably and to use sequence-to-sequence model (para 0207). 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 disclosure of Ong with the known teachings of Peters to provide the predictable result of using various neural networks as desired.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 105769222; US 20220125376; US 20210282692 A1; US 20190059756.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANA SAHAND whose telephone number is (571)272-6842. The examiner can normally be reached M-Th 8:30 am -5:30 pm; F 9 am-3 pm.
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/SANA SAHAND/Examiner, Art Unit 3796
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