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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/9/26 has been entered.
Status of Claims
Claims 1-9 and 22 are rejected. Claims 10-20 and 21 are canceled.
Response to Arguments
Specification Objections
Applicant asserts there is support for the claim limitation “hidden sates” in ¶55 and ¶59. While ¶55 and ¶59 disclose a hidden Markov model and discuss states within the hidden Markov model, there is no disclosure of “hidden states” as claimed. Applicant is encouraged to change the claim limitation to match the language in the specification.
Applicant asserts there is support for the claim limitation “a new subject” in ¶61-63. However, the Examiner disagrees. A new subject is not recited in these paragraphs, and there is no disclosure to suggest “a test subject” is the same thing as “a new subject.” Applicant is encouraged to change the claim limitation to match the language in the specification.
Applicant asserts there is support for the claim limitation “a threshold ratio” in ¶16, ¶59, and ¶55. While ¶16 discloses a threshold proportion, there is no disclosure of “a threshold ratio” as recited in the claim. Applicant is encouraged to change the claim limitation to match the language in the specification.
Claim Rejections - 35 USC § 101
Applicant's arguments filed 4/9/26 have been fully considered but they are not persuasive.
Applicant asserts that the Office Action inappropriately rejects claim 1, because the claims cannot practically be performed in the human mind, and thus does not recite a mental process. However, the Examiner disagrees. Under the broadest reasonable standard, the limitations directed to the abstract idea are nothing more than a medical professional defining features on a paper based on the triaxial accelerometer data, drawing clusters of the data based on the features, determining a cluster assigned for the features, fitting the cluster assignments to generate a plurality of hidden states that reduce noise in the cluster assignments, identifying hidden states as sleep states based on a frequency of occurrence of each of the hidden states, a hidden state is identified as a sleep states bed on the frequency of occurrence being above a threshold ratio, and classifying a sleep or wake status of a subject based on print outs of triaxial accelerometer data from a new subject.
Applicant asserts that the Office Action’s characterization of the claimed steps as “nothing more than a medical professional defining features on a paper” is exactly the kind of oversimplification that the Desjardins Memo warns against. However, the Examiner disagrees. A medical professional is capable of receiving a print out of triaxial accelerometer data and analyzing the data for features.
Applicant cites to Research Corp. Techs. v. Microsoft Corp., 627 F.3d at 868 where the court held nothing abstract in the subject matter as claimed and the invention presented functional and palpable applications in the field of computer technology. Applicant further asserts that claim 1 of the instant application similarly recites manipulating specific data structures, including accelerometer feature sets, cluster assignments, hidden Markov model states, and annotated timelines, through a multi-step process to produce a trained classifier. However, the Examiner disagrees. Unlike in Research Corp. Techs, where the court found nothing abstract in the subject matter, there are limitations directed to the abstract idea as shown in further detail in the rejection below.
Applicant asserts that the human mind is not equipped to define features for each time interval across multiple subjects, cluster those features, fit an HMM to the resulting clustering assignment sequences to generate hidden states that reduce noise, and then calculate the frequency of occurrence of each hidden state during annotated sleep periods across all subjects. However, the Examiner disagrees. A medical professional is capable of performing the steps using pen and paper. The hidden Markov model is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. The claims do not specify the size of the data and the amount of time the steps are performed. Even if the claims specified the timeframe the steps are performed, MPEP 2106.05(f) states:
Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Applicant asserts that the specification identifies a concrete improvement to how the classifier is trained. However, the Examiner disagrees. The alleged improvement to how the classifier is trained is directed to how to do the data analysis rather than something about how the artificial intelligence itself is configured or coded. The Examiner cites Applicant to Example 47, claim 2 of the July 2024 Subject Matter Eligibility Examples. Example 47, claim 2 recites training a model and was found to be ineligible.
Applicant asserts that claim 1 as amended includes the steps that provide the improvement described in the specification. Applicant additionally asserts that claim 1 as amended also explicitly recites the improvement itself. However, the Examiner disagrees. The alleged improvement to how the classifier is trained is directed to how to do the data analysis rather than something about how the artificial intelligence itself is configured or coded.
Applicant asserts that the claims provide an inventive concept under Step 2B to represent a non-conventional and non-generic arrangement. Applicant further asserts that the identifying step is not merely data gathering or insignificant post-solution activity. However, the Examiner disagrees. The computing device is recited at a high level of generality and amounts to a part of a generic computer. The identifying step is related to the hidden Markov model, which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. Therefore, the identifying step is directed to the abstract idea and is not an additional element.
Claim Rejections - 35 USC § 103
Applicant’s arguments, see Remarks, filed 4/9/26, with respect to claims 1-9 and 22 have been fully considered and are persuasive. The 103 rejection of claims 1-9 and 22 has been withdrawn. See the Examiner’s Note section below for further details.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
¶59 of the specification discloses: “while the clustering assignments themselves can be very noisy across subjects, sleep and wake are distinguishable by the frequency distributions of the specific cluster labels, which can be captured as the emission probabilities of different states in an HMM,” however the specification does not provide proper antecedent basis for the limitation “that reduce noise in the cluster assignments across the plurality of subjects for accurately determining sleep or wake status” in claim 1.
¶16 of the specification discloses “the at least one state is identified as a sleep state when the at least one state occurs beyond a threshold proportion during ground truth sleep” and ¶55 of the specification discloses “sleep states can be identified as states that occur a certain proportion (e.g., 60-40, 60%) during sleep as compared with awake based on the ground truth annotations.” However, the specification does not provide proper antecedent basis for the limitation “wherein a hidden state is identified as a sleep state if its frequency of occurrence during the sleep periods is above a threshold ratio” in claim 1.
¶55 of the specification discloses “the process 100 can identify ( 108 ) sleep and wake states based on frequency distributions of the clustering assignments. Clustering assignments can be captured as the emission probabilities of different states in an HMM. In accordance with several embodiments of the invention, sleep states can be identified as states that occur a certain proportion (e.g., 60-40, 60%) during sleep as compared with awake based on the ground truth annotations” and ¶59 of the specification discloses “sleep and wake are distinguishable by the frequency distributions of the specific cluster labels, which can be captured as the emission probabilities of different states in an HMM.” However, the specification does not provide proper antecedent basis for the limitation “the plurality of hidden states are sleep states based on a frequency of occurrence of each of the hidden states during all sleep periods from the annotated timeline of the triaxial accelerometer training data for the plurality of subjects” in claim 1.
¶6 of the specification discloses “training a classifier to determine sleep and or wake status of a subject based on triaxial accelerometer data” and ¶63 of the specification discloses “hidden Markov modeling to classify sleep vs awake in 24-hour triaxial accelerometer data. In the example data this was performed for 7 subjects using 21 features.” However, the specification does not provide proper antecedent basis for the limitation “thereby enabling the hidden Markov model to classify a sleep or wake status of a new subject based on triaxial accelerometer data from the new subject” in claim 1.
¶49 of the specification discloses “an algorithm (e.g., a classification algorithm) can perform classification of sleep and awake using an amount (e.g., a single day) of triaxial accelerometer data” and ¶55 of the specification discloses “sleep states can be identified as states that occur a certain proportion (e.g., 60-40, 60%) during sleep as compared with awake based on the ground truth annotations.” However, the specification does not provide proper antecedent basis for the limitation “wherein identifying which of the plurality of hidden states are awake states comprises identifying hidden states having a frequency of occurrence during all awake periods from the annotated timeline of the triaxial accelerometer training data based on a threshold ratio” in claim 22.
Claim Objections
Claim 9 is objected to because of the following informalities: “the clustering the features” is incorrect grammar. Applicant is encouraged to change the limitation to recite –the clustering of the features--. Appropriate correction is required.
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:
a computing device in claim 1.
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.
For a computing device in claim 1, the specification discloses “a computing device 800 can include a network interface 802, a processor 804, and a memory 806” (¶76). Therefore, the Examiner is interpreting the computing device to be a network interface, a processor, and a memory, or 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 § 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.
Claim 8 is 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 8, the limitation of “a number of small peak (local maxima) in each axis” seems unclear. It remains unclear whether “(local maxima)” is a limitation or an example.
In claim 8, the limitation of “include at least one item selected from a list including:” seems unclear. It remains unclear what other alternatives are intended to be encompassed by the claim. See In re Kiely, 2022 USPQ2d 532 at 2* (Fed. Cir. 2022) (each independent claim recites "a selection from the group comprising a person, an animal, an animated character, a creature, an alien, a toy, a structure, a vegetable, and a fruit." … (emphasis added). A Markush grouping is a closed group of alternatives, i.e., the selection is made from a group "consisting of" (rather than "comprising" or "including") the alternative members. Abbott Labs., 334 F.3d at 1280, 67 USPQ2d at 1196. See MPEP 2173.05(h). Applicant is encouraged to change the limitation of “include at least one item selected from a list including:” to recite –include at least one item selected from the list consisting of:--.
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 1-9 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea without significantly more.
Step 1
The claimed invention in claims 1-9 and 22 are directed to statutory subject matter as the claims recite a process for training a classifier to determine sleep and or wake status of a subject.
Step 2A, Prong One
Regarding claim 1, the recited steps are directed to a mental process of performing concepts in a human mind or by a human using a pen and paper (see MPEP 2106.04(a)(2) subsection (III)).
Regarding claim 1, the limitations of “for each of the plurality of subjects, defining…a set of features for each of a plurality of time intervals of the triaxial accelerometer training data; clustering…the sets of features of the triaxial accelerometer training data into a number of clusters to obtain a cluster assignment for each of the sets of features of each of the plurality of subjects; fitting…a hidden Markov model to the triaxial accelerometer training data cluster assignments to generate a plurality of hidden states that reduce noise in the cluster assignments across the plurality of subjects for accurately determining sleep or wake status; and identifying…which of the plurality of hidden states are sleep states based on a frequency of occurrence of each of the hidden states during all sleep periods from the annotated timeline of the triaxial accelerometer training data for the plurality of subjects, wherein a hidden state is identified as a sleep state if its frequency of occurrence during the sleep periods is above a threshold ratio, thereby…to classify a sleep or wake status of a new subject based on triaxial accelerometer data from the new subject” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional defining features on a paper based on the triaxial accelerometer data, drawing clusters of the data based on the features, determining a cluster assigned for the features, fitting the cluster assignments to generate a plurality of hidden states that reduce noise in the cluster assignments, identifying hidden states as sleep states based on a frequency of occurrence of each of the hidden states, a hidden state is identified as a sleep states bed on the frequency of occurrence being above a threshold ratio, and classifying a sleep or wake status of a subject based on print outs of triaxial accelerometer data from a new subject.
Step 2A, Prong Two
For claim 1, the judicial exception is not integrated into a practical application. In particular, claim 1 recites “obtaining, at a computing device, triaxial accelerometer training data and an annotated timeline of the triaxial accelerometer training data for a plurality of subjects.” The step of obtaining triaxial accelerometer data amounts to pre-solution activity of data gathering. The computing device is recited at a high level of generality and amounts to a part of a generic computer. The steps of defining, clustering, fitting, and identifying are related to the hidden Markov model, which are nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into
a practical application, the additional element of obtaining triaxial accelerometer data amounts to nothing more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. Moreover, obtaining triaxial accelerometer data is well-understood, routine, and conventional activity as evidenced by US 20100131227 (¶2-a conventional tri-axial accelerometer measures acceleration in a three-dimensional space), US 20120176309 (¶5-a conventional three dimensional (3D) pointing device 1, such as a 3D mouse, that includes a three-axis accelerometer 11), and US 20140117059 (¶8-a conventional triaxial accelerometer). Further, 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)).
Regarding dependent claims 2-9 and 22, the limitations of claim 1 further define the limitations already indicated as being directed to the abstract idea.
Claims 2-4 and 7-9 further define the details of the machine learning model, which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting.
Claims 5-6 are further directed to the abstract idea.
Claim 22 is directed to the abstract idea. The limitation of “identifying which of the plurality of hidden states are awake states, wherein a hidden state is identified as an awake state if its frequency of occurrence during all awake periods is above a threshold ratio” is a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, this limitation is nothing more than a medical professional analyzing print outs of data to determine an awake state if its frequency of occurrence during all awake periods is above a threshold ratio.
Examiner’s Note
Claims 1-9 and 22 distinguish over the prior art but are still rejected under 35 USC § 101. Claim 8 is additionally rejected under 35 USC § 112.
The following is a statement of reasons for the indication of allowable subject matter:
Applicant asserts that Nguyen’s algorithm does not generate hidden states and then go through each one to calculate how frequently it occurs during annotated sleep periods to identify sleep states. This was found to be persuasive.
Applicant further asserts that Li teaches away from the claimed approach, as Li analyzes each individual separately. This was found to be persuasive.
The closest prior art of record is Nguyen (NPL “Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry” published in 2007); however it fails to recite “generate a plurality of hidden states that reduce noise in the cluster assignments across the plurality of subjects for accurately determining sleep or wake status”; and “identifying, using the computing device, which of the plurality of hidden states are sleep states based on a frequency of occurrence of each of the hidden states during all sleep periods from the annotated timeline of the triaxial accelerometer training data for the plurality of subjects, wherein a hidden state is identified as a sleep state if its frequency of occurrence during the sleep periods is above a threshold ratio.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180000408: the classification assigned to a measurement time interval may also depend on the feature values from surrounding time intervals. For example, the classifications may be based on the most likely state in a time dependent model, such as a hidden Markov model, that takes account of the likelihood of transitions between different states and relates states to the likelihood of observed feature values (¶28).
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/LAURA HODGE/Examiner, Art Unit 3792
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