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 .
Priority
Priority documents received 10/30/24.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1- Claim 1
Claim 1 and dependent claims 2-10 are drawn to a system and thus meet the requirements for step 1.
Step 2a (prong 1) - Claim 1
Claims 1 recites the step of “issue an alarm when multiple predications indicate a risk of atrial fibrillation” Under the broadest reasonable interpretation, this step covers a concept capable of being performed in the human mind, and thus falls within the mental processes grouping of abstract ideas. Other than reciting the method is “computer-implemented” in the preamble, nothing in the claim precludes the step from practically being performed in the mind.
Accordingly, claim 1 recites an abstract idea.
Step 2a (prong 2) – Claim 1
The judicial exception is not integrated into a practical application. Claim 1 recites the additional elements of:
A data acquisition module configured to perform continuous electrocardiogram monitoring is insignificant extra-solution activity (i.e., data gathering),
A data processing module configured to preprocess electrocardiogram data is insignificant extra-solution activity (i.e., data gathering/statistics),
An AI analysis module configured to train and analyze electrocardiogram data is recited at a high level of generality (i.e., as generic devices, a “computer-implemented” method, performing generic computer functions like sending, receiving, and visually displaying data) is insignificant extra-solution activity (i.e. data processing using generic computer functions).
An alarm mechanism is recited at a high level of generality (i.e., as generic devices, a “computer-implemented” method, performing generic computer functions like sending, receiving, and visually displaying data) is insignificant extra-solution activity (i.e., data output), and
A clinical application module configured to provide real-time warnings is recited at a high level of generality (i.e., as generic devices, a “computer-implemented” method, performing generic computer functions like sending, receiving, and visually displaying data) is insignificant extra-solution activity (i.e., data output).
These steps do not integrate the abstract idea into a practical application because they are insignificant extra solution activity.
Step 2b- Claim 1
The additional elements when considered individually and in combination are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, providing a (real time warning) is recited at a high level of generality (i.e., as generic devices, a “computer-implemented” method, performing generic computer functions like sending, receiving, and visually displaying data). Further, and AI analysis module is considered data gathering/statistics. It is noted that the analysis module is recited at a high level of generality and is not utilized by the claim beyond the fact that it is included in the system.
The additional elements that were considered insignificant extra solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine and conventional when considered individually and in combination with evidence provided. Specifically:
A data acquisition module configured to perform continuous electrocardiogram monitoring is well understood, routine, and conventional (i.e., receiving data MPEP 2106.05(d)(II)).
A data processing module configured to preprocess electrocardiogram data is well-understood routine and conventional (i.e., gathering data/statistics MPEP 2106.05(d)(II)).
An AI analysis module configured to train and analyze electrocardiogram data is well-understood routine and conventional (i.e., gathering data/statistics MPEP 2106.05(d)(II)).
An alarm mechanism is considered to be well-understood, routine, and conventional (i.e., presenting data MPEP 2106.05(d)(II)).
A clinical application module configured to provide real-time warnings is considered to be well-understood, routine, and conventional (i.e., presenting data MPEP 2106.05(d)(II)).
Claim 1 is thus consider to be directed to an abstract idea without significantly more.
Claims 2-10 depend from claim 1. The type of data analyzed as stated in claims 2-10 are considered extra solution activity. Thus, the dependent claim do not change the overall analysis that claims 2-10 are also directed to an abstract idea.
Claim Rejections - 35 USC § 112(a)
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 1-10 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 claims are directed toward all machine learning or deep learning engines. The specification includes a generic placeholder that states deep learning model but fails to list or describe a single known model, let along all known and unknown models as claimed. Therefore the specification does not include support for the claimed limitations. Further the specification is silent as to the exact model and the training process for each model. The specification fails to include a single known model, and further fails to explicitly state how a single one is trained with any detail needed to accurately recreate the claimed invention. Therefore, the claimed subject matter was not described in the specification in such a way as to reasonably convey to one skilled in the art that the Applicant’s had possession of the claimed invention.
Claims 1-10 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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The claims generically state the use of deep learning model and generically state that the engines are trained but fails to explicitly state the exact engine, the exact parameters for the engine and the exact data and how it is inputted into the exact engines. Therefore, one skilled in the art would not be able to make and/or use the invention. Factors to be weighed when evaluating whether a disclosure satisfies the enablement requirement and whether any necessary experimentation is “undue” (i.e., “Wands” factors): In this case the claims are extremely broad and include, without support, all known deep learning models. Currently there are numerous known deep learning models each having separate and unique configurable parameters that need to be configured in order for the engines/models to work. A single engine/model has an almost infinite number of configurations. The specification fails to provide any support for the configuration of even a single engine/model. Therefore, the claiming that the data is input into a deep learning model, without knowing the engine/model, the parameters of the engine/model, the exact training parameters and the exact data along with the data parameters that are inputted into the engine, creates an infinite number of possibilities, creating an infinite amount of experimentation needed to recreate the claimed invention. Without knowing the exact engine, the exact parameters and configuration of that engine, the exact training procedure and exact training data parameters, there is a lack of enablement for the claimed invention.
Claim Rejections - 35 USC § 112(b)
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 1-10 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "multiple predictions" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "traditional clinical evaluations" in line 11. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-10 each recite the limitation “a dynamic electrocardiogram” in line 1. This is vague as it is unclear if the “a dynamic electrocardiogram” in each claim is the same or different as the “a dynamic electrocardiogram” from claim 1, line 1.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 3 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 2 states that the data acquisition module requires patients without a history of AF to wear the recorder for 7 days. While Claim 3 that depends from claim 2 requires all patients even those with a history to wear the recorder for 7 days. Since claim 3 is broader than claim 2, it fails to further limit the claim from which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 7 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 6 states that during training a large-scale dataset is utilized for training. While Claim 7 that depends from claim 6 states that the system utilizes the electrocardiogram data. Since claim 7 depends from claim 6, and requires a different data set than the parent claim, the claim fails to further limit the claim from which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 9 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 8 states that the warning assessments are every 5 minutes. While Claim 9 that depends from claim 8 requires the assessments at every 10 minutes. Since claim 9 is broader than claim 8, it fails to further limit the claim from which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-6 and 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bashour et al. (U.S. Pub. 2008/0167567 hereinafter “Bashour”).
Regarding claim 1, Bashour discloses an artificial intelligence (AI)-based atrial fibrillation warning system using a dynamic electrocardiogram (e.g. see Fig. 1), comprising: a data acquisition module configured to perform continuous electrocardiogram monitoring using a portable dynamic electrocardiogram recorder (e.g. 60); a data processing module configured to preprocess electrocardiogram data (61, 62, 63); an AI analysis module configured to train and analyze the preprocessed electrocardiogram data using a deep learning model (e.g. see Fig. 5; Deep learning AI); an alarm mechanism module configured to issue an alarm when multiple predictions indicate a risk of atrial fibrillation (e.g. ¶62; issue alarm when atrial fibrillation risk reaches threshold level); and a clinical application module configured to provide real-time warning assessments that are combined with traditional clinical evaluations (e.g. 324, ¶62; display or alert clinician to alarms and warnings).
Regarding claim 4, Bashour further discloses wherein the data processing module is connected to the data acquisition module (e.g. see Fig. 2); in the data processing module, the collected electrocardiogram data undergoes preprocessing, comprising noise removal and signal interference elimination (e.g. ¶14; filtering), and the electrocardiogram data is segmented to ensure independence and representativeness of each data segment (e.g. ¶19; segmentation).
Regarding claim 5, Bashour further discloses wherein during segmentation of the electrocardiogram data, the continuous electrocardiogram data is divided into independent segments, wherein each segment contains a plurality of heartbeat signals to enable the deep learning model to identify electrocardiogram features in different states (e.g. ¶¶14, 19; segmentation), and each segment is labeled to indicate whether an atrial fibrillation event is present, thereby forming a training set and a validation set (e.g. ¶¶14, 19; determine atrial fibrillation).
Regarding claim 6, Bashour further discloses wherein the data processing module is connected to the AI analysis module (e.g. see Fig. 5; AI module); the artificial intelligence analysis module optimizes sensitivity and accuracy using a receiver operating characteristic curve pattern and an F1 score pattern, respectively (e.g. ¶¶27-30; pattern and curve matching); during a training phase of the AI analysis module, a large-scale dataset is utilized for training the deep learning model, ensuring that the deep learning model possesses high sensitivity and specificity (e.g. ¶¶27-30; deep learning dataset).
Regarding claim 10, Bashour further discloses wherein in the clinical application module, the system continuously monitors and evaluates the electrocardiogram data of the patients in real time, providing reliable warning information in conjunction with the traditional clinical evaluations (e.g. ¶62); medical personnel make timely interventions and adjust medication treatment plans based on the warning information (e.g. ¶¶20, 23; continuous evaluations).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zimmerman et al. (U.S. 2022/0378379) – discloses using AI to determine features of ECG including atrial fibrillation (e.g. Figures 1-5).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REX R HOLMES whose telephone number is (571)272-8827. The examiner can normally be reached Monday-Thursday 7:00AM-5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer McDonald can be reached at (571) 270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REX R HOLMES/Primary Examiner, Art Unit 3796
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