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 .
Drawings
The drawings as submitted by Applicant on 10/06/2022 has been accepted.
Disposition of Claims
Claims 1-9 are pending in the instant application. No claims have been added. No claims have been cancelled. Claims 4 and 7 have been amended. The rejection of the pending claims is hereby made non-final.
Claim Rejections - 35 USC § 101
5. 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.
6. Claim 8 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In sum, claim 8 is rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is something “significantly more” than the judicial exception under the MPEP 2106 patentable subject matter eligibility guidance analysis which follows.
Under the MPEP 2106 step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claim is directed to the statutory category of a method. (See, e.g., MPEP §2106.03). Therefore, we proceed to step 2A, Prong 1.
Under the MPEP 2106 step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability. Here, the claim recite the abstract idea of filtering vital sign data obtained by a plurality of sensors by:
performing learning related to an output of vital data indicating vital signs of a subject by using first sensor data acquired from the subject through a first method as learning data and using training data based on second sensor data acquired from the subject in the same period as a period of acquisition of the first sensor data through a second method which is less affected by noise than the first method, wherein the learning further includes performing learning further on the basis of third sensor data which is acquired in the same period as the period of acquisition of the first sensor data and the second sensor data and available as an index indicating a magnitude of influence of the noise occurring in the first sensor data.
Here, the recited abstract idea falls within one or more of the three enumerated MPEP 2106 categories of patent ineligible subject matter, to wit: the category of certain methods of organizing human activity, which includes fundamental economic practices or principles, commercial or legal interactions, and managing personal behavior or relationships or interactions between people (e.g., collecting, dispersing and monitoring of donation funds).
Under the MPEP 2106 step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). Therefore, the claim is directed to an abstract idea.
Under the MPEP 2106 step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as: “machine learning” and “sensor” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming. (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. (See, e.g., MPEP §2106.05 I.A).
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claim as a whole, does not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field (e.g., the field of computer coding technology is not being improved); the claim does not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claim does not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claim does not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., simply claiming the use of a computer and/or computer system to implement the abstract idea). Appropriate correction and/or clarification is required.
Claim Rejections - 35 USC § 102
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 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)(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.
Claims are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Najarian et al (US 2020/0022658).
Regarding claims 1 and 8, the prior art discloses a learning device and method comprising a learning unit that performs learning related to an output of vital data indicating vital signs of a subject by using first sensor data acquired from the subject through a first method as learning data and using training data based on second sensor data acquired from the subject (see at least paragraph [0037] to Najarian et al, wherein vent prediction module may be configured to predict and/or detect one or more medical conditions based on obtained biomedical signals. In various embodiments, event prediction module may be configured to utilize a machine learning algorithm to predict and/or detect one or more medical conditions based on obtained biomedical signals. The event prediction module may be configured to obtain training data comprising a plurality of biomedical signals (“training signals”)) in the same period as a period of acquisition of the first sensor data through a second method which is less affected by noise than the first method (see at least paragraph [0054] to Najarian et al), wherein the learning unit performs learning further on the basis of third sensor data which is acquired in the same period as the period of acquisition of the first sensor data and the second sensor data and available as an index indicating a magnitude of influence of the noise occurring in the first sensor data (see at least paragraph [0073] to Najarian et al).
Regarding claim 2, the prior art discloses the learning device according to claim 1, wherein the third sensor data includes acceleration data acquired with the subject or a device predicted to come into contact with the subject as a detection target, and the learning unit performs learning based on the acceleration data as the third sensor data (see at least paragraph [0033] to Najarian et al, wherein these may include, but are not limited to accelerator operation amount, A.sub.CC, a revolution speed, N.sub.E, of ICE 14 (engine RPM), a rotational speed, N.sub.MS, of the motor 12 (motor rotational speed), and vehicle speed).
Regarding claim 3, the prior art discloses the learning device according to claim 2, wherein the learning unit performs learning based on at least the acceleration data in a direction of gravity as the third sensor data (see at least paragraph [0033] to Najarian et al, wherein these may include, but are not limited to accelerator operation amount, A.sub.CC, a revolution speed, N.sub.E, of ICE 14 (engine RPM), a rotational speed, N.sub.MS, of the motor 12 (motor rotational speed), and vehicle speed).
Regarding claim 4, the prior art discloses the learning device according to any one of claims1 to 3claim1, wherein the vital data includes data related to cardiac activity, and the learning unit learns an output of data related to cardiac activity of the subject by using a first electrocardiographic waveform acquired through the first method and the third sensor data acquired in the same period as a period of acquisition of the first electrocardiographic waveform as learning data and using training data based on a second electrocardiographic waveform acquired in the same period as the period of acquisition of the first electrocardiographic waveform through the second method (see at least paragraph [0037] to Najarian et al, wherein event prediction module may be configured to utilize a machine learning algorithm to predict and/or detect one or more medical conditions based on obtained biomedical signals. The event prediction module may be configured to obtain training data comprising a plurality of biomedical signals (“training signals”). The event prediction module may be configured to extract pre-event signals from the training data that span a predetermined time interval before a particular cardiac event).
Regarding claim 5, the prior art discloses the learning device according to claim 4, wherein the first method is a method of acquiring an electrocardiographic waveform using at least two electrodes expected to come into contact with the subject, and the second method is a method of acquiring an electrocardiographic waveform using at least two electrodes attached to the skin of the subject (see at least paragraph [0039] to Najarian et al, wherein the biomedical signals may comprise electrocardiogram (ECG) signals).
Regarding claim 6, the prior art discloses the learning device according to claim 5, wherein the two electrodes used in the first method are provided on a seat on which the subject sits and an operated device which is operated by the subject (see at least paragraph [0036] to Najarian et al, wherein a sensor configured to monitor a biomedical signal (e.g., an ECG signal) may be incorporated into a steering wheel, a seat, and/or other components in contact with or within a proximity of the driver and/or passenger(s)).
Regarding claim 7, the prior art discloses the learning device according to any one of claims1 to 6claim1, wherein the subject is a driver who drives a moving object (see at least paragraph [0022] to Najarian et al, wherein systems and methods disclosed herein may be configured to predict and/or detect medical conditions (e.g., a cardiac event) in an in-vehicle environment based on real-time biomedical signals received for drivers and/or passengers within the vehicle).
Regarding claim 9, the prior art discloses a measurement device comprising a measurement unit that outputs vital data indicating vital signs of a subject using first sensor data acquired from the subject through a first method as an input, wherein the measurement unit outputs the vital data by using the first sensor data as learning data (see at least paragraph [0037] to Najarian et al, wherein vent prediction module may be configured to predict and/or detect one or more medical conditions based on obtained biomedical signals. In various embodiments, event prediction module may be configured to utilize a machine learning algorithm to predict and/or detect one or more medical conditions based on obtained biomedical signals. The event prediction module may be configured to obtain training data comprising a plurality of biomedical signals (“training signals”)), using training data based on second sensor data acquired from the subject in the same period as a period of acquisition of the first sensor data through a second method which is less affected by noise than the first method (see at least paragraph [0067] to Najarian et al), and using a trained model having performed learning related to an output of the vital data further on the basis of third sensor data which is acquired in the same period as the period of acquisition of the first sensor data and the second sensor data and available as an index indicating a magnitude of influence of the noise occurring in the first sensor data(see at least paragraph [0073] to Najarian et al).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
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, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TALIA F CRAWLEY/Primary Examiner, Art Unit 3627