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 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 1-6, 8, 10-17, and 20-21, and 23-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) a computer implemented method including receiving video data, determining features of that video data, and determining sleep state data for the subject.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Examiner notes that these claims are most similar to claim 1 of Example 46 related to livestock management and abstract idea exceptions including mental processes of Appendix 1 to the October 2019 Update to Subject Matter Eligibility found at https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_app1.pdf
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
The claims are directed towards a method and thus meet the requirements of Step 1.
Step 2A (Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites a method for determining sleep state.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claim 1 recite an abstract idea of a mental process.
All of the components of the claimed method including:
receiving video data representing a video of a subject;
determining, using the video data, a plurality of features corresponding to the subject; and
determining, using the plurality of features, sleep state data for the subject.;
can be done visually or mentally by a user by watching a monitor, mentally distinguishing whether the patient is moving or not, and mentally determining if the subject is awake or asleep. Nothing in the elements of the claims preclude them from being performed by the mind.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claim 1 additionally recites the limitation of “computer-implemented method” to perform the steps directed to the abstract idea as indicated above.
The limitation of “computer-implemented method” is recited at a high level of generality, i.e., as a generic processor, performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
As stated above, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial except into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The specification in ¶[0106] does not provide any indication that the computer is anything other than a generic, off-the-shelf computer component. Court decisions cited in MPEP 2106.05(d)(II) indicate that computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim, as a whole, amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). Accordingly, a conclusion that the generic computer functions merely being used to implement an abstract idea is well-understood, routine, conventional activity is supported under Berkheimer Option 2. Therefore the claims are not patent eligible.
Claims 2-6, 8, 10-17, and 20-21, and 23-25
Dependent claims 2-6, 8, 10-17, and 20-21, and 23-25 further limit the abstract idea already indicated in independent claim 1 and they are ineligible for the same reasons provided for claim 1 above.
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 3 and 17 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 3 recites the phrase “ellipse fit data.” This phrase is unclear, as it is not known what is meant by the term. Where applicant acts as his or her own lexicographer to specifically define a term the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “ellipse data” in claim 3 is used by the claim to mean “an ellipse drawn around the subject,” while the accepted meaning is “way to fit the data to an ellipse.” The term is indefinite because the specification does not clearly redefine the term.
Claim 17 recites the phrase, “wherein the video captures the subject in the subject's natural state, wherein the subject's natural state comprises an absence of an invasive detection in or on the subject, and wherein the invasive detection comprises one or both of an electrode attached to and an electrode inserted into the subject.” It is unclear if the invasive detection is being positively recited in this claim, or if the claim is simply stating that no invasive detection is used.
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 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.
Claims 1-6, 8, 10-13, 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Long (WO 2020/064580, as cited by Applicant).
Regarding claim 1, Long discloses a computer-implemented method comprising:
receiving video data representing a video of a subject (e.g. video camera 10 and processing unit 20 of arrangement 100 according to the invention for monitoring sleep and wake states of an infant 30 in an incubator 40; pg. 8, ln 17-19);
determining, using the video data, a plurality of features corresponding to the subject (e.g. determining video actigraphy (VA data) which includes values for smaller and larger movements; pg. 9, ln 16 to pg. 10, ln 21 as well as determining the logarithm of time difference between each epoch and its nearest epoch with a lot of body movements, in correspondence to a large VAM value or a large VAC value, pg. 11, In 1-6); and
determining, using the plurality of features, sleep state data for the subject (e.g. classification of video frames; pg. 11, ln 31 – pg. 12, ln 4).
Regarding claim 2, Long additionally discloses processing, using a machine learning model, the video data to determine segmentation data indicating a first set of pixels corresponding to the subject and a second set of pixels corresponding to a background (e.g. use of machine learning (pg. 7, ln 1-8) in order to determine whether pixels correspond to the subject or the background (pg. 9, ln 31 to pg. 10, ln 21; pg. 13, ln 5-15)).
Regarding claim 3, as best the claim can be understood, Long discloses processing the segmentation data to determine ellipse fit data corresponding to the subject (e.g. determining whether a baby is present in the incubator; pg. 9, ln 9-15).
Regarding claim 4, Long additionally discloses wherein the determining the plurality of features comprises processing the segmentation data to determine the plurality of features (e.g. segmentation of data into video frames; Abstract).
Regarding claim 5, Long additionally discloses wherein the plurality of features comprises a plurality of visual features for each video frame of the video data (e.g. set of four features that are collected as shown on pg. 10, ln 10-20).
Regarding claim 6, Long additionally discloses determining time domain features for each visual feature of the plurality of visual features, wherein determining the time domain features comprises determining one of mean data, and wherein the plurality of features comprises the time domain features (e.g. collection of video actigraphy count data which is time domain data; pg. 10, ln 10-11).
Regarding claim 8, Long additionally discloses determining frequency domain features for each visual feature of the plurality of visual features, wherein determining the frequency domain features comprises determining one of maximum data and minimum data and wherein the plurality of features comprises the frequency domain features (e.g. collection of video actigraphy mean which is set to calculate frequency; pg. 9, ln 10-11).
Regarding claim 10, Long additionally discloses determining time domain features for each of the plurality of features (e.g. video actigraphy count); determining frequency domain features for each of the plurality of features (e.g. vido actigraphy mean); and processing, using a machine learning classifier, the time domain features and the frequency domain features to determine the sleep state data (e.g. calculation of relative possibility of sleep based on VAM and VAC; pg. 10, ln 1-20; using machine learning; pg. 7, ln 1-8).
Regarding claim 11, Long additionally discloses processing, using a machine learning classifier, the plurality of features to determine a sleep state for a video frame of the video data, the sleep state being one of a wake state, a REM sleep state and a non-REM (NREM) sleep state (e.g. determining REM and NREM sleep; pg. 3, ln 5-10).
Regarding claim 12, Long additionally discloses, wherein the sleep state data indicates one or more of a duration of time of a sleep state, a duration and/or frequency interval of one or more of a wake state, a REM state, and a NREM state; and a change in one or more sleep states (e.g. determining REM and NREM sleep; pg. 3, ln 5-10).
Regarding claim 13, Long additionally discloses, using the plurality of features, a plurality of body areas of the subject, each body area of the plurality of body areas corresponding to a video frame of the video data; and determining the sleep state data based on changes in the plurality of body areas during the video (e.g. use of the body data to determine sleep/wake status; pg. 9).
Regarding claim 17, Long additionally discloses wherein the video captures the subject in the subject's natural state, wherein the subject's natural state comprises an absence of an invasive detection in or on the subject, and wherein the invasive detection comprises one or both of an electrode attached to and an electrode inserted into the subject (e.g. the subject of Long does not have an invasive detection in or on the subject. The claim as currently filed does not require an invasive detection on the subject).
Regarding claim 20, Long additionally discloses wherein the video is a high-resolution video (e.g. video as described in pg. 9, lines 16-27).
Claim Rejections - 35 USC § 103
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.
Claims 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Long in view of Barbut et al. (US 2019/0091241).
Long disclose the claimed invention except the express mention that the subject is a genetically engineered mouse. Barbut, however, discloses that it was well known in the art of sleep/wake analysis for a subject to be a mouse and genetically engineered (e.g. PD mouse of [0091]). It would have been obvious to one of ordinary skill in the art to have modified the system of Long by including wherein the subject is a genetically engineered subject; wherein the subject is a rodent, and optionally is a mouse as taught by Barbut because the modification would identify an animal that is suffering from or at risk of suffering from a sleep disorder, sleep disturbance, or related symptom.
Conclusion
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/Amanda K Hulbert/ Primary Examiner, Art Unit 3792