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 Status
Claims 1-3, and 4-13 are currently pending in the application filed 10/04/2024
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/04/2024 and 9/30/2025 have been considered by the Examiner.
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 1-3, and 4-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts without significantly more. The claims recite statutory categories:
Regarding claim 1, under step 1 the claim recites a method and falls under a statutory category.
Under step 2A prong 1, the claim recites limitations that amount to mental processes. These steps could practically be performed in the human mind. Images could be reviewed manually by a human, or judgment of data – clearly within the "mental processes" grouping of abstract ideas under the July 2024 US PTO guidance.
“a difference information calculation unit that calculates difference information that is information indicating a difference between two frame images at different times” – This limitation recites a mathematical calculation. Calculating a difference between two images (e.g., pixel-by-pixel subtraction or comparison) is a mathematical operation that could also be performed mentally by a human comparing two images and noting differences. This falls within the “mathematical concepts” and “mental processes” groupings of abstract ideas.
“a subject attribute information acquisition unit that acquires subject attribute information that is information indicating an attribute of the subject” – This limitation recites mere data gathering. Acquiring attribute information such as the age of a subject is an insignificant extra-solution activity of collecting data, and a human could observe or record a subject’s attributes mentally or on paper.
“a sleep state related information calculation unit that calculates sleep state related information that is information related to a sleep state of the subject, using the difference information or information obtained by processing the difference information as an explanatory variable according to the subject attribute information” – This limitation recites a mathematical concept. Using difference information as an “explanatory variable” to calculate sleep state information is the application of a mathematical model or statistical/machine learning algorithm to data. A human could also mentally evaluate motion patterns in video frames and form a judgment about whether a subject is asleep or awake, especially with knowledge of the subject’s age. This falls within both the “mathematical concepts” and “mental processes” groupings.
Regarding claim 13, under Step 1 the claim recites a method and falls under a statutory category.
Under Step 2A, Prong 1, the claim recites limitations that amount to mental processes. These steps could practically be performed in the human mind. Images could be reviewed manually by a human, or judgment of data -- clearly within the "mental processes" grouping of abstract ideas under the July 2024 USPTO guidance.
"acquiring frame images including a subject during sleep in time series" -- This limitation recites data gathering. Capturing images of a subject is an insignificant pre-solution activity of collecting data, and a human could observe a sleeping subject over time.
"calculating difference information that is information indicating a difference between frame images at different times based on a comparison result of the frame images at the different times" -- This limitation recites a mathematical calculation. Comparing two frame images and calculating the difference between them is a mathematical operation that could also be performed mentally by a human comparing two images and noting differences. This falls within the "mathematical concepts" and "mental processes" groupings of abstract ideas.
"acquiring subject attribute information that is information indicating an attribute of the subject" This limitation recites mere data gathering. Acquiring attribute information such as the age of a subject is an insignificant extra-solution activity of collecting data, and a human could observe or record a subject's attributes mentally or on paper.
"calculating sleep state related information that is information related to a sleep state of the subject, using the difference information or information obtained by processing the difference information as an explanatory variable according to the subject attribute information" -- This limitation recites a mathematical concept. Using difference information as an "explanatory variable" to calculate sleep state information is the application of a mathematical model or statistical/machine learning algorithm to data. A human could also mentally evaluate motion patterns in video frames and form a judgment about whether a subject is asleep or awake, especially with knowledge of the subject's age. This falls within both the "mathematical concepts" and "mental processes" groupings.
"acquiring size related information that is information related to a relative size of all or a specific part of a subject in the frame image, wherein calculating the sleep state related information also uses the size related information as an explanatory variable" -- Acquiring size-related information from an image is mere data gathering, and using size information as an additional explanatory variable in the sleep state calculation is further mathematical processing. A human could observe the relative size of a subject in an image and factor that into their judgment.
Under Step 2A, Prong 2, the claim does not recite additional elements that integrate the judicial exception into a practical application. All of the steps in the method (acquiring frame images, calculating differences, acquiring attributes, acquiring size information, and calculating sleep state information) are either data gathering or mathematical processing. The claim does not recite any practical application of the calculated sleep state related information, such as controlling a device, generating an alert, adjusting an environmental condition, displaying a result to a user, or providing any treatment. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016).
Under Step 2B, the claim does not recite additional elements that amount to significantly more than the abstract idea. The method steps are well-understood, routine, and conventional activities of capturing video, processing images, and classifying data. See MPEP 2106.05(d). Therefore, claim 13 is not patent eligible under 35 U.S.C. 101.
Regarding claim 2, the claim adds the limitation of selecting one or a plurality of learned models according to the subject attribute information and calculating the sleep state related information by giving the difference information to the learned models. Selecting a model and running data through it is a mathematical process. This does not remedy the abstract idea of claim 1.
Regarding claim 3, the claim adds the limitation that an age of a subject is used as the subject attribute information. Specifying the type of data used in the mathematical model is a data selection step. This does not remedy the abstract idea of claim 1.
Regarding claim 5, the claim adds the limitation that the sleep state related information calculation unit normalizes the difference information by the size related information. Normalization is a well-known mathematical operation (dividing or scaling data values). This amounts to a mathematical concept and does not remedy the abstract idea of claim 1.
Regarding claim 6, the claim adds the limitation of a target area specification unit that specifies a target area having a predetermined shape including all or a specific part of a subject in the frame image. The claim also adds that the size related information is a number of area pixels of the target area or a number of length pixels in a predetermined direction. Specifying a region of interest is a data selection step, and counting pixels is a mathematical operation. This does not remedy the abstract idea of claim 1.
Regarding claim 7, the claim adds the limitation that the sleep state related information includes a sleep depth. This merely defines the type of output calculated by the mathematical model. Characterizing the output as sleep depth does not remedy the abstract idea of claim 1.
Regarding claim 8, the claim adds the limitations of an extraction unit that extracts biological information from a subject during sleep and a calculation unit that calculates time variation information of the biological information. The claim also adds that the sleep state calculation uses both the difference information and the time variation information as explanatory variables. Extracting biological information is data gathering, and calculating time variation information is a mathematical operation. Using both as input to a classifier is mathematical modeling. This does not remedy the abstract idea of claim 1.
Regarding claim 9, the claim adds the limitation that the biological information includes one or more of body motion, a respiration rate, a variance of the respiration rate, an amplitude of a respiration waveform, a heart rate, a pulse rate, a pulse interval, and a time variation of the pulse interval. Specifying the types of data gathered does not change the abstract nature of the data gathering. This does not remedy the abstract idea of claim 1.
Regarding claim 10, the claim adds the limitation that the extraction unit detects a landmark in the frame image and determines a specific part with reference to the landmark and extracts the biological information in the specific part. Detecting a landmark and identifying a body region are image recognition steps that could be performed by a human through visual observation, hence it is a mental process. This does not remedy the abstract idea of claim 1.
Regarding claim 11, the claim adds the limitation that the extraction unit determines a periphery of the specific part as a target area and extracts the biological information in the target area. Determining a periphery around a body part is a judgment which can be performed by a human, hence it is a mental process. This does not remedy the abstract idea of claim 1.
Regarding claim 12, the claim adds the limitation that the landmark is a face of a subject, the specific part is a chest and abdomen, and the biological information is respiratory information. Specifying particular body parts and data types is a judgment which can be performed by a human, hence it is a mental process. This does not remedy the abstract idea of claim 1.
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, as explained in MPEP § 2181, subsection I (note that the list of generic placeholders below is not exhaustive, and other generic placeholders may invoke 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 Generic Placeholder (A Term That Is Simply A Substitute for "Means")
With respect to the first prong of this analysis, a claim element that does not include the term "means" or "step" triggers a rebuttable presumption that 35 U.S.C. 112(f) does not apply. When the claim limitation does not use the term "means," examiners should determine whether the presumption that 35 U.S.C. 112(f) does not apply is overcome. The presumption may be overcome if the claim limitation uses a generic placeholder (a term that is simply a substitute for the term "means"). The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998). Note that there is no fixed list of generic placeholders that always result in 35 U.S.C. 112(f) interpretation, and likewise there is no fixed list of words that always avoid 35 U.S.C. 112(f) interpretation. Every case will turn on its own unique set of facts.
Such claim limitation(s) is/are:
A. “Frame image acquisition unit” in claim 1 and dependent claims thereof, described in paragraph [0016] and implemented on hardware disclosed in paragraphs [0013]–[0014] and [0017] (“The CPU and its peripheral devices cooperate with each other according to the program stored in the memory, whereby the information processing apparatus 2 functions as a frame image acquisition unit” [0016]; “For example, the frame image acquisition unit receives moving image data from the camera 1, extracts or generates a series of still images (frame images) arranged in time series at regular intervals (here, for example, every 0.5 seconds) from the moving image data, and stores the data in a predetermined area of a memory” [0017]).
B. “Difference information calculation unit” in claims 1 and dependent claims thereof, described in paragraph [0016] and implemented on hardware disclosed in paragraphs [0013]–[0014] and [0026]–[0029] (“The difference information acquisition unit calculates, for each frame image, difference information that is information indicating a difference between two frame images at different times, more specifically, a difference between a first frame image (hereinafter, also referred to as a reference frame image) of the two frame images and a frame image (hereinafter, also referred to as a comparison frame image) after a lapse of a certain time interval” [0026]; “The difference information in this embodiment is, for example, the number of pixels having different pixel values exceeding a predetermined threshold in a case where corresponding pixels are compared in the target area of the reference frame image and the target area of the comparison frame image” [0028]).
C. “Subject attribute information acquisition unit” in claims 1 and dependent claims thereof, described in paragraph [0016] and implemented on hardware disclosed in paragraphs [0013]–[0014] and [0023]–[0025] (“The subject attribute information acquisition unit acquires subject attribute information that is information indicating the attribute of the subject P input by an operator, for example. The subject attribute information here includes age, gender, height, weight, presence or absence of disease, physical condition, and the like” [0023]).
D. “Sleep state related information calculation unit” in claims 1and dependent claims thereof, described in paragraph [0016] and implemented on hardware disclosed in paragraphs [0013]–[0014], [0030], and [0044]–[0046] (“The sleep state related information calculation unit calculates the sleep state related information, which is information on the sleep state of the subject P, using the difference information and the subject attribute information as an explanatory variable” [0030]; “The learned model is an algorithm generated in advance by machine learning to output the sleep depth when the difference information and/or the intermediate parameter is input. Here, the learning data (the difference information and/or the intermediate parameter) is divided into a plurality of pieces according to the value of the subject attribute information, and machine learning is executed for each piece of the learning data to generate a learned model for each value of the subject attribute information” [0045]).
E. “Size related information acquisition unit” in claims 1 and dependent claims thereof, described in paragraph [0016] and implemented on hardware disclosed in paragraphs [0013]–[0014], [0021]–[0022], and [0035] (“The size related information acquisition unit acquires size related information that is information related to a relative size of all or a specific part of the subject P in the frame image” [0021]; “The number of area pixels of the target area or the number of length pixels along the predetermined direction of the target area is simply used as the size related information” [0022]).
F. “Target area specification unit” in claims 6 thereof described in paragraph [0016] and implemented on hardware disclosed in paragraphs [0013]–[0014] and [0018]–[0020] (“The target area specification unit specifies a target area (ROI) which is a constant image area including the whole or the specific part of the subject P in the frame image acquired by the frame image acquisition unit” [0018]; “The target area specification unit specifies, as the target area, a rectangular area set and input by the operator in such a manner of including the entire subject P in the captured image (initial frame image) displayed on the display” [0019]).
G. “Extraction unit” in claim 8 thereof, described in paragraphs [0057]–[0063] and implemented on hardware disclosed in paragraphs [0013]–[0014] and [0058] (“Examples of a biological information sensor for measuring the biological information include an electromagnetic wave sensor that detects a heartbeat, a pulse wave, or the like from a transmission amount or a reflection amount of an electromagnetic wave such as infrared rays, and a pressure sensor that detects a load change or a load movement. As a form of the biological information sensor, a sheet type, a wearable type, a microwave radar type, or the like may be used” [0058]; “When the above-described camera is considered together with the information processing apparatus that processes the image data, it can also be said that the camera has a function as a biological information sensor that detects biological information” [0063]).
H. “Calculation unit” in claim 8 thereof, described in paragraph [0016] (“The CPU and its peripheral devices cooperate with each other according to the program stored in the memory, whereby the information processing apparatus 2 functions as a frame image acquisition unit, a target area specification unit, a size related information acquisition unit, a subject attribute information acquisition unit, a difference information calculation unit, a sleep state related information calculation unit, and the like as illustrated in FIG. 2).
Claim Rejections - 35 USC § 112
Regarding §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.
Section II of §2181 reads:
II. DESCRIPTION NECESSARY TO SUPPORT A CLAIM LIMITATION WHICH INVOKES 35 U.S.C. 112(f) or Pre-AIA 35 U.S.C. 112, SIXTH PARAGRAPH
A. The Corresponding Structure Must Be Disclosed In the Specification Itself in a Way That One Skilled In the Art Will Understand What Structure Will Perform the Recited Function
The proper test for meeting the definiteness requirement is that the corresponding structure (or material or acts) of a means- (or step-) plus-function limitation must be disclosed in the specification itself in a way that one skilled in the art will understand what structure (or material or acts) will perform the recited function. See Atmel Corp. v. Information Storage Devices, Inc., 198 F.3d 1374, 1381, 53 USPQ2d 1225, 1230 (Fed. Cir. 1999).
Claim 5 and 6 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.
Claims 5 and 6 depend from cancelled claim 4 and are therefore indefinite. For purposes of examination, the examiner will interpret claims 5 and 6 to depend from claim 1.
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.
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 1-3, 5 and 13 is rejected under 35 U.S.C. 103 as being unpatentable over Long (EP 3 628 213 A1).
Regarding claim 1, (Long) teaches:
A sleep state measurement system comprising
(Long, [0007]; “an arrangement is provided that is designed to derive information about a person’s sleep and wake states from a sequence of video frames”).
a frame image acquisition unit that acquires frame images including a subject during sleep in time series
(Long, [0007]; “The arrangement comprises a video camera for capturing a sequence of video frames during a time period”).
a difference information calculation unit (Long; [0034]: “The processing unit 20”) that calculates difference information that is information indicating a difference between two frame images at different times
(Long, [0013]: “a motion value-time relation is determined from the video frames”;
[0037]: “In a first step 1 of the analysis of the video frames, a motion estimation technique is employed for quantifying motion as video actigraphy (VA). In the shown example, the motion estimation technique is assumed to be a technique known as 3D recursive search (3DRS)”;
[0037]: “The raw 3DRS motion estimates corresponding to the video recording may have a frame rate of approximately 15 Hz, but may also have another frame rate such as 8 Hz, depending on the video camera 10 that is employed”; [0038]: “A larger estimated VA value usually corresponds to a large body movement or more body movements”; [0045]: “Assuming the raw VA data within a 30 seconds epoch is u={u1, u2, . . . , uk}, where k is 450 when the frequency of the video frames is 15 Hz”; [0039]: “video actigraphy count (VAC)=(averaged) count of non-zero VA values (motion estimates) over 30 seconds, computed based on raw VA data after 3DRS motion estimations”)
Examiner Note: Applicant's specification defines "difference information" as "information indicating a difference between two frame images at different times" ([0026]) and gives the example of counting pixels that differ between two frames when compared against a threshold ([0028]). Long's 3DRS algorithm produces one VA (video actigraphy) value per video frame at 15 Hz ([0037], [0045]). Each VA value represents the motion detected between two successive frames. Each VA value is therefore information derived from comparing two frame images at different times, which reads on "difference information" as defined in Applicant's specification.
a subject attribute information acquisition unit that acquires subject attribute information that is information indicating an attribute of the subject
(Long, [0039]; “For preterm infants, 45 video recordings…from 7 infants with an average gestational age of 29.9 weeks were included. For healthy term infants, video data of 29 hours…from 8 infants aged 6 months on average were included”;
[0017]; “an arrangement of the invention that is particularly designed for use in infant care…the algorithm may be configured to set the classifier of an epoch to be a care state classifier”).
a sleep state related information calculation unit that calculates sleep state related information that is information related to a sleep state of the subject, using the difference information or information obtained by processing the difference information as an explanatory variable according to the subject attribute information
(Long, [0049]: "classification takes place on the basis of the sets of features of the respective epochs. In particular, for each epoch, the set of features of that particular epoch is classified as being representative of a wake state or a sleep state")
(Long, [0039]: "features are extracted from the raw estimated motion data...two of those features being computed based on the mean of the VA values over the respective epochs, and another two of those features being computed by counting the non-zero VA values over the respective epochs")
(Long, [0056]: "The following tables present and compare the sleep and wake classification results using different feature sets and settings/methods for preterm infants and healthy term infants, respectively"; [0056] preterm table note: "Adaptive prior probability is not available/applicable due to short recordings from preterm infants"; [0037]: "the application of RGB may be best applicable to the context of preterm infants, while the application of NIR may be best applicable to the context of term infants")
Examiner Note: Applicant's specification at [0030] describes "sleep state related information" to include "sleep depth" and "a sleep cycle," among others. Applicant's specification at [0023] describes "subject attribute information" to include "age, gender, height, weight, presence or absence of disease, physical condition." Long's classification of each epoch as wake or sleep ([0049]) reads on "sleep state related information" as defined by Applicant's specification. The features (VAM, VAC) used as input to the classifier are derived from the raw VA values ([0039]), which read on "difference information" as mapped above. Long's classification settings also differ based on subject type. Specifically, Long applies different video types for preterm vs term infants ([0037]), uses different feature sets and settings for each group ([0056]), and applies adaptive priors only for term infants and not for preterm infants ([0056] table note). Since "subject attribute information" was mapped above to the preterm/term status, and Long adjusts its classifier based on that status, the classification is performed "according to the subject attribute information" as claimed.
a size related information acquisition unit that acquires size related information that is information related to a relative size of all or a specific part of a subject in the frame image
(Long, [0009]; “the camera is positioned such that it can have a view of at least a part of the infant’s body”; see also [0039]; “45 video recordings (738x480 pixels or 768x576 pixels)…For healthy term infants, video data of 29 hours (1280x720 pixels)”,
Examiner Note: Under broadest reasonable interpretation, the pixel-based image area of the subject captured by the video camera has information related to the relative size of the subject in the frame image, which is used in the per-pixel motion estimation that quantifies video actigraphy.
(Long, [0030]; "the mean of the VA data over the epoch is computed by VAM = Σuᵢ/k";
[0031]; "the (averaged) count of non-zero VA data over the epoch is computed by VAC = Σvᵢ/k where vᵢ = 1 if uᵢ > 0, 0 otherwise";
[0014]; "the features include at least one of (i) the mean of the motion values in each of the respective epochs and (ii) the number of non-zero motion values or motion values which are larger than a certain minimum";
[0007]; "classifiers of the respective epochs are determined by classifying the respective sets of features").
Examiner Note: VAM and VAC features are pixel-count values derived from motion within the subject's image area. Pixel counts scale with how much of the frame the subject occupies, VAM and VAC inherently encode the subject's relative size in the frame. These size-dependent features are then input to Long's classifier ([0007]) to determine the sleep state. The size related information is therefore used as an explanatory variable in calculating the sleep state related information, meeting the claim limitation.
wherein the sleep state related information calculation unit calculates the sleep state related information also using the size related information as an explanatory variable
(Long, [0048]: "in order to reduce the global variability between subjects and between days conveyed by VA, features are normalized for each recording. For PSVAM and PSVAC, feature values may be normalized to zero mean and unit standard deviation of the entire recording (Z-score normalization), while for VAM and VAC, it may be practical to normalize the feature values between 0 and 1 (max-min normalization)"; [0016]: "It may be advantageous to normalize the features of each recording for the purpose of reducing between-infant and/or between-recording variability"; [0055]: "For preterm infants, 45 video recordings (738×480 pixels or 768×576 pixels) from 7 infants...For healthy term infants, video data of 29 hours (1280×720 pixels) from 8 infants")
Examiner Note: Applicant's specification at [0021] defines "size related information" as "information related to a relative size of all or a specific part of the subject P in the frame image." Long's recordings use different pixel resolutions for different subject types, with preterm infants at 738x480 or 768x576 pixels and term infants at 1280x720 pixels ([0055]). The size of the subject in the frame affects the raw VA values because a larger subject means more pixels change when there is motion. Long addresses this by normalizing the features for each recording before classification ([0048], [0016]). These normalized features are then fed into the classifier ([0049]). Since the normalization adjusts the features based on recording-specific values that reflect the subject's size in the frame, long reads on "also using the size related information as an explanatory variable" as claimed.
While Long (US 2021/0275089 A1) does disclose adjusting classification settings based on subject type, including using different video types for preterm versus term infants ([0037]: "the application of RGB may be best applicable to the context of preterm infants, while the application of NIR may be best applicable to the context of term infants"), different feature sets and settings for each group ([0056]: "The following tables present and compare the sleep and wake classification results using different feature sets and settings/methods for preterm infants and healthy term infants, respectively"), and applying adaptive priors only for term infants ([0056] preterm table note: "Adaptive prior probability is not available/applicable due to short recordings from preterm infants"), it does not fully disclose explicitly acquiring subject attribute information and using it as an explanatory variable to condition the sleep state calculation, but it would have been obvious to have done so. The reason is Long already recognizes that different subjects require different classification approaches and that classification performance varies due to differences between subjects ([0016]: "It may be advantageous to normalize the features of each recording for the purpose of reducing between-infant and/or between-recording variability"), and Long already applies different settings based on whether the subject is a preterm or term infant ([0056]). Therefore, a person of ordinary skill in the art would understand that explicitly acquiring the subject's attributes (such as preterm/term status) and using that information to select the appropriate classification settings is simply formalizing what Long already does in practice. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include explicitly acquiring subject attribute information and using it to condition the sleep state calculation, because it allows the system to automatically select the correct classification settings for each subject and improve measurement accuracy, as Long itself demonstrates by achieving different accuracy results for preterm versus term infants ([0056]).
Regarding Claim 2, (Long) teaches:
wherein the sleep state related information calculation unit selects one or a plurality of learned models according to content of the subject attribute information from among a plurality of learned models that are identical to each other or different from each other, and calculates the sleep state related information by giving the difference information to the learned models
(Long, [0016]; “the algorithm may be configured to determine machine learning classifiers on the basis of differences between (i) an initial set of classifiers determined on the basis of the features and (ii) a final set of classifiers determined by applying at least one of the adaptive prior probability and the smoothing filter, and to use the machine learning classifiers for making adjustments in the algorithm as far as determining the classifiers of the respective epochs is concerned”;
[0040]; the tables in [0040] compare sleep and wake classification results using different feature sets and settings/methods for preterm infants and healthy term infants, respectively, demonstrating that distinct classifiers are selected and applied based on the infant subject attribute).
Regarding claim 3, (Long) teaches:
wherein an age of a subject is used as the subject attribute information
(Long, [0039]; "For preterm infants, 45 video recordings… from 7 infants with an average gestational age of 29.9 weeks were included. For healthy term infants, video data of 29 hours… from 8 infants aged 6 months on average were included",
Examiner Note: Long expressly acquires and records the age of each subject gestational age (29.9 weeks) for preterm infants and postnatal age (6 months) for term infants and uses this age information to distinguish between the two subject populations for separate classifier training and evaluation, as shown by the separate result tables for preterm versus term infants in [0040]. Hence Age is used as subject attribute information.
Regarding claim 5, (Long) teaches:
wherein the sleep state related information calculation unit normalizes the difference information by the size related information, and calculates the sleep state related information based on the difference information normalized and the subject attribute information
(Long, [0032]; “features are normalized for each recording…for VAM and VAC, it may be practical to normalize the feature values between 0 and 1 (max-min normalization)”;
[0015]; “the algorithm may be configured to normalize the features, so that the influence of variations, such as variations between infants and/or variations between recordings may be reduced”,
Examiner Note: Long normalizes the VAM and VAC features per recording. VAM and VAC are pixel-count values, so normalizing them equates to normalizing the difference information by the size related information. The normalized features are then fed into Long's classifier along with the subject attribute (preterm/term/age), equating to the claimed calculation of the sleep state related information based on the normalized difference information and the subject attribute.
Regarding claim 13, (Long) teaches:
A sleep state measurement method comprising
(Long, [0007]; “an arrangement is provided that is designed to derive information about a person’s sleep and wake states from a sequence of video frames”;
[0018]; “a computer program product may be provided, comprising a program code of a computer program to make a computer execute the algorithm when the computer program is loaded on the computer”):
acquiring frame images including a subject during sleep in time series
(Long, [0007]; “a video camera for capturing a sequence of video frames during a time period”);
calculating difference information that is information indicating a difference between frame images at different times based on a comparison result of the frame images at the different times
(Long, [0026]; “a motion estimation technique is employed for quantifying motion as video actigraphy (VA). In the shown example, the motion estimation technique is assumed to be a technique known as 3D recursive search (3DRS)”;
[0008]; “the algorithm may be configured to apply a technique known as 3D recursive search motion estimation so as to identify video actigraphy through time”,
acquiring size related information that is information related to a relative size of all or a specific part of a subject in the frame image,
(Long, [0009]; “the camera is positioned such that it can have a view of at least a part of the infant’s body”; see also [0039]; “45 video recordings (738x480 pixels or 768x576 pixels)…For healthy term infants, video data of 29 hours (1280x720 pixels)”,
Examiner Note: Under broadest reasonable interpretation, the pixel-based image area of the subject captured by the video camera has information related to the relative size of the subject in the frame image, which is used in the per-pixel motion estimation that quantifies video actigraphy.
(Long, [0030]; "the mean of the VA data over the epoch is computed by VAM = Σuᵢ/k";
[0031]; "the (averaged) count of non-zero VA data over the epoch is computed by VAC = Σvᵢ/k where vᵢ = 1 if uᵢ > 0, 0 otherwise";
[0014]; "the features include at least one of (i) the mean of the motion values in each of the respective epochs and (ii) the number of non-zero motion values or motion values which are larger than a certain minimum";
[0007]; "classifiers of the respective epochs are determined by classifying the respective sets of features").
Examiner Note: VAM and VAC features are pixel-count values derived from motion within the subject's image area. Pixel counts scale with how much of the frame the subject occupies, VAM and VAC inherently encode the subject's relative size in the frame. These size-dependent features are then input to Long's classifier ([0007]) to determine the sleep state. The size related information is therefore used as an explanatory variable in calculating the sleep state related information, meeting the claim limitation.
wherein calculating the sleep state related information also uses the size related information as an explanatory variable.
(Long, [0048]: "in order to reduce the global variability between subjects and between days conveyed by VA, features are normalized for each recording. For PSVAM and PSVAC, feature values may be normalized to zero mean and unit standard deviation of the entire recording (Z-score normalization), while for VAM and VAC, it may be practical to normalize the feature values between 0 and 1 (max-min normalization)"; [0016]: "It may be advantageous to normalize the features of each recording for the purpose of reducing between-infant and/or between-recording variability"; [0055]: "For preterm infants, 45 video recordings (738×480 pixels or 768×576 pixels) from 7 infants...For healthy term infants, video data of 29 hours (1280×720 pixels) from 8 infants")
Examiner Note: Applicant's specification at [0021] defines "size related information" as "information related to a relative size of all or a specific part of the subject P in the frame image." Long's recordings use different pixel resolutions for different subject types, with preterm infants at 738x480 or 768x576 pixels and term infants at 1280x720 pixels ([0055]). The size of the subject in the frame affects the raw VA values because a larger subject means more pixels change when there is motion. Long addresses this by normalizing the features for each recording before classification ([0048], [0016]). These normalized features are then fed into the classifier ([0049]). Since the normalization adjusts the features based on recording-specific values that reflect the subject's size in the frame, long reads on "also using the size related information as an explanatory variable" as claimed.
Regarding claim 13, while Long (US 2021/0275089 A1) does disclose adjusting classification settings based on subject type, including using different video types for preterm versus term infants ([0037]: "the application of RGB may be best applicable to the context of preterm infants, while the application of NIR may be best applicable to the context of term infants"), different feature sets and settings for each group ([0056]: "The following tables present and compare the sleep and wake classification results using different feature sets and settings/methods for preterm infants and healthy term infants, respectively"), and applying adaptive priors only for term infants ([0056] preterm table note: "Adaptive prior probability is not available/applicable due to short recordings from preterm infants"), it does not fully disclose explicitly acquiring subject attribute information and using it as an explanatory variable to condition the sleep state calculation, but it would have been obvious to have done so. The reason is Long already recognizes that different subjects require different classification approaches and that classification performance varies due to differences between subjects ([0016]: "It may be advantageous to normalize the features of each recording for the purpose of reducing between-infant and/or between-recording variability"), and Long already applies different settings based on whether the subject is a preterm or term infant ([0056]). Therefore, a person of ordinary skill in the art would understand that explicitly acquiring the subject's attributes (such as preterm/term status) and using that information to select the appropriate classification settings is simply formalizing what Long already does in practice. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include explicitly acquiring subject attribute information and using it to condition the sleep state calculation, because it allows the system to automatically select the correct classification settings for each subject and improve measurement accuracy, as Long itself demonstrates by achieving different accuracy results for preterm versus term infants ([0056]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Long (EP 3 628 213 A1) further in view of Long et al. (Video-Based Actigraphy for Monitoring Wake and Sleep in Healthy Infants: A Laboratory Study), herein referred to as Long et al. 2019.
Regarding claim 6, Long fails to teach
a target area specification unit that specifies a target area having a predetermined shape including all or a specific part of a subject in the frame image, wherein the difference information calculation unit is configured to calculate difference information between the target area and the target area in two frame images
the size related information acquisition unit acquires, as the size related information, a number of area pixels of the target area or a number of length pixels of the target area in a predetermined direction
Long et al. 2019 teaches:
a target area specification unit that specifies a target area having a predetermined shape including all or a specific part of a subject in the frame image, wherein the difference information calculation unit is configured to calculate difference information between the target area and the target area in two frame images
(Long et al. 2019, p. 2, Section 2.2; “Video recordings were obtained from an IR camera placed in a ‘look-down’ view above an infant bed placed in the BabyLab at Tilburg University. The whole mattress of the bed was visible”;
Long et al. 2019, p. 3, Section 2.2; “we employed a spatiotemporal-based recursive search (RS) motion detection algorithm to quantify motions from IR videos…The IR video recording (at 376×480 pixels) and its corresponding raw RS motion estimates had a frame rate of 10 Hz”).
Examiner Note: The look-down mattress region equates to the target area of predetermined shape including the subject. The RS motion estimation between IR video frames within that region equates to calculating difference information between the target area and the target area in two frame images, as recited in the claim.
the size related information acquisition unit acquires, as the size related information, a number of area pixels of the target area or a number of length pixels of the target area in a predetermined direction
(Long et al. 2019, p. 3, Section 2.2; "The IR video recording (at 376×480 pixels) and its corresponding raw RS motion estimates had a frame rate of 10 Hz").
Examiner Note: The IR video frame containing the look-down mattress target area has a defined dimension of 376×480 pixels, which equates to acquiring the number of area pixels (376 × 480) and the number of length pixels of the target area in a predetermined direction (376 pixels horizontal or 480 pixels vertical), as recited in the claim.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Long and Long et al. 2019. Long calculates motion differences between frames but does not specify a target area containing the subject. Long et al. 2019 adds this target area, containing the motion reflection of the subject and no other movement in the frame (Long et al. 2019, p. 6, Section 3; "for some recordings external disturbances (e.g., from parental activity) also contributed to the motion captured by the videos,").
Claim 7-9 is rejected under 35 U.S.C. 103 as being unpatentable over Long (EP 3 628 213 A1) further in view of Nochino (Sleep stage estimation method using a camera for home use, 2019).
Regarding Claim 7, Long fails to teach:
wherein the sleep state related information includes a sleep depth
(Nochino) teaches:
wherein the sleep state related information includes a sleep depth
(Nochino, p. 258, Section 2.1; “Stage N1 and stage N2 were defined as LIGHT, stage N3 were defined as DEEP. Stage R was defined as REM and stage W was defined as WAKE”;
Abstract; “Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these five parameters to a support vector machine”,
Examiner Note: the classification output among LIGHT/DEEP/REM/WAKE sleep stages constitutes sleep depth information.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Long and Nochino. Long outputs only a sleep or wake state but does not classify how deep the sleep is. Nochino adds the sleep depth classification (LIGHT and DEEP stages), (Nochino, Abstract; "Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these five parameters to a support vector machine").
Regarding claim 8, Long teaches:
according to the subject attribute information
(Long, [0049]: "classification takes place on the basis of the sets of features of the respective epochs. In particular, for each epoch, the set of features of that particular epoch is classified as being representative of a wake state or a sleep state")
(Long, [0039]: "features are extracted from the raw estimated motion data...two of those features being computed based on the mean of the VA values over the respective epochs, and another two of those features being computed by counting the non-zero VA values over the respective epochs")
(Long, [0056]: "The following tables present and compare the sleep and wake classification results using different feature sets and settings/methods for preterm infants and healthy term infants, respectively"; [0056] preterm table note: "Adaptive prior probability is not available/applicable due to short recordings from preterm infants")
Examiner Note: Applicant's specification at [0043] equates "explanatory variables" with "input parameters" for the learning device, and at [0045] describes the concept of "according to the subject attribute information" as dividing the learning data according to the value of the subject attribute information and generating a separate learned model for each value. In Long, the motion-derived features (VAM, VAC, PSVAM, PSVAC) extracted from the video actigraphy data ([0039]) are the input parameters that Long's classifier uses to determine whether each epoch represents a sleep or wake state ([0049]). Long then conditions how those input parameters are processed by selecting different classifier configurations based on whether the subject is a preterm or term infant: Long applies adaptive prior probability only for term infants and not for preterm infants ([0056] table note), and uses different feature sets and settings for each group ([0056]). Because the preterm or term status of the subject determines which classifier configuration is applied to the input parameters that serve as explanatory variables, the sleep state calculation in Long is performed "according to the subject attribute information" as claimed.
Long fails to teach:
an extraction unit that extracts biological information from a subject during sleep
a calculation unit that calculates time variation information of the biological information
wherein the sleep state related information calculation unit calculates sleep state related information that is information related to a sleep state of the subject, using the difference information or information obtained by processing the difference information and the time variation information or information obtained by processing the time variation information as an explanatory variable
Nocino teaches:
an extraction unit that extracts biological information from a subject during sleep
(Nochino, Abstract: "Body movement was extracted by video processing from the video data"; p. 259, Section 2.3: "Five parameters representing these characteristics were calculated...Parameter 1: mean value of the body movement in every 30 s...Parameter 2: occurrence frequency of body movement over 5 min...Parameter 3: volume of body movement for 5 min...Parameter 4: number of seconds passed since the last body movement was detected")
a calculation unit that calculates time variation information of the biological information
(Nochino, p. 260, Section 2.3, Parameter 2 description: "this parameter represents a moving average over 5 min...This parameter represents the occurrence frequency of body movement over 5 min"; Parameter 4: "measures the number of seconds passed since the last body movement was detected")
Examiner Note: The moving averages, occurrence frequencies, and elapsed-time-since-last-movement calculations represent time variation information of the body-movement biological information.
wherein the sleep state related information calculation unit calculates sleep state related information that is information related to a sleep state of the subject, using the difference information or information obtained by processing the difference information and the time variation information or information obtained by processing the time variation information as an explanatory variable
(Nochino, p. 260, Section 2.3: "These five parameters were used for training and test data for SVM"; p. 259, Section 2.3: "Sleep stages were estimated through an SVM machine learning using only body movement data")
Examiner Note: Nochino's SVM classifies each epoch into a sleep stage using all five body movement parameters as input to the classifier (p. 259, p. 260). Parameters 1-3 are derived from the inter-frame difference values, reading on "difference information." Parameters 4-5 are time-based features, reading on "time variation information."
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Long and Nochino. Long does not teach an extraction unit that extracts biological information from the subject, a calculation unit that calculates time variation information of that biological information, or the use of time variation information as an explanatory variable in the sleep state calculation. Nochino teaches each of these the extraction of body movement as biological information, the calculation of its time variation (moving averages, occurrence frequency, time since last movement), and the use of those time variation values as inputs to the sleep stage classifier (Nochino, p. 260, Section 2.3; "These five parameters were used for training and test data for SVM").
Regarding claim 9, (the combination of Long and Nochino) teaches:
wherein the biological information includes one or more of body motion, a respiration rate, a variance of the respiration rate, an amplitude of a respiration waveform, a heart rate, a pulse rate, a pulse interval, and a time variation of the pulse interval
(Nochino, [Abstract]; “Body movement was extracted by video processing from the video data”;
Abstract; “Subjects’ body movements were simultaneously recorded by the web camera”,
Examiner Note: Body movement constitutes body motion as recited
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Long (EP 3 628 213 A1) in further view of Nochino (Sleep stage estimation method using a camera for home use, 2019), and further in view of Janssen et al. (Video-based respiration monitoring with automatic region of interest detection, Physiological Measurement").
Regarding claim 10, the combination of Long and Nochino fails to teach:
wherein the extraction unit detects a landmark in the frame image, and determines a specific part with reference to the landmark and extracts the biological information in the specific part
Janssen teaches:
wherein the extraction unit detects a landmark in the frame image, and determines a specific part with reference to the landmark and extracts the biological information in the specific part
(Janssen, [Abstract]; "we propose a video-based respiration monitoring method that automatically detects a respiratory region of interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization";
(Janssen, [p. 100]; "we propose a video-based respiration monitoring method that automatically detects respiratory Region of Interest (RoI) and signal using a camera").
Examiner Note: Janssen automatically detects an anatomical region of the subject (the chest/abdomen respiratory region) as a landmark in the frame image, determines the chest/abdomen specific part with reference to that detected region, and extracts respiratory biological information (the respiratory signal) from that specific part via motion factorization. This matches the claimed extraction unit that detects a landmark, determines a specific part with reference to the landmark, and extracts the biological information in the specific part.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Long, Nochino, and Janssen. Long and Nochino calculate motion across the whole frame and do not detect any anatomical landmark of the subject or determine a specific body part of the subject from which biological information is extracted. Janssen teaches automatically detecting an anatomical region of interest (the chest/abdomen) of the subject in the frame, locating that specific body part, and extracting respiratory biological information from that specific part (Janssen, [Abstract]; "respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization").
Regarding claim 11, the combination of Long and Nocino fails to teach:
wherein the extraction unit detects a landmark in the frame image, determines a specific part with reference to the landmark, determines a periphery of the specific part as a target area, and extracts the biological information in the target area
Janssen teaches:
wherein the extraction unit detects a landmark in the frame image, determines a specific part with reference to the landmark, determines a periphery of the specific part as a target area, and extracts the biological information in the target area
(Janssen, [Abstract]; "we propose a video-based respiration monitoring method that automatically detects a respiratory region of interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization").
Examiner Note: Janssen detects the respiratory anatomical region of the subject as a landmark in the frame image, determines the chest/abdomen as the specific part, and uses the surrounding region of interest (the periphery of the detected chest/abdomen) as the target area within which the respiratory biological information is extracted via motion factorization. This matches the claimed extraction unit that determines a periphery of the specific part as a target area and extracts the biological information in the target area.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Long, Nochino, and Janssen. Long and Nochino calculate motion across the whole frame and do not determine a periphery surrounding a specific body part of the subject as a target area for biological information extraction. Janssen teaches automatically detecting the chest/abdomen of the subject and defining the respiratory region of interest surrounding that detected chest/abdomen as the target area, within which the respiratory biological information is extracted (Janssen, Abstract; "find the respiratory RoI and extract the respiratory signal via motion factorization").
Regarding claim 12, the combination of Long, Nochino, and Janssen teaches:
wherein the landmark is a face of a subject, the specific part is a chest and abdomen, and the biological information is respiratory information
(Janssen, [Abstract]; "respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization"; see also Janssen, p. 100; "automatically detects a respiratory region of interest (RoI) and signal using a camera"; further Janssen describes video collection scenarios including "zoomed top-view of head and a part of the chest" of the subject).
Examiner Note: Janssen teaches video-based monitoring of the subject in which the face/head and chest/abdomen are both present in the frame, the chest/abdomen specific part is detected as the respiratory RoI, and respiratory information is extracted from that specific part.
Conclusion
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/SHIVANGI SARKAR/Examiner, Art Unit 2666
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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