DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claims 3-6 and 17-19 are withdrawn from consideration.
Claims 1-2, 7-16 and 20 are being examined.
Response to Arguments (Election Requirement of Species)
Applicant's election with traverse of Species 1 (claims 2 and 17, together with generic claims 1, 7, 16, and 20) in the reply filed on 4/23/2026 is acknowledged. The traversal is on the ground(s) that examination of all nine (9) species would not constitute a serious burden.
The traverse is noted but found not fully persuasive. The nine species require searches in distinct fields and classifications, satisfying the burden requirement under MPEP § 803 and 37 C.F.R. § 1.142.
In the interest of compact prosecution, the examiner withdraws the restriction in part and agrees to examine Species 1 and 6-9 together (claims 1-2, 7-17 and 20 with generic claims included) in the present application. The restriction is maintained as to Species 2-5 (claims 3-6 and 18-19), as their cumulative search burden remains undue. Claims directed solely to Species 2-5 are withdrawn from consideration per 37 C.F.R. § 1.142(b).
Applicant's traverse as to Species 2-5 is preserved for the record. Should applicant wish to contest the maintained restriction, a petition may be filed under 37 C.F.R. § 1.144 following a final restriction requirement.
Prosecution continues on all claims directed to Species 1 and 6-9.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-2, 7-8, 16-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ostadabbas et al (US20220051437A1) in view of Nikolenko et al (US20200320345A1).
Regarding claims 1, 16 and 20, Ostadabbas teaches a method comprising:
receiving subject training data comprising a plurality of images of subjects in a plurality of positions on a person support apparatus;
(Ostadabbas, "The technology described herein provides a dataset of lying poses ... which includes images from participants while lying in a bed ", [0011]; receiving subject training data; the data comprises images of subjects on a person support apparatus, specifically a bed; "pose data was collected from each participant, while lying in a bed and randomly changing their poses under three main categories of supine, left side, and right side.", [0175]; capturing subjects in a plurality of positions while on the person support apparatus)
labeling the plurality of images based on the positions of the subjects to generate labeled subject training data;
(Ostadabbas, "(b) labeling the gathered images with ground truth poses;", [0019]; labeling the plurality of images based on the subject's position; "the collected LWIR pose images were labeled by finding 14 body joints in each", [0178]; the labels are based on finding the specific positions of body joints, generating labeled training data)
generating synthetic training data comprising computer generated images of artificial subjects in a plurality of positions on an artificial person support apparatus;
(Ostadabbas, "The augmentation included rotation, shifting, scaling, color jittering, as well as synthetic occlusion to simulate the potential objects blocking the view point such as a bed table.", [0188]; while Ostadabbas teaches synthetic augmentations and creating datasets for lying poses in beds, it does not explicitly disclose generating entirely synthetic training data comprising computer-generated images of artificial subjects in positions on an artificial person support apparatus; however, Nikolenko teaches: ““synthetic image” and “synthetic data” refer to generated data or generated images, which can be produced by CGI”, [0017]; generating synthetic training data via computer generated imagery; "In accordance with an aspect of the invention, the synthetic data is generated using computer graphics to prepare artificial imagery with objects similar to the objects present in the seed data.", [0021]; the synthetic data comprises artificial imagery; when applied to the in-bed context of Ostadabbas, this naturally entails generating artificial subjects on an artificial person support apparatus based on the real seed data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Nikolenko into the system or method of Ostadabbas in order to augment the real-world captured images with labeled computer-generated synthetic data allows the system to easily and cost-effectively expand the diversity, volume, and scaling of the training dataset, thereby improving the training efficiency and accuracy of the machine learning model without the prohibitive burdens of exhaustive manual data collection. The combination of Ostadabbas and Nikolenko also teaches other enhanced capabilities.
The combination of Ostadabbas and Nikolenko further teaches:
labeling the synthetic training data based on the positions of the artificial subjects to generate labeled synthetic training data; and
(Nikolenko, "As the synthetic data is generated, labels are used to provide accurate information as part of the dataset that is used for training the system.", [0004]; labeling the synthetic training data; "In accordance with the various aspects of the invention, the data that makes up the dataset, which is used for training the system, is also labeled as part of the dataset generation", [0019]; the synthetic datasets are labeled during generation to provide accurate positional/class information)
training a machine learning model based on the labeled subject training data and the labeled synthetic training data, using supervised learning techniques, to generate a trained model to predict a subject position based on an image of the subject.
(Ostadabbas, “(b) in a computer system comprising a processor and memory, training a model for estimating human in-bed poses with the dataset”, [0055]; training a machine learning model based on the labeled subject training data; "(c) with the model, estimating a human in-bed pose from an input image.", [0056]; the trained model predicts a subject position based on an image of the subject; Nikolenko, "In accordance with one aspect of the invention, the training dataset is made up of real data and synthetic data resulting in a hybrid dataset. The training dataset is provided to the system or model and used to train the system or model", [0031]; training the machine learning model based on both the real subject training data and the synthetic training data; the combined data is used to train the system)
Regarding claims 2 and 17, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein the machine learning model comprises one or more convolutional neural networks.
(Ostadabbas, “The dataset can be used with a variety of machine learning or artificial intelligence (AI) systems, algorithms and techniques, such as, without limitation, convolution pose machines and deep neural networks”, [0144]; “It includes two convolution layers with kernel 1×1 and channel 256 and 32 followed by 3 fully connected layers”, [0268]; Nikolenko, “deep learning based models such as Mask-RCNN or DeepMask”, [0038]; employed and tested machine learning models can include convolutional neural networks)
Regarding claim 7, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, further comprising:
receiving a real-time image of a first subject on a first person support apparatus;
(Ostadabbas, "(b) transmitting to the processor one or more images of a human lying in a bed from one or more imaging devices oriented toward the bed", [0045]; "Observation Interpretation at Run-Time", [0233]; "The computer system can run the software with instructions to periodically capture LWIR/depth images", [0141]; periodically capturing and transmitting run-time (real-time) images of a human lying in a bed to the processing system)
inputting the real-time image into the trained model; and predicting a first position of the first subject based on an output of the trained model.
(Ostadabbas, "the processor is in communication with the one or more imaging devices to receive one or more images of a human lying in the bed and is operative to determine a pose of the human lying in the bed via the trained model.", [0048]; predicting the position/pose of the subject using the trained model based on the newly inputted image)
Regarding claim 8, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination further teaches the method of claim 7, further comprising displaying information about the first position of the first subject.
(Ostadabbas, "The computer system can run the software with instructions to periodically capture LWIR/depth images and display a skeleton pose figure to the user.", [0141]; outputting and displaying a skeleton pose figure, which provides visual information about the position of the subject)
Claim(s) 9-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ostadabbas et al (US20220051437A1) in view of Nikolenko et al (US20200320345A1) and further in view of Al-Ali et al (US20170055896A1).
Regarding claim 9, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination does not expressly disclose but Al-Ali teaches the method of claim 7, further comprising causing a handheld device to display information about the first position of the first subject.
(Al-Ali, "portable clinician devices 114, such as, for example, tablets, PDAs or the like, may be used by caregivers to access the required patient-specific information while at the patient's bedside.", [0088]; causing a handheld device (such as tablets or PDAs) to display required patient-specific information, which includes the orientation/position data of the subject)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the remote monitoring and alerting features of Al-Ali into the in-bed human pose estimation system of Ostadabbas in order to practically utilize the estimated pose data to track the duration a patient remains in a specific position, trigger warnings for dangerous positions or prolonged immobility to prevent pressure ulcers, and efficiently communicate this status to caregivers by displaying predetermined representative images of the patient's position on remote and handheld computing devices. The combination of Ostadabbas, Nikolenko and Al-Ali also teaches other enhanced capabilities.
Regarding claim 10, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination of Ostadabbas, Nikolenko and Al-Ali teaches the method of claim 7, further comprising causing a remote computing device to display information about the first position of the first subject.
(Al-Ali, "This representation can be displayed on the patient monitor 106 or transmitted to a nurses' station or other processing node to enable caregivers to monitor the patient's position in bed.", [0138]; transmitting the representation of the patient's position to a remote computing device, such as a nurses' station processing node, to display the information)
Regarding claim 11, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination of Ostadabbas, Nikolenko and Al-Ali teaches the method of claim 7, further comprising displaying a predetermined image associated with the first position of the first subject.
(Al-Ali, "the graphical icons are used to visually depict the detected orientation of the patient. In particular, the icons of FIGS. 13A-F show, in stick figure-type format, the patient sitting, standing, and lying in the supine position (on the back), the prone position (on the belly), on the left side, and on the right side, respectively.", [0196]; displaying predetermined images, such as graphical stick-figure icons, that are directly associated with the specific detected position/orientation of the subject)
Regarding claim 12, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination of Ostadabbas, Nikolenko and Al-Ali teaches the method of claim 7, further comprising causing a handheld device to display a predetermined image associated with the first position of the first subject.
(Al-Ali, "portable clinician devices 114, such as, for example, tablets, PDAs or the like, may be used by caregivers to access the required patient-specific information while at the patient's bedside.”, [0088]; "the graphical icons are used to visually depict the detected orientation of the patient.", [0196]; displaying predetermined images (graphical icons) of the patient's orientation and transmitting patient-specific information to handheld devices (tablets, PDAs); It would have been obvious to a person having ordinary skill in the art to display the predetermined images depicting the patient's orientation on the handheld devices explicitly taught by Al-Ali to allow a clinician to visually ascertain the patient's status on their portable device)
Regarding claim 13, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination of Ostadabbas, Nikolenko and Al-Ali teaches the method of claim 7, further comprising causing a remote computing device to display a predetermined image associated with the first position of the first subject.
(Al-Ali, "This representation can be displayed on the patient monitor 106 or transmitted to a nurses' station or other processing node to enable caregivers to monitor the patient's position in bed.", [0138]; "the graphical icons are used to visually depict the detected orientation of the patient.", [0196]; displaying predetermined images (graphical icons) of the patient's orientation and transmitting representations of the patient's position to remote computing devices (nurses' stations); It would have been obvious to a person having ordinary skill in the art to display the predetermined images on the remote computing device to allow remote caregivers to visually monitor the patient's specific position)
Regarding claim 14, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination of Ostadabbas, Nikolenko and Al-Ali teaches the method of claim 7, further comprising:
determining an amount of time that the first subject has been in the first position; and
(Al-Ali, "The patient turn and movement monitoring system can identify the present orientation of the patient and determine how long the patient has been in the present orientation.", [0011]; determining the amount of time that the subject has been in the current position/orientation)
upon determination that the first subject has been in the first position for greater than a predetermined threshold amount of time, outputting a warning.
(Al-Ali, "If the patient remains in an orientation beyond a predefined, clinician-prescribed patient orientation duration, the system can notify the patient and/or caretakers that the patient is due to be repositioned.", [0011]; outputting a warning (notification) to caretakers upon determining that the subject has remained in the position for greater than a predetermined threshold amount of time (a predefined duration))
Regarding claim 15, the combination of Ostadabbas and Nikolenko teaches its/their respective base claim(s).
The combination of Ostadabbas, Nikolenko and Al-Ali teaches the method of claim 7, further comprising:
determining whether the first subject is in a dangerous position; and
(Al-Ali, "the patient monitor determines the mobility status of the patient, e.g., whether the patient is ambulatory, standing, sitting, reclining, or falling.", [0017]; determining whether the subject is in a dangerous or prohibited position, such as falling)
upon determination that the first subject is in the dangerous position, outputting a warning.
(Al-Ali, "In certain aspects, the wireless monitoring system can include an alert system to alert the caregiver that the patient is falling, getting out of bed, or otherwise moving in a prohibited manner or in a manner that requires caregiver attention.", [0017]; outputting a warning (alert) upon determining that the subject is in a dangerous or prohibited position)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 5/30/2026
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