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 .
Information Disclosure Statement
The information disclosure statement (IDS) was filed on 02/21/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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. Such claim limitations are “an image capturing module…”, “a pose estimation module…”, “an accident prevention module…” in claims 1, 2, 4, 6-18.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-9, 13, 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et. al. (AU Pub. No. 2021101323A4).
As per claim 1, Wang teaches “An accident prevention system for early detection of a potential accident of a target user, comprising:
an image capturing module arranged to capture one or more images associated with the movement of the target user within a real-world environment; (See paragraph 26 “[0026] Our project is based on the Microsoft Azure Kinect depth camera, which is a device that can capture depth frames. A depth frame shows the distance from the camera and object in each pixel. A windows computer is applied to collect depth frames from the depth camera using the Microsoft Azure Kinect Software development kit in the C++ environment. Once depth frames are collected, skeleton data, including the coordinates of joints in the human body, is extracted from the depth frames. Both skeleton data and depth frame should be written into a named pipe (Interprocess Communications) once the Python program requests a new frame. The Python program can read skeleton data and depth frames from the named pipe. The Python program sends depth frames to the server via the Real-Time Messaging Protocol (RTMP). Limited amount of depth frame is also stored in a buffer so that a video can be generated later.” See also paragraph 31 “[0031] Furthermore, the systems will also display a real-time prediction of the patient's fall risk level, computed by the local computer 103 using Al and machine learning algorithms. If the predicted risk is over a preset threshold, the system will automatically send the alert to relevant healthcare workers 208 so that they can quickly attend to the patient's room to intervene and prevent a potential patient 101 fall.” Wang
“a pose estimation module arranged to estimate the pose of the target user based on the captured image associated with the movement of the target user within the real-world environment; and
an accident prediction module arranged to predict the potential hazardous event of the target user within the real-world environment based on the estimated pose.” (See paragraphs 37-38 “[0037]…Furthermore, CaptureFall has an extremely useful function that wearable sensors do not possess - it can assess the risk of the fall and only alert nurses of the danger if the patient's fall is serious. Using the skeletal tracking system, CaptureFall analyses the movements and positions of the patient's joints and limbs and makes a risk level assessment. The longer the patient remains on the floor after the fall, the higher the risk assessment. The CaptureFall system will then alert nurses to the fall once it reaches a certain risk level.” See also paragraphs 10 and 17. Wang)
As per claim 2, Wang teaches “An accident prevention system in accordance with claim 1, wherein the accident prediction module further comprises a machine learning network model configured to determine one or more parameters associated with the potential hazardous event.” (See paragraphs 10-11 “[0011] The proposed fall detection algorithm takes the depth frame captured by the sensor as an input, then retrieves the spatial coordinates of the body joints of a person and other information from the depth frame. With the extracted real-time data, the developed machine learning method is able to successfully detect patient falls with an accuracy of at least 90%. With regards to personal security, the privacy of both patients and hospital workers can be protected by transforming the appearance of those monitored into a simple stick-figure format.” See also paragraphs 21-26. Wang)
As per claim 3, Wang teaches “An accident prevention system in accordance with claim 1, wherein the identity of the target user is filtered through the pose estimation whereby the privacy of the target user is protected.” (See paragraphs 37-39 “[0038] With regards to vision-based devices, CaptureFall also utilises a camera that streams the patient's room, and hence shares the controversy surrounding the privacy of patients. However, footage of patients captured by CaptureFall can reduce the patient's appearance to a skeletal format, thus completely protecting the patient's identity. Additionally, unlike many other vision-based devices, the CaptureFall skeletal tracking system is able to track the patient's body and movements through furniture, allowing falls obscured by furniture to be still picked up and analysed by the CaptureFall system by employing prediction of patient positions. It can also detect falls with multiple people in the same room, a feature that is non-existent in many vision-based devices.” See also paragraph 11 “[0011] The proposed fall detection algorithm takes the depth frame captured by the sensor as an input, then retrieves the spatial coordinates of the body joints of a person and other information from the depth frame. With the extracted real-time data, the developed machine learning method is able to successfully detect patient falls with an accuracy of at least 90%. With regards to personal security, the privacy of both patients and hospital workers can be protected by transforming the appearance of those monitored into a simple stick-figure format.” Wang)
As per claim 4, Wang teaches “An accident prevention system in accordance with claim 3, wherein the pose estimation module is configured to generate a graphical representation associated with the estimated pose of the target user from the captured image.” (See paragraphs 11 “[0011] The proposed fall detection algorithm takes the depth frame captured by the sensor as an input, then retrieves the spatial coordinates of the body joints of a person and other information from the depth frame. With the extracted real-time data, the developed machine learning method is able to successfully detect patient falls with an accuracy of at least 90%. With regards to personal security, the privacy of both patients and hospital workers can be protected by transforming the appearance of those monitored into a simple stick-figure format.” See also paragraph 43 “0043] …Thus, mechanisms would be put into place to ensure the safe storage of any video footage and processing of videos (such as modifying a person's appearance to a stick-figure format) would be undertaken to protect the identity and relieve the concerns for patients and hospital staff who will experience this ongoing surveillance…” See also paragraphs 30-32 and 38 “[0030] The fall detection and prevention system provides a feature that allows the healthcare workers 208 to monitor the actions of the patients 101 in real time by watching the live stream session recorded by the depth sensors. With the camera set in different locations (e.g. bathroom), the mobile application provides a page that shows one or more live stream sessions…” Wang)
As per claim 5, Wang teaches “An accident prevention system in accordance with claim 4, wherein the generated graphical representation associated with the estimated pose of the target user is independent of the identity of the target user.” (Examiner interprets “independent” as protecting the privacy of the target. See paragraphs 30-32, 43, 11 and 37-38 “[0038] With regards to vision-based devices, CaptureFall also utilises a camera that streams the patient's room, and hence shares the controversy surrounding the privacy of patients. However, footage of patients captured by CaptureFall can reduce the patient's appearance to a skeletal format, thus completely protecting the patient's identity. Additionally, unlike many other vision-based devices, the CaptureFall skeletal tracking system is able to track the patient's body and movements through furniture, allowing falls obscured by furniture to be still picked up and analysed by the CaptureFall system by employing prediction of patient positions. It can also detect falls with multiple people in the same room, a feature that is non-existent in many vision-based devices.” Wang)
As per claim 6, Wang teaches “An accident prevention system in accordance with claim 5, wherein the pose estimation module is configured to generate a skeleton associated with the estimated pose of the target user.” (See paragraphs 37-38 “[0038] With regards to vision-based devices, CaptureFall also utilises a camera that streams the patient's room, and hence shares the controversy surrounding the privacy of patients. However, footage of patients captured by CaptureFall can reduce the patient's appearance to a skeletal format, thus completely protecting the patient's identity. Additionally, unlike many other vision-based devices, the CaptureFall skeletal tracking system is able to track the patient's body and movements through furniture, allowing falls obscured by furniture to be still picked up and analysed by the CaptureFall system by employing prediction of patient positions. It can also detect falls with multiple people in the same room, a feature that is non-existent in many vision-based devices.” See paragraphs 11 “[0011] The proposed fall detection algorithm takes the depth frame captured by the sensor as an input, then retrieves the spatial coordinates of the body joints of a person and other information from the depth frame. With the extracted real-time data, the developed machine learning method is able to successfully detect patient falls with an accuracy of at least 90%...” See also paragraphs 23-28. Wang)
As per claim 7, Wang teaches “An accident prevention system in accordance with claim 1, wherein the accident prediction module is configured to predict the occurrence of the potential hazardous event prior to the actual occurrence of the predicted hazardous event.” (See paragraphs 10, 12, 14. “0010] The proposed fall detection system, named CaptureFall, consists of a server, a mobile application and one or more depth sensors, each sensor is connected with a computer to execute fall detection and risk analysis algorithms. When a fall is detected or is predicted to occur, the computer sends notifications to the server along with a short recording captured by the depth sensor, and the developed application will automatically send a notification to the doctors and nurses associated with the patient. This allows hospital staff to quickly intervene and stop the fall before it occurs, or provide immediate assistance to the patient after the fall has occured to mitigate the severity of any fall-inflicted injury.” “[0012] Fall prediction is a method intended to predict falls risk and send alerts before a fall occurs… ” See also paragraphs 37-39. Wang)
As per claim 8, Wang teaches “An accident prevention system in accordance with claim 7, wherein the accident prediction module is configured to compare the estimated pose against one or more predetermined pose references.” (See paragraphs 12-20 and 21-28) “[0014] Our fall prediction method is based on similar patterns and activities that patients undertake that could cause them to be at risk of a fall. Therefore, features should be able to capture action that may lead to falling. Changing their posture could be a high fall-risk action for elderly people as it could lead to postural hypotension (Nyberg, 1997) (13). Based on research conducted by the Canadian Institutes of Health Research (CIHR) (14) team during the period between 2007 and 2010, 227 falls by elderly people were captured and analysed. The most common cause of falls in elderly people was the incorrect transfer or shifting of body weight, which accounted for 41% of all captured falls. This can be caused whilst performing ordinary activities such as walking forward, turning, getting up, sitting down, etc. Therefore, for the purpose of fall prediction, we found a feature which is closely related to the balance of human body posture. [0015] Considering the main scenario where aged people need help when attempting to sit down or stand up, a feature of the detection technology should capture the transition between a sitting and standing posture. This feature is the horizontal distance between the pelvis joint and the feet joints' centre which can change significantly if the person is about to lose balance or fall. Thus, we choose the speed of this distance change as one of the features used to predict fall risk. [0016] Another important feature is the vertical speed of the pelvis joint. A high vertical speed can happen when the person is about to lose balance or has a tendency to fall. These kinds of actions may require too much strength for elderly people. Thus, this is also a factor in calculating the risk of fall. [0017] Finally, the time that a person lays down or sits on the floor should be taken into consideration. If someone is lying on the floor for an extended period, the underlying reason for their inability to get up could be a serious health condition like a heart attack. According to research by the CIHR team, 11% of falls in elderly people lead to collapse or loss of consciousness (14). Therefore, our fall prediction algorithm also takes these scenarios into account. We regard the pelvis joint located below a specific height as an indication that the person is on the floor. Thus, the time that the pelvis joint is below a specific height is used as a feature to predict fall risk.[0018] Three features mentioned above are chosen to predict the fall risk: " The horizontal speed between the pelvis joint and the feet joints' centre " The vertical speed of the pelvis joint. " The length of time in which the pelvis joint is below a specific height” Wang)
As per claim 9, Wang “An accident prevention system in accordance with claim 7, wherein the accident prediction module is arranged to determine the elevated level of the target user relative to the ground level in the real-world environment.” (See paragraphs 12-20, 21-28 “[0027] In our designed method, the skeleton data is analyzed by the Python program and organized into several features. These features are: " The distance between pelvis joint and floor " The vertical speed of the pelvis joint. " Step symmetric index which is calculated by the difference of step length between different feet. " Trunk sway index which is calculated by the angle that the trunk rolls from the normal vector. " Balance index which is calculated by the horizontal distance between the pelvis joint and the centre of both feet.” “0022] The second algorithm takes the magnitude of the acceleration of the waist IMU, and vertical height of the spine base joint from the floor into consideration. They use the vector-mathematics to calculate the real-time vertical height of the spine base joint of the subject from the floor. If the vertical height of the spine base joint is below 20cm, and the acceleration of the waist obtained by IMU is over 3g within the 2s time window, the center of the time window is the time instance where the first feature is triggered, the system will recognize the fall behaviour of the subject… [0023] On the other hand, Nizam et al.'s team (2018) (15) proposed a fall detection method which is purely based on the Microsoft Azure Kinect. The proposed method adopts two basic features for fall detection: the velocity and the height of the subject skeleton. The algorithm uses the floor plan and the human body skeleton coordinates generated from the Kinect as the basis for the computation of the velocity and height.” See also paragraphs 37-39. Wang)
As per claim 13, Wang teaches “An accident prevention system in accordance with claim 7, wherein the accident prediction module is configured to determine the velocity of the target user.” (See paragraphs 23-26 “[0025] For the entire fall detection algorithm, the system will calculate the fall risk factor, the height and the velocity of the subject. If a high velocity of the subject is detected, a potential fall alert will be directly triggered for fall confirmation. If the fall risk factor is high, the system will calculate another new velocity and height for the fall detection. If both the fall risk factor and the velocity is low, the system will use the height and acceleration (derivative of velocity) to identify the potential fall event. The accuracy of the proposed method is 88.57% based on the University of Rzeszow Fall Detection (URFD) dataset” Wang)
As per claim 15, Wang teaches “An accident prevention system in accordance with claim 1, wherein the accident prediction module is configured to determine the occurrence of an actual hazardous event in real-time.” (See paragraphs 30-31 and 37-39 “[0037]… It also has a higher accuracy as its skeletal tracking system will be able to differentiate between real falls and other types of falls that wearable sensors cannot detect, such as a braced fall and rolling onto the floor. Furthermore, CaptureFall has an extremely useful function that wearable sensors do not possess - it can assess the risk of the fall and only alert nurses of the danger if the patient's fall is serious. Using the skeletal tracking system, CaptureFall analyses the movements and positions of the patient's joints and limbs and makes a risk level assessment. The longer the patient remains on the floor after the fall, the higher the risk assessment. The CaptureFall system will then alert nurses to the fall once it reaches a certain risk level. This is beneficial as it reduces the potential of false positives occurring, reducing the effect of alarm fatigue of other devices previously mentioned.” See also paragraphs 21-28 and 10-20. “[0031] Furthermore, the systems will also display a real-time prediction of the patient's fall risk level, computed by the local computer 103 using Al and machine learning algorithms. If the predicted risk is over a preset threshold, the system will automatically send the alert to relevant healthcare workers 208 so that they can quickly attend to the patient's room to intervene and prevent a potential patient 101 fall.” Wang )
As per claim 16, Wang teaches “An accident prevention system in accordance with claim 7, wherein the accident prediction module is configured to record a high-risk activity of the target user contributing to the predicted hazardous event.” (See paragraphs 30-38 and 21-28. See also paragraphs 10-19. “[0014] Our fall prediction method is based on similar patterns and activities that patients undertake that could cause them to be at risk of a fall. Therefore, features should be able to capture action that may lead to falling. Changing their posture could be a high fall-risk action for elderly people as it could lead to postural hypotension (Nyberg, 1997) (13)…” “[0024] Besides this, the team also introduced a falls risk factor in the fall detection process. The different fall risk level of the subject can lead to different orders in which the two basic features (height and velocity) are applied in the fall detection procedure. The fall risk factor is computed based on the three parameters including step symmetry, trunk sway, and arm spread- features which are widely used in the fall risk assessment. These features are determined based on the obtained subject skeleton data from the Kinect sensor. [0025] For the entire fall detection algorithm, the system will calculate the fall risk factor, the height and the velocity of the subject. If a high velocity of the subject is detected, a potential fall alert will be directly triggered for fall confirmation. If the fall risk factor is high, the system will calculate another new velocity and height for the fall detection. If both the fall risk factor and the velocity is low, the system will use the height and acceleration (derivative of velocity) to identify the potential fall event. The accuracy of the proposed method is 88.57% based on the University of Rzeszow Fall Detection (URFD) dataset.” “0031] Furthermore, the systems will also display a real-time prediction of the patient's fall risk level, computed by the local computer 103 using Al and machine learning algorithms. If the predicted risk is over a preset threshold, the system will automatically send the alert to relevant healthcare workers 208 so that they can quickly attend to the patient's room to intervene and prevent a potential patient 101 fall.” See also paragraphs 28, 30, 33 and 10 for recording. Wang)
As per claim 17, Wang teaches “An accident prevention system in accordance with claim 16, wherein the accident prediction module is configured to record the statistics associated with the high-risk activity of the target user.” (See paragraphs 30-38 and 21-28. See also paragraphs 10-19. “[0014] Our fall prediction method is based on similar patterns and activities that patients undertake that could cause them to be at risk of a fall. Therefore, features should be able to capture action that may lead to falling. Changing their posture could be a high fall-risk action for elderly people as it could lead to postural hypotension (Nyberg, 1997) (13)…” “[0024] Besides this, the team also introduced a falls risk factor in the fall detection process. The different fall risk level of the subject can lead to different orders in which the two basic features (height and velocity) are applied in the fall detection procedure. The fall risk factor is computed based on the three parameters including step symmetry, trunk sway, and arm spread- features which are widely used in the fall risk assessment. These features are determined based on the obtained subject skeleton data from the Kinect sensor. [0025] For the entire fall detection algorithm, the system will calculate the fall risk factor, the height and the velocity of the subject. If a high velocity of the subject is detected, a potential fall alert will be directly triggered for fall confirmation. If the fall risk factor is high, the system will calculate another new velocity and height for the fall detection. If both the fall risk factor and the velocity is low, the system will use the height and acceleration (derivative of velocity) to identify the potential fall event. The accuracy of the proposed method is 88.57% based on the University of Rzeszow Fall Detection (URFD) dataset.” “0031] Furthermore, the systems will also display a real-time prediction of the patient's fall risk level, computed by the local computer 103 using Al and machine learning algorithms. If the predicted risk is over a preset threshold, the system will automatically send the alert to relevant healthcare workers 208 so that they can quickly attend to the patient's room to intervene and prevent a potential patient 101 fall.” See also paragraphs 28, 30, 33 and 10 for recording. “[0026]… Once depth frames are collected, skeleton data, including the coordinates of joints in the human body, is extracted from the depth frames. Both skeleton data and depth frame should be written into a named pipe (Interprocess Communications) once the Python program requests a new frame. The Python program can read skeleton data and depth frames from the named pipe. The Python program sends depth frames to the server via the Real-Time Messaging Protocol (RTMP)… [0027] In our designed method, the skeleton data is analyzed by the Python program and organized into several features.” Wang )
As per claim 18, Wang teaches “An accident prevention system in accordance with claim 1, wherein the accident prediction module is configured to alert a remote user the occurrence of a potential hazardous event.” (See paragraphs 37-39 and 29-36 “[0030] The fall detection and prevention system provides a feature that allows the healthcare workers 208 to monitor the actions of the patients 101 in real time by watching the live stream session recorded by the depth sensors. With the camera set in different locations (e.g. bathroom), the mobile application provides a page that shows one or more live stream sessions. This functionality allows medical staff to keep an eye on their patients through this panel and possibly predict an accident before it happens. Even if all medical staff are too busy to sit in front of the screen, the system will automatically react to emergency situations. For example, once the camera detects a patient falling down, the application will send a notification to the medical staff and their family members, so that they can react to the situation immediately. RTMP transmission protocol is used to send the captured data from the local computer 103 to the server 104.” “[0031] Furthermore, the systems will also display a real-time prediction of the patient's fall risk level, computed by the local computer 103 using Al and machine learning algorithms. If the predicted risk is over a preset threshold, the system will automatically send the alert to relevant healthcare workers 208 so that they can quickly attend to the patient's room to intervene and prevent a potential patient 101 fall.” Wang)
As per claim 19, Wang teaches “An accident prevention system in accordance with claim 18, further comprising a display module configured to graphically display to the remote user in real-time a skeleton associated with the estimated pose of the target user.” (See paragraphs 37-39 and 29-36 “[0030] The fall detection and prevention system provides a feature that allows the healthcare workers 208 to monitor the actions of the patients 101 in real time by watching the live stream session recorded by the depth sensors. With the camera set in different locations (e.g. bathroom), the mobile application provides a page that shows one or more live stream sessions. This functionality allows medical staff to keep an eye on their patients through this panel and possibly predict an accident before it happens. Even if all medical staff are too busy to sit in front of the screen, the system will automatically react to emergency situations. For example, once the camera detects a patient falling down, the application will send a notification to the medical staff and their family members, so that they can react to the situation immediately. RTMP transmission protocol is used to send the captured data from the local computer 103 to the server 104.” “[0038] With regards to vision-based devices, CaptureFall also utilises a camera that streams the patient's room, and hence shares the controversy surrounding the privacy of patients. However, footage of patients captured by CaptureFall can reduce the patient's appearance to a skeletal format, thus completely protecting the patient's identity. Additionally, unlike many other vision-based devices, the CaptureFall skeletal tracking system is able to track the patient's body and movements through furniture, allowing falls obscured by furniture to be still picked up and analysed by the CaptureFall system by employing prediction of patient positions. It can also detect falls with multiple people in the same room, a feature that is non-existent in many vision-based devices.” Wang)
As per claim 20, Wang teaches “An accident prevention system in accordance with claim 18, wherein the display module is further configured to display the information associated with the predicted hazardous event.” (See paragraphs 37-39 and 29-36. “[0031] Furthermore, the systems will also display a real-time prediction of the patient's fall risk level, computed by the local computer 103 using Al and machine learning algorithms. If the predicted risk is over a preset threshold, the system will automatically send the alert to relevant healthcare workers 208 so that they can quickly attend to the patient's room to intervene and prevent a potential patient 101 fall.” Wang)
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.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Kim (KR Pub. No. 20250046717 A) .
As per claim 10, Wang teaches “An accident prevention system in accordance with claim 7, wherein the accident prediction module is configured to…”, however Wang does not completely teach “determine the presence of a real-world object within the real-world environment associated with the captured image.”
Kim teaches “determine the presence of a real-world object within the real-world environment associated with the captured image.” (See page 4 last paragraph and page 5 paragraphs 1-6. “Next, objects included in the input two-dimensional image are detected, and each is classified and recognized (S2200). In detail, the task is to broadly classify objects in the input 2D image into people, objects, animals, etc., and define (label) each object. Next, skeletal features of objects recognized as humans among the recognized objects are extracted (S2300)”. It is based on the real world as seen on page 2 paragraphs 2-8. See also page 6 paragraphs 1-7. Kim)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Wang with the teachings of Kim to also determine the presence of objects. The modification would have been motivated by the desire to improve detection accuracy and faster response to incidents and accidents, therefore it is an improvement, as suggested by Kim (See page 5 paragraphs 1-7 “In detail, the task is to broadly classify objects in the input 2D image into people, objects, animals, etc., and define (label) each object. Next, skeletal features of objects recognized as humans among the recognized objects are extracted (S2300). Here, the skeleton features of the person are features that express the positions of the bones according to the joints of the person based on coordinates, for example, features that express the positions of the head, both shoulders, neck, both elbows, both hips, both hands, both knees, and both feet as coordinates. Next, the extracted human skeleton features are input into the learning model created by the learning model creation method for action recognition and estimation based on the 3D image (S1000) (S2400). Next, the skeleton features of the undetected areas among the extracted human skeleton features are reconstructed into 3D skeleton features viewed from a 360-degree direction used in learning the learning model (S2500)… Accordingly, there is an advantage in that the person's actions can be detected based on the skeleton features of the entire area of the person, thereby improving detection accuracy.” See also page 5 last paragraph and page 6 paragraphs 1-7 “Accordingly, the present invention has the advantage of being able to predict future behavior based on
behavior detected in a video of a person being filmed, thereby being able to predict and quickly respond to incidents and accidents such as crimes and suicides.” )
As per claim 11, Wang teaches “An accident prevention system in accordance with claim 10, wherein the accident prediction module is configured to…”, however Wang does not completely teach “determine the interaction between the target user and the real-world object within the real-world environment.”
Kim teaches “determine the interaction between the target user and the real-world object within the real-world environment.” (See page 6 paragraphs 1-8. “Next, when both a person and an object are recognized in the input 2D image (S2200), it is determined whether there is an interaction between the person and the object, and if there is an interaction, the type of interaction is detected and predicted (S2600)” Kim)
As per claim 12, Wang teaches “An accident prevention system in accordance with claim 11, wherein the accident prediction module is configured to determine…”, however Wang does not completely teach “the lifting of a real-world object by the target user.”
Kim teaches “the lifting of a real-world object by the target user.” (See page 6 paragraphs 1-8. “Next, when both a person and an object are recognized in the input 2D image (S2200), it is determined whether there is an interaction between the person and the object, and if there is an interaction, the type of interaction is detected and predicted (S2600)… Furthermore, by calculating the distance between the skeletal features of the person and the 3D object of the object, the movement speed and the movement direction of each, it is possible to determine whether there is an interaction between the person and the object, and if it is determined that there is an interaction between the person and the object, it is possible to detect and predict what the interaction is (S2600)… For example, the act of interaction between the person and the object refers to an act including an act of the person wearing the object, an act of the person lifting the object, an act of the person sitting on the object, an act of the person leaning on the object, etc” Kim)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Littwitz et. al. (US Pub. No. 20220323285 A1 ).
As per claim 14, Wang teaches “An accident prevention system in accordance with claim 7, wherein the accident prediction module is configured to…”, however Wang does not completely teach “determine the obstacle obstructing the movement of the target user within the real-world environment.”
Littwitz teaches “determine the obstacle obstructing the movement of the target user within the real-world environment.” (See paragraphs 7-22, 123-124, 88, 39-45 and 151-153. “[0010] In at least certain embodiments there may be provided a method and/or system for identifying obstacles in a walking path that may relate to the position and/or orientation of where a foot of a user may be laid down on a ground surface and/or a projected path/route that a foot may traverse to perform a walking step (possibly an expected step to be performed—e.g. an expected step that will be taken three steps from now or the like)… 0015] In at least certain embodiments, such system may include measures for example for predicting a location and/or timing that an obstacle may potentially pose a problem to a person—that takes into consideration issues such as delay time from obstacle detection by the person and his/her typical response time, which may be defined as depending on his/her physical condition…” “[0152] Image data (either with or without determination of ‘usefulness’) may be processed at step 103 to detect objects of interest within the images, such as potential obstacles that may impede a user's walking.” Littwitz)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Wang with the teachings of Littwitz to determine an obstacle that obstructs movement. The modification would have been motivated by the desire to increase safety when walking, provide better assistance, and avoid accidents, therefore it is an improvement, as suggested by Littwitz (See paragraphs 10-15, and 76 “0076] In an aspect of the present invention, at least certain system embodiment may be employ learning techniques for assisting in avoiding accidents in a known, possibly closed environment, such as a home of a user.” See paragraph 2 “…When a mature or disabled individual misses to identify and react in time to an obstacle, unnecessary falls and injuries may relatively often occur. Various solutions are available for assisting old people and disabled users with walking or reacting to their environment.” See also paragraph 7 “[0007] In a broad aspect, at least certain embodiments of the present invention may be defined as relating to assisting a person in walking and/or moving by providing wider and/or additional fields or view and/or indications of expected element(s) in a walking route that lie possibly ahead or transverse to direction of advancement and/or outside of his/her eye-sight—possibly as virtual indications in the field of view of such person's eye sight or physical warning signals such as vibration or audio”. See also paragraphs 20-21 “[0020] When a person matures in age and/or encounters difficulty in walking, the glance downwards towards the ground increases likelihood of missing to identify obstacles in time in the route ahead, and thus react to such sudden appearing obstacles possibly too late. [0021] Therefore, a system's beacon-axis directed to gathering data relating to the direction of walking—may assist in obtaining information relating to such potential obstacles ahead and in return provide feedback regarding same in a reasonable distance/time in advance so that e.g. a pedestrian can respond in time.” Littwitz)
Conclusion
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/DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675
/VU LE/Supervisory Patent Examiner, Art Unit 2668
