DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to the Application Filed on 04/30/2024.
Claims 1-17 are pending in this application.
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 limitation(s) is/are:
“the processing module” in claim 14
“the comparison module” in claim 14
“the intervention module” in claim 14
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
“the processing module” corresponds to element 52 in Figure 6, “the processor 52 includes a processing module, a comparison module, and an intervention module,” (Applicant Spec. ¶ [0235]).
“the comparison module” corresponds to element 52 in Figure 6, “the processor 52 includes a processing module, a comparison module, and an intervention module,” (Applicant Spec. ¶ [0235])
“the intervention module” corresponds to element 52 in Figure 6, “the processor 52 includes a processing module, a comparison module, and an intervention module,” (Applicant Spec. ¶ [0235])
If applicant does not intend to have this/these limitation(s) 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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because they cover both statutory and non-statutory embodiments (under the broadest reasonable interpretation of the claim when read in light of the specification and in view of one skilled in the art) and embraces subject matter that is not eligible for patent protection, and therefore is directed to non-statutory subject matter.
“[a] transitory, propagating signal … is not a “process, machine, manufacture, or composition of matter.” Those four categories define the explicit scope and reach of subject matter patentable under 35 U.S.C. § 101; thus, such a signal cannot be patentable subject matter.” (In re Petrus A.C.M. Nuijten; Fed Cir, 2006-1371, 9/20/2007). Claim 17 recites “A computer-readable storage medium.” The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to a signal per se. Specifically, Applicant’s specification describes “the storage medium can be a volatile or non-volatile computer-readable storage medium” (¶ [0259]). As a result, the claim is drawn to a medium that covers transitory embodiments. Thus, the claim is not eligible under 35 USC 101. It is recommended to amend the claim by adding the limitation “non-transitory” to the preamble of the claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pathak et al. (US 2017/0287146 A1, hereinafter, “Pathak”).
Regarding claim 1, Pathak discloses a health intervention and correction method based on vision and sensors (See Pathak, [0053] FIG. 3 is a flow chart illustrating a process 300 for collecting and modeling user movements; [0059] FIG. 4 is a flow chart illustrating a process 400 for analyzing motion data for medical treatment and diagnosis by a motion analysis system), comprising:
acquiring various motion monitoring data (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity…the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data) obtained by detecting a user in a target state (See Pathak, [0031] the motion data indicates the user is performing a specific task; [0054] capture motion of the user while the user is performing ordinary activities, such as walking, performing household chores, eating. Examiner considers the target state to be any activity mentioned above);
determining a target motion model of the user (See Pathak, [0034] pre-processes the captured data by generating a user model; [0055] constructs a model of the user form the captured 3D data. Examiner considers the user model as a target motion model) based on the various motion monitoring data (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity…the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data), wherein the target motion model is used to indicate state information of the user (See Pathak, [0034] user model (e.g., location and tracking of specific points associated with the user). Examiner considers the location and tracking as state information) at each detection moment (See Pathak, [0030] movement of those points in space over a period of time (e.g., a movement of the point from one location (xi, yi, zi, ti) to another location (xj, yj, zj, tj) over an interval of time). Examiner considers the change in movement as a detection moment in which the user is being detected in motion);
comparing the target motion model with a target reference model (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease. Examiner considers the baseline model as a target reference model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results); and
generating health intervention and correction information based on the comparison results (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information which is generated based on analysis of motion features), and sending the health intervention and correction information to a health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414). Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device).
Regarding claim 2, in which claim 1 is incorporated, Pathak discloses wherein acquiring the various motion monitoring data obtained (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity…the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data) by detecting the user in the target state (See Pathak, [0031] the motion data indicates the user is performing a specific task; [0054] capture motion of the user while the user is performing ordinary activities, such as walking, performing household chores, eating. Examiner considers the target state to be any activity mentioned above) comprises:
acquiring motion monitoring data obtained by performing motion detection on the user by a plurality of wearable devices (See Pathak, [0032] the wearable device 155 may be a plurality of different or the same wearable devices, such as wearable devices along each joint of a user, a wearable device on limbs of interests; [0032] motion data from one or more assistance devices (e.g., a cane or walker tracking user motions via one or more accelerometers, an eating implement that tracks user motions, etc.)) in communication connection with a terminal device to obtain the various motion monitoring data (See Pathak, [0032] user motion data is transferred, such as by a wireless communication link, to one or more of depth mapping capture system 120. Examiner considers the device to have an established connection to a device for data to be transferred).
Regarding claim 3, in which claim 2 is incorporated, Pathak discloses wherein the various motion monitoring data is obtained by performing motion detection on various monitoring parts of the user (See Pathak, [0032] motion data captured by a wearable device 155; [0032] plurality of different or the same wearable devices, such as wearable devices along each joint of a user. Examiner considers the monitoring parts to be the joints at which the wearable devices are placed along), wherein the monitoring parts comprise but are not limited to the following parts: skeletons, joints and muscles (See Pathak, [0032] plurality of different or the same wearable devices, such as wearable devices along each joint of a user. Examiner considers the monitoring parts to be the joints at which the wearable devices are placed along);
the motion monitoring data comprise but are not limited to the following types: visual images, photoelectric data, neuroelectrophysiological data, audio data and speed data (See Pathak, [0054] the three-dimensional data may be depth sensor data and includes a plurality of frames of image data).
Regarding claim 4, in which claim 1 is incorporated, Pathak discloses wherein determining the target motion model of the user (See Pathak, [0034] pre-processes the captured data by generating a user model. Examiner considers the user model as a target motion model) based on the various motion monitoring data (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity …the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data) comprises:
transforming each of the various motion monitoring data into motion posture data of the user (See Pathak, [0036] postural sway observed in a user who is sitting in a chair, standing, or otherwise in a resting position, asked to make a movement (e.g., standing from the chair), and then attempts to remain motionless, is another indicator of a chronic neurodegenerative disease that can be captured and quantified using depth sensor data. Examiner considers the depth data, a type of 3D data, to be used to get posture data of a user by transforming, or converting, the sensor data to the type of data of interest); and
performing data fusion (See Pathak, [0012] FIG. 6B illustrates on embodiment of movement of the skeletal model of the user over a period of time. Examiner considers the combination of multiple type of data as data fusion as see in the skeletal model of Fig. 6B) on the motion posture data obtained after transforming the various motion monitoring data (See Pathak, [0036] postural sway observed in a user who is sitting in a chair, standing, or otherwise in a resting position, asked to make a movement (e.g., standing from the chair), and then attempts to remain motionless, is another indicator of a chronic neurodegenerative disease that can be captured and quantified using depth sensor data. Examiner considers the depth data, a type of 3D data, to be used to get posture data of a user by transforming, or converting the sensor data to the type of data of interest) based on the detection moment of the motion posture data (See Pathak, [0057] each node/point in the model may be tracked and associated with a series of (x, y, z, t) coordinates that provide a spatial (x, y, z) and a temporal (t) location of that point) to obtain the target motion model (See Pathak, [0034] pre-processes the captured data by generating a user model. Examiner considers the user model as a target motion model).
Regarding claim 5, in which claim 4 is incorporated, Pathak discloses wherein (See Pathak, [0012] FIG. 6B illustrates on embodiment of movement of the skeletal model of the user over a period of time. Examiner considers the combination of multiple type of data as data fusion as see in the skeletal model of Fig. 6B) on the motion posture data obtained after transforming the various motion monitoring data (See Pathak, [0036] postural sway observed in a user who is sitting in a chair, standing, or otherwise in a resting position, asked to make a movement (e.g., standing from the chair), and then attempts to remain motionless, is another indicator of a chronic neurodegenerative disease that can be captured and quantified using depth sensor data. Examiner considers the depth data, a type of 3D data, to be used to get posture data of a user by transforming, or converting the sensor data to the type of data of interest) based on the detection moment of the motion posture data (See Pathak, [0057] each node/point in the model may be tracked and associated with a series of (x, y, z, t) coordinates that provide a spatial (x, y, z) and a temporal (t) location of that point) to obtain the target motion model (See Pathak, [0034] pre-processes the captured data by generating a user model. Examiner considers the user model as a target motion model) comprises:
determining motion posture data with the same detection moment in the motion posture data obtained after transforming the various motion monitoring data (See Pathak, [0057] each node/point in the model may be tracked and associated with a series of (x, y, z, t) coordinates that provide a spatial (x, y, z) and a temporal (t) location of that point. Examiner considers the posture data to have a time point associated with it. To correlate different motion data, one would use the time information when it is the same);
processing the motion posture data with the same detection moment to obtain target motion posture data at the detection moment (See Pathak, [0057] each node/point in the model may be tracked and associated with a series of (x, y, z, t) coordinates that provide a spatial (x, y, z) and a temporal (t) location of that point. Examiner considers data fusion to combine multiple factors, one being time, into one user model as seen in Fig. 6B. By taking time factor into account, the data is processed to yield combined data like seen in the spatial coordinate); and
determining the target motion model (See Pathak, [0034] pre-processes the captured data by generating a user model. Examiner considers the user model as a target motion model) based on the target motion posture data at each detection moment (See Pathak, [0057] The collected motion of the points therefore provides the motion of the model over time, and is indicative of the captured user's movements. Spatial location and time are used and seen in the user model (Fig. 6B) to represent user movement at any given time).
Regarding claim 6, in which claim 1 is incorporated, Pathak discloses wherein the target reference model is a reference motion model (See Pathak, [0035] the baseline models may be models captured of users with known chronic neurodegenerative or musculoskeletal conditions), and comparing the target motion model with the target reference model (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease. Examiner considers the baseline model as a target reference model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results) comprises:
determining object information of the user based on the target motion model, wherein the object information is used to indicate a motion state of the user and/or object attributes (See Pathak, [0036] both amplitude and frequency of arm swing can be extracted from movements of a user's model. Examiner considers the amplitude and frequency as object information);
determining a reference motion model matched with the object information (See Pathak, [00035] the baseline models are machine learning models trained using one or more movement features… such that each trained machine learning model represents the motion patterns of an entire population of people having a chronic neurodegenerative or musculoskeletal condition. Examiner considers the machine learning models to be trained to represent one specific population. The objected information is used to match movement to a trained model to be used as the reference model for a specific state), wherein the matched reference motion model is used to indicate expected posture information of the user under the motion state (See Pathak, [0035] FIG. 7B, however, illustrates a side view 702-b of a frame of a model generated from a user's movements who is afflicted with a chronic neurodegenerative or musculoskeletal disease; [0036] comparison of the baseline model of FIG. 7A with the model of FIG. 7B enables motion assessment system 130 to locate certain motion features relevant to the discovery and diagnosis of a chronic neurodegenerative or musculoskeletal disease, such as forward leaning posture 710. Examiner considers the matched baseline model to be one of a person with a musculoskeletal disease which would give the expected posture of forward leaning during motion to indicate a diagnosis of the disease); and
comparing the matched reference motion model (See Pathak, [0035] FIG. 7B, however, illustrates a side view 702-b of a frame of a model generated from a user's movements who is afflicted with a chronic neurodegenerative or musculoskeletal disease; [0036] comparison of the baseline model of FIG. 7A with the model of FIG. 7B enables motion assessment system 130 to locate certain motion features relevant to the discovery and diagnosis of a chronic neurodegenerative or musculoskeletal disease, such as forward leaning posture 710. Examiner considers the baseline model of someone afflicted with a disease as the matched reference motion model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results).
Regarding claim 7, in which claim 1 is incorporated, Pathak discloses wherein comparing the target motion model with the target reference model (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease. Examiner considers the baseline model as a target reference model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results) comprises:
determining target data pairs with corresponding timestamps (See Pathak, [0047] the stored depth sensor data is associated with time information indicative of when the data was captured, such as with a timestamp; [0057] each node/point in the model may be tracked and associated with a series of (x, y, z, t) coordinates that provide a spatial (x, y, z) and a temporal (t) location of that point. Examine considers x-, y-, z-, and t- data to be mapped as seen in the user model in Fig. 6B) in the target motion model and the target reference model (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease. Examiner considers the comparison of movement, which can be in the x-direction, between models to yield data pairs, one from each respective model);
determining a data similarity of the target data pairs (See Pathak, [0036] When compared to stride length and frequency of a user's model 720-b undergoing an assessment for a chronic neurodegenerative or musculoskeletal disease, the diminished stride length and increased stride frequency can be indicators relevant to a diagnosis or risk score for a chronic neurodegenerative or musculoskeletal disease. Examiner considers when comparing the user model to a baseline model, similar data pairs, including length or frequency information, can be observed when determining indicators of a condition)); and
determining the comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features as comparison results which use data similarity when determining indicators of a condition) based on the data similarity of each of the target data pairs (See Pathak, [0036] When compared to stride length and frequency of a user's model 720-b undergoing an assessment for a chronic neurodegenerative or musculoskeletal disease, the diminished stride length and increased stride frequency can be indicators relevant to a diagnosis or risk score for a chronic neurodegenerative or musculoskeletal disease. Examiner considers when comparing the user model to a baseline model, similar data pairs, including length or frequency information, can be observed when determining indicators of a condition)).
Regarding claim 8, in which claim 1 is incorporated, Pathak disclose wherein generating health intervention and correction information based on the comparison results (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information which is generated based on analysis of motion features), and sending the health intervention and correction information to a health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414);. Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device) comprises:
determining a model difference between the target motion model and the target reference model (See Pathak, [0015] FIG. 7C illustrates a comparison of postural and motion differences of the healthy user and the user having the chronic neurodegenerative disease during the walking motion; [0036] FIG. 7E illustrates X and Z position (e.g., left/right and to/from the depth mapping capture system 120) of a user model's shoulder/spine showing a significant difference in a health user's sway 740-a and the sway 740-b of a user undergoing diagnosis for a chronic neurodegenerative disease) based on the comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison, such as posture away, as comparison results);
determining a health intervention strategy matched with the model difference (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease, to track severity of symptoms of a chronic neurodegenerative or musculoskeletal disease over time, to compute a risk score indicative of a patient having or developing a chronic neurodegenerative or musculoskeletal disease, etc. The model difference, like difference in movements, can help identify the existence of a disease, track severity, all of which are examples of a healthy intervention; [0036] locate certain motion features relevant to the discovery and diagnosis of a chronic neurodegenerative or musculoskeletal disease);
determining a health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414). Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device) matched with the health intervention strategy (See Pathak, [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system. Examiner considers the report as a health intervention strategy) and generating the health intervention and correction information matched with the health intervention strategy (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information); and
sending the health intervention and correction information (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information) to the matched health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414);. Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device).
Regarding claim 9, in which claim 1 is incorporated, Pathak discloses wherein the target reference model is medical image data (See Pathak, [0035] the baseline models may be models captured of users with known chronic neurodegenerative or musculoskeletal conditions. Examiner considers the models to be medical image data, the medical image data being models of chronic neurodegenerative or musculoskeletal conditions), and comparing the target motion model with the target reference model (See Pathak, [0035] the movement of the baseline models may provide movement data for a healthy baseline. Examiner considers the baseline model as a target reference model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features as comparison results) comprises:
extracting state information (See Pathak, [0036] both amplitude and frequency of arm swing can be extracted from movements of a user's model. Examiner considers the amplitude and frequency as state information) of monitoring parts of the user (See Pathak, [0032] motion data captured by a wearable device 155; [0032] plurality of different or the same wearable devices, such as wearable devices along each joint of a user. Examiner considers the monitoring parts to be the joints at which the wearable devices are placed along) based on the target motion model (See Pathak, [0034] pre-processes the captured data by generating a user model. Examiner considers the user model as a target motion model); wherein the monitoring parts comprise but are not limited to the following parts: skeletons, joints and muscular systems (See Pathak, [0032] plurality of different or the same wearable devices, such as wearable devices along each joint of a user. Examiner considers the monitoring parts to be the joints at which the wearable devices are placed along); and
comparing the state information of the target monitoring parts with an expected skeletal state of the target monitoring parts (See Pathak, [0036] comparison of the baseline model of FIG. 7A with the model of FIG. 7B enables motion assessment system 130 to locate certain motion features relevant to the discovery and diagnosis of a chronic neurodegenerative or musculoskeletal disease. Examiner considers Fig. 7B to give an expected skeletal state associated with a disease or condition) in the medical image data (See Pathak, [0035] the baseline models may be models captured of users with known chronic neurodegenerative or musculoskeletal conditions. Examiner considers the models to be medical image data, the medical image data being models of chronic neurodegenerative or musculoskeletal conditions) to obtain the comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features as comparison results).
Regarding claim 13, Pathak discloses a health intervention and correction system based on vision and sensors (See Pathak, [0053] FIG. 3 is a flow chart illustrating a process 300 for collecting and modeling user movements; [0059] FIG. 4 is a flow chart illustrating a process 400 for analyzing motion data for medical treatment and diagnosis by a motion analysis system), comprising multiple sensors (See Pathak, [0032] motion data captured by a wearable device 155; [0032] plurality of different or the same wearable devices, such as wearable devices along each joint of a user. Examiner considers the wearable devices to have sensors) and a processor (See Pathak, [0070] a processor 510), wherein
the multiple sensors are configured to perform motion detection on a user in a target state to obtain various motion monitoring data (See Pathak, [0032] motion data captured by a wearable device 155; [0031] the motion data indicates the user is performing a specific task. Examiner considers user to be motion detect while performing a task); and
the processor (See Pathak, [0070] a processor 510) is configured to acquire various motion monitoring data (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity…the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data) and determine a target motion model of the user (See Pathak, [0034] pre-processes the captured data by generating a user model. Examiner considers the user model as a target motion model) based on the various motion monitoring data (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity…the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data), wherein the target motion model is used to indicate state information of the user at each detection moment (See Pathak, [0034] user model (e.g., location and tracking of specific points associated with the user). Examiner considers the location and tracking as state information and examiner considers the specified points of the location and tracking as detection moment);
compare the target motion model with a target reference model (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease. Examiner considers the baseline model as a target reference model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results); and
generate health intervention and correction information based on the comparison results (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information which is generated based on analysis of motion features), and send the health intervention and correction information to a health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414). Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device).
Regarding claim 14, in which claim 13 is incorporated, Pathak discloses wherein the processor comprises a processing module (See Pathak, [0034] depth mapping sensor system 120), a comparison module (See Pathak, [0035] motion assessment system 130), and an intervention module (See Pathak, [0035] motion assessment system 130);
the processing module is configured to acquire the various motion monitoring data and determine the target motion model of the user based on the various motion monitoring data (See Pathak, [0034] depth mapping sensor system 120 captures depth sensor data and pre-processes the captured data by generating a user model (e.g., location and tracking of specific points associated with the user). Examiner considers the depth mapping sensor system as the processing module);
the comparison module is configured to compare the target motion model with the target reference model to obtain the comparison results (See Pathak, [0035] motion assessment system 130 compares the movements of the user model over a period of time with movement of one or more baseline models. Examiner considers the motion assessment system 130 as the comparison and intervention module); and
the intervention module is configured to generate health intervention and correction information based on the comparison results and send the health intervention and correction information to the health intervention device (See Pathak, [0040] motion assessment system 130 can measure a treatment response, such as through 3D motion analysis of one or more motion features. Examiner considers the motion assessment system 130 as the comparison and intervention module).
Regarding claim 15, in which claim 13 is incorporated, Pathak discloses wherein the multiple sensors comprise wearable devices and camera devices (See Pathak, [0028] a depth camera sensor, an assisted depth camera sensor, etc., that captures image/video data simultaneously from multiple camera; [0032] the wearable device 155 may be a fitness tracker, smart watch, pedometer, etc. that includes one or more sensors (e.g., accelerometers, gyroscopes, GPS, etc.), wherein the wearable devices and the camera devices are configured to connect to the processor via wireless or wired connections (See Pathak, [0032] user motion data is transferred, such as by a wireless communication link, to one or more of depth mapping capture system 120. Examiner considers the devices to have an established connection to a device for data to be transferred).
Regarding claim 16, Pathak discloses an electronic device (See Pathak, [0024] an apparatus for performing the operations herein), comprising: a processor, a memory, and a bus (See Pathak, [0075] The embodiments discussed herein may also be embodied in a handheld or portable device containing a subset of the computer hardware components described above. For example, the handheld device may be configured to contain only the bus 515, the processor 510, and memory 550 or 525); wherein the memory stores machine-readable instructions executable by the processor; when the electronic device is operational, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor (See Pathak, [0074] This control logic or software may also be resident on an article of manufacture comprising a computer readable medium having computer readable program code embodied therein and being readable by the mass storage device 525 and for causing the processor 510 to operate in accordance with the methods and teachings herein), they perform the steps of the health intervention and correction method based on vision and sensors according to claim 1 (See claim 1 as cited above).
Regarding claim 17, Pathak discloses a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor (See Pathak, [0024] may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium), causing the processor to perform the steps of the health intervention and correction method based on vision and sensors according to claim 1 (See claim 1 as cited above).
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 non-obviousness.
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Pathak et al. (US 2017/0287146 A1, hereinafter, “Pathak”) in view of Zhou et al. (CN 112199846 A, hereinafter, “Zhou”).
Regarding claim 10, in which claim 1 is incorporated, Pathak discloses [wherein the target motion model is a sleep posture model determined based on body state data obtained from the user in a sleep state, and the target reference model is a sleep reference model;]
acquiring various motion monitoring data (See Pathak, [0054] capturing three-dimensional (3D) data of a user performing an activity …the user can be considered as a target person whose motions will be analyzed; [0054] the three dimensional data may include a combination of any of the different types of three dimensional data) obtained by detecting a user in a target state (See Pathak, [0031] the motion data indicates the user is performing a specific task; [0054] capture motion of the user while the user is performing ordinary activities, such as walking, performing household chores, eating. Examiner considers the target state to be any activity mentioned above) comprises:
acquiring various body state data obtained by performing posture measurement on the user (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements. Examiner considers posture measurement to be performed which a skeletal model that can give body state information such as length, frequency, lack of arm sway, or any of the above listed features) [in a sleep state] through sensors of various dimensions (See Pathak, [0032] the wearable device 155 may be a plurality of different or the same wearable devices, such as wearable devices along each joint of a user, a wearable device on limbs of interests. Examiner considers the different wearable devices to hold sensors of different sizes), [wherein the body state data comprises at least one of the following types of data: sleep posture monitoring data, body photoelectric monitoring signals, body myoelectric monitoring signals and body vital sign signals;] and
comparing the target motion model with the target reference model (See Pathak, [0035] compares the movement of the user model with one or more of the baseline model's movements to extract features indicative of existence of a chronic neurodegenerative or musculoskeletal disease. Examiner considers the baseline model as a target reference model) to obtain comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results) comprises:
[comparing the sleep posture model with the sleep reference model to obtain the comparison results, wherein the sleep reference model is used to indicate an expected sleep posture of the user.]
However, Pathak does not explicitly disclose wherein the target motion model is a sleep posture model determined based on body state data obtained from the user in a sleep state, and the target reference model is a sleep reference model; [acquiring various body state data obtained by performing posture measurement on the user] in a sleep state; wherein the body state data comprises at least one of the following types of data: sleep posture monitoring data, body photoelectric monitoring signals, body myoelectric monitoring signals and body vital sign signals; and comparing the sleep posture model with the sleep reference model to obtain the comparison results, wherein the sleep reference model is used to indicate an expected sleep posture of the user.
Zhou teaches wherein the target motion model is a sleep posture model determined based on body state data obtained from the user in a sleep state (See Zhou, Pg. 4, line 40, construct the user’s exclusive sleep model; Pg. 4, line 42, monitor the user's position and sleeping posture in real time), and the target reference model is a sleep reference model (See Zhou, Pg. 4, lines 41-43, monitor the user's position and sleeping posture in real time, and automatically follow the user's posture to adjust the degree of support of each part of the mattress to maintain the optimal comfort level. Examiner considers the sleep reference model as the user’s previous sleep posture which the device is actively monitoring); [acquiring various body state data obtained by performing posture measurement on the user] in a sleep state (See Zhou, Pg. 4, line 42, monitor the user's position and sleeping posture in real time); wherein the body state data comprises at least one of the following types of data: sleep posture monitoring data, body photoelectric monitoring signals, body myoelectric monitoring signals and body vital sign signals (See Zhou, Pg. 4, line 42, monitor the user's position and sleeping posture in real time); and comparing the sleep posture model with the sleep reference model to obtain the comparison results, wherein the sleep reference model is used to indicate an expected sleep posture of the user (See Zhou, Pg. 4, lines 41-43, monitor the user's position and sleeping posture in real time, and automatically follow the user's posture to adjust the degree of support of each part of the mattress to maintain the optimal comfort level. Examiner considers the adjusted supporting degree as the comparison that leads to an expected sleep posture of the user).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Pathak’s reference to wherein the target motion model is a sleep posture model determined based on body state data obtained from the user in a sleep state, and the target reference model is a sleep reference model; [acquiring various body state data obtained by performing posture measurement on the user] in a sleep state; wherein the body state data comprises at least one of the following types of data: sleep posture monitoring data, body photoelectric monitoring signals, body myoelectric monitoring signals and body vital sign signals; and comparing the sleep posture model with the sleep reference model to obtain the comparison results, wherein the sleep reference model is used to indicate an expected sleep posture of the user based on the method of Zhou’s reference. The suggestion/motivation would have been to monitor the user's position and sleeping posture in real time as suggested by Zhou in Pg. 2, line 5.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Zhou with Pathak to obtain the invention as specified in claim 10.
Regarding claim 11, in which claim 10 is incorporated, Pathak discloses wherein generating health intervention and correction information based on the comparison results (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information), and sending the health intervention and correction information to the health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414). Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device) comprises:
[in response to determining that a posture difference between the sleep posture of the user and the expected sleep posture is large based on the comparison results, sending the health intervention and correction information to a sleep intervention device of the user, wherein the health intervention and correction information is used to adjust sleep posture of the user through the sleep intervention device.]
However, Pathak does not explicitly disclose in response to determining that a posture difference between the sleep posture of the user and the expected sleep posture is large based on the comparison results, sending the health intervention and correction information to a sleep intervention device of the user, wherein the health intervention and correction information is used to adjust sleep posture of the user through the sleep intervention device.
Zhou teaches in response to determining that a posture difference between the sleep posture of the user and the expected sleep posture is large based on the comparison results, sending the health intervention and correction information to a sleep intervention device of the user, wherein the health intervention and correction information is used to adjust sleep posture of the user through the sleep intervention device (See Zhou, Pg. 4, lines 41-43, monitor the user's position and sleeping posture in real time, and automatically follow the user's posture to adjust the degree of support of each part of the mattress to maintain the optimal comfort level. Examiner considers the adjusted supporting degree as the comparison that leads to an expected sleep posture of the user. Any deviation from the posture is considered to be a large change to the normal posture. Correction information is the optimal comfort degree, and the sleep intervention device is the mattress).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Pathak’s reference to in response to determining that a posture difference between the sleep posture of the user and the expected sleep posture is large based on the comparison results, sending the health intervention and correction information to a sleep intervention device of the user, wherein the health intervention and correction information is used to adjust sleep posture of the user through the sleep intervention device based on the method of Zhou’s reference. The suggestion/motivation would have been to automatically follow the user's posture to adjust the degree of support as suggested by Zhou in Pg. 2, line 6.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Zhou with Pathak to obtain the invention as specified in claim 11.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Pathak et al. (US 2017/0287146 A1, hereinafter, “Pathak”) in view of Lee (US 2024/0145062 A1, hereinafter, “Lee”).
Regarding claim 12, in which claim 1 is incorporated, Pathak discloses wherein generating health intervention and correction information based on the comparison results See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information which is generated based on analysis of motion features) comprises:
generating health intervention and correction information (See Pathak, [0040] measure a treatment response, such as through 3D motion analysis of one or more motion features (e.g., through comparison to a trained machine learning motion model, comparison to one or more baseline models, etc.). Examiner considers the measurement of a treatment response as health intervention and correction information) matched with the health intervention device (See Pathak, [0026] health professional system 140 may also be computing devices, such as one or more server computers, desktop computers, etc.; [0068] utilizes the analysis results to generate a report (processing block 412) and communicate the report to a health professional system (processing block 414);. Examiner considers the analysis results as including a treatment response which is sent to the health professional system, or the health intervention device) based on the comparison results (See Pathak, [0036] Motion features, such as characteristic forward-leaning (e.g., stooped) posture, postural sway, diminished stride length, increased stride frequency, lack of arm sway, tremor motions, etc., are quantifiable from a skeletal model generated from user movements or based on comparison to a baseline model. Examiner considers the motion features extracted from a comparison as comparison results), [wherein the health intervention and correction information comprises the following types: vibration information, video information, audio information, textual information and command information.]
However, Pathak does not explicitly disclose wherein the health intervention and correction information comprises the following types: vibration information, video information, audio information, textual information and command information.
Lee teaches wherein the health intervention and correction information comprises the following types: vibration information, video information, audio information, textual information and command information (See Pathak, [0047] the user's posture may be detected, and a notification service, such as the notification “your shoulders are bent too much and you need to stretch your deltoids!” may be provided through the user terminal 110. Examiner considers the notification text information as correction information).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Pathak’s reference wherein the health intervention and correction information comprises the following types: vibration information, video information, audio information, textual information and command information based on the method of Lee’s reference. The suggestion/motivation would have been to receive appropriate feedback and correction suitable for each individual user as suggested by Lee at [0005].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Pathak with Lee to obtain the invention as specified in claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gamarnik et al. (US 20180330810 A1) discloses a system and method that provide guidance and support during physical therapy treatments. Providing feedback can enhance patient compliance and success of treatments. Motion data is received from a motion tracking device when a user is performing an exercise movement. The motion data is then compared to a reference model to determine if the exercise movement is being performed correctly. The motion tracking device then provides feedback or a recommendation to the user to improve the exercise movement.
Surbur et al. (US 20170156666 A1) discloses a lifestyle service device for monitoring human lifestyle and providing insightful notifications. Data is tracked and collected by a wearable electronic device while a user performs a physical activity or during a sleep state. Tracked data can be compared with historical data to give insights into the user’s movements and provide the user with an audible, visual, or vibration notification.
Haas et al. (US 20240260892 A1) discloses a system and method that provides a dynamic spinal assessment to provide a more personalized treatment of spinal conditions. The patient wears an assessment tool that collects data for a predetermined period of time. The data is processed into a report that can be used by medical professionals for more patient-specific information. Data can be compared with other records saved in the system for tracking treatment progress.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jasmin Marcelino Hernandez whose telephone number is (571)270-0211. The examiner can normally be reached 7am-3pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JASMIN MARCELINO HERNAND/Examiner, Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
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