DETAILED ACTION
The following is a Non-Final Office Action in response to communications filed November 20, 2025. Claims 1, 9, and 17 are amended. Claims 1–22 are currently pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 20, 2025 has been entered.
Response to Amendment/Argument
Applicant’s Response is not sufficient to overcome the previous rejection of claims 1–22 under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. More particularly, the amendments to claims 9 and 17 do not overcome the previously asserted rejection under 35 U.S.C. 112(b). Accordingly, Applicant is directed to the relevant clarification below.
Further, Applicant’s Response necessitates a new ground of rejection under 35 U.S.C. 112(b), and Applicant is directed to the relevant explanation below.
With respect to the previous rejection of claims 1–20 under 35 U.S.C. 101, Applicant’s remarks have been fully considered but are not persuasive. More particularly, Applicant asserts that the first deep learning framework and second deep learning framework integrate the abstract idea into a practical application because the first learning framework and the second learning framework embody an interrelated, cascading process requiring that the first learning framework determine joint locations and risk ratings to enable the second learning framework to determine risk root-causes. Examiner disagrees.
Although the output of the first learning framework is used as a basis for generating the input required by the second learning framework, each learning framework operates as a standalone learning framework because the output of the first learning framework is not directly input into the second learning framework. Further, each learning framework is recited at a high level of generality, such that each learning framework, individually, does no more than generally link the use of the recited abstract idea to a particular technological environment. Accordingly, the amended elements do not integrate the abstract idea into a practical application under Step 2A Prong Two, and Examiner directs Applicant to the relevant explanation below.
With respect to the previous rejections under 35 U.S.C. 103, Applicant’s remarks have been fully considered but are not persuasive.
Applicant first asserts that the references of record do not disclose the claimed subject matter because Baek and Kaszuba do not disclose the use of two deep learning frameworks. Examiner disagrees.
As noted above, each recited learning framework is an individual, standalone learning framework. As a result, Applicant’s remarks are not persuasive because Baek discloses a first learning framework, Kaszuba discloses a second learning framework, and the combination of references disclose a first deep learning framework and a second deep learning framework.
Applicant’s remaining arguments have been fully considered but are directed to amended subject matter, which is addressed for the first time herein. Accordingly, Applicant’s remarks are not persuasive, and Examiner directs Applicant to the updated rejection asserted below.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, and 17 recite “the risk ratings” as the final element in the limitation to “determine ergonomic risk root-causes”. There is insufficient antecedent basis for this limitation in the claims.
For purposes of examination, the claims are interpreted as reciting “the ergonomic risk root-causes specifying reasons related to the worker performing the job as to why each of the plurality of body regions had [[the]] a respective risk rating[[s]]”.
Claims 9 and 17 further recite “the respective body region of the worker” in the element to “determine ergonomic risk root-causes”. There is insufficient antecedent basis for this limitation in the claims.
For purposes of examination, the claims are interpreted as reciting “using a second deep learning framework including an expert or knowledge-based diagnostic or evaluation system that relates the risk rating for each of the plurality of body regions to one or more ergonomic risk root-causes for [[the]] a respective body region of the worker”.
In view of the above, claims 1, 9, and 17 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2–8, 10–16, and 18–22, which depend from claims 1, 9, and 17, inherit the deficiencies described above. As a result, claims 2–8, 10–16, and 18–22 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–22 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements to “obtain information relating to forces being exerted during the job”, “process the video signals to determine joint locations of the worker”, “calculate joint angles for each of a plurality of body regions of the worker based on the joint locations”, “calculate, based at least upon the joint angles and the information relating to the forces, a risk score for each of the plurality of body regions of the worker in each of a plurality of risk categories”, “calculate a risk rating for each of the plurality of body regions of the worker based on the risk score”, “responsive to the risk rating for each of the plurality of body regions being calculated after the joint locations of the worker are determined, determine ergonomic risk root-causes for each of the plurality of body regions of the worker using an expert or knowledge-based diagnostic or evaluation system that relates the risk rating for each of the plurality of body regions to one or more ergonomic risk root-causes for a respective body region of the worker, the ergonomic risk root-causes specifying reasons related to the worker performing the job as to why the plurality of body regions had the risk ratings”, and “provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the elements recite a process for determining ergonomic risks in employees and providing risk control recommendations. Additionally, each element to “calculate” recites a mathematical concept because the elements recite mathematical calculations, and the elements to “process” and “determine” recite a mental process because the elements describe observations or evaluations that could be practically performed in the mind. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 9 and 17 include substantially similar limitations to those included with respect to claim 1. As a result, claims 9 and 17 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–8, 10–16, and 18–22 further describe the process for determining ergonomic risks in employees and providing risk control recommendations and recite certain methods of organizing human activity, mathematical concepts, and/or mental processes for the same reasons as stated above. As a result, claims 2–8, 10–16, and 18–22 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a computing server system, a computer-readable storage medium, a processor, a first deep learning framework, a second deep learning framework, and an element to receive video signals. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing components that are merely used as a tool to perform the recited abstract idea; the first deep learning framework and the second deep learning framework do no more than generally link the use of the recited abstract idea to a particular technological environment; and the function to “receive” is an insignificant extrasolution activity to the recited abstract idea. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 9 and 17 include substantially similar limitations to those included with respect to claim 1. Further, claims 9 and 17 do not include any additional elements beyond those recited with respect to claim 1. As a result, claims 9 and 17 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2–8, 10–16, and 18–22 do not include any additional elements beyond those included with respect to the claims from which claims 2–8, 10–16, and 18–22 depend. As a result, claims 2–8, 10–16, and 18–22 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a computing server system, a computer-readable storage medium, a processor, a first deep learning framework, a second deep learning framework, and an element to receive video signals. The additional elements do not amount to significantly more than the recited abstract idea because the additional computing elements are generic computing components that are merely used as a tool to perform the recited abstract idea; the first deep learning framework and the second deep learning framework do no more than generally link the use of the recited abstract idea to a particular technological environment; and the function to “receive” is a well-understood, routine, and conventional computing function in view of MPEP 2106.05(d)(II). Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claims 9 and 17 include substantially similar limitations to those included with respect to claim 1. Further, claims 9 and 17 do not include any additional elements beyond those recited with respect to claim 1. As a result, claims 9 and 17 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2–8, 10–16, and 18–22 do not include any additional elements beyond those included with respect to the claims from which claims 2–8, 10–16, and 18–22 depend. As a result, claims 2–8, 10–16, and 18–22 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–22 are rejected under 35 U.S.C. 103 as being unpatentable over BAEK et al. (U.S. 2020/032465) in view of Kaszuba et al. (U.S. 2020/0273580), and in further view of Burgess-Limerick et al. (AU 2009238336).
Claims 1, 9, and 17: Baek discloses a computing server system (See FIG. 12 and paragraph 114), comprising:
a non-transitory computer-readable storage medium storing machine readable instructions (See paragraph 109); and
a processor coupled to the non-transitory computer-readable storage medium and configured to execute the machine readable instructions (See FIG. 12 and paragraphs 108–109) to:
receive video signals of a worker performing a job at a workplace (See FIG. 1 and paragraph 41, wherein video signals are received from image capturing devices),
obtain information relating to forces being exerted during the job (See paragraph 96, wherein joint movement velocity information is obtained from the time series data),
process at least the video signals through a first deep learning framework to determine joint locations of the worker (See paragraph 42, wherein the image data is processed to determine joint positions and angles using deep learning algorithms),
calculate joint angles for each of a plurality of body regions of the worker based on the joint locations (See paragraph 42, wherein the image data is processed to determine joint positions and angles, and FIG. 7 and paragraph 58, wherein joint positions and angles are determined for each body region),
calculate, based at least upon the joint angles and the information relating to the forces, a risk score for each of the plurality of body regions of the worker in each of a plurality of risk categories (See paragraphs 42 and 45, in view of paragraph 58, wherein ergonomic metrics are determined for each of the plurality of body regions, and wherein ergonomic metrics are risk scores; see also paragraphs 96–97),
calculate a risk rating for each of the plurality of body regions of the worker based on the risk score (See paragraphs 42–43, in view of FIG. 10, wherein a risk assessment is derived from the ergonomic metrics, and wherein risks are identified with respect to each body region), and
provide ergonomic risk control recommendations to mitigate the ergonomic risk (See paragraphs 17–18, wherein a recommended course of action is provided to address identified risks). Baek does not expressly disclose the remaining claim elements.
Kaszuba discloses functionality to, responsive to the risk rating for each of the plurality of body regions being calculated after the joint locations of the worker are determined using the first framework, determine ergonomic risk root-causes for each of the plurality of body regions of the worker using a second deep learning framework including an expert of knowledge-based diagnostic or evaluation system that relates the risk rating for each of the plurality of body regions to one or more ergonomic risk root-causes for the respective body region of the worker (See paragraphs 34 and 36, wherein a risk assessment is produced from posture analysis, and wherein root causes are determined from the risk assessment; paragraphs 15 and 36, wherein ergonomic science is integrated into the deep learning AI framework; and FIG. 4-5 and paragraphs 41–45, wherein risks and root causes are identified by body part as the deep learning AI framework is trained using collected data, ergonomic science, and assessment outputs), and
provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes (See paragraphs 15 and 36, wherein root causes are mitigated).
Baek discloses a system directed to analyzing ergonomic risks by evaluating joint positioning risks. Kaszuba discloses a system directed to assessing ergonomic risks by evaluating posture data. Each reference discloses a system directed to assessing ergonomic risks in the workplace. The technique of determining root-causes is applicable to the system of Baek as they each share characteristics and capabilities, namely, they are directed to assessing ergonomic risks in the workplace.
One of ordinary skill in the art would have recognized that applying the known technique of Kaszuba would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Kaszuba to the teachings of Baek would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate ergonomic risk assessments into similar systems. Further, applying root-cause determinations to Baek would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results. Baek and Kaszuba do not expressly disclose the remaining claim elements.
Burgess-Limerick discloses the ergonomic risk root-causes specifying reasons related to the worker performing the job as to why the plurality of body regions had the risk (See FIG. 3C, wherein risk factor causes are explained with respect to performance of a given task, and wherein risk factor causes are identified with respect to body regions).
As disclosed above, Baek discloses a system directed to analyzing ergonomic risks by evaluating joint positioning risks, and Kaszuba discloses a system directed to assessing ergonomic risks by evaluating posture data. Burgess-Limerick discloses a system directed to assessing ergonomic injury risks. Each reference discloses a system directed to assessing ergonomic risks. The technique of providing root-cause explanations is applicable to the systems of Baek and Kaszuba as they each share characteristics and capabilities, namely, they are directed to assessing ergonomic risks.
One of ordinary skill in the art would have recognized that applying the known technique of Burgess-Limerick would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Burgess-Limerick to the teachings of Baek and Kaszuba would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate ergonomic risk assessments into similar systems. Further, applying root-cause explanations to Baek and Kaszuba would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claims 2, 10, and 18: Baek discloses the computing server system of claim 1, wherein the plurality of body regions include a neck region, a back region, a hand/wrist region, a shoulder region including a left shoulder and a right shoulder, an elbow region including a left elbow and a right elbow, and a leg region including a left knee and a right knee (See paragraph 58, wherein “the following joints and landmarks may be tracked: left hip; right hip; chest; neck; left shoulder; right shoulder; left elbow; right elbow; left wrist; right wrist; left knee; right knee; left ankle; right ankle; nose; left ear; right ear; left eye; and right eye”).
Claims 3 and 11: Baek discloses the computing server system of claim 1, wherein the plurality of risk categories comprises a posture category, a duration category, a frequency category, and a force category (See paragraph 42, wherein ergonomic metrics include posture, duration, repetition, and force).
Claims 4, 12, and 19: Baek discloses the computing server system of claim 3, wherein the processor is configured to execute the machine readable instructions to calculate the risk score by comparing the joint angles with a plurality of threshold values determined for each body region in each risk category (See paragraphs 42–43, in view of paragraphs 96–97, wherein body region assessment scores are determined by comparing monitored joint postures to thresholds).
Claims 5 and 13: Baek discloses the computing server system of claim 4, wherein the processor is further configured to execute the machine readable instructions to determine a first portion of the plurality of threshold values for each body region in the posture category based upon a range of motion for a body joint, wherein the joint angles near an upper limit of the range of motion are determined to have higher risks (See paragraphs 42–43, in view of paragraphs 96–97, wherein body region assessment scores are determined by comparing monitored joint postures to neutral posture thresholds, and wherein risk implicitly increases when joint postures exceed neutral posture thresholds).
Claims 6, 14, and 20: Baek discloses the computing server system of claim 4, wherein the processor is further configured to execute the machine readable instructions to determine a second portion of the plurality of threshold values for each body region in the duration category, determine a percentage of time of one or more body regions maintained in an identified posture based on a frame-by-frame analysis of the video signals, and compare the percentage of time of the one or more body regions with the second portion of the plurality of threshold values (See paragraphs 40 and 42–43, in view of paragraphs 96–97, wherein body region assessment scores are determined according to extreme posture duration proportions derived from the joint time series data; see also paragraph 51, wherein frame-by-frame analysis is performed).
Claims 7, 15, and 21: Baek discloses the computing server system of claim 4, wherein the processor is further configured to execute the machine readable instructions to determine a third portion of the plurality of threshold values for each body region in the frequency category, identify a frequency of occurrence of one or more body regions during a selected period of time based on the video signals, and compare the frequency of occurrence with the third portion of the plurality of threshold values (See paragraph 11, wherein posture exposure event frequency is disclosed; see also paragraphs 96–97, wherein a number of posture events are tracked within the time series data, and paragraph 40, wherein repetitions are monitored).
Claims 8, 16, and 22: Baek discloses the computing server system of claim 4, wherein the information relating to the forces include a force magnitude and a force (See paragraph 96, wherein joint movement velocity information is obtained from the time series data, and paragraph 11, wherein time series posture and movement information may be expressed by an exposure magnitude), wherein the processor is further configured to execute the machine readable instructions to determine a fourth portion of the plurality of threshold values for each body region in the force category based on a force, and compare the force with the fourth portion of the plurality of threshold values in the force (See paragraphs 42–43, in view of paragraphs 96–97, wherein body region assessment scores are determined by comparing monitored joint postures to neutral posture thresholds, and wherein risk implicitly increases when joint postures exceed neutral posture thresholds). Baek and Kaszuba do not expressly disclose the remaining claim elements.
Burgess-Limerick discloses a force magnitude and a force direction to determine a fourth portion of the plurality of threshold values for each body region in the force category based on a maximum force allowed in the force direction, and compare the force magnitude with the fourth portion of the plurality of threshold values in the force direction (See pg. 22, ll. 10–19, wherein risk is identified according to force magnitude and direction as compared to a range of movement).
One of ordinary skill in the art would have recognized that applying the known technique of Burgess-Limerick would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Bradbury et al. (U.S. 2021/0241919) discloses a system directed to analyzing worksite risk by evaluating tasks and body movements.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM S BROCKINGTON III whose telephone number is (571)270-3400. The examiner can normally be reached M-F, 8am-5pm, EST.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623