DETAILED ACTION
The following is a Final Office Action in response to communications filed June 17, 2026. Claims 1, 9, and 17 are amended. Claims 1–22 are currently pending.
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 claim 9 do not overcome the previously asserted rejection under 35 U.S.C. 112(b). Accordingly, Applicant is directed to the relevant clarification below.
With respect to the previous rejection of claims 1–22 under 35 U.S.C. 101, Applicant’s remarks have been fully considered but are not persuasive.
Applicant first asserts that the claims do not recite mental processes. More particularly, Applicant asserts that the claims do not recite mental processes because deep learning operations are not practically performable in the mind. Examiner disagrees. Examiner notes that the rejection of record does not assert that the deep learning elements recite mental processes. Instead, the rejection of record addresses the deep learning elements under Step 2A Prong Two and Step 2B. As a result, Applicant’s remarks are not persuasive because the remarks are not commensurate with the rejection of record.
Applicant next asserts that the claims do not recite mathematical concepts because the recited elements are analogous to the claims of McRO, Inc. v. Bandai Namco Games America, Inc. 837 F.3d 1299, 1314, 120 USPQ2d 1091, 1102 (Fed. Cir. 2016) and Thales Visionix, Inc. v. United States, 850 F.3d 1343, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017). Examiner disagrees. Unlike the claims of McRO and Thales, the pending claims expressly recite elements to “calculate”, and MPEP 2106.04(a)(2)(I)(C) expressly identifies steps for calculating as mathematical calculations. As a result, Applicant’s remarks are not persuasive.
Applicant further asserts that the claims do not recite certain methods of organizing human activity. However, Applicant does not present any argument or rationale underpinning in support of Applicant’s assertion. As a result, Applicant’s remarks are not persuasive.
With respect to Step 2A Prong Two, Applicant first asserts that the claims provide a technical solution to a technical problem by reciting how to automatically and accurately assess ergonomic risks. Examiner disagrees. MPEP 2106.05(a)(II) distinguishes between improvements in a business process and improvements in technology, and Examiner submits that assessing ergonomic risks is a business problem rather than a technical problem. Specifically, assessing ergonomic risks is a business problem because assessing risk in a workplace is a business practice that does not require any technical features. As a result, Applicant’s remarks are not persuasive because, although the claims utilize technical elements, the claims do not address a technical problem.
Applicant next asserts that the claims are eligible under Step 2A Prong Two for the same reasons as asserted in USPTO Example 39. Examiner disagrees. As an initial matter, Examiner notes that, in asserting that the claim of Example 39 was found eligible at Step 2A Prong Two, Applicant mischaracterizes Example 39. Example 39 was instead found eligible at Step 2A Prong One because the example claim did not recite an abstract idea. Unlike Example 39, the pending claims recite an abstract idea at Step 2A Prong One. Further, unlike Example 39, which expressly claims elements for iteratively training a neural network, the pending claims generically recite steps to “process … through a first deep learning framework” and “determine … using a second deep learning framework” without reciting any elements defining a specific deep learning implementation. As a result, the pending claims are not analogous to Example 39, and Applicant’s remarks are not persuasive.
Applicant further assets that the deep learning framework is eligible in view of Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision). Examiner disagrees. In Desjardins, the claims were deemed eligible because the claims embodied improvements in machine learning technology, which were supported by the Specification. As noted above, the deep learning elements of the pending claims are generically recited, and Applicant’s Specification does not disclose any improvements in deep learning technology. Accordingly, the pending claims are not analogous to the claims of Desjardins, and Applicant’s remarks are not persuasive.
Under Step 2B, Applicant asserts that combination of two deep learning frameworks is unconventional and patent-eligible. Examiner disagrees. As noted above, the deep learning elements are generically recited without any corresponding details, and Applicant’s Specification does not disclose any improvements or unconventionality in the recited deep learning elements. Further, each deep learning framework operates as a standalone framework, such that each framework does no more than generally link the use of the recited abstract idea to a particular technological environment. Accordingly, Applicant’s remarks are not persuasive.
In view of the above, the previous rejection under 35 U.S.C. 101 is maintained and reasserted 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 asserts that the rejection of record is deficient because Burgess-Limerick discloses risk factors rather than worker-specific risk root-causes. Examiner disagrees. As asserted previously and below, the reference to Kaszuba is asserted as disclosing determining risk root-causes (See e.g., Non-Final Act., pg. 11–12). More particularly, Kaszuba is asserted as disclosing risk root-causes, whereas Burgess-Limerick is asserted in combination as disclosing the reasons related to risk for each of the plurality of body regions. MPEP 2145(IV) states that “[o]ne cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).” As a result, Applicant’s remarks are not persuasive because the rejection is based on a combination of references, and Applicant’s remarks do not address the combination.
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 9–16 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.
Claim 9 recites “the risk ratings” as the final element in the limitation for “determining ergonomic risk root-causes”. There is insufficient antecedent basis for this limitation in the claim.
For purposes of examination, the claim is 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
In view of the above, claim 9 is 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 10–16, which depend from claim 9, inherit the deficiencies described above. As a result, claims 10–16 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 each of the plurality of body regions had a respective risk rating”, 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 each of the plurality of body regions had a 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623
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