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 .
Response to Amendment
This action is responsive to the request for continued examination (RCE), amendments and remarks received 18 July 2026. Claims 1 - 6, 9 and 10 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 18 July 2026 has been entered.
Claim Objections
Claim 5 is objected to because of the following informalities: Lines 4 - 5 of claim 5 recite, in part, “from appearing with the at least the second object” which appears to contain a grammatical error and/or a minor informality. The Examiner suggests amending the claim to --from appearing with the at least [[the]] second object-- or --from appearing with [[the]] at least the second object-- in order to improve the clarity and precision of the claim Appropriate correction is required.
Claim 9 is objected to because of the following informalities: Lines 9 - 10 of claim 9 recite, in part, “and determining, that the at least one object is prohibited” which appears to contain a grammatical error and/or a minor informality. The Examiner suggests amending the claim to --and determining[[,]] that the at least one object is prohibited-- in order to improve the clarity and precision of the claim Appropriate correction is required.
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: “a machine vision component processing”, “a learning system…analyzing” and “a state machine…analyzing” in claim 9.
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.
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 § 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.
The rejections to claims 1 - 8 and 10 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, are hereby withdrawn in view of the amendments and remarks received 18 July 2026.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The rejections to claims 7 and 8 under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, are hereby withdrawn in view of the amendments and remarks received 18 July 2026.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 - 6, 9 and 10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1 - 6 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhry et al. U.S. Publication No. 2023/0230379 A1 in view of Hsu U.S. Patent No. 10,249,163 in view of Osman et al. U.S. Publication No. 2024/0281954 A1.
- With regards to claim 1, Chaudhry et al. disclose a method for executing a learning system, (Chaudhry et al., Abstract, Figs. 3 & 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0018, Pg. 3 ¶ 0023 - 0024, 0026 and 0031 - 0032, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0045 - 0049, Pg. 6 ¶ 0052 - 0053) the method comprising: processing, by a machine vision component in communication with the learning system, (Chaudhry et al., Figs. 1 - 6, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 and 0031, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0049 - Pg. 6 ¶ 0053) a video file to detect at least one object in the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024, 0026 and 0028 - 0032, Pg. 4 ¶ 0036 - 0037) generating, by the machine vision component, (Chaudhry et al., Figs. 1 - 6, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 and 0031, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0049 - Pg. 6 ¶ 0053) an output including data relating to the at least one object and the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024 and 0028 - 0032, Pg. 4 ¶ 0035 - 0040, Pg. 5 ¶ 0042 - 0044) analyzing, by the learning system, the output; (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0034 - 0040, Pg. 5 ¶ 0042 - 0044) identifying, by the learning system, an attribute of the video file, the attribute associated with the at least one object; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0032, Pg. 4 ¶ 0034 - 0040, Pg. 5 ¶ 0042 - 0044) analyzing, by the learning system, the output and the attribute and the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0036 - 0040) determining, by the learning system, that the at least one object is prohibited from appearing with the attribute in the video file by at least one rule, (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 4 ¶ 0036 - 0040) wherein the at least one object comprises an individual, (Chaudhry et al., Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0024 and 0031, Pg. 4 ¶ 0036 - 0039) wherein the at least one rule requires the individual to execute a specified step, (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0024, Pg. 4 ¶ 0036 - 0039) and wherein the determining comprises determining, in response to the output and the attribute, whether the individual executed the specified step; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0017 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0030 - 0031, Pg. 4 ¶ 0036 - 0040) generating, by the learning system, a reminder for improving a level of compliance with the at least one rule; (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) and modifying, by the learning system, a user interface to display an indication of the determination by the learning system, (Chaudhry et al., Figs. 1, 2, 4 & 6, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) wherein modifying the user interface further comprises modifying the user interface to display a description of the generated reminder. (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) Chaudhry et al. fail to disclose explicitly analyzing and determining by a state machine in communication with the learning system, wherein the at least one rule requires the individual to execute a plurality of specified steps in a specified order, determining, by the state machine changing from one state to another, whether the individual executed the plurality of specified steps in the specified order, and generating a recommendation for improving a level of compliance with the at least one rule and displaying a description of the generated recommendation. Pertaining to analogous art, Hsu discloses a method for executing a learning system, (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 57 - 67, Col. 3 Lines 1 - 16, Col. 4 Line 54 - Col. 5 Line 19, Col. 6 Lines 19 - 36) the method comprising: processing, by a machine vision component in communication with the learning system, (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 19, Col. 5 Line 66 - Col. 6 Line 36) a video file to detect at least one object in the video file; (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 32 and Lines 61 - 65, Col. 3 Line 61 - Col. 4 Line 53) generating, by the machine vision component, an output including data relating to the at least one object and the video file; (Hsu, Abstract, Figs. 2 & 3, Col. 1 Lines 16 - 32 and Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 37 and Lines 52 - 59) analyzing, by the learning system, the output; (Hsu, Figs. 2 & 3, Col. 1 Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 7, Col. 4 Line 58 - Col. 5 Line 43, Col. 7 Lines 6 - 37 and Lines 52 - 59) identifying, by the learning system, an attribute of the video file, the attribute associated with the at least one object; (Hsu, Col. 1 Lines 16 - 29 and Lines 61 - 65, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 39, Col. 5 Line 54 - Col. 6 Line 12, Col. 7 Lines 6 - 20) analyzing, by a state machine in communication with the learning system, (Hsu, Abstract, Col. 1 Lines 16 - 26 and Lines 57 - 65, Col. 4 Line 54 - Col. 5 Line 19, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 37 and Lines 52 - 65) the output and the attribute and the video file; (Hsu, Abstract, Figs. 2 & 3, Col. 1 Lines 16 - 26 and Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 34, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 37, Col. 7 Line 52 - Col. 8 Line 12) determining, by the state machine, that the at least one object is prohibited from appearing with the attribute in the video file by at least one rule, (Hsu, Abstract, Figs. 2 & 3, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 37, Col. 7 Line 52 - Col. 8 Line 12) wherein the at least one object comprises an individual, (Hsu, Col. 1 Lines 16 - 29 and Lines 61 - 65, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 34, Col. 7 Lines 6 - 37) wherein the at least one rule requires the individual to execute a plurality of specified steps in a specified order, (Hsu, Figs. 2 & 3, Col. 1 Lines 49 - 60, Col. 3 Lines 1 - 32, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Line 52 - Col. 8 Line 12) and wherein the determining comprises determining, by the state machine changing from one state to another in response to the output and the attribute, (Hsu, Figs. 2 & 3, Col. 1 Lines 16 - 26 and Lines 49 - 60, Col. 3 Line 61 - Col. 4 Line 7, Col. 5 Lines 1 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 28, Col. 7 Line 52 - Col. 8 Line 12) whether the individual executed the plurality of specified steps in the specified order; (Hsu, Figs. 2 & 3, Col. 1 Lines 56 - 60, Col. 3 Lines 1 - 16, Col. 5 Lines 1 - 43, Col. 7 Lines 21 - 28, Col. 7 Line 52 - Col. 8 Line 12) generating, by the learning system, a warning for improving a level of compliance with the at least one rule; (Hsu, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 21 - 28 and Lines 52 - 65) and modifying, by the learning system, a user interface to display an indication of the determination by the state machine, (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 36 - 44, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 52, Col. 7 Lines 21 - 28, Col. 7 Line 52 - Col. 8 Line 12) wherein modifying the user interface further comprises modifying the user interface to display a description of the generated warning. (Hsu, Abstract, Col. 1 Lines 16 - 26 and Lines 37 - 40, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 53) Hsu fails to disclose explicitly generating a recommendation for improving a level of compliance with the at least one rule; and displaying a description of the generated recommendation. Pertaining to analogous art, Osman et al. disclose generating, by the learning system, a recommendation for improving a level of compliance with the at least one rule; (Osman et al., Figs. 1A - 3B, 7A & 7B, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0011 - 0012, Pg. 3 ¶ 0059 - Pg. 4 ¶ 0066, Pg. 10 ¶ 0107 - 0109, Pg. 11 ¶ 0114 - 0116) and modifying, by the learning system, a user interface to display an indication of the determination by the state machine, (Osman et al., Figs. 1A - 3B, 7A & 7B, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0011 - 0012, Pg. 3 ¶ 0059 - Pg. 4 ¶ 0066, Pg. 10 ¶ 0107 - 0109, Pg. 11 ¶ 0114 - 0116) wherein modifying the user interface further comprises modifying the user interface to display a description of the generated recommendation. (Osman et al., Figs. 1A - 3B, 7A & 7B, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0011 - 0012, Pg. 3 ¶ 0059 - Pg. 4 ¶ 0066, Pg. 10 ¶ 0107 - 0109, Pg. 11 ¶ 0114 - 0116) Chaudhry et al. and Hsu are combinable because they are both directed towards analyzing sensor data of an environment with machine learning techniques to detect the occurrence of one or more safety issues in the environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaudhry et al. with the teachings of Hsu. This modification would have been prompted in order to enhance the base device of Chaudhry et al. with the well-known and applicable technique Hsu applied to a comparable device. Utilizing a state machine in communication with the learning system to determine compliance with at least one rule requiring the individual to execute a plurality of specified steps in a specified order by determining, by the state machine changing from one state to another in response to the output and the attribute, whether the individual executed the plurality of specified steps in the specified order; as taught by Hsu, would enhance the base device of Chaudhry et al. by enabling it to assess an individual’s compliance with a greater variety and/or complexity of safety rules or requirements, such as safety rules or requirements that require a person to perform a set of steps in a particular sequence, so as to allow for it to be utilized in an increased number and diversity of environments and/or applications, such as ones with safety rules that require an individual to perform an ordered sequence of steps, and thereby improve its overall appeal, usefulness and marketability to potential end-users. Furthermore, this modification would have been prompted by the teachings and suggestions of Chaudhry et al. that images may be analyzed over time to detect and determine characteristics of people in an environment, that the characteristics of the people may be compared against safety requirements or regulations, such as directional requirements, personal protective equipment requirements, workplace safety and/or behavior requirements and/or rules or other requirements established by an organization, to determine compliance and detect possible safety issues due to breaches of the safety requirements, that an algorithm or machine learning model may be utilized to analyze safety requirements with detected object characteristics, that implementation of their teachings is not limited to any specific combination of hardware circuitry and/or software and that the resulting implementation of their teachings and disclosed logical operations is a matter of choice, see at least page 1 paragraph 0012, page 2 paragraph 0021, page 4 paragraphs 0036 and 0039 - 0040, page 5 paragraph 0049 and page 6 paragraph 0052 of Chaudhry et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that the base device of Chaudhry et al. would utilize a state machine in communication with the learning system to determine compliance with at least one rule that requires an individual to execute a plurality of specified steps in a specified order so as to enable it to determine an individual’s compliance with a greater variety and/or complexity of safety rules or requirements in the monitored environment and thereby improve its overall appeal, usefulness and marketability to potential end-users by allowing for it to be utilized in an increased number and diversity of environments and/or applications, such as ones with safety requirements requiring an individual to perform an ordered sequence of steps. In addition, Chaudhry et al. in view of Hsu and Osman et al. are combinable because they are all directed towards analyzing sensor data of an environment with machine learning techniques to detect the occurrence of one or more safety issues in the environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Chaudhry et al. in view of Hsu with the teachings of Osman et al. This modification would have been prompted in order to enhance the combined base device of Chaudhry et al. in view of Hsu with the well-known and applicable technique Osman et al. applied to a comparable device. Generating and displaying a recommendation for improving a level of compliance with the at least one rule, as taught by Osman et al., would enhance the combined base device by helping improve compliance with the at least one rule by monitored individuals so as to help ensure mitigation of identified safety issues and reduce a number of possible dangerous incidents resulting from non-compliance with safety requirements from occurring. Furthermore, this modification would have been prompted by the teachings and suggestions of Chaudhry et al. that compliance information, including a reminder on requirements for compliance with rules and/or requirements based on identified compliance issues, may be output to users to help mitigate or remedy identified violations, see at least page 2 paragraph 0018 and page 4 paragraph 0040 - page 5 paragraph 0041 of Chaudhry et al. Moreover, this modification would have been prompted by the teachings and suggestions of Hsu that displayed text can be output to warn people that an unsafe work area has been created due to a step being performed incorrectly or out-of-order, see at least column 3 lines 1 - 16 and column 5 lines 20 - 43 of Hsu. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that a recommendation for improving a level of compliance with the at least one rule would be generated and displayed in order to help improve compliance with at least one rule by monitored individuals thereby facilitating mitigation of identified safety issues and increasing an overall level of safety of the monitored individuals. Therefore, it would have been obvious to combine Chaudhry et al. with Hsu and Osman et al. to obtain the invention as specified in claim 1.
- With regards to claim 2, Chaudhry et al. in view of Hsu in view of Osman et al. disclose the method of claim 1, wherein analyzing further comprises analyzing, by the learning system, a plurality of objects detected in the video file. (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024, 0026 and 0028 - 0031, Pg. 4 ¶ 0036 - 0040) In addition, analogous art Hsu discloses analyzing, by the learning system, a plurality of objects detected in the video file. (Hsu, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 20)
- With regards to claim 3, Chaudhry et al. in view of Hsu in view of Osman et al. disclose the method of claim 1, wherein identifying further comprises identifying an attribute identifying a physical location depicted in the video file. (Chaudhry et al., Figs. 3 - 5, Pg. 1 ¶ 0012, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0032, Pg. 4 ¶ 0035 - 0039, Pg. 5 ¶ 0042 - 0044) In addition, analogous art Hsu discloses identifying an attribute identifying a physical location depicted in the video file. (Hsu, Col. 3 Lines 1 - 32, Col. 3 Line 44 - Col. 4 Line 34, Col. 4 Line 47 - Col. 5 Line 43, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 20 and Lines 38 - 51)
- With regards to claim 4, Chaudhry et al. in view of Hsu in view of Osman et al. disclose the method of claim 1, wherein identifying further comprises identifying an attribute identifying a time of day depicted in the video file. (Chaudhry et al., Pg. 2 ¶ 0017 - 0018 and 0021, Pg. 3 ¶ 0027 and 0032, Pg. 4 ¶ 0038 and 0040, Pg. 5 ¶ 0042 - 0044)
- With regards to claim 5, Chaudhry et al. in view of Hsu in view of Osman et al. disclose the method of claim 1, wherein identifying further comprises: identifying, by the learning system, an attribute identifying at least a second object in the video file; (Chaudhry et al., Pg. 1 ¶ 0012, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0036 - 0039) and determining that the at least one object is prohibited from appearing with the at least the second object in the video file by the at least one rule. (Chaudhry et al., Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024, Pg. 4 ¶ 0037 - Pg. 5 ¶ 0041) In addition, analogous art Hsu discloses identifying, by the learning system, an attribute identifying at least a second object in the video file; (Hsu, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 20) and determining that the at least one object is prohibited from appearing with the at least the second object in the video file by the at least one rule. (Hsu, Abstract, Figs. 2 & 3, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 37)
- With regards to claim 6, Chaudhry et al. in view of Hsu in view of Osman et al. disclose the method of claim 1 further comprising: generating, by the learning system, an alert regarding the determination; (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0013, Pg. 2 ¶ 0018, Pg. 5 ¶ 0041) and transmitting, by the learning system, to at least one user of the learning system, the alert. (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0013, Pg. 2 ¶ 0018, Pg. 5 ¶ 0041) In addition, analogous art Hsu discloses generating, by the learning system, an alert regarding the determination; (Hsu, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 21 - 28 and Lines 52 - 65) and transmitting, by the learning system, to at least one user of the learning system, the alert. (Hsu, Col. 1 Lines 37 - 44, Col. 5 Line 20 - Col. 6 Line 12)
- With regards to claim 9, Chaudhry et al. disclose a system (Chaudhry et al., Abstract, Figs. 3 & 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0018, Pg. 3 ¶ 0023 - 0024, 0026 and 0031 - 0032, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0045 - 0049, Pg. 6 ¶ 0052 - 0053) comprising: a machine vision component (Chaudhry et al., Figs. 1 - 6, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 and 0031, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0049 - Pg. 6 ¶ 0053) processing a video file to detect at least one object in the video file (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024, 0026 and 0028 - 0032, Pg. 4 ¶ 0036 - 0037) and generating an output including data relating to the at least one object and the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024 and 0028 - 0032, Pg. 4 ¶ 0035 - 0040, Pg. 5 ¶ 0042 - 0044) a learning system, in communication with the machine vision component, (Chaudhry et al., Figs. 1 - 6, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 and 0031, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0049 - Pg. 6 ¶ 0053) analyzing the output Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0034 - 0040, Pg. 5 ¶ 0042 - 0044) and identifying an attribute of the video file, the attribute associated with the at least one object (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0032, Pg. 4 ¶ 0034 - 0040, Pg. 5 ¶ 0042 - 0044) and generating a user interface; (Chaudhry et al., Figs. 1, 2, 4 & 6, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) and the learning system analyzing the output and the attribute and the video file (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0036 - 0040) and determining, that the at least one object is prohibited from appearing with the attribute in the video file by at least one rule, (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 4 ¶ 0036 - 0040) wherein the at least one object comprises an individual, (Chaudhry et al., Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0024 and 0031, Pg. 4 ¶ 0036 - 0039) wherein the at least one rule requires the individual to execute a specified step, (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0024, Pg. 4 ¶ 0036 - 0039) and wherein the learning system determines, in response to the output and the attribute, whether the individual executed the specified step; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0017 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0030 - 0031, Pg. 4 ¶ 0036 - 0040) wherein the learning system further comprises functionality for generating a reminder for improving a level of compliance with the at least one rule, (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) and wherein the learning system further comprises functionality for modifying the user interface to display an indication of the determination by the learning system (Chaudhry et al., Figs. 1, 2, 4 & 6, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) and to display a description of the generated reminder. (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) Chaudhry et al. fail to disclose explicitly analyzing and determining by a state machine in communication with the learning system, wherein the at least one rule requires the individual to execute a plurality of specified steps in a specified order, wherein the state machine determines, by changing from one state to another, whether the individual executed the plurality of specified steps in the specified order, and generating a recommendation for improving a level of compliance with the at least one rule and displaying a description of the generated recommendation. Pertaining to analogous art, Hsu discloses a system (Hsu, Abstract, Fig. 1, Col. 1 Lines 16 - 26, Col. 3 Lines 1 - 16 and Lines 33 - 46, Col. 4 Line 54 - Col. 5 Line 19, Col. 5 Line 54 - Col. 6 Line 12) comprising: a machine vision component (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 19, Col. 5 Line 66 - Col. 6 Line 36) processing a video file to detect at least one object in the video file (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 32 and Lines 61 - 65, Col. 3 Line 61 - Col. 4 Line 53) and generating an output including data relating to the at least one object and the video file; (Hsu, Abstract, Figs. 2 & 3, Col. 1 Lines 16 - 32 and Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 37 and Lines 52 - 59) a learning system, in communication with the machine vision component, (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 57 - 67, Col. 3 Lines 1 - 16, Col. 4 Line 54 - Col. 5 Line 19, Col. 6 Lines 19 - 36) analyzing the output (Hsu, Figs. 2 & 3, Col. 1 Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 7, Col. 4 Line 58 - Col. 5 Line 43, Col. 7 Lines 6 - 37 and Lines 52 - 59) and identifying an attribute of the video file, the attributed associated with the at least one object (Hsu, Col. 1 Lines 16 - 29 and Lines 61 - 65, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 39, Col. 5 Line 54 - Col. 6 Line 12, Col. 7 Lines 6 - 20) and generating a user interface; (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 37 - 44, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 21 - 28 and Lines 52 - 65) and a state machine, in communication with the learning system, (Hsu, Abstract, Col. 1 Lines 16 - 26 and Lines 57 - 65, Col. 4 Line 54 - Col. 5 Line 19, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 37 and Lines 52 - 65) analyzing the output and the attribute and the video file (Hsu, Abstract, Figs. 2 & 3, Col. 1 Lines 16 - 26 and Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 34, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 37, Col. 7 Line 52 - Col. 8 Line 12) and determining, that the at least one object is prohibited from appearing with the attribute in the video file by at least one rule, (Hsu, Abstract, Figs. 2 & 3, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 37, Col. 7 Line 52 - Col. 8 Line 12) wherein the at least one object comprises an individual, (Hsu, Col. 1 Lines 16 - 29 and Lines 61 - 65, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 34, Col. 7 Lines 6 - 37) wherein the at least one rule requires the individual to execute a plurality of specified steps in a specified order, (Hsu, Figs. 2 & 3, Col. 1 Lines 49 - 60, Col. 3 Lines 1 - 32, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Line 52 - Col. 8 Line 12) and wherein the state machine determines, by changing from one state to another in response to the output and the attribute, (Hsu, Figs. 2 & 3, Col. 1 Lines 16 - 26 and Lines 49 - 60, Col. 3 Line 61 - Col. 4 Line 7, Col. 5 Lines 1 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 28, Col. 7 Line 52 - Col. 8 Line 12) whether the individual executed the plurality of specified steps in the specified order; (Hsu, Figs. 2 & 3, Col. 1 Lines 56 - 60, Col. 3 Lines 1 - 16, Col. 5 Lines 1 - 43, Col. 7 Lines 21 - 28, Col. 7 Line 52 - Col. 8 Line 12) wherein the learning system further comprises functionality for generating a warning for improving a level of compliance with the at least one rule, (Hsu, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 21 - 28 and Lines 52 - 65) and wherein the learning system further comprises functionality for modifying the user interface to display an indication of the determination by the state machine (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 36 - 44, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 52, Col. 7 Lines 21 - 28, Col. 7 Line 52 - Col. 8 Line 12) and to display a description of the generated warning. (Hsu, Abstract, Col. 1 Lines 16 - 26 and Lines 37 - 40, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 53) Hsu fails to disclose explicitly generating a recommendation for improving a level of compliance with the at least one rule and displaying a description of the generated recommendation. Pertaining to analogous art, Osman et al. disclose wherein the learning system further comprises functionality for generating a recommendation for improving a level of compliance with the at least one rule, (Osman et al., Figs. 1A - 3B, 7A & 7B, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0011 - 0012, Pg. 3 ¶ 0059 - Pg. 4 ¶ 0066, Pg. 10 ¶ 0107 - 0109, Pg. 11 ¶ 0114 - 0116) and wherein the learning system further comprises functionality for modifying the user interface to display a description of the generated recommendation. (Osman et al., Figs. 1A - 3B, 7A & 7B, Pg. 1 ¶ 0004 - 0005, Pg. 2 ¶ 0011 - 0012, Pg. 3 ¶ 0059 - Pg. 4 ¶ 0066, Pg. 10 ¶ 0107 - 0109, Pg. 11 ¶ 0114 - 0116) Chaudhry et al. and Hsu are combinable because they are both directed towards analyzing sensor data of an environment with machine learning techniques to detect the occurrence of one or more safety issues in the environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaudhry et al. with the teachings of Hsu. This modification would have been prompted in order to enhance the base device of Chaudhry et al. with the well-known and applicable technique Hsu applied to a comparable device. Utilizing a state machine in communication with the learning system to determine compliance with at least one rule requiring the individual to execute a plurality of specified steps in a specified order by determining, by the state machine changing from one state to another in response to the output and the attribute, whether the individual executed the plurality of specified steps in the specified order; as taught by Hsu, would enhance the base device of Chaudhry et al. by enabling it to assess an individual’s compliance with a greater variety and/or complexity of safety rules or requirements, such as safety rules or requirements that require a person to perform a set of steps in a particular sequence, so as to allow for it to be utilized in an increased number and diversity of environments and/or applications, such as ones with safety rules that require an individual to perform an ordered sequence of steps, and thereby improve its overall appeal, usefulness and marketability to potential end-users. Furthermore, this modification would have been prompted by the teachings and suggestions of Chaudhry et al. that images may be analyzed over time to detect and determine characteristics of people in an environment, that the characteristics of the people may be compared against safety requirements or regulations, such as directional requirements, personal protective equipment requirements, workplace safety and/or behavior requirements and/or rules or other requirements established by an organization, to determine compliance and detect possible safety issues due to breaches of the safety requirements, that an algorithm or machine learning model may be utilized to analyze safety requirements with detected object characteristics, that implementation of their teachings is not limited to any specific combination of hardware circuitry and/or software and that the resulting implementation of their teachings and disclosed logical operations is a matter of choice, see at least page 1 paragraph 0012, page 2 paragraph 0021, page 4 paragraphs 0036 and 0039 - 0040, page 5 paragraph 0049 and page 6 paragraph 0052 of Chaudhry et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that the base device of Chaudhry et al. would utilize a state machine in communication with the learning system to determine compliance with at least one rule that requires an individual to execute a plurality of specified steps in a specified order so as to enable it to determine an individual’s compliance with a greater variety and/or complexity of safety rules or requirements in the monitored environment and thereby improve its overall appeal, usefulness and marketability to potential end-users by allowing for it to be utilized in an increased number and diversity of environments and/or applications, such as ones with safety requirements requiring an individual to perform an ordered sequence of steps. In addition, Chaudhry et al. in view of Hsu and Osman et al. are combinable because they are all directed towards analyzing sensor data of an environment with machine learning techniques to detect the occurrence of one or more safety issues in the environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Chaudhry et al. in view of Hsu with the teachings of Osman et al. This modification would have been prompted in order to enhance the combined base device of Chaudhry et al. in view of Hsu with the well-known and applicable technique Osman et al. applied to a comparable device. Generating and displaying a recommendation for improving a level of compliance with the at least one rule, as taught by Osman et al., would enhance the combined base device by helping improve compliance with the at least one rule by monitored individuals so as to help ensure mitigation of identified safety issues and reduce a number of possible dangerous incidents resulting from non-compliance with safety requirements from occurring. Furthermore, this modification would have been prompted by the teachings and suggestions of Chaudhry et al. that compliance information, including a reminder on requirements for compliance with rules and/or requirements based on identified compliance issues, may be output to users to help mitigate or remedy identified violations, see at least page 2 paragraph 0018 and page 4 paragraph 0040 - page 5 paragraph 0041 of Chaudhry et al. Moreover, this modification would have been prompted by the teachings and suggestions of Hsu that displayed text can be output to warn people that an unsafe work area has been created due to a step being performed incorrectly or out-of-order, see at least column 3 lines 1 - 16 and column 5 lines 20 - 43 of Hsu. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that a recommendation for improving a level of compliance with the at least one rule would be generated and displayed in order to help improve compliance with at least one rule by monitored individuals thereby facilitating mitigation of identified safety issues and increasing an overall level of safety of the monitored individuals. Therefore, it would have been obvious to combine Chaudhry et al. with Hsu and Osman et al. to obtain the invention as specified in claim 9.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chaudhry et al. U.S. Publication No. 2023/0230379 A1 in view of Hsu U.S. Patent No. 10,249,163.
- With regards to claim 10, Chaudhry et al. disclose a method for executing a learning system, (Chaudhry et al., Abstract, Figs. 3 & 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0018, Pg. 3 ¶ 0023 - 0024, 0026 and 0031 - 0032, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0045 - 0049, Pg. 6 ¶ 0052 - 0053) the method comprising: processing, by a machine vision component in communication with the learning system, (Chaudhry et al., Figs. 1 - 6, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 and 0031, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0049 - Pg. 6 ¶ 0053) a video file to detect at least one object in the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024, 0026 and 0028 - 0032, Pg. 4 ¶ 0036 - 0037) generating, by the machine vision component, (Chaudhry et al., Figs. 1 - 6, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 and 0031, Pg. 4 ¶ 0036 - 0040, Pg. 5 ¶ 0049 - Pg. 6 ¶ 0053) an output including data relating to the at least one object and the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024 and 0028 - 0032, Pg. 4 ¶ 0035 - 0040, Pg. 5 ¶ 0042 - 0044) analyzing, by the learning system, the output; (Chaudhry et al., Fig. 4, Pg. 1 ¶ 0012, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0034 - 0040, Pg. 5 ¶ 0042 - 0044) identifying, by the learning system, an attribute of the video file, the attribute associated with the at least one object; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0032, Pg. 4 ¶ 0034 - 0040, Pg. 5 ¶ 0042 - 0044) analyzing, by the learning system, the output and the attribute and the video file; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0016 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0031 - 0032, Pg. 4 ¶ 0036 - 0040) determining, by the learning system, that the at least one object is prohibited from appearing with the attribute in the video file by at least one rule, (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 4 ¶ 0036 - 0040) wherein the at least one object comprises an individual, (Chaudhry et al., Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0024 and 0031, Pg. 4 ¶ 0036 - 0039) wherein the at least one rule requires the individual to execute a specified step, (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0021, Pg. 3 ¶ 0024, Pg. 4 ¶ 0036 - 0039) and wherein the determining comprises determining, by the learning system in response to the output and the attribute, whether the individual executed the specified step; (Chaudhry et al., Abstract, Fig. 4, Pg. 1 ¶ 0012, Pg. 2 ¶ 0017 - 0018 and 0021, Pg. 3 ¶ 0023 - 0024 and 0030 - 0031, Pg. 4 ¶ 0036 - 0040) and modifying, by the learning system, a user interface to display an indication of the determination by the learning system. (Chaudhry et al., Figs. 1, 2, 4 & 6, Pg. 1 ¶ 0012 - 0013, Pg. 2 ¶ 0017 - 0019, Pg. 4 ¶ 0040 - Pg. 5 ¶ 0041) Chaudhry et al. fail to disclose explicitly wherein the at least one rule requires the individual to execute a plurality of specified steps in a specified order, and wherein the determining comprises determining, by the learning system changing from one state to another, whether the individual executed the plurality of specified steps in the specified order. Pertaining to analogous art, Hsu discloses a method for executing a learning system, (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 57 - 67, Col. 3 Lines 1 - 16, Col. 4 Line 54 - Col. 5 Line 19, Col. 6 Lines 19 - 36) the method comprising: processing, by a machine vision component in communication with the learning system, (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 19, Col. 5 Line 66 - Col. 6 Line 36) a video file to detect at least one object in the video file; (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 32 and Lines 61 - 65, Col. 3 Line 61 - Col. 4 Line 53) generating, by the machine vision component, an output including data relating to the at least one object and the video file; (Hsu, Abstract, Figs. 2 & 3, Col. 1 Lines 16 - 32 and Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 37 and Lines 52 - 59) analyzing, by the learning system, the output; (Hsu, Figs. 2 & 3, Col. 1 Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 7, Col. 4 Line 58 - Col. 5 Line 43, Col. 7 Lines 6 - 37 and Lines 52 - 59) identifying, by the learning system, an attribute of the video file, the attribute associated with the at least one object; (Hsu, Col. 1 Lines 16 - 29 and Lines 61 - 65, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 39, Col. 5 Line 54 - Col. 6 Line 12, Col. 7 Lines 6 - 20) analyzing, by the learning system, the output and the attribute and the video file; (Hsu, Abstract, Figs. 2 & 3, Col. 1 Lines 16 - 26 and Lines 57 - 65, Col. 3 Lines 1 - 32, Col. 3 Line 61 - Col. 4 Line 34, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 37, Col. 7 Line 52 - Col. 8 Line 12) determining, by the learning system, that the at least one object is prohibited from appearing with the attribute in the video file by at least one rule, (Hsu, Abstract, Figs. 2 & 3, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 53, Col. 5 Lines 1 - 43, Col. 7 Lines 6 - 37, Col. 7 Line 52 - Col. 8 Line 12) wherein the at least one object comprises an individual, (Hsu, Col. 1 Lines 16 - 29 and Lines 61 - 65, Col. 3 Lines 1 - 32, Col. 4 Lines 8 - 34, Col. 7 Lines 6 - 37) wherein the at least one rule requires the individual to execute a plurality of specified steps in a specified order, (Hsu, Figs. 2 & 3, Col. 1 Lines 49 - 60, Col. 3 Lines 1 - 32, Col. 5 Lines 1 - 43, Col. 6 Lines 13 - 36, Col. 7 Line 52 - Col. 8 Line 12) and wherein the determining comprises determining, by the learning system changing from one state to another in response to the output and the attribute, (Hsu, Figs. 2 & 3, Col. 1 Lines 16 - 26 and Lines 49 - 60, Col. 3 Line 61 - Col. 4 Line 7, Col. 5 Lines 1 - 53, Col. 6 Lines 13 - 36, Col. 7 Lines 6 - 28, Col. 7 Line 52 - Col. 8 Line 12) whether the individual executed the plurality of specified steps in the specified order; (Hsu, Figs. 2 & 3, Col. 1 Lines 56 - 60, Col. 3 Lines 1 - 16, Col. 5 Lines 1 - 43, Col. 7 Lines 21 - 28, Col. 7 Line 52 - Col. 8 Line 12) and modifying, by the learning system, a user interface to display an indication of the determination by the learning system. (Hsu, Abstract, Figs. 1 - 3, Col. 1 Lines 16 - 26 and Lines 36 - 44, Col. 3 Lines 1 - 16, Col. 5 Lines 20 - 52, Col. 7 Lines 21 - 28, Col. 7 Line 52 - Col. 8 Line 12) Chaudhry et al. and Hsu are combinable because they are both directed towards analyzing sensor data of an environment with machine learning techniques to detect the occurrence of one or more safety issues in the environment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaudhry et al. with the teachings of Hsu. This modification would have been prompted in order to enhance the base device of Chaudhry et al. with the well-known and applicable technique Hsu applied to a comparable device. Determining compliance with at least one rule requiring the individual to execute a plurality of specified steps in a specified order by determining, by the learning system changing from one state to another, whether the individual executed the plurality of specified steps in the specified order; as taught by Hsu, would enhance the base device of Chaudhry et al. by enabling it to assess an individual’s compliance with a greater variety and/or complexity of safety rules or requirements, such as safety rules or requirements that require a person to perform a set of steps in a particular sequence, so as to allow for it to be utilized in an increased number and diversity of environments and/or applications, such as ones with safety rules that require an individual to perform an ordered sequence of steps, and thereby improve its overall appeal, usefulness and marketability to potential end-users. Furthermore, this modification would have been prompted by the teachings and suggestions of Chaudhry et al. that images may be analyzed over time to detect and determine characteristics of people in an environment and that the characteristics of the people may be compared against safety requirements or regulations, such as directional requirements, personal protective equipment requirements, workplace safety and/or behavior requirements and/or rules or other requirements established by an organization, to determine compliance and detect possible safety issues due to breaches of the safety requirements, see at least page 1 paragraph 0012, page 2 paragraph 0021 and page 4 paragraphs 0036 and 0039 - 0040 of Chaudhry et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that compliance with at least one rule that requires an individual to execute a plurality of specified steps in a specified order would be determined by the base device of Chaudhry et al. so as to enable it to determine an individual’s compliance with a greater variety and/or complexity of safety rules or requirements in the monitored environment thereby improving its overall appeal, usefulness and marketability to potential end-users by allowing for it to be utilized in an increased number and diversity of environments and/or applications, such as ones with safety requirements requiring an individual to perform an ordered sequence of steps. Therefore, it would have been obvious to combine Chaudhry et al. with Hsu to obtain the invention as specified in claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhatt et al. U.S. Publication No. 2020/0167715 A1; which is directed towards systems and methods for assessing and monitoring tasks, wherein image data of a task comprising a plurality of sub-tasks is captured and the image data is analyzed to determine that the plurality of sub-tasks of the task are completed in the correct order.
Starr et al. U.S. Publication No. 2022/0284566 A1; which is directed towards a system and method for detecting safety violations, wherein image data is analyzed to identify a time series of actions, and an unsafe practice is determined based on the identified time series of actions.
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/ERIC RUSH/Primary Examiner, Art Unit 2677
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