DETAILED ACTION
The following is a Final Office Action in response to communications filed July 10, 2026. Claims 1–3 and 5–9 are amended. Claims 1–9 are currently pending.
Response to Amendment/Argument
Applicant’s Response is sufficient to overcome the previous objections to claims 1, 3, and 9 for informalities. Accordingly, the previous objections to claims 1, 3, and 9 are withdrawn.
Applicant’s Response is sufficient to overcome the previous rejections of claims 1–9 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. Accordingly, the previous rejections of claims 1–9 under 35 U.S.C. 112(b) are withdrawn.
However, Applicant’s Response necessitates a new ground of rejection under 35 U.S.C. 112(b), and Examiner directs Applicant to the relevant explanation below.
Applicant’s Response is sufficient to overcome the previous rejections of claims 1–9 under 35 U.S.C. 101 as being directed to non-statutory subject matter. More particularly, the additional elements of independent claims 1 and 9, including the trained machine-learning model, the AI classification model, and the elements to “extract, from an object model database, a 3D object model having a same shape as the object, and to plot the 3D object model in a 3D virtual space” and “extract, from a body model database, a 3D body model of the worker, and to plot the 3D body model in the3D virtual space”, integrate the abstract idea into a practical application because the additional elements apply or use the recited abstract idea in some other meaningful way beyond generally linking the use of the recited abstract idea to a particular technological environment. Accordingly, the previous rejections of claims 1–9 under 35 U.S.C. 101 are withdrawn.
With respect to the previous rejections under 35 U.S.C. 103, Applicant’s remarks have been fully considered but are moot in view of the updated grounds of 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.
Claim 2 is 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 2 recites “a simultaneous recognition probability” in the elements reciting functionality that “records” and “extracts”. As a result, the scope of the claim is indefinite because it is unclear whether Applicant intends for the second recitation to reference the first recitation or intends to introduce a second, different “simultaneous recognition probability”.
For purposes of examination, the claim is interpreted as reciting “the object position/orientation estimating unit extracts object models based at least in part on [[a]] the simultaneous recognition probability recorded in the object model database”.
In view of the above, claim 2 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.
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, 3–7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over KOVACH et al. (U.S. 2019/0080274) in view of Sharma et al. (U.S. 2024/0341903).
Claims 1 and 9: Kovach discloses a behavior analysis system that analyzes an interrelationship between a worker and an object, the behavior analysis system comprising:
a computer including a processor device configured to execute predetermined processing and a storage device connected to the processor device (See FIG. 3), wherein the processor device includes:
an input unit (See FIG. 2 and paragraphs 40–41, wherein the system executes applications and programs) configured to receive an input of measurement data collected from the worker and the object, the measurement data including one or more images (See paragraphs 17 and 25, wherein an image capture system captures images of objects and workers within a facility);
a human movement estimating unit configured to estimate a movement of the worker based on the measurement data (See paragraphs 25–26 and 72, wherein worker movements and motions are detected);
an object position/orientation estimating unit configured to estimate a position of the object based on the measurement data (See paragraphs 25, 27, 63, and 72, wherein movement associated with tools, vehicles, and equipment is monitored to determine positions of objects);
an object model plotting unit configured to extract an object model having a same shape as the object (See paragraph 67, wherein object recognition processes identify an object by matching shapes/features of the object to known objects; see also FIG. 3 and paragraphs 49–50, wherein storage devices/components are disclosed as storing information related to the disclosed operations), and to plot the object model in a virtual space based on the position estimated for the object by the object position/orientation estimating unit (See FIG. 1D and paragraphs 27–28, wherein identified objects are mapped with respect to the facility; see also paragraph 66);
a body model plotting unit configured to extract a body model of the worker (See paragraphs 69–70, wherein worker recognition processes identify workers by matching features of the worker to known workers; see also FIG. 3 and paragraphs 49–50, wherein storage devices/components are disclosed as storing information related to the disclosed operations), and to plot the body model in the virtual space based on the movement estimated for the worker by the human movement estimating unit (See FIG. 1D and paragraphs 27–28, wherein identified workers are mapped with respect to the facility; see also paragraph 66);
an interrelationship analyzing unit configured to analyze an interrelationship between the body model and the object model based on a position of the body model and the position of the object model plotted in the virtual space (See paragraphs 72 and 78, in view of paragraphs 86–87 and 92, wherein activities are determined by measuring worker and object motions and distances, and wherein worker and object motion analytics are performed using the generated map; see also paragraph 21, wherein a threshold distance between a worker and an object is determined);
an interrelationship output unit configured to output the interrelationship analyzed by the interrelationship analyzing unit (See paragraph 108, wherein a report is generated based on worker and object movements and proximities; see also FIG. 1D and paragraphs 27–28, wherein the map displays workers, objects, and activities); and
a work-type classifying unit configured to determine a work type corresponding to the interrelationship by employing an artificial intelligence (Al) model having been trained based on relationships between a plurality of images and work types, the Al model receiving, as input, the interrelationship and the one or more images of the measurement data used by the trained machine-learning model, wherein, based on the input, the Al model outputs the work type corresponding to the interrelationship (See paragraphs 72 and 78, in view of paragraph 79, wherein a worker activity is identified using a trained artificial intelligence model, and wherein the activity is identified based on the images and identified interrelationships). Although Kovach implicitly discloses estimating an orientation of the objection (See citations above), Kovach does not expressly disclose the remaining claim elements.
Sharma discloses a human movement estimating unit configured to estimate a movement of the worker based on the measurement data, the human movement estimating unit using a trained machine-learning model to infer an orientation of a body of the worker in three-dimensional (3D) space based on the one or more images included in the measurement data (See paragraphs 29–30, in view of paragraph 36, wherein the pose/orientation of people and objects are estimated within the virtual space using a machine learning model pre-trained for 3D human/object pose/shape estimation, and paragraph 51, wherein the medical environment is continuously updated; see also paragraphs 31–33);
an object position/orientation estimating unit configured to estimate a position and an orientation of the object based on the measurement data (See paragraphs 29–30, in view of paragraph 36, wherein the pose/orientation of people and objects are estimated within the virtual space using a machine learning model pre-trained for 3D human/object pose/shape estimation, and paragraph 51, wherein the medical environment is continuously updated; see also paragraphs 31–33);
an object model plotting unit configured to extract, from an object model database, a 3D object model having a same shape as the object, and to plot the 3D object model in a 3D virtual space based on the position and the orientation estimated for the object by the object position/orientation estimating unit (See paragraphs 29–30, in view of paragraphs 36 and 46, wherein a 3D model of the object/person is selected using classification matching, and wherein the position/orientation of the 3D model are mapped in the 3D virtual space);
a body model plotting unit configured to extract, from a body model database, a 3D body model of the worker, and to plot the 3D body model in the 3D virtual space based on the movement estimated for the worker by the human movement estimating unit (See paragraphs 29–30, in view of paragraphs 36 and 46, wherein a 3D model of the object/person is selected using classification matching, and wherein the position/orientation of the 3D model are mapped in the 3D virtual space; see also paragraphs 31–33); and
a classification model (See paragraph 36, wherein classification models are disclosed).
Kovach discloses a system directed to tracking and analyzing workers, objects, and activities in a facility. Sharma discloses a system directed to visualizing medical environments using predetermined 3D models. Each reference discloses a system directed to managing work by monitoring workers and objects. The technique of utilizing body/object 3D mapping is applicable to the system of Kovach as they both share characteristics and capabilities; namely, they are directed to managing work by monitoring workers and objects.
One of ordinary skill in the art would have recognized that applying the known technique of Sharma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Sharma to the teachings of Kovach would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate work management using movement monitoring into similar systems. Further, applying body/object 3D mapping to Kovach 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.
Claim 3: Kovach discloses the behavior analysis system according to claim 1, wherein the object model plotting unit plots the object model in the virtual space by using the position of the body model plotted by the body model plotting unit (See FIG. 1D and paragraphs 27–28, in view of paragraph 65, wherein identified objects are mapped relative to workers, objects, and activities; see also paragraph 66). Kovach does not expressly disclose the remaining claim elements.
Sharma discloses wherein the object model plotting unit plots the 3D object model in the 3D virtual space (See paragraphs 29–30, wherein predetermined 3D body models and 3D object models are plotted in a virtual space; see also paragraphs 31–33).
One of ordinary skill in the art would have recognized that applying the known technique of Sharma would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 4: Kovach discloses the behavior analysis system according to claim 1, further comprising a behavior analysis database that stores the interrelationship analyzed by the interrelationship analyzing unit for each type of work (See FIG. 1D and paragraph 28, wherein analytics are monitored with respect to types of bays, and wherein a type of bay indicates a type of work; see also paragraphs 72 and 78, in view of paragraphs 86–87 and 92, wherein activities are determined by measuring worker and object motions and distances).
Claim 5: Kovach discloses the behavior analysis system according to claim 4, further comprising a work evaluating unit configured to make an evaluation of work by comparing a stored interrelationship stored in the behavior analysis database, the stored interrelationship being used as a criterion of an evaluation, with the interrelationship that is to be evaluated, and that is obtained from the analysis performed by the interrelationship analyzing unit (See paragraphs 108 and 21, wherein proximity thresholds are used to evaluate work performance).
Claim 6: Kovach discloses the behavior analysis system according to claim 4, further comprising an analysis result presenting unit configured to present the interrelationship obtained from the analysis performed by the interrelationship analyzing unit (See FIG. 1D, wherein the map is generated). Although Kovach discloses presenting the interrelationship, Kovach does not expressly disclose the remaining claim elements.
Sharma discloses an analysis result presenting unit configured to present, to the worker, the interrelationship obtained from the analysis performed by the interrelationship analyzing unit (See paragraphs 29–30, wherein people and/or objects are visualized on a visualization device that may include VR goggles worn by the doctor).
One of ordinary skill in the art would have recognized that applying the known technique of Sharma would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 7: Kovach discloses the behavior analysis system according to claim 4, further comprising a cause analyzing unit that records a result of determining a quality of work in the behavior analysis database, and extracts an interrelationship having an effect on the quality of work (See paragraph 77, wherein worker and object movements are analyzed to identify bottlenecks in the facility; see also paragraphs 87–88 and 92).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over KOVACH et al. (U.S. 2019/0080274) in view of Sharma et al. (U.S. 2024/0341903), and in further view of Bronicki et al. (U.S. 2021/0374836).
Claim 2: As disclosed above, Kovach and Sharma disclose the elements of claim 1. Kovach and Sharma do not expressly disclose the elements of claim 2.
Bronicki discloses wherein the object model database records a simultaneous recognition probability between object models being stored, and the object position/orientation estimating unit extracts object models based at least in part on a simultaneous recognition probability recorded in the object model database (See paragraphs 355–356, wherein product recognition models identify a plurality of possible products according to relative probability scores).
As disclosed above, Kovach discloses a system directed to tracking and analyzing workers, objects, and activities in a facility, and Sharma discloses a system directed to visualizing medical environments using predetermined 3D models. Bronicki discloses a system directed to managing product placements by monitoring worker and target locations. Each reference discloses a system directed to managing work by monitoring workers and objects. The technique of utilizing recognition probabilities is applicable to the systems of Kovach and Sharma as they each share characteristics and capabilities; namely, they are directed to managing work by monitoring workers and objects.
One of ordinary skill in the art would have recognized that applying the known technique of Bronicki would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bronicki to the teachings of Kovach and Sharma would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate work management using movement monitoring into similar systems. Further, applying recognition probabilities to Kovach and Sharma 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.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Wegbreit et al. (U.S. 2010/0085358) discloses a system directed to constructing a 3D scene using predetermined object models; and
GRABNER et al. (U.S. 2019/0147221) discloses a system directed to estimating an object pose and selecting an object model based on an input image.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 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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