DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to the communications filed on 4/3/2025. Claims 1-20 are currently pending and have been considered below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-20 are determined to be directed to an abstract idea.
The claims 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), without providing a practical application integration and without providing significantly more.
As per Step 1 of the subject matter eligibility analysis, Claims 1, 8 and 15 are directed to a method (i.e., process), a system (i.e., apparatus), and non-transitory medium (i.e., product) which are statutory categories of invention.
As per Step 2A-Prong 1 of the subject matter eligibility analysis, Claim 1, 8 and 15 are directed specifically to the abstract idea of monitoring work tasks by gathering respective information about a respective at least one work task that is being monitored; obtaining the respective information about the respective at least one work task that is being monitored, and providing the respective information to at least one model to cause the at least one model to output a respective performance score that corresponds to the respective at least one work task; and outputting, for each performance score, based on their respective performance scores; all of which include mental processes (observing and evaluating task data for making an opinion or judgment on performance scores), and certain methods of organizing human activity based on fundamental economic practice (monitoring work tasks), and based on managing personal behavior and interactions between people (following rules and instruction for scoring performance or work tasks). Claims 2-7, 9-14 and 16-20 are directed to the abstract idea of claim 1, 8 or 15 with further details on the parameters/attributes of the abstract idea which includes mental processes and certain methods of organizing human activity for similar reasons as provided above for claim 1, 8 or 15. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
As per Step 2A-Prong 2 of the subject matter eligibility analysis, while the claims 1-20 recite additional limitations which are hardware or software elements, such as a plurality of sensor groups, at least one computing device, at least one machine learning model, output a user interface (UI) to at least one display device, wherein the UI includes a respective sub-UI that is ordered within the UI relative to other sub-Uls, non-transitory computer readable storage medium configured to store instructions that, when executed by at least one processor included in a computing device, cause the computing device to…, these limitations are not enough to qualify as a practical application being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Alternatively, receiving and/or transmitting data between devices is mere data gathering and insignificant extrasolution activity, which does not provide a practical application for the abstract idea (MPEP 2106.05(g)).
As per Step 2B of the subject matter eligibility analysis, while the claims 1-20 recite additional limitations which are hardware or software elements, such as a plurality of sensor groups, at least one computing device, at least one machine learning model, output a user interface (UI) to at least one display device, wherein the UI includes a respective sub-UI that is ordered within the UI relative to other sub-Uls, non-transitory computer readable storage medium configured to store instructions that, when executed by at least one processor included in a computing device, cause the computing device to…, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of an abstract idea in a particular technological environment, and mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do provide significantly more to an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Alternatively, receiving and/or transmitting data between devices is mere data gathering and insignificant extrasolution activity, and also is well-understood, routine and conventional which do not provide a practical application for the abstract idea (MPEP 2106.05(g) & (d)).
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lonsberry et al (US 20220016776 A1)
As per Claim 1, Lonsberry teaches a system (Abstract), comprising:
a plurality of sensor groups, wherein each sensor group of the plurality of sensor groups gathers respective information about a respective at least one work task that is being monitored by the sensor group; and at least one computing device configured to: for each sensor group of the plurality of sensor groups (para. 0008-0009, regarding plurality of sensors and different types/groups of sensors; para. 0006, “The computer-implemented method can include receiving data of a workspace that includes a part to be welded via at least one sensor. ”):
obtain the respective information about the respective at least one work task that is being monitored by the sensor group (para. 0006, 0034-0036, 0042, 0045-0061, regarding various sensors receiving/monitoring data about work task),
provide the respective information to at least one machine learning model to cause the at least one machine learning model to output a respective performance score that corresponds to the respective at least one work task (para. 0076, “The neural network can also be trained to also output an indication of the resultant weld quality, weld shape, weld penetration, measure of porosity, or a combination thereof.”; para. 0071, “The two-dimensional images and the three-dimensional point cloud data from the sensor(s) 102 can be used to recognize the root gap, hole, void, or weld and further can be used to adapt and/or update the robotic weld program to account for the root gap, hole, void, or weld.”; para. 0073, “The input data to these neural networks can be point cloud data and/or two-dimensional image data. The data flows into a one or more networks which then classifies regions or boundaries of holes, voids, or root gap”) and
output a user interface (UI) to at least one display device, wherein the UI includes, for each performance score, a respective sub-UI that is ordered within the UI relative to other sub-Uls based on their respective performance scores (para. 0076, “The neural network can also be trained to also output an indication of the resultant weld quality, weld shape, weld penetration, measure of porosity, or a combination thereof.”; para. 0084, “a user interface 106 that can allow users to provide weld parameters and/or update weld parameters. The user interface 106 can enable a user to interact with the system 100. Some non-limiting examples of the user interface 106 include menu-driven interface, graphical user interface (GUI), touchscreen GUI, a combination thereof, and/or the like.”;
while the reference teaches multiple performance scores for the work task and UIs; it does not explicitly specify sub-UIs for each performance score. It would be obvious for an ordinary skill before the effective filing date of the invention to reorganize parts of an invention (MPEP 2144.04), in this case to output/display each performance score in a separate UI or UI region for increased user-friendly UI and to enable the interested party to easily recognize each score from each other).
As per Claim 2, Lonsberry teaches a system as provided in claim 1 above. Lonsberry further teaches wherein, for a given sensor group of the plurality of sensor groups, the respective information includes a plurality of performance values, and the respective performance score is based on the plurality of performance values (para. 0006, 0034-0036, 0042, 0045-0061, regarding various sensors receiving/monitoring data about work task; para. 0076, “The neural network can also be trained to also output an indication of the resultant weld quality, weld shape, weld penetration, measure of porosity, or a combination thereof.”; para. 0071, “The two-dimensional images and the three-dimensional point cloud data from the sensor(s) 102 can be used to recognize the root gap, hole, void, or weld and further can be used to adapt and/or update the robotic weld program to account for the root gap, hole, void, or weld.”; para. 0073, “The input data to these neural networks can be point cloud data and/or two-dimensional image data. The data flows into a one or more networks which then classifies regions or boundaries of holes, voids, or root gap”).
As per Claim 4, Lonsberry teaches a system as provided in claim 1 above. Lonsberry further teaches wherein the at least one machine learning model is trained and fine-tuned to output the performance scores based on the respective information (para. 0067-0068, 0070, 0073-0077, 0079-0080, regarding training neural networks).
As per Claim 5, Lonsberry teaches a system as provided in claim 1 above. Lonsberry further teaches wherein, for a given sensor group of the plurality of sensor groups, the respective at least one work task comprises at least one welding task (abstract, para. 0006, 0034-0036, 0042, 0045-0061, regarding various sensors receiving/monitoring data about welding task).
As per Claim 6, Lonsberry teaches a system as provided in claim 5 above. Lonsberry further teaches wherein the respective information indicates, for a weld associated with the at least one welding task, a straightness of the weld, a thickness of the weld, a temperature of the weld, a travel speed of the weld, a gas presence of the weld, an amount of welding material used, a porosity of the weld, a penetration of the weld, or some combination thereof (para. 0076, “The neural network can also be trained to also output an indication of the resultant weld quality, weld shape, weld penetration, measure of porosity, or a combination thereof.”).
As per claims 8-9 and 11-13, Claims 8-9 and 11-13 recite substantially similar limitations as claims 1-2 and 4-6, respectively; therefore, claims 8-9 and 11-13 are rejected with the same reasoning, rationale and motivation as recited above for claim 1-2 and 4-6, respectively.
As per claims 15-16 and 18-20, Claims 15-16 and 18-20 recite substantially similar limitations as claims 1-2 and 4-6, respectively; therefore, claims 15-16 and 18-20 are rejected with the same reasoning, rationale and motivation as recited above for claim 1-2 and 4-6, respectively.
As per claim 15, Lonsberry further teaches a non-transitory computer readable storage medium configured to store instructions that, when executed by at least one processor included in a computing device, cause the computing device to … (para. 0095).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lonsberry et al (US 20220016776 A1) in view of Mullen (US 20160019813 A1).
As per Claim 3, Lonsberry teaches a system as provided in claim 2 above. Lonsberry does not teach; however, Mullen teaches wherein respective weights are assigned to the plurality of performance values, and the respective performance score is calculated based on a weighted average of the plurality of performance values (para. 0011).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Lonsberry with the aforementioned teachings of Mullen, in the field of monitoring tasks, with the motivation to provide a more inclusive score that includes multiple performance aspects.
As per claims 10 and 17, Claims 10 and 17 recite substantially similar limitations as claim 3; therefore, claim 10 and 17 are rejected with the same reasoning, rationale and motivation as recited above for claim 3.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lonsberry et al (US 20220016776 A1) in view of Elhawary et al (US 20190343429 A1).
As per Claim 7, Lonsberry teaches a system as provided in claim 1 above. Lonsberry further teaches wherein, for a given at least one work task, the respective sub-UI includes: first information based at least in part on the respective performance score (para. 0076, “The neural network can also be trained to also output an indication of the resultant weld quality, weld shape, weld penetration, measure of porosity, or a combination thereof.”), and second information based at least in part on the respective information on which the respective performance score is based (para. 0076, “The neural network can also be trained to also output an indication of the resultant weld quality, weld shape, weld penetration, measure of porosity, or a combination thereof.”), a first option to adjust at least one aspect of the respective at least one work task (para. 0036 “As discussed above, the technology described herein can adjust motion/trajectory (e.g., weld path) for a welding robot based on data obtained from sensors. The adjustment can be made dynamically based on the comparison of the expected state with the desired state. Accordingly, in contrast to existing feedback systems, the technology described herein does not require a user to provide the shape of the seams or gaps. Additionally, even if a portion of the seam or the gap is occluded, the technology described herein can still dynamically adjust welding instructions by automatically recognizing seams and/or gaps based on the sensor data.”), a second option to view at least one camera feed of the given at least one work task as it is being performed (para. 0067, “In some embodiments, different CNNs can be used to process image data from optical cameras, thermal cameras, and/or other sensors… For instance, by receiving data of the welding operation from two or more sensors can provide a wide-angle view of the part and the weld tip during the welding process. In contrast, when a single sensor such as a laser emitter is used to provide data, it is possible that some portion of the seam that is being welded may be occluded from the laser emitter. Such occlusion can lead to large deviations from the desired state. With complete view of the part during the welding process, the controller can accurately identify the desired state and make updates to it in real time.”),
Lonsberry does not teach; however, Elhawary teaches a third option to summon at least one manager individual who is in proximity to the given at least one work task as it is being performed (para. 0277, “The platform described may provide immediate feedback to workers themselves, or it may provide feedback directly to managers, either through on screen notifications at their workstations or through text messages to immediately notify a manager to an increase risk level for an employee. Similarly, the platform may provide rankings for individual workers, or may alert the manager when the workplace as a whole has generated an increased risk profile.”).
It would be obvious to one of ordinary skill in the art, before the earliest effective filing date of the invention, to modify Lonsberry with the aforementioned teachings of Elhawary, in the field of monitoring tasks, with the motivation to manage increased risk in a work environment.
As per claim 14, Claim 14 recites substantially similar limitations as claim7; therefore, claim 14 is rejected with the same reasoning, rationale and motivation as recited above for claim 7.
Conclusion
Additional relevant art not relied upon includes:
Becker (US 20240207982 A1), regarding “Described herein are examples of tool based welding technique monitoring systems with sloped workpiece calibrations. Using two calibration steps (or one fluid calibrating movement), the disclosed system is able to monitor welding technique along a straight welding joint of any slope, be the slope 0/180/360 degrees (i.e., horizontal), 90/270 degrees (i.e., vertical), or any slope in between. The system provides an inexpensive, intuitive, and relatively robust way of tracking an orientation of a welding-type tool in relation to a welding joint and/or workpiece, and providing welding technique feedback based on the relationship.”;
Akella (US 12093022 B2), regarding “In various embodiments, a computer implemented method of determining a work task assignment for an actor within a production system is disclosed. The method includes receiving a sensor stream at a computing device, the sensor stream including sensor information obtained from a sensor operable to sense progress of a work task performed by a plurality of actors, receiving with the computing device an identity of each of a plurality of actors identified within the sensor stream, using the computing device and an engine to identify an action within the sensor stream that is performed by each of the plurality of actors performing the work task, using the computing device to store, in a data structure, the received sensor stream, an identity of each action, and an identity of each of the plurality of actors, using the computing device to map respective actions performed by each of the plurality of actors to the sensor stream, using the computing device and the engine to characterize the respective actions performed by each of the plurality of actors to produce determined characterizations thereof, and based on the determined characterizations of the plurality of actors performing the action, automatically determining the work task assignment which assigns an actor of the plurality of actors to perform the action.”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Sincerely,
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
Prosecution Insights