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 .
Drawings
The drawings were received on May 19th 2025. These drawings are accepted.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Status of the Claims
This action is in response to the applicant’s filing on June 23rd 2025;
Claims 1-20 are pending and examined 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 1-4, 6-8, 11-12, 14-16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis for claim 1:
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 falls within one of the statutory categories? Yes. The claim is directed toward a data collection.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to evaluation.
Claim 1
A safety system for a machine, comprising:
one or more image sensors positioned to image a predetermined work zone
around the machine;
a plurality of detectors defining a predetermined plurality of operator zones of the machine that are non-overlapping, the plurality of detectors being suitable to detect personnel in the plurality of operator zones without imaging the plurality of operator zones, the plurality of operator zones at least partially non-overlapping the work zone; and
a controller communicatively coupled to the one or more image sensors and
the plurality of detectors, the controller configured to
receive data from the one or more image sensors indicative of imaged
scenes of the work zone,
use a machine learning model to detect personnel in the imaged scenes
based on the data,
cause a safety indicator to indicate unsafe operation of the machine in
response to the machine learning model detecting personnel in
the work zone,
cause the safety indicator to indicate safe operation of the machine in
response to the plurality of detectors detecting personnel in a
non-predetermined first operator zone of the plurality of operator
zones while detecting no personnel in the plurality of operator
zones other than the first operator zone and while the machine
learning model detects no personnel in the work zone, and
cause the safety indicator to indicate unsafe operation of the machine in
response to the plurality of detectors detecting personnel in a
non-predetermined second operator zone of the plurality of
operator zones while detecting personnel in the first operator
zone, the second operator zone being separate from the first
operator zone.
The method in claim 1 includes a mental process that can be practicably performed with pen and paper, therefore, an abstract idea the limitations of claim 1 highlighted above merely consist of indicating that there is a person in the image provided by visual data (picture or just visually inspecting the area), this all can be done by mentally and with pen and paper by looking at the work area or available image. More specifically, a person can observe/evaluate where the personnel is located in the work zone. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
A safety system for a machine, comprising:
one or more image sensors positioned to image a predetermined work zone
around the machine;
a plurality of detectors defining a predetermined plurality of operator zones of the machine that are non-overlapping, the plurality of detectors being suitable to detect personnel in the plurality of operator zones without imaging the plurality of operator zones, the plurality of operator zones at least partially non-overlapping the work zone; and
a controller communicatively coupled to the one or more image sensors and
the plurality of detectors, the controller configured to
receive data from the one or more image sensors indicative of imaged
scenes of the work zone,
use a machine learning model to detect personnel in the imaged scenes
based on the data,
cause a safety indicator to indicate unsafe operation of the machine in
response to the machine learning model detecting personnel in
the work zone,
cause the safety indicator to indicate safe operation of the machine in
response to the plurality of detectors detecting personnel in a
non-predetermined first operator zone of the plurality of operator
zones while detecting no personnel in the plurality of operator
zones other than the first operator zone and while the machine
learning model detects no personnel in the work zone, and
cause the safety indicator to indicate unsafe operation of the machine in
response to the plurality of detectors detecting personnel in a
non-predetermined second operator zone of the plurality of
operator zones while detecting personnel in the first operator
zone, the second operator zone being separate from the first
operator zone.
Claim 1 does not recite any of the exemplary considerations that are indicative of a mental process/evaluation having been integrated into a practical application. The Machine learning model it is recited at a high level of generality; which is a form of extra solution activity, nothing more than signal/data collection [see paragraph 00126]. As such, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
Also, as noted above, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Selecting and transmitting data are fundamental, i.e. WURC, activities performed by processors, such as the device in claim 20.
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional
elements that integrate the judicial exception into a practical application, and (c) does not
recite additional elements that amount to significantly more than the judicial exception, it is
clear that claim 1 is directed towards non-statutory subject matter.
Dependent claims 2-4, 6-8, 11-12 and 14-15 are further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more. As such, claims 1-4, 6-8, 11-12 and 14-15 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
Analysis for claim 16:
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 falls within one of the statutory categories? Yes. The claim is directed toward a data collection.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to evaluation.
Claim 16
A method of safely operating a machine, comprising:
receiving data indicative of imaged scenes of a predetermined work zone
defined around the machine;
using a machine learning model to detect personnel in the imaged scenes
based on the data;
causing a safety indicator to indicate unsafe operation of the machine in
response to the machine learning model detecting personnel in the work
zone;
receiving a first signal, from a plurality of non-image detectors defining a
plurality of operator zones of the machine, indicative of detection of
personnel in a non-predetermined first operator zone of the plurality of
operator zones;
causing the safety indicator to indicate safe operation of the machine in
response to detecting personnel in the first operator zone while detecting
no personnel in the plurality of operator zones other than the first
operator zone and while the machine learning model detects no
personnel in the work zone;
receiving a second signal, from the plurality of non-image detectors, indicative
of detection of personnel in a non-predetermined second operator zone
of the plurality of operator zones, the second operator zone being
separate from the first operator zone; and
causing the safety indicator to indicate unsafe operation of the machine in
response to detecting personnel in the second operator zone while
detecting personnel in the first operator zone.
The method in claim 16 includes a mental process that can be practicably performed with pen and paper, therefore, an abstract idea the limitations of claim 1 highlighted above merely consist of indicating that there is a person in the image provided by visual data (picture or just visually inspecting the area), this all can be done by mentally and with pen and paper by looking at the work area or available image. More specifically, a person can observe/evaluate where the personnel is located in the work zone. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
A method of safely operating a machine, comprising:
receiving data indicative of imaged scenes of a predetermined work zone
defined around the machine;
using a machine learning model to detect personnel in the imaged scenes
based on the data;
causing a safety indicator to indicate unsafe operation of the machine in
response to the machine learning model detecting personnel in the work
zone;
receiving a first signal, from a plurality of non-image detectors defining a
plurality of operator zones of the machine, indicative of detection of
personnel in a non-predetermined first operator zone of the plurality of
operator zones;
causing the safety indicator to indicate safe operation of the machine in
response to detecting personnel in the first operator zone while detecting
no personnel in the plurality of operator zones other than the first
operator zone and while the machine learning model detects no
personnel in the work zone;
receiving a second signal, from the plurality of non-image detectors, indicative
of detection of personnel in a non-predetermined second operator zone
of the plurality of operator zones, the second operator zone being
separate from the first operator zone; and
causing the safety indicator to indicate unsafe operation of the machine in
response to detecting personnel in the second operator zone while
detecting personnel in the first operator zone.
Claim 16 does not recite any of the exemplary considerations that are indicative of a mental process/evaluation having been integrated into a practical application. The Machine learning model it is recited at a high level of generality; which is a form of extra solution activity, nothing more than signal/data collection [see paragraph 00126]. As such, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
Also, as noted above, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 16 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Selecting and transmitting data are fundamental, i.e. WURC, activities performed by processors, such as the device in claim 20.
CONCLUSION
Thus, since claim 16 is: (a) directed toward an abstract idea, (b) does not recite additional
elements that integrate the judicial exception into a practical application, and (c) does not
recite additional elements that amount to significantly more than the judicial exception, it is
clear that claim 16 is directed towards non-statutory subject matter.
Dependent claim 18 are further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more.
As such, claims 16 and 18 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
Claims 5, 9-10, 13, 17, 19 and 20 overcomes the rejection.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-10 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Li-Heng Hsu (Patent no. TWI830617B) in view of Mitsuta (Patent No. US20130162830A1) and Oblak el al (Patent No. US20220180131A1).
Regarding claim 1 Li-Heng Hsu teaches a safety system for a machine, comprising; (See Li-Heng Hsu paragraph 0005 and 0009; “…a prevention mechanism for machine accidents and safety measures settings… automatic monitoring systems, to establish a protection mechanism.”);
use a machine learning model to detect personnel in the imaged scenes based on the data, cause a safety indicator to indicate unsafe operation of the machine in response to the machine learning model detecting personnel in the work zone; (See Li-Heng Hsu paragraph 0012 and 0017; “a model trained by a neural network, which uses an image recognition model (such as YOLO v4) or other similar recognition models to extract image features, and uses the bounding box of image recognition to mark objects to generate object marking positions… In order to prevent accidents, the first prediction model determines whether an accident is likely to occur through the captured features before the machine operator has an accident, and provides a preventive warning based on the predicted safety level (predicted value) to achieve a similar prediction effect…”; cause the safety indicator to indicate safe operation of the machine in response to the plurality of detectors detecting personnel in a non-predetermined first operator zone of the plurality of operator zones while detecting no personnel in the plurality of operator zones other than the first operator zone and while the machine learning model detects no personnel in the work zone; (“See Li-Heng Hsu paragraph 0021; “Please refer to Figure 2, which shows a flow chart of a machine accident prediction method according to an embodiment of the present invention. First, in step S21, an image related to machine personnel maintenance is input into the first prediction model. In step S22, the first prediction model predicts the actions of the machine and the machine personnel. In step 23, is it possible to confirm whether an accident is likely to occur? For example: setting the prediction value to be greater than 0.5 indicates that the actions of the machine and the machine personnel meet the above-mentioned "incorrect operation of the machine" standard, and the prediction value is less than 0.5, indicating that the actions of the machine and the machine personnel meet the above-mentioned "normal operation of the machine" standard. When the predicted value is greater than 0.5, the probability of an accident occurring is greater than the probability of no accident occurring, and further judgment is made as to whether the predicted value is greater than 0.6 (step S24), whether the predicted value is greater than 0.8 (step S25), and whether the predicted value is greater than 0.9 (step S26). When the predicted value is greater than 0.6, the first prediction model notifies the system to issue a Class A preventive warning; when the predicted value is greater than 0.8, the first prediction model notifies the system to issue a Class B preventive warning; when the predicted value is greater than 0.9, the first prediction model notifies the system to issue a Class C preventive warning.”); and cause the safety indicator to indicate unsafe operation of the machine in response to the plurality of detectors detecting personnel in a non-predetermined second operator zone of the plurality of operator zones while detecting personnel in the first operator -1-zone, the second operator zone being separate from the first operator zone; (“See Li-Heng Hsu paragraph 0021; “Please refer to Figure 2, which shows a flow chart of a machine accident prediction method according to an embodiment of the present invention. First, in step S21, an image related to machine personnel maintenance is input into the first prediction model. In step S22, the first prediction model predicts the actions of the machine and the machine personnel. In step 23, is it possible to confirm whether an accident is likely to occur? For example: setting the prediction value to be greater than 0.5 indicates that the actions of the machine and the machine personnel meet the above-mentioned "incorrect operation of the machine" standard, and the prediction value is less than 0.5, indicating that the actions of the machine and the machine personnel meet the above-mentioned "normal operation of the machine" standard. When the predicted value is greater than 0.5, the probability of an accident occurring is greater than the probability of no accident occurring, and further judgment is made as to whether the predicted value is greater than 0.6 (step S24), whether the predicted value is greater than 0.8 (step S25), and whether the predicted value is greater than 0.9 (step S26). When the predicted value is greater than 0.6, the first prediction model notifies the system to issue a Class A preventive warning; when the predicted value is greater than 0.8, the first prediction model notifies the system to issue a Class B preventive warning; when the predicted value is greater than 0.9, the first prediction model notifies the system to issue a Class C preventive warning.”).
Li-Heng Hsu does not explicitly teach but Oblak teaches, one or more image sensors positioned to image a predetermined work zone around the machine; (See Oblak paragraph 0026 and figure 1Aand B; “... The machine 10 may have various external sensors, for instance, to provide information for advanced safety systems and/or various levels of automated driving. The various external sensors may include, but are not limited to, one or more high definition cameras, RADAR sensors, and LiDAR.”);
and a controller communicatively coupled to the one or more image sensors and the plurality of detectors, the controller configured to receive data from the one or more image sensors indicative of imaged scenes of the work zone; (See Oblak paragraph 0028 and Figure 2; “FIG. 2 is a block diagram of a system 200 for the machine 10. In embodiments, the system 200 may be characterized as an object detection system. p The system 200 may include the three dimensional scanner 32 and a control system 201 (sometimes referred to as a controller 201). In certain embodiments, the control system 201 is in communication with the three dimensional scanner 32 and the machine 10, for example, via wired and/or wireless connections…”).
Li-Heng Hsu and Oblak are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with Oblak image sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu by applying Oblak Image sensors which will add visual information to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Li-Heng Hsu does not explicitly teach but Mitsuta teaches, a plurality of detectors defining a predetermined plurality of operator zones of the machine that are non-overlapping, the plurality of detectors being suitable to detect personnel in the plurality of operator zones without imaging the plurality of operator zones, the plurality of operator zones at least partially non-overlapping the work zone; (See Mitsuta paragraph 0045 and figure 4; “in FIG. 4, the abovementioned six imaging units 11 to 16 are able to obtain images of substantially the whole surrounding area of the work vehicle 1. Two adjacent regions among the first to sixth region 16R partially overlap each other as illustrated in the center figure in FIG. 4. Specifically, the first region 11R partially overlaps the second region 12R in a first overlapping region OA1. The first region 11R partially overlaps the third region 13R in a second overlapping region OA2. The second region 12R partially overlaps the fourth region 14R in a third overlapping region OA3. The third region 13R partially overlaps the fifth region 15R in a fourth overlapping region OA4. The fourth region 14R partially overlaps the sixth region 16R in a fifth overlapping region OA5. Moreover, the fifth region 15R partially overlaps the sixth region 16R in a sixth overlapping region OA6. The first to sixth imaging units 11 to 16 transmit the image data showing the imaged images to the controller 19.”).
Li-Heng Hsu and Mitsuta are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, wherein the safety indicator includes an audible alarm configured to generate sound to indicate unsafe operation of the machine; (See Li-Heng Hsu paragraph 0022; “…preventive warning may include, for example, a warning sound…”).
Regarding claim 3 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, wherein the safety indicator includes a lighting assembly configured to generate light to indicate unsafe operation of the machine; (See Li-Heng Hsu paragraph 0022; “…preventive warning may include, for example, a warning sound, a warning light, or an image…”).
Regarding claim 4 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng Hsu does not explicitly teach but Mitsuta teaches, wherein the safety indicator is configured to generate a plurality of zone-specific indicators; (See Mitsuta paragraph 0045 and figure 4; “in FIG. 4, the abovementioned six imaging units 11 to 16 are able to obtain images of substantially the whole surrounding area of the work vehicle 1. Two adjacent regions among the first to sixth region 16R partially overlap each other as illustrated in the center figure in FIG. 4. Specifically, the first region 11R partially overlaps the second region 12R in a first overlapping region OA1. The first region 11R partially overlaps the third region 13R in a second overlapping region OA2. The second region 12R partially overlaps the fourth region 14R in a third overlapping region OA3. The third region 13R partially overlaps the fifth region 15R in a fourth overlapping region OA4. The fourth region 14R partially overlaps the sixth region 16R in a fifth overlapping region OA5. Moreover, the fifth region 15R partially overlaps the sixth region 16R in a sixth overlapping region OA6. The first to sixth imaging units 11 to 16 transmit the image data showing the imaged images to the controller 19.”).
Li-Heng Hsu and Mitsuta are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Li-Heng Hsu does not explicitly teach but Oblak teaches, to indicate at least one of presence of personnel or no presence of personnel in the work zone and the plurality of operator zones; (See Oblak paragraph 0060 and 0083; “…an object may be a person or persons, or another machine in which detection is essential… embodiments of the disclosed subject matter can identify the person (or a region containing the person)”).
Li-Heng Hsu and Oblak are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with Oblak image sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu by applying Oblak Image sensors which will add visual information to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, wherein the safety indicator is operably coupled to the machine and is configured to indicate unsafe operation of the machine by causing stoppage of the machine when the controller detects no personnel in the work zone and the controller detects no personnel in the plurality of operator zones; (See Li-Heng Hsu paragraph 0022; “…preventive warning may include, for example, in addition to a warning sound, a warning light, or an image, further turning off the power of the machine to stop the machine from operating…”).
Regarding claim 7 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, wherein the machine learning model is an object detection model suitable to recognize objects in the imaged scenes based on the data; (See Li-Heng Hsu paragraph 0013; “…model is a convolutional neural network that first finds matching objects and then determines which area has the matching object with the highest probability …model can reduce the amount of computation required for object detection and learn more diverse objects, thus achieving better accuracy…”).
Regarding claim 8 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU does not explicitly teach but Mitsuta teaches, wherein each detector of the plurality of detectors defines an exclusive one of the plurality of operator zones; (See Mitsuta paragraph 0045 and figure 4; “in FIG. 4, the abovementioned six imaging units 11 to 16 are able to obtain images of substantially the whole surrounding area of the work vehicle 1. Two adjacent regions among the first to sixth region 16R partially overlap each other as illustrated in the center figure in FIG. 4. Specifically, the first region 11R partially overlaps the second region 12R in a first overlapping region OA1. The first region 11R partially overlaps the third region 13R in a second overlapping region OA2. The second region 12R partially overlaps the fourth region 14R in a third overlapping region OA3. The third region 13R partially overlaps the fifth region 15R in a fourth overlapping region OA4. The fourth region 14R partially overlaps the sixth region 16R in a fifth overlapping region OA5. Moreover, the fifth region 15R partially overlaps the sixth region 16R in a sixth overlapping region OA6. The first to sixth imaging units 11 to 16 transmit the image data showing the imaged images to the controller 19.”).
Li-Heng Hsu and Mitsuta are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, wherein the safety indicator is operably coupled to the machine and is configured to indicate unsafe operation of the machine by causing stoppage of the machine; (See Li-Heng Hsu paragraph 0022; “…preventive warning may include, for example, in addition to a warning sound, a warning light, or an image, further turning off the power of the machine to stop the machine from operating…”).
Regarding claim 10 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, wherein the safety indicator is controllably connected to a power source of the machine and is configured to indicate unsafe operation of the machine by controlling power supplied from the power source to the machine to prevent operation of the machine; (See Li-Heng Hsu paragraph 0002 and 0018; “…a machine, and more particularly to a method for predicting machine accidents, which is used to establish a preventive mechanism for machine accidents and safety measures… Preventive warnings may include, for example, warning sounds, warning lights, and/or images, etc., to remind machine operators of possible dangers… if a signboard is not set up, when a machine operator enters to repair or start a suspended machine, the system may issue a warning sound, warning light, or image, or reduce the machine's operating time or shut down the machine's power supply to stop the machine from running, so as to achieve preventive warnings of different degrees.”).
Regarding claim 12 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU does not explicitly teach but Oblak teaches, wherein the one or more image sensors are optical image sensors; (See Oblak paragraph 0026; “…The machine 10 may have various external sensors, for instance, to provide information for advanced safety systems and/or various levels of automated driving. The various external sensors may include, but are not limited to, one or more high definition cameras, RADAR sensors, and LiDAR.”).
Li-Heng Hsu and Oblak are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with Oblak image sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu by applying Oblak Image sensors which will add visual information to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU further teaches, a monitored machine system, comprising: machinery; and a safety system according to Claim 1, wherein the machinery includes the machine; (See Li-Heng Hsu paragraph 0002;” The present invention relates to a machine…”).
Regarding claim 14 Li-Heng HSU in view of Mitsuta and Oblak teaches,the monitored machine system of Claim 13, Li-Heng HSU does not explicitly teach but Oblak teaches, wherein the one or more image sensors are mounted on the machinery; (See Oblak paragraph 0026 and Figure 1A and 1B; “... The machine 10 may have various external sensors, for instance, to provide information for advanced safety systems and/or various levels of automated driving. The various external sensors may include, but are not limited to, one or more high definition cameras, RADAR sensors, and LiDAR.”).
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Li-Heng Hsu and Oblak are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with Oblak image sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu by applying Oblak Image sensors which will add visual information to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 15 Li-Heng HSU in view of Mitsuta and Oblak teaches, the monitored machine system of Claim 14, Li-Heng HSU does not explicitly teach but Oblak teaches, wherein the plurality of detectors is mounted on the machinery; (See Oblak paragraph 0026 and Figure 1A and 1B; “... The machine 10 may have various external sensors, for instance, to provide information for advanced safety systems and/or various levels of automated driving. The various external sensors may include, but are not limited to, one or more high definition cameras, RADAR sensors, and LiDAR.”).
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Li-Heng Hsu and Oblak are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with Oblak image sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu by applying Oblak Image sensors which will add visual information to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Li-Heng Hsu (Patent no. TWI830617B) in view of Mitsuta (Patent No. US20130162830A1), Oblak el al (Patent No. US20220180131A1) and Johnson (US10885758B2).
Regarding claim 6 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU does not explicitly teach but Johnson teaches, wherein the plurality of detectors includes a plurality of motion sensors; (See Johnson column 16, line 15-22; “the machine sensor 237 may include any suitable type of sensor or transducer, or transceiver device, receiver device, transmitter device, and/or the like. The machine sensor 237 is configured to generate data corresponding to position and/or motion the machine to which it is operably coupled, e.g., for use in predicting and preventing collisions between the machine and personnel, as described in further detail herein.”).
Li-Heng Hsu and Johnson are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Johnson motion sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Johnson motion sensors which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Li-Heng Hsu (Patent no. TWI830617B) in view of Mitsuta (Patent No. US20130162830A1), Oblak el al (Patent No. US20220180131A1) and Subramanian (US20210149369A1).
Regarding claim 11 Li-Heng HSU in view of Mitsuta and Oblak teaches, the safety system of Claim 1, Li-Heng HSU does not explicitly teach but Subramanian teaches, wherein the plurality of detectors includes a plurality of passive infrared sensors; (See Subramanian paragraph 0024; “additional sensors 116a, 116b…illustrated in FIG. 1. For example, the sensors 116 may include one or more sensor modalities, e.g., a motion sensor, a camera, a position sensor, a microphone, to surveille conditions in the environment 100.”).
Li-Heng Hsu and Subramanian are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Subramanian passive infrared sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Subramanian passive infrared sensors which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li-Heng Hsu (Patent no. TWI830617B) in view of Mitsuta (Patent No. US20130162830A1).
Regarding claim 16 a method of safely operating a machine, comprising; (See Li-Heng Hsu paragraph 0005 and 0009; “…a prevention mechanism for machine accidents and safety measures settings… automatic monitoring systems, to establish a protection mechanism.”); receiving data indicative of imaged scenes of a predetermined work zone defined around the machine; (See Li-Heng Hsu paragraph 0015; ”In step S11, images of machine operation, such as "machine normal operation", "machine error operation", "accident" and other status images, are collected as the first training data. The first training data can be input image data or data generated by the neural network simulation based on the input image data…”); using a machine learning model to detect personnel in the imaged scenes based on the data; (See Li-Heng Hsu paragraph 0012 and 0017; “a model trained by a neural network, which uses an image recognition model (such as YOLO v4) or other similar recognition models to extract image features, and uses the bounding box of image recognition to mark objects to generate object marking positions… In order to prevent accidents, the first prediction model determines whether an accident is likely to occur through the captured features before the machine operator has an accident, and provides a preventive warning based on the predicted safety level (predicted value) to achieve a similar prediction effect…”); causing a safety indicator to indicate unsafe operation of the machine in response to the machine learning model detecting personnel in the work zone; (See Li-Heng Hsu paragraph 0021; “Please refer to Figure 2, which shows a flow chart of a machine accident prediction method according to an embodiment of the present invention. First, in step S21, an image related to machine personnel maintenance is input into the first prediction model. In step S22, the first prediction model predicts the actions of the machine and the machine personnel. In step 23, is it possible to confirm whether an accident is likely to occur? For example: setting the prediction value to be greater than 0.5 indicates that the actions of the machine and the machine personnel meet the above-mentioned "incorrect operation of the machine" standard, and the prediction value is less than 0.5, indicating that the actions of the machine and the machine personnel meet the above-mentioned "normal operation of the machine" standard. When the predicted value is greater than 0.5, the probability of an accident occurring is greater than the probability of no accident occurring, and further judgment is made as to whether the predicted value is greater than 0.6 (step S24), whether the predicted value is greater than 0.8 (step S25), and whether the predicted value is greater than 0.9 (step S26). When the predicted value is greater than 0.6, the first prediction model notifies the system to issue a Class A preventive warning; when the predicted value is greater than 0.8, the first prediction model notifies the system to issue a Class B preventive warning; when the predicted value is greater than 0.9, the first prediction model notifies the system to issue a Class C preventive warning.”);
causing the safety indicator to indicate safe operation of the machine in response to detecting personnel in the first operator zone while detecting no personnel in the plurality of operator zones other than the first operator zone and while the machine learning model detects no personnel in the work zone; (See Li-Heng Hsu paragraph 0021; “Please refer to Figure 2, which shows a flow chart of a machine accident prediction method according to an embodiment of the present invention. First, in step S21, an image related to machine personnel maintenance is input into the first prediction model. In step S22, the first prediction model predicts the actions of the machine and the machine personnel. In step 23, is it possible to confirm whether an accident is likely to occur? For example: setting the prediction value to be greater than 0.5 indicates that the actions of the machine and the machine personnel meet the above-mentioned "incorrect operation of the machine" standard, and the prediction value is less than 0.5, indicating that the actions of the machine and the machine personnel meet the above-mentioned "normal operation of the machine" standard. When the predicted value is greater than 0.5, the probability of an accident occurring is greater than the probability of no accident occurring, and further judgment is made as to whether the predicted value is greater than 0.6 (step S24), whether the predicted value is greater than 0.8 (step S25), and whether the predicted value is greater than 0.9 (step S26). When the predicted value is greater than 0.6, the first prediction model notifies the system to issue a Class A preventive warning; when the predicted value is greater than 0.8, the first prediction model notifies the system to issue a Class B preventive warning; when the predicted value is greater than 0.9, the first prediction model notifies the system to issue a Class C preventive warning.”); receiving a second signal, from the plurality of non-image detectors, indicative of detection of personnel in a non-predetermined second operator zone of the plurality of operator zones, the second operator zone being separate from the first operator zone; (See Li-Heng Hsu paragraph 0021; “Please refer to Figure 2, which shows a flow chart of a machine accident prediction method according to an embodiment of the present invention. First, in step S21, an image related to machine personnel maintenance is input into the first prediction model. In step S22, the first prediction model predicts the actions of the machine and the machine personnel. In step 23, is it possible to confirm whether an accident is likely to occur? For example: setting the prediction value to be greater than 0.5 indicates that the actions of the machine and the machine personnel meet the above-mentioned "incorrect operation of the machine" standard, and the prediction value is less than 0.5, indicating that the actions of the machine and the machine personnel meet the above-mentioned "normal operation of the machine" standard. When the predicted value is greater than 0.5, the probability of an accident occurring is greater than the probability of no accident occurring, and further judgment is made as to whether the predicted value is greater than 0.6 (step S24), whether the predicted value is greater than 0.8 (step S25), and whether the predicted value is greater than 0.9 (step S26). When the predicted value is greater than 0.6, the first prediction model notifies the system to issue a Class A preventive warning; when the predicted value is greater than 0.8, the first prediction model notifies the system to issue a Class B preventive warning; when the predicted value is greater than 0.9, the first prediction model notifies the system to issue a Class C preventive warning.”); and causing the safety indicator to indicate unsafe operation of the machine in response to detecting personnel in the second operator zone while detecting personnel in the first operator zone; (See Li-Heng Hsu paragraph 0022; “…preventive warning may include, for example, in addition to a warning sound, a warning light, or an image, further turning off the power of the machine to stop the machine from operating…”).
Li-Heng Hsu does not explicitly teach but Mitsuta teaches, receiving a first signal, from a plurality of non-image detectors defining a plurality of operator zones of the machine, indicative of detection of personnel in a non-predetermined first operator zone of the plurality of operator zones; (See Mitsuta paragraph 0045 and figure 4; “in FIG. 4, the above mentioned six imaging units 11 to 16 are able to obtain images of substantially the whole surrounding area of the work vehicle 1. Two adjacent regions among the first to sixth region 16R partially overlap each other as illustrated in the center figure in FIG. 4. Specifically, the first region 11R partially overlaps the second region 12R in a first overlapping region OA1. The first region 11R partially overlaps the third region 13R in a second overlapping region OA2. The second region 12R partially overlaps the fourth region 14R in a third overlapping region OA3. The third region 13R partially overlaps the fifth region 15R in a fourth overlapping region OA4. The fourth region 14R partially overlaps the sixth region 16R in a fifth overlapping region OA5. Moreover, the fifth region 15R partially overlaps the sixth region 16R in a sixth overlapping region OA6. The first to sixth imaging units 11 to 16 transmit the image data showing the imaged images to the controller 19.”).
Li-Heng Hsu and Mitsuta are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Mitsuta plurality of detectors defining a predetermined plurality of operator zones of the machine which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 17 Li-Heng HSU in view of Mitsuta teaches, the method of Claim 16, Li-Heng HSU further teaches, wherein the safety indicator, to indicate unsafe operation, causes stoppage of the machine by preventing supply of power to the machine, and the safety indicator, to indicate safe operation, causes supply of power to the machine; (See Li-Heng Hsu paragraph 0022; “…preventive warning may include, for example, in addition to a warning sound, a warning light, or an image, further turning off the power of the machine to stop the machine from operating…”).
Regarding claim 19 Li-Heng HSU in view of Mitsuta teaches, the method of Claim 16, Li-Heng HSU further teaches, further comprising: receiving a third signal, from the plurality of non-image detectors, indicative of detection of no personnel in the plurality of operator zones; and causing stoppage of the machine in response to the third signal; (See Li-Heng Hsu paragraph 0024; “In one embodiment, a Class D abnormal warning, for example, stops the machine from running and does not restart the machine until the foreign object is removed. A Class E abnormal warning, for example, stops the machine from running and emits a warning sound, warning light or image, and then notifies personnel to handle it until the safety measures are placed in the correct position or the foreign object has been removed. In addition, a Class F abnormal warning, for example, there is a foreign object in the machine, stops the machine from running and emits a warning sound, warning light or image, and then notifies personnel to handle it until all abnormal warnings are eliminated. The above-mentioned Class D and E abnormal warnings and the above-mentioned Class A, B, and C preventive warnings can be set in priority according to the level to achieve the effect of graded warnings. For example, the safety level of A, B, and C level preventive warnings is relatively low, while the safety level of D and E level abnormal warnings is relatively high. The lower the safety level, the safer it is, and its priority is relatively low; the higher the safety level, the more dangerous it is, and its priority is relatively high. In addition, when one of the A, B, and C level preventive warnings and one of the D and E level abnormal warnings occur at the same time, the F level abnormal warning will be triggered until all abnormal conditions are eliminated.”).
Regarding claim 20 please see rejection above with respect to claim 16 which is commensurate in scope to claim 20, with claim 16 being drawn to method and claim 20 being drawn to an invention method as well except for; energizing the machine to prevent stoppage of the machine in response to detecting personnel in the first operator zone while detecting no personnel in the plurality of operator zones other than the first operator zone and while the machine learning model detects no personnel in the work zone; (See Li-Heng Hsu paragraph 0021-0022; “Please refer to Figure 2, which shows a flow chart of a machine accident prediction method according to an embodiment of the present invention. First, in step S21, an image related to machine personnel maintenance is input into the first prediction model. In step S22, the first prediction model predicts the actions of the machine and the machine personnel. In step 23, is it possible to confirm whether an accident is likely to occur? For example: setting the prediction value to be greater than 0.5 indicates that the actions of the machine and the machine personnel meet the above-mentioned "incorrect operation of the machine" standard, and the prediction value is less than 0.5, indicating that the actions of the machine and the machine personnel meet the above-mentioned "normal operation of the machine" standard. When the predicted value is greater than 0.5, the probability of an accident occurring is greater than the probability of no accident occurring, and further judgment is made as to whether the predicted value is greater than 0.6 (step S24), whether the predicted value is greater than 0.8 (step S25), and whether the predicted value is greater than 0.9 (step S26). When the predicted value is greater than 0.6, the first prediction model notifies the system to issue a Class A preventive warning; when the predicted value is greater than 0.8, the first prediction model notifies the system to issue a Class B preventive warning; when the predicted value is greater than 0.9, the first prediction model notifies the system to issue a Class C preventive warning. In one embodiment, a Class A preventive warning may include, for example, a warning sound, a warning light, or an image. A Class B preventive warning may include, for example, in addition to a warning sound, a warning light, or an image, further controlling the voltage to reduce the operating time of the machine. A Class C preventive warning may include, for example, in addition to a warning sound, a warning light, or an image, further turning off the power of the machine to stop the machine from operating. The above-mentioned Class A, B, and C preventive warnings may be set according to the predicted safety level (predicted value) to achieve the effect of graded warnings.”).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Li-Heng Hsu (Patent no. TWI830617B) in view of Mitsuta (Patent No. US20130162830A1) and Subramanian (US20210149369A1).
Regarding claim 18 Li-Heng HSU in view of Mitsuta teaches, the method of Claim 16, Li-Heng HSU does not explicitly teach but Subramania teaches, wherein the plurality of detectors includes a plurality of passive infrared sensors; (See Subramanian paragraph 0024; “additional sensors 116a, 116b (collectively, and when referring to additional and/or alternative sensors not associated with one of the machines 104 and/or personnel 106, the “additional sensors 116” or “sensors 116”) also are illustrated in FIG. 1. For example, the sensors 116 may include one or more sensor modalities, e.g., a motion sensor, a camera, a position sensor, a microphone, a LiDAR sensor, a radar sensor, and/or the like, to surveille conditions in the environment 100.”).
Li-Heng Hsu and Subramanian are in the same field of mechanism for machine safety system and method. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Li-Heng Hsu prevention mechanism for machine accidents and safety measures with and Oblak image sensors with Subramanian passive infrared sensors. No new functionality would arise from the combination and the combination would improve usability of Li-Heng Hsu and Oblak Image sensors by applying Subramanian passive infrared sensors which will add addition information on the machine surroundings to improve safety of the workplace and systems of machinery. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
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/L.K./ Examiner, Art Unit 3666
/JESS WHITTINGTON/ Primary Examiner, Art Unit 3666c
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