DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/17/2026 has been entered.
Status of Claims
This action is in reply to the RCE filed on 03/17/2026.
Claims 1, 3-4, 6-14, 16-19, and 21-26 are currently pending and have been examined.
Claims 1, 3, 4, 6-14, 16-19, and 21 are amended.
Claims 22-26 are added.
Claims 1, 3-4, 6-14, 16-19, and 21-26 are currently rejected.
This action is made NON-FINAL.
Response to Arguments
Applicant’s arguments filed 03/17/2026 have been fully considered but they are not persuasive.
Applicant argues that the amended independent claims recite “generate a radiation exposure model estimating ultraviolet propagation and attenuation throughout the environment” is not taught by Pierson since Pierson only “calculates a predicted dosage at individual points using an inverse-squared distance relationship from a single UVC lamp”. Applicant cites [0018] for disclosing “The dosage of the UV lamps tapers off as a function of distance from the lamp, proportional to 1/a2 where a is the distance from the lamp”. The examiner is unclear of the perceived difference between the teaching of Pierson with that of applicants paragraph [0018]. Applicant further argues “This is a point-by-point dosage calculation based on distance from the lamp, not a radiation exposure model that estimates ultraviolet propagation and attenuation throughout the environment as a whole. Pierson's approach does not model how ultraviolet radiation propagates through the environment or how it is attenuated by objects within the environment. Rather, Pierson calculates dosage at discrete points based on a simple distance relationship and uses those calculations to assign weights to graph edges for path planning.” Pierson teaches “the D-AMR 100 can operate to create a model to accurately predict a disinfection dosage a surface in an area will receive from the D-AMR. The dosage information can be used to control a speed of the D-AMR and to control the operation of the UVC lamps during the time it traverses a path through the area [Pierson, 0022]” which clearly discloses a “radiation exposure model”. Pierson additionally further describes the dosage model in paragraphs [0032-0042]. Since applicants specification does not define “propagation and attenuation”, under BRI the examiner is interpreting this phrase to be any model that determines how the signal is affected as it goes from the emitting source to the surface being measured. This would include losses and scattering in the air. Therefore applicant’s arguments are not persuasive and the rejections of Pierson are maintained as mapped in the updated rejection below.
Applicant additionally argues Jeong, Byrnes, Chae, Candelore, Slycke, and Lei do not cure the deficiencies of Pierson, however these arguments are moot in light of the arguments pertaining to Pierson supra and the updated rejections below.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: The terms “ultraviolet radiation transmission characteristics”, “radiation exposure model”, and “ultraviolet propagation and attenuation”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 4. 6-14, 16-19, and 21-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 8, and 17 recite “classify the plurality of objects based on ultraviolet radiation transmission characteristics; generate a radiation exposure model estimating ultraviolet propagation and attenuation throughout the environment; predict ultraviolet exposure levels for areas of the interior environment based on the radiation exposure model;”. Applicant’s specification teaches calculating a dosage score primarily in paragraphs [0022-0027] which is calculated based on the distance from the light source and an shadows cast by objects in the environment depending on if they are transparent or not but does not explicitly disclose classifying objects based on ultraviolet radiation characteristics, generating a radiation exposure model, or predicting ultraviolet exposure levels based on the model. The specification also does not disclose that the sensors equipped, either the camera or the lidar, are capable of detecting UV light or characteristics. Independent claims 8 and 17 recite similar subject matter and are rejected for the same reasons described supra.
Claim 3 additionally recites “wherein the radiation exposure model predicts ultraviolet attenuation caused by objects within the environment”. As discussed above, the specification does not provide written description for modelling ultraviolet attenuation.
Claim 22 additionally recites “wherein the classification of objects distinguishes between objects that substantially block ultraviolet radiation and objects that partially transmit ultraviolet radiation”. As discussed above, the specification does not provide written description for detecting UV characteristics and only describes determining general light transitiveness of visible light, not specifically UV light and therefore the is not written description for the subject matter of claim 22.
All additional dependent claims are rejected due to their dependence upon their respective rejected independent claims.
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.
Claim(s) 1, 3, 7-9, 11, 14, 16, 17, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierson et. al. (US 2022/0143250), herein Pierson in view of Byrnes et. al. (US 2022/0313855), herein Byrnes, and Rephaeli et. al. (US 2022/0124260), herein Rephaeli.
Regarding claim 1:
Pierson teaches:
A mobile disinfecting device (mobile robotic devices capable of disinfecting an area [0015]) comprising:
an autonomous vehicle unit (Autonomous, mobile robotic devices (AMRs) [0002]) comprising a drive system (driven wheels 120 that can be controlled by a drive system to move the D-AMR around an interior area [0021]);
at least one sensor comprising at least one of a camera device (The D-AMR can also be configured to have one or more depth cameras [0021]) or a lidar device (The laser sensor functionality can be implemented in a 2-D or 3-D light detection and ranging (Lidar) device [0026]) attached to the autonomous vehicle unit (the D-AMR is comprised of a robot body, drive and sensor systems [0019]);
at least one ultraviolet lamp (a D-AMR 100 having two UVC light sources (hereinafter referred to as UVC lamps) 110a and 110b [0021]), attached to the autonomous vehicle unit (see at least fig. 1a showing lamps 110a and 110b attached to robot.);
at least one processor (fig. 3, processor 310); and
memory (fig. 3, memory 320) that stores computer-executable instructions that, cause the mobile disinfecting device to (A processor 310 is in communication with a non-transitory, computer memory device (memory) 320 that maintains information generated by any of the functional modules 300, and which maintains information that can be used by the processor to control the operation of the D-AMRs. More specifically, the processor 310 can operate to control functionality associated with each of the modules 300 to generate and store information used by the D-AMR to disinfect particular features in an interior area [0025]):
generate (Information collected from one or more laser sensing devices 330A can be used by a SLAM (Simultaneous Location and Mapping) algorithm to construct a visual map [0026]) a spatial map of an interior environment (to map and localize objects in an area and to generate a visual representation of the map [0021]) using simultaneous localization and mapping (simultaneous localization and mapping functionality (SLAM) [0021]);
identify (identify [0021]) a plurality of objects in the interior room (one or more depth cameras used for capturing image information used to identify different types of objects [0021]) using the at least one sensor (one or more depth cameras [0021]);
classify the plurality of objects (construct a visual map showing the various features comprising an area, such as walls, floors, tables, chairs, counters, etc. The identify and position of at least some features comprising the area can be determined by the feature ID functionality 330C, which can be implemented in a neural network that is trained to identify features that a user is interested in disinfecting, such as floors, walls, tables, chairs, counters, etc. [0026])
generate a radiation exposure model estimating ultraviolet propagation and attenuation throughout the environment (To construct a dosage model based on a UVC light configuration, a summation of all lights on the payload is considered, their visibility relative to points in the environment is considered, and the orientation of the robot, or the orientation of the lamps, within the environment or the orientation of the UVC lamps is considered [0040]);
predict ultraviolet exposure levels for areas of the interior environment based on the radiation exposure model (it is possible to calculate a model of UVC radiation that can be applied to accurately predict a dosage any selected feature or surface within a particular area can receive. [0033]);
determine a traversal route (A path-planning and navigation function 340 generally operates on information generated by the mapping function 330 to determine an optimal path for the D-AMR to traverse through the area [0025]) including path segments (to determine a particular rate of speed at which the D-AMR can move along particular portions of the path while disinfecting the identified features [0025]) and speeds (The dosage information can be used to control a speed of the D-AMR [0022]) based on the predicted exposure levels (the D-AMR 100 can operate to create a model to accurately predict a disinfection dosage a surface in an area will receive from the D-AMR [0022]);
calculate ultraviolet dosage scores in real time as the mobile disinfection device traverses the route (during each traverse of a path, the D-AMR collects odometry information, sensor data, and applied dosage information generated using Equation 5. From the trajectory history and map information, the function 344B can estimate a total dosage of UVC radiation that was applied to all surfaces within the environment [0044]);
generate a dosage map (Subsequent to dosages delivered in an area being verified by the estimating function 344B, a map visualizing the effectiveness of the disinfection process can be generated. Using information generated by the estimating function 344B, a 2D heatmap can be generated having a color gradient that denotes the various dosage thresholds delivered by the robot in time [0046]) indicating whether areas satisfy a minimum disinfection threshold (a 2D heatmap can be generated having a color gradient that denotes the various dosage thresholds delivered by the robot in time. Color can range from red to blue, with red representing an area in which an adequate or high dosage of disinfectant is applied and blue representing an area in which a low or inadequate dosage of disinfectant is applied [0046]); and
Pierson does not explicitly teach, however Byrnes teaches:
implement a dosage feedback control process (In step 616, the disinfection management computing device 20 may determine if the disinfection of the area has been completed. By way of example, the disinfection management computing device 20 may monitor the targeted disinfection of the one of the areas with, by way of example only, one or more of the front LIDAR 46, rear LIDAR 48, front camera 50, IMU 52, and/or encoders 54 by way of example only, and analyze the captured data to determine when the targeted disinfection to the one areas is completed, although other manners for determining when disinfection is completed may be used, such as based on a monitored length of time for the disinfection cycle by way of example [0059]) that dynamically modifies traversal speed (taught by Pierson), exposure time (taught by Pierson), ultraviolet irradiation characteristics (taught by Pierson), or route segments (the SLAM algorithm that may include feature extraction from the data and short term and long term data association to generate or modify drive system controls for navigation [0051]) when calculated dosage scores fall below the minimum disinfection threshold (If in step 616 the disinfection management computing device 20 determines the targeted disinfection to the one areas is not completed, then the No branch is taken back to step 614 to continue the disinfection process with the targeted disinfection using the disinfection emitter 42 with ultraviolet light in this example [0059]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson to include the teachings as taught by Byrnes with a reasonable expectation of success. Both references are in the same field of endeavor of robotic UV surface disinfection. Byrnes also teaches the benefit of “this technology are able to selectively sanitize dynamic environments in the proximity of humans, eliminating a major limitation of prior full-room single-source UV radiation based robots that require the room to be unoccupied. Further, examples of this technology are able to radically increase the speed of disinfection in these dynamic environments, such as in hospitals, malls, offices, airports, and campuses by way of example only. With examples of this technology, the selective UV light exposure capability with the use of the arm-mounted disinfecting emitter alleviates prior concerns of over exposure with UV because the UV emitter can be directed to be close to the area requiring disinfection [Byrnes, 0008]”.
Pierson in view of Byrnes does not explicitly teach, however Rephaeli teaches:
classify the plurality of objects based on ultraviolet radiation transmission characteristics (a calibration UV image representing a transparency to UV light of an optical path of each pixel may be generated by determining the response of each pixel to a corresponding portion of a scene [0028]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson and Byrnes to include the teachings as taught by Rephaeli with a reasonable expectation of success. Rephaeli teaches the benefit of “UV images may allow for identification of some otherwise-invisible features [Rephaeli, 0023]”. This coupled with Pierson would allow combining known methods to achieve a predictable outcome. Being able to determine the UV transmissiveness of transparent objects would aid in the models ability to calculate the UV dosage to a more accurate degree.
Regarding claim 3:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Rephaeli further teaches:
wherein the radiation exposure model predicts ultraviolet attenuation caused by objects within the environment (a calibration UV image representing a transparency to UV light of an optical path of each pixel may be generated by determining the response of each pixel to a corresponding portion of a scene [0028]).
Regarding claim 7:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Pierson further teaches:
wherein traversal speed is reduced near objects predicted to have insufficient ultraviolet exposure (The dosage information can be used to control a speed of the D-AMR and to control the operation of the UVC lamps during the time it traverses a path through the area [0022]).
Regarding claim 8:
Pierson teaches:
A computer-implemented method (the processor 310 can operate to control functionality associated with each of the modules 300 [0025]) comprising:
mapping an environment (to map and localize objects in an area and to generate a visual representation of the map [0021]) using simultaneous localization and mapping (simultaneous localization and mapping functionality (SLAM) [0021]);
identifying (identify [0021]) objects (one or more depth cameras used for capturing image information used to identify different types of objects [0021]) using at least one sensor (one or more depth cameras [0021]);
classifying the objects (construct a visual map showing the various features comprising an area, such as walls, floors, tables, chairs, counters, etc. The identify and position of at least some features comprising the area can be determined by the feature ID functionality 330C, which can be implemented in a neural network that is trained to identify features that a user is interested in disinfecting, such as floors, walls, tables, chairs, counters, etc. [0026])
generating a radiation exposure model for ultraviolet propagation (To construct a dosage model based on a UVC light configuration, a summation of all lights on the payload is considered, their visibility relative to points in the environment is considered, and the orientation of the robot, or the orientation of the lamps, within the environment or the orientation of the UVC lamps is considered [0040]);
predicting exposure levels for areas of the environment (it is possible to calculate a model of UVC radiation that can be applied to accurately predict a dosage any selected feature or surface within a particular area can receive. [0033]);
determining a traversal route (A path-planning and navigation function 340 generally operates on information generated by the mapping function 330 to determine an optimal path for the D-AMR to traverse through the area [0025]) based on the predicted exposure levels (the D-AMR 100 can operate to create a model to accurately predict a disinfection dosage a surface in an area will receive from the D-AMR [0022]);
calculating ultraviolet dosage scores during traversal (during each traverse of a path, the D-AMR collects odometry information, sensor data, and applied dosage information generated using Equation 5. From the trajectory history and map information, the function 344B can estimate a total dosage of UVC radiation that was applied to all surfaces within the environment [0044]); and
Pierson does not explicitly teach, however Byrnes teaches:
modifying traversal parameters using a dosage feedback control process (In step 616, the disinfection management computing device 20 may determine if the disinfection of the area has been completed. By way of example, the disinfection management computing device 20 may monitor the targeted disinfection of the one of the areas with, by way of example only, one or more of the front LIDAR 46, rear LIDAR 48, front camera 50, IMU 52, and/or encoders 54 by way of example only, and analyze the captured data to determine when the targeted disinfection to the one areas is completed, although other manners for determining when disinfection is completed may be used, such as based on a monitored length of time for the disinfection cycle by way of example [0059]) when exposure thresholds are not satisfied (If in step 616 the disinfection management computing device 20 determines the targeted disinfection to the one areas is not completed, then the No branch is taken back to step 614 to continue the disinfection process with the targeted disinfection using the disinfection emitter 42 with ultraviolet light in this example [0059]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson to include the teachings as taught by Byrnes with a reasonable expectation of success. Both references are in the same field of endeavor of robotic UV surface disinfection. Byrnes also teaches the benefit of “this technology are able to selectively sanitize dynamic environments in the proximity of humans, eliminating a major limitation of prior full-room single-source UV radiation based robots that require the room to be unoccupied. Further, examples of this technology are able to radically increase the speed of disinfection in these dynamic environments, such as in hospitals, malls, offices, airports, and campuses by way of example only. With examples of this technology, the selective UV light exposure capability with the use of the arm-mounted disinfecting emitter alleviates prior concerns of over exposure with UV because the UV emitter can be directed to be close to the area requiring disinfection [Byrnes, 0008]”.
Pierson in view of Byrnes does not explicitly teach, however Rephaeli teaches:
classify the plurality of objects based on ultraviolet radiation transmission characteristics (a calibration UV image representing a transparency to UV light of an optical path of each pixel may be generated by determining the response of each pixel to a corresponding portion of a scene [0028]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson and Byrnes to include the teachings as taught by Rephaeli with a reasonable expectation of success. Rephaeli teaches the benefit of “UV images may allow for identification of some otherwise-invisible features [Rephaeli, 0023]”. This coupled with Pierson would allow combining known methods to achieve a predictable outcome. Being able to determine the UV transmissiveness of transparent objects would aid in the models ability to calculate the UV dosage to a more accurate degree.
Regarding claim 9:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson further teaches:
generating a dosage map representing cumulative ultraviolet exposure values (The model of UVC dosage assumes a radially-symmetric light source, such as a single UVC lamp. Consider a UVC lamp at location b.sup.i=(x.sub.b, y.sub.b, z.sub.b, θ.sub.b) in a 3D grid, where (x.sub.b, y.sub.b, z.sub.b) is the position of the D-AMR and θ.sub.b is the heading of the D-AMR. The dosage, d.sup.i.sub.τ, of UVC light received at a point p=(x, y, z) at a single time step is expressed in Equation 1 as being proportional to the inverse squared distance to that point [0035]).
Regarding claim 11:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson further teaches:
wherein traversal speed is dynamically modified based on dosage deficits (the speed at which the D-AMR moves from one node to another can be determined by an amount of disinfection that is needed as the D-AMR traverses a path between the nodes. And this traversal speed corresponds to a disinfection dosage assigned to an edge, as a weight, that corresponds to the path between the two nodes. This weight encodes a particular speed at which the D-AMR moves along the path between the two nodes and it encodes the state of the UVC lamps comprising the D-AMR [0049]).
Regarding claim 14:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Byrnes further teaches:
wherein route segments are recomputed dynamically based on updated dosage maps (In step 616, the disinfection management computing device 20 may determine if the disinfection of the area has been completed. By way of example, the disinfection management computing device 20 may monitor the targeted disinfection of the one of the areas with, by way of example only, one or more of the front LIDAR 46, rear LIDAR 48, front camera 50, IMU 52, and/or encoders 54 by way of example only, and analyze the captured data to determine when the targeted disinfection to the one areas is completed, although other manners for determining when disinfection is completed may be used, such as based on a monitored length of time for the disinfection cycle by way of example [0059]).
Regarding claim 16:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson further teaches:
wherein the at least one sensor comprises at least one of a camera (cameras or other image sensing devices [0026]), lidar (The laser sensor functionality can be implemented in a 2-D or 3-D light detection and ranging (Lidar) device [0026]), sonar (examiner is interpreting this limitation in the alternative which does not require it to be mapped.), and a proximity sensor (examiner is interpreting this limitation in the alternative which does not require it to be mapped.).
Regarding claim 17:
Pierson teaches:
A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system (A processor 310 is in communication with a non-transitory, computer memory device (memory) 320 that maintains information generated by any of the functional modules 300, and which maintains information that can be used by the processor to control the operation of the D-AMRs [0025]), cause the computer system to at least:
identify (identify [0021]) objects in an environment (one or more depth cameras used for capturing image information used to identify different types of objects [0021]);
classify objects (construct a visual map showing the various features comprising an area, such as walls, floors, tables, chairs, counters, etc. The identify and position of at least some features comprising the area can be determined by the feature ID functionality 330C, which can be implemented in a neural network that is trained to identify features that a user is interested in disinfecting, such as floors, walls, tables, chairs, counters, etc. [0026])
generate a radiation exposure model (To construct a dosage model based on a UVC light configuration, a summation of all lights on the payload is considered, their visibility relative to points in the environment is considered, and the orientation of the robot, or the orientation of the lamps, within the environment or the orientation of the UVC lamps is considered [0040]);
predict ultraviolet exposure levels (it is possible to calculate a model of UVC radiation that can be applied to accurately predict a dosage any selected feature or surface within a particular area can receive. [0033]);
determine a traversal route for a mobile disinfection robot (A path-planning and navigation function 340 generally operates on information generated by the mapping function 330 to determine an optimal path for the D-AMR to traverse through the area [0025]); and
modify traversal speed (the speed or speeds at which the D-AMR moves along at least some portions of a path to disinfect an interior area can depend upon a configuration of UVC light sources [0018]) and ultraviolet radiation characteristics (a configuration of UVC light sources (i.e., number, type, intensity, and/or orientation of UVC light sources) attached to the D-AMR [0018])
Pierson does not explicitly teach, however Byrnes teaches:
modify traversal speed (the SLAM algorithm that may include feature extraction from the data and short term and long term data association to generate or modify drive system controls for navigation [0051]) and ultraviolet radiation characteristics (An additional motor may be coupled between the end of the arm 45(a) and the disinfection emitter 42, under the control of the arm controller 44 based on one or more commands from the disinfection management computing device 20, and used to adjust the position of the disinfection emitter 42 [0018]) in response to real-time dosage calculations (If in step 616 the disinfection management computing device 20 determines the targeted disinfection to the one areas is not completed, then the No branch is taken back to step 614 to continue the disinfection process with the targeted disinfection using the disinfection emitter 42 with ultraviolet light in this example [0059]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson to include the teachings as taught by Byrnes with a reasonable expectation of success. Both references are in the same field of endeavor of robotic UV surface disinfection. Byrnes also teaches the benefit of “this technology are able to selectively sanitize dynamic environments in the proximity of humans, eliminating a major limitation of prior full-room single-source UV radiation based robots that require the room to be unoccupied. Further, examples of this technology are able to radically increase the speed of disinfection in these dynamic environments, such as in hospitals, malls, offices, airports, and campuses by way of example only. With examples of this technology, the selective UV light exposure capability with the use of the arm-mounted disinfecting emitter alleviates prior concerns of over exposure with UV because the UV emitter can be directed to be close to the area requiring disinfection [Byrnes, 0008]”.
Pierson in view of Byrnes does not explicitly teach, however Rephaeli teaches:
classify objects based on ultraviolet transmission characteristics (a calibration UV image representing a transparency to UV light of an optical path of each pixel may be generated by determining the response of each pixel to a corresponding portion of a scene [0028]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson and Byrnes to include the teachings as taught by Rephaeli with a reasonable expectation of success. Rephaeli teaches the benefit of “UV images may allow for identification of some otherwise-invisible features [Rephaeli, 0023]”. This coupled with Pierson would allow combining known methods to achieve a predictable outcome. Being able to determine the UV transmissiveness of transparent objects would aid in the models ability to calculate the UV dosage to a more accurate degree.
Regarding claim 21:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Byrnes further teaches:
further comprising dynamically recomputing traversal paths in response to updated dosage maps (In step 616, the disinfection management computing device 20 may determine if the disinfection of the area has been completed. By way of example, the disinfection management computing device 20 may monitor the targeted disinfection of the one of the areas with, by way of example only, one or more of the front LIDAR 46, rear LIDAR 48, front camera 50, IMU 52, and/or encoders 54 by way of example only, and analyze the captured data to determine when the targeted disinfection to the one areas is completed, although other manners for determining when disinfection is completed may be used, such as based on a monitored length of time for the disinfection cycle by way of example [0059]).
Regarding claim 22:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Rephaeli further teaches:
wherein the classification of objects distinguishes between objects that substantially block ultraviolet radiation and objects that partially transmit ultraviolet radiation (a calibration UV image representing a transparency to UV light of an optical path of each pixel may be generated by determining the response of each pixel to a corresponding portion of a scene [0028]);
Claim(s) 4, 12, 24, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierson et. al. (US 2022/0143250), herein Pierson in view of Byrnes et. al. (US 2022/0313855), herein Byrnes and Rephaeli et. al. (US 2022/0124260), herein Rephaeli in further view of Chae (US 2024/0252705), herein Chae.
Regarding claim 4:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Chae teaches:
redirect ultraviolet radiation towards areas predicted to receive insufficient exposure (In determining the sterilization angle using the distance measurement, the control unit 115 may be preset to control, for example, to form a sterilization angle of a predetermined obtuse angle if the distance measurement value generated by the distance detection sensor is below a first threshold (wide-range mode), to control to form a sterilization angle of 180 degrees if the distance measurement value is above the first threshold and below a second threshold (mid-range mode), and to control to form a sterilization angle of a predetermined acute angle if the distance measurement value is above the second threshold (narrow-range mode). [0068]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Chae with a reasonable expectation of success. Chae teaches “an ultraviolet sterilization device and a method for controlling the operation thereof that can be universally utilized in various spaces (e.g., restaurants, operating rooms in hospitals, toilets in rest areas, elevators, etc.) as a sterilization power can be adaptively adjusted corresponding to the distance from a target surface [Chae, 0007]”.
Regarding claim 12:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Chae teaches:
further comprising redirecting ultraviolet radiation using a moveable mirror system (depending on the size of the sterilization angle θ formed by each of the pair of reflectors 107a, 107b reflecting ultraviolet ray generated by the ultraviolet lamp 105 in a rotated position, the ultraviolet sterilization device 100 may be operated for sterilization processing in a narrow-range mode, a mid-range mode, or a wide-range mode. [0052]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Chae with a reasonable expectation of success. Chae teaches “an ultraviolet sterilization device and a method for controlling the operation thereof that can be universally utilized in various spaces (e.g., restaurants, operating rooms in hospitals, toilets in rest areas, elevators, etc.) as a sterilization power can be adaptively adjusted corresponding to the distance from a target surface [Chae, 0007]”.
Regarding claim 24:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Chae teaches:
further comprising detecting moving objects and modifying ultraviolet irradiation to avoid exposure to the moving objects (a method of operation thereof that can ensure safety even during uninterrupted sterilization processing by preemptively adjusting a range of sterilization region when a moving object (e.g., a person, etc.) is expected to enter the sterilization region. [0008]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Chae with a reasonable expectation of success. Chae teaches “an ultraviolet sterilization device and a method for controlling the operation thereof that can be universally utilized in various spaces (e.g., restaurants, operating rooms in hospitals, toilets in rest areas, elevators, etc.) as a sterilization power can be adaptively adjusted corresponding to the distance from a target surface [Chae, 0007]”.
Regarding claim 25:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 17, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Chae teaches:
further comprising instructions for redirecting ultraviolet radiation using movable mirrors (depending on the size of the sterilization angle θ formed by each of the pair of reflectors 107a, 107b reflecting ultraviolet ray generated by the ultraviolet lamp 105 in a rotated position, the ultraviolet sterilization device 100 may be operated for sterilization processing in a narrow-range mode, a mid-range mode, or a wide-range mode. [0052]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Chae with a reasonable expectation of success. Chae teaches “an ultraviolet sterilization device and a method for controlling the operation thereof that can be universally utilized in various spaces (e.g., restaurants, operating rooms in hospitals, toilets in rest areas, elevators, etc.) as a sterilization power can be adaptively adjusted corresponding to the distance from a target surface [Chae, 0007]”.
Claim(s) 6, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierson et. al. (US 2022/0143250), herein Pierson in view of Byrnes et. al. (US 2022/0313855), herein Byrnes and Rephaeli et. al. (US 2022/0124260), herein Rephaeli in further view of Candelore (US 2022/0323623), herein Candelore.
Regarding claim 6:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Byrnes further teaches:
wherein high touch objects are identified (the first object 704 (such as the door knob of a door for a hospital room) may be a high touch surface compared to the second object 706 (such as the chair), or the third object 708 (such as the floor mat) [0076]) using an artificial intelligence and machine learning classification technique (the disinfection management computing device 20 may execute an artificial intelligence target area identification algorithm based on the obtained imaging data [0048]).
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Candelore teaches:
wherein high touch objects are identified (the first object 704 (such as the door knob of a door for a hospital room) may be a high touch surface compared to the second object 706 (such as the chair), or the third object 708 (such as the floor mat) [0076])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Candelore with a reasonable expectation of success. Candelore teaches “Based on the determined level of priority, the circuitry 202 may control the ultraviolet light source 104 to irradiate each object (such as, the first object 704, the second object 706, and the third object 708) of the set of objects in the first physical space 110 in an order based on the determined level of priority for disinfection of each object (for example, disinfect the first object 704 prior to disinfection of the second object 706 and the third object 708) [Candelore, 0076]”.
Regarding claim 13:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Candelore teaches:
wherein high-touch objects receive increased ultraviolet exposure (the circuitry 202 may be configured to determine a level of priority for disinfection of each object (such as, the first object 704, the second object 706, and the third object 708) of the set of objects based on the determined type of each object in the first physical space 110. For example, the first object 704 (such as the door knob of a door for a hospital room) may be a high touch surface compared to the second object 706 (such as the chair), or the third object 708 (such as the floor mat) [0076]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Candelore with a reasonable expectation of success. Candelore teaches “Based on the determined level of priority, the circuitry 202 may control the ultraviolet light source 104 to irradiate each object (such as, the first object 704, the second object 706, and the third object 708) of the set of objects in the first physical space 110 in an order based on the determined level of priority for disinfection of each object (for example, disinfect the first object 704 prior to disinfection of the second object 706 and the third object 708) [Candelore, 0076]”.
Regarding claim 19:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 17, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Candelore teaches:
further comprising instructions for prioritizing ultraviolet exposure near high-touch objects (the circuitry 202 may be configured to determine a level of priority for disinfection of each object (such as, the first object 704, the second object 706, and the third object 708) of the set of objects based on the determined type of each object in the first physical space 110. For example, the first object 704 (such as the door knob of a door for a hospital room) may be a high touch surface compared to the second object 706 (such as the chair), or the third object 708 (such as the floor mat) [0076]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Candelore with a reasonable expectation of success. Candelore teaches “Based on the determined level of priority, the circuitry 202 may control the ultraviolet light source 104 to irradiate each object (such as, the first object 704, the second object 706, and the third object 708) of the set of objects in the first physical space 110 in an order based on the determined level of priority for disinfection of each object (for example, disinfect the first object 704 prior to disinfection of the second object 706 and the third object 708) [Candelore, 0076]”.
Claim(s) 10, 18, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierson et. al. (US 2022/0143250), herein Pierson in view of Byrnes et. al. (US 2022/0313855), herein Byrnes and Rephaeli et. al. (US 2022/0124260), herein Rephaeli in further view of Slycke et. al. (US 2024/0181109), herein Slycke.
Regarding claim 10:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 8, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not teach, however Slycke teaches:
further comprising detecting shadow regions caused by ultraviolet-blocking objects (If at stage 1003 it is determined that the ray intersects a non-robot surface, then the process 1000 flows to stage 1005 wherein the point is flagged as a shadow point receiving zero UVC power at this step. [0064]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Slycke with a reasonable expectation of success. Slycke teaches the benefit of “a mobile service robot running appropriate software and algorithms mitigates the described problems by minimizing the amount of electrical energy needed to disinfect surfaces and objects of interest using UVC light by accurately modelling the precise amount of UVC dosage delivered to each surface in a room, to provide accurate estimates of active pathogen reduction to ensure inactivation of the pathogen of interest and by careful automated planning of the path the robot should take to achieve these objectives with minimum robot movement and minimum use of electrical energy. [Slycke, 0032]”
Regarding claim 18:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 17, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Slycke teaches:
identify shadow regions within the environment (If at stage 1003 it is determined that the ray intersects a non-robot surface, then the process 1000 flows to stage 1005 wherein the point is flagged as a shadow point receiving zero UVC power at this step. [0064]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Slycke with a reasonable expectation of success. Slycke teaches the benefit of “a mobile service robot running appropriate software and algorithms mitigates the described problems by minimizing the amount of electrical energy needed to disinfect surfaces and objects of interest using UVC light by accurately modelling the precise amount of UVC dosage delivered to each surface in a room, to provide accurate estimates of active pathogen reduction to ensure inactivation of the pathogen of interest and by careful automated planning of the path the robot should take to achieve these objectives with minimum robot movement and minimum use of electrical energy. [Slycke, 0032]”
Regarding claim 23:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Pierson further teaches:
wherein the instructions further cause the system to identify [shadow] regions and increase ultraviolet dosage delivery to the [shadow] regions (to calculate dosages of UVC radiation to be applied to features located at different points along the path, and to determine a particular rate of speed at which the D-AMR can move along particular portions of the path while disinfecting the identified features. The module 340 also has functionality that can operate to control the state of each UVC lamp to be on or off, and to control the movement of each articulated arm in order to orient the lamps to be pointing towards a feature identified for disinfection. [0025]).
Pierson in view of Byrnes and Rephaeli does not teach, however Slycke teaches:
wherein the instructions further cause the system to identify shadow regions (If at stage 1003 it is determined that the ray intersects a non-robot surface, then the process 1000 flows to stage 1005 wherein the point is flagged as a shadow point receiving zero UVC power at this step. [0064]) and increase ultraviolet dosage delivery to the shadow regions (Accurate computation of shadows or regions where the UVC light cannot reach is also useful in ensuring as complete disinfection as possible [0048]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Slycke with a reasonable expectation of success. Slycke teaches the benefit of “a mobile service robot running appropriate software and algorithms mitigates the described problems by minimizing the amount of electrical energy needed to disinfect surfaces and objects of interest using UVC light by accurately modelling the precise amount of UVC dosage delivered to each surface in a room, to provide accurate estimates of active pathogen reduction to ensure inactivation of the pathogen of interest and by careful automated planning of the path the robot should take to achieve these objectives with minimum robot movement and minimum use of electrical energy. [Slycke, 0032]”
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pierson et. al. (US 2022/0143250), herein Pierson in view of Byrnes et. al. (US 2022/0313855), herein Byrnes and Rephaeli et. al. (US 2022/0124260), herein Rephaeli in further view of Chae (US 2024/0252705), herein Chae and Candelore (US 2022/0323623), herein Candelore.
Regarding claim 26:
Pierson in view of Byrnes and Rephaeli teaches all the limitations of claim 1, upon which this claim is dependent.
Pierson in view of Byrnes and Rephaeli does not explicitly teach, however Chae teaches:
wherein traversal delays and angular intervals (calculating the sterilization angle and the operation time for performing the sterilization operation [0014]) are adjusted (depending on the size of the sterilization angle θ formed by each of the pair of reflectors 107a, 107b reflecting ultraviolet ray generated by the ultraviolet lamp 105 in a rotated position, the ultraviolet sterilization device 100 may be operated for sterilization processing in a narrow-range mode, a mid-range mode, or a wide-range mode. [0052])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes and Rephaeli to include the teachings as taught by Chae with a reasonable expectation of success. Chae teaches “an ultraviolet sterilization device and a method for controlling the operation thereof that can be universally utilized in various spaces (e.g., restaurants, operating rooms in hospitals, toilets in rest areas, elevators, etc.) as a sterilization power can be adaptively adjusted corresponding to the distance from a target surface [Chae, 0007]”.
Pierson in view of Byrnes, Rephaeli, and Chae does not explicitly teach, however Candelore teaches:
to ensure minimum ultraviolet dosage delivery for high-touch surfaces (the first object 704 (such as the door knob of a door for a hospital room) may be a high touch surface compared to the second object 706 (such as the chair), or the third object 708 (such as the floor mat) [0076])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Pierson in view of Byrnes, Rephaeli, and Chae to include the teachings as taught by Candelore with a reasonable expectation of success. Candelore teaches “Based on the determined level of priority, the circuitry 202 may control the ultraviolet light source 104 to irradiate each object (such as, the first object 704, the second object 706, and the third object 708) of the set of objects in the first physical space 110 in an order based on the determined level of priority for disinfection of each object (for example, disinfect the first object 704 prior to disinfection of the second object 706 and the third object 708) [Candelore, 0076]”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lanigan (WO 2019/162666) discloses A method of determining the ultraviolet (UV) transparency of a gemstone (18, e.g. diamond) is provided. The method comprises irradiating the gemstone with at least one excitation pulse of incident UV light (by UV light source 11) and detecting UV light (by UV detector 12) that is transmitted into, internally reflected within, and returned from the gemstone (18). An image is generated using the internally reflected UV light. A UV transparency or a nitrogen content of the gemstone is determined, based upon the brightness within the image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott R Jagolinzer whose telephone number is (571)272-4180. The examiner can normally be reached M-Th 8AM - 4PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571)272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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