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 .
DETAILED ACTION
Claims 1-3, 8-9, 11, 14-17, 19-22, 26-27, 29, 32-34 have been examined. Claims 4-7, 10, 12-13, 18, 23-25, 28, 30-31, 35-36 have been canceled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims are 26, 27, 32, 33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 26 recites:
“The system of claim 19, wherein the first position of interest is centered on the first location.”
The limitation “the first location” lacks antecedent basis in claim 19. Claim 19 recites a “first virtual location of the signaling device,” but does not previously recite or otherwise identify “a first location.”
Thus, when claim 26 is read together with claim 19, it is unclear what particular “first location” is being referred to. In particular, claim 19 distinguishes between the virtual location of the signaling device and the first position of interest, but does not introduce a separate “first location” corresponding to the location recited in claim 1.
This is not merely a matter of preferred claim terminology. The limitation expressly requires the first position of interest to be “centered on” an unidentified object, and therefore the scope of the claimed spatial relationship cannot be ascertained with reasonable certainty from the claim language.
The Office therefore cannot determine with reasonable certainty whether “the first location” in claim 26 refers to:
the physical location at which the signaling device was placed;
the first virtual location determined by the processor; or
some other location.
Claim 27 recites:
“The system of claim 19, wherein the first position of interest is offset from the first location.”
For the same reasons discussed with respect to claim 26, claim 19 does not introduce “a first location.”
The claim therefore fails to establish with reasonable certainty the reference point from which the first position of interest is “offset.”
This ambiguity is particularly material because the claim expressly distinguishes the first position of interest from the location to which it is offset. Without identification of the referenced “first location,” the metes and bounds of the claimed spatial relationship cannot be determined with reasonable certainty.
Claim 32 recites, in pertinent part:
“obtain a second scan of the patient space when a second signal is received at the receiver;”
and subsequently:
“determine a second virtual location of the signaling device within the patient space based on a difference between the first scan and the baseline scan”
The claim therefore establishes a sequence in which receipt of a second signal causes the processor to obtain a second scan, but the subsequently recited determination of the second virtual location is expressly based on a difference between the first scan and the baseline scan.
This creates an internal inconsistency concerning the scan data from which the second virtual location is determined.
In particular, the claim does not reasonably establish whether:
the second virtual location is determined from the newly obtained second scan and the baseline scan; or
the second virtual location is determined from the previously obtained first scan and the baseline scan.
The ambiguity is material because these alternatives can produce different virtual locations and therefore different second positions of interest.
The Office is not rejecting the claim merely because the language is broad or because another formulation might be preferable. Rather, the claim contains two internally conflicting scan references that leave the scope of the claimed determination unclear.
Claim 33 is rejected under 35 U.S.C. §112(b) at least insofar as it depends from claim 32.
Claim 33 recites:
“The system of claim 32, wherein the processor is further configured to merge the first position of interest and the second position of interest.”
Because claim 33 depends from claim 32, it incorporates all limitations of claim 32, including the internally inconsistent requirement concerning determination of the second virtual location.
Accordingly, claim 33 likewise fails to define with reasonable certainty the claimed system because the identity of the scan data used to establish the second virtual location—and consequently the second position of interest to be merged—is unclear.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-3,8-9,14,16,19-22,26-27,32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iagnemma et al. (US 2018/0196417 A1), in view of Lee et al. (US 2019/0108909 A1), and further in view of Kim et al. (US 8,948,501 B1).
Claim 1:
Iagnemma et al. discloses a location-signaling system in which a signaling device sends a location indication signal that is received by a stimulus detector and used to estimate the precise location of the signaling device. Iagnemma further discloses determining a precise goal location based on the location-signaling activity and the location indication signal sent by the signaling device. See, e.g., US 2018/0196417 A1, paras. [0033], [0054], [0102], and [0112]-[0113].
Iagnemma further discloses use of LiDAR for determining distance to the signaling device/rider, including determining distance from LiDAR reflections and using spatial information to estimate the location of the signaling device. See id., paras. [0063] and [0121].
Lee et al. discloses a location system associated with a healthcare facility. The location system receives wireless signals, including RF, optical, and acoustic signals, from a client device to facilitate determining the location of the client device. Lee further discloses determining locations of patients and visitors using optical ranging and positioning devices including LiDAR. See US 2019/0108909 A1, paras. [0031] and [0095].
Lee additionally discloses facility information identifying the relative locations of physical structures and points of interest in three-dimensional space and an accurate three-dimensional model/map of the healthcare facility. See id., paras. [0038], [0054], and [0114].
Kim et al. discloses LiDAR-based three-dimensional point-cloud processing using a baseline/background point cloud and an input point cloud. Kim teaches generating the respective point clouds from a 3D scanner, obtaining foreground objects using background subtraction, and generating a difference map from the baseline and input point clouds. See US 8,948,501 B1, col. 4, lines 28-45 and col. 7, lines 48-56.
“establishing virtual positions of interest in a patient space”
Iagnemma teaches determining a precise goal location from location-signaling activity. See US 2018/0196417 A1, paras. [0014]-[0015]. Lee teaches a healthcare-facility location system that determines precise locations of patients and devices and maintains three-dimensional facility information identifying physical structures and points of interest. See US 2019/0108909 A1, paras. [0031] and [0038].
Thus, the combination suggests establishing a precisely determined location/position of interest in a healthcare facility or patient environment.
“obtaining a baseline scan of the patient space using a LiDAR scanner”
Kim expressly discloses a baseline/background point cloud obtained from a 3D scanner, including LiDAR point clouds. Kim describes processing collected LiDAR point clouds by computing a baseline/background point cloud and an input point cloud. See US 8,948,501 B1, col. 4, lines 28-32 and col. 7, lines 48-50.
Lee teaches the use of LiDAR for determining locations within a healthcare facility, including locations of patients and visitors. See US 2019/0108909 A1, para. [0031].
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to apply Kim's known baseline LiDAR point-cloud acquisition technique to the healthcare-facility LiDAR location system of Lee. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, providing a baseline spatial representation for subsequent LiDAR-based location processing.
“placing a signaling device at a first location within the patient space”
Iagnemma expressly discloses a signaling device, typically possessed by a rider/user, that transmits a location indication signal used to estimate the precise location of the signaling device. See US 2018/0196417 A1, paras. [0033] and [0102]. Iagnemma further describes the signaling device being operated by a rider at the location that is to be identified as a precise goal location. See paras. [0014]-[0015] and [0110].
Lee teaches client devices that are carried, worn, or otherwise attached to patients and other entities in a healthcare facility and whose locations are determined by the healthcare-facility location system. See US 2019/0108909 A1, paras. [0031]-[0038] concerning client device 120 and the location system.
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to employ Iagnemma's known signaling device in Lee's healthcare-facility environment and position the signaling device at a selected location within that environment. This would be an implementation of use of known techniques to improve a similar device in the same way, namely, using a signaling device to identify a selected physical location within a location-determination system.
“signaling a receiver using the signaling device”
Iagnemma expressly discloses a location indication signal sent by a signaling device and received by stimulus detectors, which may be located on an autonomous vehicle or elsewhere. See US 2018/0196417 A1, paras. [0033], [0054], [0102], and [0112]-[0113].
Lee independently teaches receiving RF, optical, and acoustic signals from a client device for determining the device's location. See US 2019/0108909 A1, paras. [0031] and [0095].
Thus, this limitation is expressly taught by the combination.
“to indicate the first location is a position of interest”
Iagnemma teaches that the location indication signal is used to estimate the precise location of the signaling device and that a process determines a precise goal location based on the location-signaling activity. See US 2018/0196417 A1, paras. [0014]-[0015], [0054], [0102], and [0113].
Lee teaches a healthcare-facility environment containing mapped physical locations and points of interest in three-dimensional space. See US 2019/0108909 A1, paras. [0038] and [0054].
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to use the location indication signal of Iagnemma to identify a selected location within Lee's healthcare facility as the location/point of interest to which the system associates the signaling-device location. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, using a known location-signaling mechanism to associate a selected physical location with a mapped point of interest.
The references need not use the exact claim terminology “position of interest.” The relevant inquiry is whether the claimed arrangement would have been obvious from the teachings of the references.
“obtaining a first scan of the patient space using the LiDAR scanner”
Kim teaches an input point cloud obtained from a 3D scanner in addition to a baseline point cloud. Kim expressly describes collected LiDAR point clouds and an input point cloud used for comparison with the baseline point cloud. See US 8,948,501 B1, col. 4, lines 28-37 and col. 7, lines 48-56.
Lee teaches LiDAR-based spatial determination in the healthcare-facility environment. See US 2019/0108909 A1, para. [0031].
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to obtain a subsequent/input LiDAR scan after positioning and signaling with the signaling device. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, acquiring a subsequent spatial representation for comparison with the baseline representation.
“determining a first virtual location of the signaling device within the patient space based on the difference between the first scan and the baseline scan”
Kim expressly teaches the relevant scan-difference processing. Kim generates a baseline/background point cloud and an input point cloud and obtains foreground objects through background subtraction. Kim further teaches comparing the baseline map with the input projection, with subtraction producing a difference map. See US 8,948,501 B1, col. 4, lines 28-45 and col. 7, lines 48-56.
Iagnemma teaches determining the precise location of the signaling device using the received location indication signal and spatial measurements, including bearing and distance, and further teaches LiDAR-based distance determination. See US 2018/0196417 A1, paras. [0054], [0063], [0102], and [0113].
Lee teaches LiDAR-based location determination of entities in a healthcare facility. See US 2019/0108909 A1, para. [0031].
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to use Kim's known baseline-versus-input LiDAR comparison technique in the Iagnemma/Lee location system so that the spatial change corresponding to the positioned signaling device could be identified relative to the baseline environment and used in determining the signaling device's spatial location. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, using known background-subtraction processing to identify a spatially changed object within a LiDAR-monitored environment.
“determining a first position of interest based on the first virtual location of the signaling device”
Iagnemma expressly teaches determining the precise location of the signaling device from the location indication signal and determining a precise goal location based on the location-signaling activities. See US 2018/0196417 A1, paras. [0014]-[0015], [0054], [0102], and [0113].
Lee teaches correlating position measurements, including x-, y-, and z-coordinate measurements, with facility information identifying physical structures and points of interest in three-dimensional space. See US 2019/0108909 A1, paras. [0038] and [0054].
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to designate the determined signaling-device location as the selected position/point of interest within the healthcare environment, consistent with Iagnemma's teaching of using the determined signaling-device location to establish a precise goal location. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, associating a determined spatial location with a mapped point of interest.
Iagnemma and Lee both concern determining a precise physical location using signaling and location-determination technologies. Iagnemma provides a signaling-device mechanism for identifying a precise location, while Lee provides a healthcare-facility implementation in which patient/device locations are determined and correlated with a three-dimensional map containing physical structures and points of interest.
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to apply Iagnemma's known location-signaling mechanism to Lee's healthcare-facility location system. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, providing a known signaling mechanism for communicating or identifying a selected precise location within a mapped healthcare environment.
The modification would use the respective components for their established purposes: Iagnemma's signaling device would provide a location indication signal, while Lee's healthcare-facility location system would determine and correlate physical locations with the three-dimensional facility model.
Kim teaches a known technique for detecting spatial changes by comparing a baseline/background LiDAR point cloud with a subsequent/input LiDAR point cloud and generating a difference map. See US 8,948,501 B1, col. 4, lines 28-45 and col. 7, lines 48-56.
It would have been obvious at the time the invention before the effective filing date of the claimed invention was made to apply Kim's known baseline-versus-input point-cloud technique to the LiDAR-based location system of Lee and the signaling-device localization system of Iagnemma. This would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results, namely, distinguishing a subsequently positioned object, such as the signaling device, from stationary background structure.
The combination would not change the respective basic functions of the references: Iagnemma's signaling device would continue to provide a location indication signal; Lee's healthcare location system would continue to determine positions within the healthcare facility; and Kim's LiDAR processing would continue to identify spatial differences between baseline and subsequent scans.
The combination therefore would have had a reasonable expectation of success because each reference applies the relevant technology for the same general purpose of determining or distinguishing physical locations, and the proposed combination merely applies known spatial-difference processing to the existing LiDAR/location-determination system.
Claim 2:
Claim 2 depends from claim 1 and further recites that “the receiver is an RF receiver, and signaling the receiver is performed using an RF transmitter.”
As discussed with respect to claim 1, Iagnemma et al. teach a signaling device that transmits a location indication signal to a stimulus detector, with the received signal being used to estimate the precise location of the signaling device and thereby determine a precise goal location. See, e.g., Iagnemma et al., paras. [0033], [0054], [0102], and [0112]–[0113]. Iagnemma et al. further teach wireless signaling and electronic signaling devices.
Lee et al. teach a location system in a healthcare-facility environment for determining the locations of patients, visitors, and other entities. Lee et al., para. [0031]. In particular, Lee et al. disclose that the location system receives wireless signals, including radio frequency (RF) signals, from a client device and uses such signals to determine the location of the client device. Lee et al., para. [0031]. Lee et al. further disclose that the client device includes communication hardware and software comprising, for example, a transmitter, receiver, or transceiver, for wired or wireless communication with other devices. Lee et al., para. [0038].
Thus, Lee et al. disclose an implementation in which a client device transmits RF signals to a location system that receives the RF signals for determining the location of the client device. Accordingly, the combination of Iagnemma et al. and Lee et al. teaches or suggests the claimed RF receiver and RF transmitter.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the signaling-device and location-determination techniques of Iagnemma et al. in the healthcare-facility location system of Lee et al. using an RF receiver and an RF transmitter as taught by Lee et al. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. In particular, Lee et al. expressly identify RF signaling as a suitable wireless communication technique for determining the location of a client device. Applying the RF signaling technique of Lee et al. to the signaling-device/location-determination arrangement of Iagnemma et al. would merely employ a known communication modality for transmitting the location-indication signal to the receiver, thereby providing the predictable result of communicating the signaling-device location through RF signaling.
Further, as discussed with respect to claim 1, Kim et al. teach collecting LiDAR point clouds including a baseline/background point cloud and an input point cloud and comparing the baseline and input point clouds to generate difference information. See Kim et al., col. 4, lines 28–45; col. 7, lines 48–56.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the baseline-versus-input LiDAR difference-processing technique of Kim et al. to the combined signaling and healthcare-facility location system of Iagnemma et al. and Lee et al. to facilitate identification and localization of the signaling device relative to the baseline spatial representation of the patient space. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. The application of Kim et al.'s known baseline-versus-input point-cloud comparison would provide the predictable result of identifying spatial differences between a baseline representation and a subsequently acquired representation of the patient space.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests all limitations of claim 2 and would have provided a person of ordinary skill in the art with a reasonable expectation of success in implementing the RF-based signaling arrangement in the healthcare-facility LiDAR location system.
Claim 3:
Claim 3 depends from claim 1 and further recites that “the receiver is a microphone, and signaling the receiver is performed using a sound.”
As discussed with respect to claim 1, Iagnemma et al. teach a signaling device that transmits a location indication signal to a stimulus detector, with the received signal being used to estimate the precise location of the signaling device and thereby determine a precise goal location. See, e.g., Iagnemma et al., paras. [0033], [0054], [0102], and [0112]–[0113]. Iagnemma et al. further teach that the signaling device may emit a sound-based location indication signal and that auditory sensors, including microphones, may be used as stimulus detectors.
In particular, Iagnemma et al. disclose emitting an uncommon sound or sequence of sounds from the signaling device, where the sound is detected by sensors on the vehicle. Iagnemma et al. further disclose that a microphone array may be used for detecting the emitted sound and that the detected sound may be analyzed to determine a bearing to the sound source. See paras. [0033] and [0054].
Thus, Iagnemma et al. expressly teach both a microphone serving as a receiver/stimulus detector and signaling the receiver by emitting a sound from the signaling device.
Lee et al. teach a location system in a healthcare-facility environment for determining the locations of patients, visitors, and other entities. Lee et al., para. [0031]. Lee et al. further teach determining spatial positions in the healthcare facility, including three-dimensional x/y/z coordinates and relationships between such position measurements and physical structures, rooms, walkways, and points of interest within the healthcare facility. See paras. [0038], [0054], [0095], and [0114].
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the location-signaling technique of Iagnemma et al. in the healthcare-facility location system of Lee et al., including using the sound-based signaling and microphone detection expressly taught by Iagnemma et al. It would be an implementation of use of know techniques to improve similar device in the same way. In particular, Iagnemma et al. expressly identify sound emission and microphone detection as techniques for communicating and detecting a location-indication signal. Applying these known techniques to the healthcare-facility location system of Lee et al. would provide the predictable result of allowing a signaling device to communicate a location indication through an emitted sound that is detected by a microphone.
Further, as discussed with respect to claim 1, Kim et al. teach collecting LiDAR point clouds including a baseline/background point cloud and an input point cloud and comparing the baseline and input point clouds to generate difference information. See Kim et al., col. 4, lines 28–45; col. 7, lines 48–56.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the baseline-versus-input LiDAR difference-processing technique of Kim et al. to the combined signaling and healthcare-facility location system of Iagnemma et al. and Lee et al. to facilitate identification and localization of the signaling device relative to the baseline spatial representation of the patient space. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. The application of Kim et al.'s known baseline-versus-input point-cloud comparison would provide the predictable result of identifying spatial differences between a baseline representation and a subsequently acquired representation of the patient space.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests all limitations of claim 3 and would have provided a person of ordinary skill in the art with a reasonable expectation of success in implementing the microphone-based, sound-signaling arrangement in the healthcare-facility LiDAR location system.
Claim 8:
Claim 8 depends from claim 1 and further recites that “the first position of interest is centered on the first location.”
As discussed with respect to claim 1, Iagnemma et al. teach a signaling device that transmits a location indication signal to a stimulus detector, with the received signal being used to estimate the precise location of the signaling device and thereby determine a precise goal location. See, e.g., Iagnemma et al., paras. [0014]–[0015], [0033], [0054], [0102], and [0112]–[0113].
In particular, Iagnemma et al. disclose that a location indication signal sent by a signaling device is received by a stimulus detector and is used to estimate the precise location of the signaling device. See para. [0054]. Iagnemma et al. further disclose that the precise goal location is the precise location of the user in certain implementations and that the AV system typically sets the precise location of the signaling device or rider as the precise goal location. See paras. [0014]–[0015] and [0112]–[0113]. Iagnemma et al. additionally describe determining the bearing of the signaling device, including the center of the signaling device, when determining the signaling-device location. See para. [0121] and the corresponding description of the signaling-device center.
Thus, Iagnemma et al. specifically disclose a positional relationship in which the precise goal location corresponds to the precise location of the signaling device. In particular, where the signaling device is positioned at the signaling-device location, setting the precise location of the signaling device as the precise goal location places the precise goal location at the signaling-device location. This corresponds to the claimed first position of interest being centered on the first location.
Lee et al. teach a location system in a healthcare-facility environment for determining locations of patients, visitors, and other entities. Lee et al., para. [0031]. Lee et al. further teach determining spatial positions in the healthcare facility, including three-dimensional x/y/z coordinates and relationships between such position measurements and physical structures, rooms, walkways, and points of interest within the healthcare facility. See paras. [0038], [0054], [0095], and [0114].
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the precise-location and precise-goal-location relationship of Iagnemma et al. in the healthcare-facility location system of Lee et al., such that the position of interest corresponding to the determined signaling-device location is centered on the signaling-device location. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. In particular, Iagnemma et al. expressly teach setting the precise location of the signaling device or rider as the precise goal location. Applying this positional relationship in the healthcare-facility environment of Lee et al. would result in the selected position of interest corresponding to, and being centered on, the location at which the signaling device is positioned.
Further, as discussed with respect to claim 1, Kim et al. teach collecting LiDAR point clouds including a baseline/background point cloud and an input point cloud and comparing the baseline and input point clouds to generate difference information. See Kim et al., col. 4, lines 28–45; col. 7, lines 48–56.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the baseline-versus-input LiDAR difference-processing technique of Kim et al. to the combined signaling and healthcare-facility location system of Iagnemma et al. and Lee et al. to facilitate identification and localization of the signaling device relative to the baseline spatial representation of the patient space. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. The application of Kim et al.'s baseline-versus-input point-cloud comparison would provide the predictable result of identifying spatial differences between a baseline representation and a subsequently acquired representation of the patient space.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests all limitations of claim 8 and would have provided a person of ordinary skill in the art with a reasonable expectation of success in implementing the claimed position-of-interest relationship in the healthcare-facility LiDAR location system.
Claim 9:
Claim 9 depends from claim 1 and further recites that “the first position of interest is offset from the first location.”
As discussed with respect to claim 1, Iagnemma et al. disclose location signaling in which a signaling device sends a location indication signal that is received by a stimulus detector and used to estimate the precise location of the signaling device. See Iagnemma et al., paras. [0033] and [0054]. Iagnemma et al. further disclose determining a precise goal location based on the location-signaling activities and the location indication signal. See paras. [0014]-[0015] and [0112]-[0113]. Iagnemma et al. also disclose LiDAR-based determination of spatial information. See paras. [0063] and [0121].
Lee et al. disclose applying location determination techniques in a healthcare facility for determining locations of patients, devices, and other entities. Lee et al. disclose that a location system receives wireless signals from a client device, including radio-frequency (RF), optical, and acoustic signals, to facilitate determining the location of the client device, and further disclose use of a light detection and ranging (LiDAR) device for optical ranging and positioning. See Lee et al., para. [0031]. Lee et al. further disclose correlating x, y, and z position measurements with facility information identifying physical structures, including points of interest, in three-dimensional space. See Lee et al., para. [0054]. Lee et al. additionally disclose communication hardware including a transmitter, receiver, and transceiver. See Lee et al., para. [0038].
Kim et al. disclose LiDAR point-cloud processing in which a baseline point cloud is compared with an input point cloud and subtraction of the baseline map from the input projection map produces a difference map. See Kim et al., col. 4, lines 28-45. Kim et al. further disclose identifying differences between baseline and input data using difference-map processing. See Kim et al., col. 7, lines 48-56.
With respect to the additional limitation of claim 9, Iagnemma et al. specifically disclose that the precise location associated with the signaling activity need not be the same as the resulting precise goal location. Iagnemma et al. disclose that a rider may signal “a precise location” that is “a different precise location desired for the activity” or “a different precise location nearby to the user's actual precise location.” See Iagnemma et al., para. [0112]. Thus, Iagnemma et al. expressly disclose a spatial relationship in which the location associated with the signaling device and the resulting precise goal location are different locations, with the goal location being located nearby the actual location of the signaling device or user.
Iagnemma et al. further expressly disclose an implementation in which the precise goal location is identified as an intersection point adjusted by a fixed offset distance, thereby offsetting the precise pickup location from the edge of the drivable road surface. See Iagnemma et al., para. [0121]. Accordingly, the reference does not merely suggest that an offset could be used; it specifically teaches determining a location and applying a spatial offset to obtain a different precise goal location.
Therefore, the additional limitation that “the first position of interest is offset from the first location” is specifically disclosed by Iagnemma et al. in the form of determining a precise location associated with the signaling device and selecting a different, nearby precise goal location, including by applying a fixed offset distance.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the location-signaling and precise-location techniques of Iagnemma et al. in the healthcare-facility location system of Lee et al., such that a precise position of interest corresponding to the signaling-device location could be selected at a different, offset location within the healthcare facility. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The motivation is further supported by Lee et al.'s disclosure of correlating three-dimensional location measurements with healthcare-facility information identifying physical structures and points of interest. See Lee et al., para. [0054]. Applying Iagnemma et al.'s disclosed offset between a detected/signaled location and a resulting goal location to such a three-dimensional healthcare-facility environment would provide the predictable result of defining a selected location at a specified spatial offset from the signaling-device location.
It would further have been obvious at the time the invention before the effective filing date of the claim invention was made to use the baseline-versus-input LiDAR difference processing of Kim et al. with the combined Iagnemma et al. and Lee et al. system to distinguish changes in the scanned patient-space environment and determine the spatial location of the signaling device. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Kim et al. specifically teach comparing baseline and input LiDAR-derived point-cloud information and generating a difference map from the comparison. See Kim et al., col. 4, lines 28-45 and col. 7, lines 48-56. Applying that disclosed baseline/difference processing to the LiDAR scans of the healthcare facility would have provided a predictable technique for identifying the spatial change associated with the signaling device.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests each limitation of claim 9, including determining a virtual location associated with a signaling device and determining a position of interest that may be spatially offset from the signaling-device location. The combination would have yielded predictable results and therefore renders claim 9 unpatentable under 35 U.S.C. § 103.
Claim 14:
Claim 14 depends from claim 1 and further recites:
“moving the signaling device at a second location within the patient space; signaling the receiver to indicate the second location is a position of interest; obtaining a second scan of the patient space using the LiDAR scanner; identifying a second virtual location of the signaling device within the patient space based on a difference between the second scan and the baseline scan; and determining a second position of interest based on the second virtual location of the signaling device.”
As discussed with respect to claim 1, Iagnemma et al. disclose a signaling device that transmits a location indication signal to a receiver or stimulus detector, where the received signal is processed to determine the precise location of the signaling device and a corresponding precise goal location. See Iagnemma et al., paras. [0033], [0054], [0076], [0112]-[0113]. Iagnemma et al. further disclose LiDAR-based determination of the distance and spatial location of the signaling device. See Iagnemma et al., para. [0063] and the LiDAR-based signaling-device localization described in the application.
Iagnemma et al. additionally disclose the specific circumstance in which the rider moves while broadcasting a location indication signal. In particular, Iagnemma et al. disclose that while the rider is broadcasting a location indication signal, the rider may also move, and that the autonomous vehicle may receive a series of location indication signals and update the precise goal location with time. See Iagnemma et al., para. [0090]. Thus, Iagnemma et al. specifically disclose repeated location-signaling events associated with different positions of the signaling device or rider and corresponding successive location determinations.
Iagnemma et al. further disclose that a processor analyzes received location indication signals and computes the precise location of the signaling device or derives a precise goal location from the location signal. See Iagnemma et al., para. [0076]. Accordingly, the successive signaling events disclosed by Iagnemma et al. provide successive location information corresponding to the signaling device.
Lee et al. disclose a healthcare-facility location system configured to determine precise locations of entities relative to a healthcare facility. Lee et al. disclose receiving wireless signals from a client device and determining the location of the client device using ranging and/or angulating techniques. Lee et al. further disclose determining locations of non-transmitting objects using optical ranging and positioning devices, including a light detection and ranging (LiDAR) device. See Lee et al., para. [0031].
Lee et al. further disclose determining x, y, and z coordinate positions and correlating those position measurements with facility information identifying physical structures of the healthcare facility, including points of interest, in three-dimensional space. See Lee et al., para. [0054]. Lee et al. therefore provide a healthcare-facility spatial framework in which determined three-dimensional locations can be associated with points of interest.
Kim et al. disclose obtaining and processing a baseline point cloud and input point clouds captured from a three-dimensional scanner. Kim et al. disclose generating a set of voxels for each of a baseline point cloud and an input point cloud and performing difference processing between the baseline and input data. See Kim et al., col. 4, lines 28-45. Kim et al. further disclose that the input point clouds may form a three-dimensional motion sequence and that subtraction of a baseline map from an input projection map produces a difference map. See Kim et al., col. 7, lines 48-56.
With respect to the additional limitation requiring a second location, Iagnemma et al. specifically disclose that the rider may move while continuing to broadcast the location indication signal and that a series of location indication signals may therefore be received and processed to update the precise goal location with time. See Iagnemma et al., para. [0090]. Thus, the signaling device is disclosed as being associated with successive locations and successive signaling events.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the successive location-signaling technique of Iagnemma et al. in the healthcare-facility location system of Lee et al., such that a signaling device moved to a second location within the healthcare facility and transmitted a subsequent location indication signal corresponding to the second location. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The combination would have provided the predictable result of allowing the healthcare-facility location system of Lee et al. to receive updated location information when a signaling device or associated user moved to a different location. Iagnemma et al. expressly teach receiving successive signals after the rider moves and updating the precise goal location with time, thereby providing a specific reason to implement successive signaling and location determination in the healthcare-facility system.
With respect to the second scan, Kim et al. specifically disclose processing a baseline point cloud and successive input point clouds captured by a three-dimensional scanner and generating difference information by comparing the input data with the baseline data. See Kim et al., col. 4, lines 28-45 and col. 7, lines 48-56. Lee et al. expressly disclose use of a LiDAR device for location determination within a healthcare facility. See Lee et al., para. [0031].
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the baseline-versus-input LiDAR difference processing of Kim et al. to successive LiDAR scans of the healthcare facility system of Lee et al., including a second scan corresponding to the subsequent signaling event disclosed by Iagnemma et al. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The resulting second input scan would be compared with the previously obtained baseline scan to identify spatial differences associated with the changed position of the signaling device. Iagnemma et al. supply the signaling-device location information, Lee et al. supply the healthcare-facility LiDAR location environment, and Kim et al. supply the specific baseline/input difference-processing technique.
With respect to determining the second position of interest, Iagnemma et al. disclose determining a precise goal location from the received location indication signal, including determining the precise location of the signaling device. See Iagnemma et al., paras. [0076] and [0090]. Lee et al. disclose correlating determined three-dimensional positions with healthcare-facility information identifying points of interest. See Lee et al., para. [0054].
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to associate the subsequently determined signaling-device location with the corresponding point of interest in the three-dimensional healthcare-facility model of Lee et al., thereby determining a second position of interest corresponding to the second virtual location of the signaling device. It would be an implementation of use of know techniques to improve similar device in the same way.
The combination therefore provides a specific teaching and rationale for successively determining locations and corresponding positions of interest: Iagnemma et al. teach repeated signaling and updated precise locations, Lee et al. teach healthcare-facility three-dimensional locations and points of interest, and Kim et al. teach baseline-versus-input three-dimensional scanner difference processing.
Accordingly, the combined teachings of Iagnemma et al., Lee et al., and Kim et al. teach or suggest each limitation of claim 14, including moving the signaling device to a second location, receiving a subsequent location indication signal, obtaining a subsequent LiDAR input relative to a baseline, determining the changed spatial location of the signaling device, and determining a corresponding second position of interest. Claim 14 is therefore unpatentable under 35 U.S.C. § 103.
Claim 16:
Claim 16 depends from claim 1 and further recites:
“tracking a patient location relative to the first position of interest within the patient space.”
As set forth above with respect to claim 1, Iagnemma et al. discloses location signaling using a signaling device and a receiver/stimulus detector to determine a precise location associated with the signaling device and a corresponding precise goal location. Iagnemma et al. describes a signaling device transmitting a location indication signal, a stimulus detector receiving the signal, and a location determination process using information from the signal to determine a precise goal location. See, e.g., Iagnemma et al., paras. [0047]-[0049], [0076], [0082]-[0083], [0087], [0112]-[0113]. Iagnemma et al. further discloses use of LiDAR for determining a distance and/or position associated with the signaling device. See Iagnemma et al., paras. [0063], [0121].
Lee et al. discloses a healthcare-facility location system for determining locations of patients, visitors, and devices within a healthcare facility. Lee et al. discloses that the location system can determine precise locations of entities within the healthcare facility, including locations of patients and devices, and can determine three-dimensional x, y, and z position information. See Lee et al., paras. [0031], [0038]. Lee et al. further discloses correlating such position measurements with facility information identifying physical structures, including points of interest, in three-dimensional space. See Lee et al., para. [0054].
Lee et al. additionally discloses continuously or regularly determining current location information for an entity relative to the physical space of the healthcare facility and monitoring changes in location information to track movement of the entity. Lee et al. expressly provides a patient tracking component 146 tailored to track information regarding the location and movement of a patient. See Lee et al., para. [0038]. Lee et al. further discloses that the tracking component monitors changes in location information to track movement of an entity about the healthcare facility and that current location information can be used to identify the entity's current position within the healthcare facility.
Lee et al. also discloses determining a patient's current location relative to an appointment location and, in certain implementations, determining whether the patient is located within a defined distance relative to that location. See Lee et al., paras. [0095], [0114]. Thus, Lee et al. teaches determining and tracking a patient's location relative to a designated location within a healthcare facility.
Kim et al. discloses LiDAR-based three-dimensional motion processing using a baseline point cloud and input point clouds. Kim et al. describes collecting LiDAR point clouds and computing voxel representations for a baseline point cloud and an input point cloud, followed by comparison/subtraction processing to identify differences between the baseline and input data. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses that LiDAR data may comprise a series of input point clouds forming a three-dimensional motion sequence and that baseline data are compared with input data to identify objects and their motion. See Kim et al., col. 4, lines 28-45; col. 7, lines 48-56.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 1, including establishing a position of interest from a signaled location using LiDAR-based spatial information, and further teaches tracking a patient's location within the healthcare facility relative to locations and points of interest represented in the healthcare-facility spatial model.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the patient-location tracking techniques of Lee et al. in the signaling-based spatial-location system of Iagnemma et al., as further implemented using the baseline-versus-input LiDAR processing of Kim et al., so that a patient's current location could be continuously monitored relative to an established position of interest within the healthcare environment.
It would be an implementation of use of know techniques to improve similar device in the same way.
Lee et al. expressly provides the healthcare-facility environment, patient-location determination, point-of-interest spatial model, and patient tracking functionality. Iagnemma et al. provides the signaling-device technique for establishing a precise location/goal location, while Kim et al. provides the baseline and subsequent LiDAR data comparison technique. Applying Lee et al.'s patient tracking functionality to the resulting healthcare spatial-location system would use each prior-art element for its disclosed function and would provide the predictable result of monitoring a patient's location relative to an established spatial position of interest.
A person of ordinary skill in the art would have had a reasonable expectation of success because Lee et al. already teaches determining and continuously updating an entity's location within a healthcare facility, tracking patient movement, and relating location information to physical structures and points of interest. Iagnemma et al. likewise provides an established technique for determining a precise signaled location, while Kim et al. provides established LiDAR baseline/input comparison processing. The proposed combination therefore would not require a change in the basic operation of these techniques, but rather their application together in the same healthcare spatial-location environment.
Claim 19 recites:
“A system for establishing virtual positions of interest in a patient space, comprising:
a processor;
a LiDAR scanner in electronic communication with the processor and configured to provide scans of the patient space;
a receiver in electronic communication with the processor;
wherein the processor is configured to:
obtain a baseline scan of the patient space from the LiDAR scanner;
obtain a first scan of the patient space when a trigger signal from a signaling device is received at the receiver;
determine a first virtual location of the signaling device within the patient space based on the difference between the first scan and the baseline scan; and
determine a first position of interest based on the first virtual location of the signaling device.”
Iagnemma et al. discloses a location-signaling system including a signaling device and a stimulus detector/receiver. The signaling device sends a location indication signal that is received by a stimulus detector and used to estimate the precise location of the signaling device. Iagnemma et al. further discloses that a processor performs a position determination process that computes the precise location of the signaling device or derives a precise goal location from the location indication signal. See Iagnemma et al., paras. [0047]-[0049], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further discloses that a precise goal location may be determined based on a location indication signal sent by a user or a device of the user. See Iagnemma et al., paras. [0145]-[0152]. The signaling device may transmit a location indication signal to a stimulus detector, and the system determines a precise location associated with the signaling device. See Iagnemma et al., paras. [0047]-[0049].
Iagnemma et al. additionally discloses LiDAR-based determination of spatial information associated with the signaling device. In particular, LiDAR sensors may emit light toward the signaling device and determine the distance between the autonomous vehicle and the signaling device based on the reflected LiDAR light. See Iagnemma et al., para. [0063]. Iagnemma et al. further describes processing information from LiDAR sensors to determine spatial information associated with a signaling device. See Iagnemma et al., para. [0121].
Lee et al. discloses a healthcare-facility location system configured to determine precise locations of entities within a healthcare facility. The location system determines the location of a client device or another signal-transmitting device and receives wireless signals from the device, including radio-frequency, optical, and acoustic signals, to facilitate determining the location of the device using ranging and/or angulating techniques. See Lee et al., para. [0031].
Lee et al. further expressly discloses determining the actual location of non-transmitting objects, including patients and visitors, using optical ranging and positioning devices such as a light detection and ranging (LiDAR) device. Lee et al. also discloses determining x, y, and z coordinate positions of a user or device within the healthcare facility. See Lee et al., para. [0031].
Lee et al. further discloses correlating x, y, and z position measurements with facility information identifying physical structures of the healthcare facility, including points of interest, rooms, halls, walls, floors, and other structures in three-dimensional space. See Lee et al., para. [0031]. The facility information may include a three-dimensional model of the healthcare facility. See Lee et al., para. [0031].
Lee et al. also discloses a processor-based healthcare-facility system having a location system, receiving component, client devices, and associated computer-implemented components that receive and process location information. The location system provides location information to the other system components for use in determining an entity's current location within the healthcare facility. See Lee et al., paras. [0038], [0095].
Thus, Iagnemma et al. and Lee et al. collectively disclose a processor-based healthcare spatial-location system having a LiDAR-based location system, a receiver for receiving a signal from a signaling/client device, and processing circuitry for determining a spatial location of the signaling device or other entity and associating the determined location with locations and points of interest in a three-dimensional healthcare-facility model.
Kim et al. further discloses obtaining three-dimensional LiDAR scans as point clouds and processing a baseline point cloud and an input point cloud. Kim et al. expressly describes collecting LiDAR point clouds from a three-dimensional scanner and computing a set of voxels for each of a baseline/background point cloud and an input point cloud. See Kim et al., col. 4, lines 28-32.
Kim et al. further discloses computing a ground-plane map for the baseline point cloud and comparing the baseline and input data to obtain foreground objects through background subtraction. See Kim et al., col. 4, lines 28-37.
Kim et al. further discloses comparing projection maps derived from the baseline and input point clouds and subtracting the baseline map from the input map to generate a difference map. See Kim et al., col. 4, lines 28-45. Kim et al. also discloses that LiDAR data may comprise a sequence of point clouds taken consecutively over time to form a three-dimensional motion sequence. See Kim et al., col. 4, lines 28-32.
Accordingly, Kim et al. teaches the claimed use of a baseline LiDAR scan and a subsequent LiDAR scan, with the difference between the baseline and subsequent scan being processed to identify spatial changes or objects represented by the subsequent scan.
The combination of Iagnemma et al., Lee et al., and Kim et al. therefore provides the claimed system architecture and processing operations. Iagnemma et al. supplies the signaling-device/receiver location-signaling relationship and LiDAR-based localization; Lee et al. supplies the healthcare-facility environment, processor-based location system, LiDAR location determination, three-dimensional spatial model, and points of interest; and Kim et al. supplies the baseline-versus-input LiDAR scan comparison and difference processing.
More particularly, the claimed “baseline scan” and “first scan” correspond to the baseline/background and input point clouds processed by Kim et al. The claimed signaling-device trigger received at the receiver corresponds to the location indication signal received from the signaling device as taught by Iagnemma et al. and Lee et al. The resulting difference information is used to identify the changed/spatially distinct location associated with the signaling device, while the healthcare-facility three-dimensional model and point-of-interest framework of Lee et al. provides the spatial context for determining a corresponding position of interest.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the baseline-versus-input LiDAR processing of Kim et al. in the signaling-device location system of Iagnemma et al., as implemented in the healthcare-facility spatial-location environment of Lee et al., so that the system could distinguish spatial changes represented in a subsequent LiDAR scan from the baseline environment and determine the corresponding spatial location of the signaled device within the healthcare facility.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The proposed combination uses each reference for the function expressly disclosed by that reference. Iagnemma et al. provides a signaling device and receiver/stimulus detector for identifying a precise spatial location. Lee et al. provides a healthcare-facility location system using LiDAR and a three-dimensional model containing points of interest. Kim et al. provides a baseline/input point-cloud comparison technique for identifying spatial differences using LiDAR data.
Applying Kim et al.'s baseline-versus-input LiDAR comparison to the location-signaling system of Iagnemma et al., within Lee et al.'s healthcare-facility location architecture, would predictably permit the system to distinguish the baseline patient-space environment from the environment represented by a subsequent scan and thereby determine the spatial location associated with the received signaling event.
A person of ordinary skill in the art would have had a reasonable expectation of success because the proposed combination does not require changing the basic operation of the individual systems. The LiDAR scanner would continue to acquire three-dimensional spatial data, the receiver would continue to receive a location indication signal from the signaling device, the processor would continue to process the received and scanned information, and the baseline/input comparison would continue to identify spatial differences between successive LiDAR datasets.
Claim 20:
Claim 20 depends from claim 19 and further recites:
“wherein the receiver is an RF receiver, and signaling the receiver is performed using an RF transmitter.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a processor-based location-signaling system including a signaling device and a stimulus detector/receiver. The signaling device transmits a location indication signal that is received by the stimulus detector and processed to determine a precise location associated with the signaling device. See Iagnemma et al., paras. [0047]-[0049], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further discloses wireless signaling between the signaling device and the stimulus detector. See, e.g., Iagnemma et al., paras. [0053], [0102], [0112]-[0113]. Iagnemma et al. also discloses LiDAR-based spatial localization of the signaling device. See Iagnemma et al., paras. [0063], [0121].
Lee et al. discloses a healthcare-facility location system that receives wireless signals from a client device to facilitate determining the location of the client device. The wireless signals expressly include radio-frequency (RF) signals, as well as optical and acoustic signals. See Lee et al., para. [0031].
Lee et al. further discloses communication components associated with a client device that include a transmitter, receiver, and/or transceiver for communicating with the healthcare-facility location system. See Lee et al., para. [0031]. Thus, Lee et al. expressly teaches an implementation in which a client/signaling device transmits an RF signal to a receiver of the healthcare-facility location system for location determination.
Lee et al. further discloses determining locations within a healthcare facility using LiDAR and determining x, y, and z coordinates of entities within the facility. See Lee et al., para. [0031]. Lee et al. additionally discloses correlating such three-dimensional position measurements with healthcare-facility information identifying points of interest and other physical structures in three-dimensional space. See Lee et al., para. [0054].
Kim et al. discloses obtaining three-dimensional LiDAR point clouds and processing a baseline point cloud and an input point cloud to identify differences between the baseline and input spatial data. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses processing a series of input point clouds to form a three-dimensional motion sequence and comparing input data with baseline data. See Kim et al., col. 4, lines 28-45; col. 7, lines 48-56.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 19. In addition, Lee et al. expressly teaches the added limitation of claim 20: an RF receiver receiving RF signals transmitted from a client device having transmitter functionality.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the RF signaling architecture expressly taught by Lee et al. in the signaling-device location system of Iagnemma et al., as implemented in the healthcare-facility and LiDAR spatial-location environment of Lee et al. and using the baseline-versus-input LiDAR processing of Kim et al.
It would be an implementation of simple substitution of one known element for another to obtain predictable results.
More particularly, Lee et al. expressly provides RF signaling as one of the communication modalities used for determining the location of a client device. Substituting the RF transmitter/receiver implementation expressly taught by Lee et al. for another wireless signaling implementation in the location-signaling system of Iagnemma et al. would retain the same basic function of transmitting a location indication signal from the signaling device to the receiver for location determination.
The RF transmitter and RF receiver would perform their respective disclosed functions of transmitting and receiving RF location signals. The use of RF signaling would therefore provide the predictable result of communicating the signaling-device location indication to the receiver in the healthcare-facility location system.
A person of ordinary skill in the art would have had a reasonable expectation of success because Lee et al. expressly demonstrates the use of RF signals and transmitter/receiver communication components for determining the location of a client device in a healthcare-facility location system. The proposed implementation would not require modification of the fundamental operation of the signaling, receiving, or LiDAR-location components.
Claim 21:
Claim 21 depends from claim 19 and further recites:
“wherein the receiver is a microphone.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a location-signaling system including a signaling device and one or more stimulus detectors for receiving a location indication signal from the signaling device. The received signal is processed to estimate the precise location of the signaling device and determine a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. expressly discloses auditory signaling and microphones as receiving stimulus detectors. In particular, Iagnemma et al. discloses that a signaling device may emit an uncommon sound or sequence of sounds that is detected by sensors mounted on the receiving vehicle. Iagnemma et al. further discloses that a microphone array may be used to detect the emitted sound and determine the bearing of the signaling device based on differences in detection time among the microphone-array sensor elements. See Iagnemma et al., discussion of auditory signaling and microphone-array detection.
Thus, Iagnemma et al. expressly teaches an implementation in which the receiver/stimulus detector is a microphone and receives a signal emitted by the signaling device.
Iagnemma et al. further discloses LiDAR-based spatial localization of the signaling device. LiDAR sensors may determine distance to the signaling device based on reflected LiDAR light, and captured LiDAR information may be processed to determine spatial information associated with a signaling device. See Iagnemma et al., paras. [0063], [0121].
Lee et al. discloses a healthcare-facility location system for determining locations of patients, visitors, and devices within a healthcare facility. Lee et al. discloses receiving wireless signals from a client device and determining the location of the client device. The location system further uses LiDAR for determining locations and determines three-dimensional x, y, and z coordinates within the healthcare facility. See Lee et al., para. [0031].
Lee et al. further discloses correlating the three-dimensional position measurements with healthcare-facility information identifying physical structures and points of interest in three-dimensional space. See Lee et al., para. [0054].
Kim et al. discloses obtaining three-dimensional LiDAR point clouds and processing a baseline point cloud and an input point cloud to identify differences between the baseline and input spatial data. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses processing a series of input point clouds to form a three-dimensional motion sequence and comparing input data with baseline data. See Kim et al., col. 4, lines 28-45; col. 7, lines 48-56.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 19 and further teaches the added limitation of claim 21, namely, a receiver implemented as a microphone for receiving a signal from the signaling device.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the microphone-based receiving technique expressly disclosed by Iagnemma et al. in the signaling-device location system of Iagnemma et al., as implemented in the healthcare-facility spatial-location environment of Lee et al. and using the baseline-versus-input LiDAR processing of Kim et al.
It would be an implementation of use of know techniques to improve similar device in the same way.
Iagnemma et al. expressly teaches use of microphones as stimulus detectors for receiving signaling-device signals and determining spatial information from the received signals. Lee et al. provides a healthcare-facility location system using receivers, LiDAR, three-dimensional spatial coordinates, and points of interest. Kim et al. provides baseline-versus-input LiDAR processing for identifying spatial differences.
Applying the microphone-based receiving technique of Iagnemma et al. within the healthcare-facility location system of Lee et al., while using the LiDAR baseline/input comparison of Kim et al., would use each element for its disclosed function and would provide the predictable result of receiving a signaling-device location indication through a microphone while determining the associated spatial location within the healthcare facility.
A person of ordinary skill in the art would have had a reasonable expectation of success because Iagnemma et al. expressly demonstrates microphone-based reception and spatial localization of a signaling device. The microphone would perform its disclosed function of detecting the signaling-device signal, while the LiDAR and spatial-location components would perform their respective disclosed functions.
Claim 22:
Claim 22 depends from claim 19 and further recites:
“further comprising a signaling device operable to provide a signal to the receiver.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a location-signaling system including a signaling device and one or more stimulus detectors for receiving a location indication signal from the signaling device. Iagnemma et al. further discloses determining a precise location of the signaling device and determining a corresponding precise goal location based on the location-signaling activity. See Iagnemma et al., paras. [0047]-[0049], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. expressly discloses the added limitation of claim 22. In particular, Iagnemma et al. defines a location indication signal as a signal sent by a signaling device and received by stimulus detectors, the signal being used to estimate the precise location of the signaling device. See Iagnemma et al., para. [0053]. Iagnemma et al. further discloses that a signaling device may be a mobile device, smartphone, tablet, smart wearable device, or other portable signaling device, and that a signal-broadcasting process running on the signaling device broadcasts a location indication signal. See Iagnemma et al., paras. [0047]-[0049] and [0053].
Iagnemma et al. further discloses that the location indication signal may be received by a stimulus detector and processed by a location determination process to determine information associated with the signaling device, including bearing and distance, for determining a precise goal location. See Iagnemma et al., paras. [0054], [0076], [0112]-[0113].
Accordingly, Iagnemma et al. expressly teaches a signaling device operable to provide a signal to a receiving detector, corresponding to the signaling device and receiver recited in claim 22.
Lee et al. discloses a healthcare-facility location system configured to determine precise locations of people, objects, and devices relative to a healthcare facility. Lee et al. discloses that the location system communicates with a client device or other signal-transmitting device to receive wireless signals from the device and determine its location. Lee et al. further discloses use of LiDAR to determine locations within the healthcare facility and correlation of x, y, and z position measurements with facility information identifying points of interest and other physical structures in three-dimensional space. See Lee et al., para. [0031] and para. [0054].
Kim et al. discloses collecting three-dimensional LiDAR point clouds and processing a baseline point cloud and an input point cloud captured from a LiDAR scanner. Kim et al. computes a difference between the baseline and input spatial information to identify changes or objects in the scene. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses processing a sequence of LiDAR point clouds and generating object tracks from the detected changes over time. See Kim et al., col. 7, lines 48-56.
Thus, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 19 and expressly teaches the added limitation of claim 22, namely, a signaling device operable to provide a signal to the receiver.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the signaling-device architecture expressly disclosed by Iagnemma et al. in the healthcare-facility location system of Lee et al., while using the baseline-versus-input LiDAR processing of Kim et al.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Iagnemma et al. expressly teaches using a signaling device to provide a location indication signal to a receiving detector for determining the location of the signaling device. Lee et al. provides a healthcare-facility location system in which signals from client devices are received and used for spatial localization, including localization using LiDAR and three-dimensional facility information. Kim et al. provides the baseline-versus-input LiDAR processing used to identify spatial differences.
Applying the signaling-device architecture of Iagnemma et al. to the healthcare-facility location system of Lee et., while employing the baseline-versus-input LiDAR processing of Kim et al., would use the respective elements for their disclosed functions and would provide the predictable result of receiving a signal from a signaling device and using the received signal and LiDAR spatial information to determine a corresponding location within the healthcare facility.
A person of ordinary skill in the art would have had a reasonable expectation of success because Iagnemma et al. expressly demonstrates the operation of a signaling device that transmits a location indication signal to a receiving detector for location determination, while Lee et al. expressly demonstrates signal-based and LiDAR-based location determination within a healthcare facility.
Claim 26:
Claim 26 depends from claim 19 and further recites:
“wherein the first position of interest is centered on the first location.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a location-signaling system including a signaling device, a receiver or stimulus detector, determination of a precise location of the signaling device, and determination of a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0053]-[0054], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further discloses that the precise location of the signaling device may be determined and that the system may set the precise location of the signaling device as the precise goal location. See Iagnemma et al., paras. [0082]-[0083].
Iagnemma et al. also expressly identifies the bearing of the signaling device with the center of the signaling device. See Iagnemma et al., para. [0087].
Accordingly, under an interpretation in which “the first location” refers to the location of the signaling device, Iagnemma et al. discloses that the location corresponding to the signaling device, including its center, is used as the precise goal location. Thus, the claimed relationship in which the first position of interest is centered on the location of the signaling device is taught by Iagnemma et al.
Lee et al. discloses a healthcare-facility location system configured to determine locations of entities, including devices, within a healthcare facility. Lee et al. discloses receiving signals from a client device to determine the location of the client device and further discloses LiDAR-based location determination. Lee et al. further discloses correlating x, y, and z position measurements with facility information identifying points of interest and other physical structures within the healthcare facility in three-dimensional space. See Lee et al., paras. [0031], [0054].
Kim et al. discloses obtaining three-dimensional LiDAR point clouds and processing a baseline point cloud and an input point cloud to determine spatial differences between the baseline and input data. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses processing a series of input point clouds to form a three-dimensional motion sequence and processing differences between baseline and input spatial data. See Kim et al., col. 7, lines 48-56.
Thus, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 19 and further teaches the claimed spatial relationship between the position of interest and the location of the signaling device.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the signaling-device-centered location relationship expressly disclosed by Iagnemma et al. in the healthcare-facility spatial-location system of Lee et al., while using the baseline-versus-input LiDAR processing of Kim et al.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Iagnemma et al. expressly teaches determining the precise location of a signaling device and using that precise location as the precise goal location, including identifying the center of the signaling device as a positional reference. Lee et al. provides a healthcare-facility environment in which device locations are determined and correlated with three-dimensional facility information and points of interest. Kim et al. provides baseline-versus-input LiDAR processing for determining spatial differences.
Applying the signaling-device-centered location determination of Iagnemma et al. within the healthcare-facility spatial-location system of Lee et., while using the baseline-versus-input LiDAR processing of Kim et al., would maintain the respective functions of the signaling, spatial-location, and LiDAR components and would predictably establish a position of interest corresponding to the location of the signaling device.
A person of ordinary skill in the art would have had a reasonable expectation of success because Iagnemma et al. expressly demonstrates determining a precise signaling-device location and setting that location as a precise goal location. Lee et al. expressly demonstrates location determination and POI correlation within a healthcare facility, while Kim et al. expressly demonstrates baseline-versus-input LiDAR difference processing.
Claim 27:
Claim 27 depends from claim 19 and further recites:
“wherein the first position of interest is offset from the first location.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a location-signaling system including a signaling device, a receiver or stimulus detector, determination of a precise location associated with the signaling device, and determination of a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0053]-[0054], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. expressly discloses that the precise goal location may be different from the actual precise location of the rider or signaling device. Iagnemma et al. describes a user signaling a precise location that may be the user's actual precise location or a different precise location nearby, with the selected location being termed the precise goal location. See Iagnemma et al., para. [0030] and corresponding description.
Iagnemma et al. further expressly discloses an embodiment in which a precise goal location is determined from an intersection point and the precise goal location is identified as the intersection point adjusted by a fixed offset distance. The fixed offset is used to offset the precise pickup location from the edge of the drivable road surface. See Iagnemma et al., para. [approximately corresponding to the offset-location embodiment].
Accordingly, under an interpretation in which “the first location” refers to the location associated with the signaling device, Iagnemma et al. expressly teaches the claimed spatial relationship in which the resulting precise goal location is offset from that location.
Iagnemma et al. further discloses that the location indication signal is used to estimate the precise location of the signaling device and that the AV system may use the resulting location information to determine the precise goal location. See Iagnemma et al., paras. [0053], [0076], [0082]-[0083], [0112]-[0113].
Lee et al. discloses a healthcare-facility location system configured to determine locations of entities within a healthcare facility. Lee et al. discloses receiving signals from a client device and determining the location of the client device, as well as using LiDAR to determine locations and three-dimensional x, y, and z coordinates. Lee et al. further discloses correlating such spatial measurements with facility information identifying points of interest and other physical structures within the healthcare facility. See Lee et al., paras. [0031] and [0054].
Kim et al. discloses obtaining three-dimensional LiDAR point clouds and processing a baseline point cloud and an input point cloud to determine spatial differences between the baseline and input data. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses processing a series of input point clouds and forming three-dimensional motion data based on the series. See Kim et al., col. 7, lines 48-56.
Thus, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 19 and further teaches the claimed offset relationship between the position of interest and the location of the signaling device.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the offset-location technique expressly disclosed by Iagnemma et al. in the healthcare-facility spatial-location system of Lee et al., while using the baseline-versus-input LiDAR processing of Kim et al.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Iagnemma et al. expressly teaches determining a precise goal location that may be different from the actual location of the signaling device or rider and further expressly teaches adjusting a location by a fixed offset distance to establish the precise goal location. Lee et al. provides a healthcare-facility spatial-location environment in which device locations and points of interest are represented in three-dimensional space. Kim et al. provides baseline-versus-input LiDAR processing for determining spatial differences.
Applying the offset-location technique of Iagnemma et al. within the healthcare-facility spatial-location system of Lee et., while employing the baseline-versus-input LiDAR processing of Kim et al., would use the respective components for their disclosed purposes and would predictably permit a position of interest to be established at a selected offset from the signaling-device location.
A person of ordinary skill in the art would have had a reasonable expectation of success because Iagnemma et al. expressly demonstrates determining a precise goal location that is spatially offset from another reference location. Lee et al. expressly demonstrates spatial location determination and points-of-interest representation in a healthcare facility, while Kim et al. expressly demonstrates baseline-versus-input LiDAR difference processing.
Claim 32:
Claim 32 depends from claim 19 and further recites:
“wherein the processor is further configured to:
obtain a second scan of the patient space when a second signal is received at the receiver;
determine a second virtual location of the signaling device within the patient space based on a difference between the first scan and the baseline scan; and
determine a second position of interest based on the second virtual location of the signaling device.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a location-signaling system in which a signaling device transmits a location indication signal that is received by a stimulus detector and processed to determine a precise location of the signaling device and a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0053]-[0054], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further expressly discloses repeated signaling and repeated location determination. In particular, Iagnemma et al. discloses that while a rider is broadcasting a location indication signal, the rider may move, and the autonomous vehicle may receive a series of location indication signals and consequently update the precise location of the user or signaling device and the precise goal location over time. See Iagnemma et al., para. [0090].
Thus, Iagnemma et al. specifically teaches receiving successive signaling events from a signaling device and using the successive signals to obtain successive location estimates and update the resulting precise goal location.
Kim et al. expressly discloses the corresponding repeated LiDAR spatial-data processing. Kim et al. discloses that three-dimensional motion data may include a series of input point clouds forming a three-dimensional motion sequence. Kim et al. further discloses processing a baseline point cloud and an input point cloud captured from a three-dimensional scanner and comparing the corresponding spatial representations to generate difference information. See Kim et al., col. 4, lines 28-45.
Kim et al. further discloses that the three-dimensional motion data comprises a sequence of three-dimensional point clouds taken over time and that moving objects are detected by subtracting the baseline background point cloud from an input point cloud. See Kim et al., col. 7, lines 48-56.
Accordingly, Kim et al. expressly teaches obtaining successive LiDAR scans or input point clouds and processing those successive scans using a baseline spatial representation and difference processing.
Lee et al. discloses the healthcare-facility spatial environment in which such location determinations may be performed. Lee et al. discloses a location system that communicates with client devices to receive signals and determine locations, and further discloses use of LiDAR to determine locations within a healthcare facility. Lee et al. further discloses determining x, y, and z coordinates and correlating the resulting spatial measurements with facility information identifying points of interest and other physical structures in three-dimensional space. See Lee et al., paras. [0031], [0054].
Thus, Iagnemma et al. provides the successive signaling and successive precise-location determinations; Kim et al. provides the successive LiDAR scans and baseline-versus-input difference processing; and Lee et al. provides the healthcare-facility three-dimensional spatial and point-of-interest framework.
The references do not expressly state that the second LiDAR scan is initiated at the precise instant that a second signaling-device signal is received. However, Iagnemma et al. expressly teaches a series of location indication signals producing successive location estimates, while Kim et al. expressly teaches a series of input point clouds producing successive spatial-difference determinations. These disclosures provide a specific technical basis for correlating a successive signaling event with a corresponding successive spatial scan and location determination.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the successive-signal location-update technique of Iagnemma et al. together with the successive LiDAR-scan and baseline-difference processing of Kim et al. in the healthcare-facility spatial-location system of Lee et al., such that a further signaling event would cause the system to obtain corresponding updated spatial information and determine an updated position of interest.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Iagnemma et al. expressly teaches receiving a series of location indication signals and updating the precise location and precise goal location over time. Kim et al. expressly teaches obtaining a series of LiDAR point clouds and comparing successive input point clouds with baseline spatial data to determine differences. Lee et al. expressly teaches determining three-dimensional locations and correlating those locations with points of interest in a healthcare facility.
Applying the successive-signal processing of Iagnemma et al. to the successive LiDAR spatial-data processing of Kim et al. within the healthcare-facility location framework of Lee et al. would provide updated spatial information when successive signaling events are received. The resulting processing would use the baseline spatial representation and successive scan data to determine an updated virtual location of the signaling device and an associated position of interest.
The modification would use the respective elements for their disclosed purposes: Iagnemma et al.'s signaling device would provide successive location indication signals; Kim et al.'s LiDAR system would provide successive spatial scans and baseline-versus-input difference processing; and Lee et al.'s healthcare-facility location system would provide the three-dimensional spatial and point-of-interest framework.
A person of ordinary skill in the art would have had a reasonable expectation of success because each individual operation is expressly demonstrated in the cited references: successive signaling and location updating by Iagnemma et al., successive LiDAR scanning and baseline-difference processing by Kim et al., and three-dimensional healthcare-facility location and point-of-interest correlation by Lee et al.
Claim 11 and 29 are rejected under 35 U.S.C. § 103 as being unpatentable over Iagnemma et al., (US 2018/0196417 A1), in view of Lee et al. (U.S. 2019/0108909 A1), further in view of Kim et al. (U.S. 8,948,501 B1), and further in view of Fredrickson et al. (U.S. 2021/0093407 A1).
Claim 11 depends from claim 1 and further recites that “the first position of interest is flagged as a keep out zone.”
As discussed with respect to claim 1, Iagnemma et al. disclose a location-signaling technique in which a signaling device transmits a location indication signal that is received by a stimulus detector and used to estimate the precise location of the signaling device. See Iagnemma et al., paras. [0033] and [0054]. Iagnemma et al. further disclose determining a precise goal location based on the location-signaling activity. See paras. [0014]-[0015] and [0112]-[0113]. Iagnemma et al. also disclose LiDAR-based spatial determination. See paras. [0063] and [0121].
Lee et al. disclose a healthcare-facility location system in which wireless signals from a client device are received and used to determine the location of the client device, including through radio-frequency, optical, and acoustic signals. Lee et al. further disclose optical ranging using LiDAR and determining x, y, and z position information in a healthcare facility. See Lee et al., para. [0031]. Lee et al. additionally disclose correlating such three-dimensional position measurements with facility information identifying physical structures and points of interest. See Lee et al., para. [0054].
Kim et al. disclose comparing a baseline LiDAR point cloud with an input point cloud and performing subtraction/difference processing to identify changes between the baseline and input data. See Kim et al., col. 4, lines 28-45 and col. 7, lines 48-56.
With respect to the additional limitation of claim 11, Fredrickson et al. disclose a robotic medical system in which a user can define or select a three-dimensional region within a medical-system model to create a “keep-out zone.” Fredrickson et al. explain that the system can allow a user to define one or more three-dimensional regions near the system model within which equipment is to be placed, thereby creating a keep-out zone, and that the robotic system restricts movement of robotic arms into the keep-out zones. See Fredrickson et al., U.S. 2021/0093407 A1, paragraphs describing “Keep-Out Zones for Non Pre-Modeled Objects.”
Fredrickson et al. further expressly disclose that the system receives an indication of one or more keep-out zones based on user input, updates the model to include the keep-out zones, and prevents movement of robotic arms into the keep-out zones. See Fredrickson et al., FIG. 26 and accompanying description. The published application also expressly discloses constructing a dynamic model from a LiDAR sensor. See Fredrickson et al., FIG. 27 and accompanying description.
Thus, Fredrickson et al. specifically disclose designating a spatial region within a medical-system model as a keep-out zone. This disclosure supplies the claimed function of flagging a determined spatial position/region as a keep-out zone.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the keep-out-zone designation technique of Fredrickson et al. to the precise location/position-of-interest system of Iagnemma et al., as implemented in the healthcare-facility spatial environment of Lee et al., such that a determined position of interest could be designated as a keep-out zone. It would be an implementation of use of know techniques to improve similar device in the same way.
The combination would have provided the predictable benefit of allowing a location selected or identified through the signaling-device and spatial-localization techniques of Iagnemma et al. and Lee et al. to be assigned a safety-related spatial restriction, as expressly taught by Fredrickson et al. for a medical system. Fredrickson et al. specifically use keep-out zones to restrict movement within a medical-system workspace, making the application of that same spatial restriction to a selected position of interest in a healthcare environment a predictable implementation of the disclosed technique.
Further, it would have been obvious at the time the invention before the effective filing date of the claim invention was made to use the baseline-versus-input LiDAR difference processing of Kim et al. with the combined Iagnemma et al., Lee et al., and Fredrickson et al. system to determine changes in the scanned patient-space environment and spatially locate the signaling device. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Kim et al. specifically disclose baseline and input LiDAR point-cloud comparison and difference processing. See Kim et al., col. 4, lines 28-45 and col. 7, lines 48-56. Applying that disclosed processing to the LiDAR scans of the healthcare environment would provide a predictable mechanism for identifying spatial changes associated with the signaling device.
Accordingly, the combined teachings of Iagnemma et al., Lee et al., Kim et al., and Fredrickson et al. teach or suggest each limitation of claim 11, including determining a position of interest from the location of a signaling device and designating that position of interest as a keep-out zone.
Claim 29 depends from claim 19 and further recites:
“wherein the first position of interest is flagged as a keep out zone.”
As set forth above with respect to claim 19, Iagnemma et al. discloses a location-signaling system including a signaling device, a receiver or stimulus detector, determination of a precise location of the signaling device, and determination of a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0053]-[0054], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further discloses that the precise location of a signaling device may be estimated from a location indication signal received by a stimulus detector and that the system may set the precise location of the signaling device or rider as the precise goal location. See Iagnemma et al., paras. [0076], [0082]-[0083], [0112]-[0113].
Lee et al. discloses a healthcare-facility location system configured to determine locations within a healthcare facility. Lee et al. discloses receiving signals from client devices and determining their locations, including through use of LiDAR. Lee et al. further discloses determining three-dimensional x, y, and z coordinates and correlating those spatial measurements with facility information identifying physical structures and points of interest in three-dimensional space. See Lee et al., paras. [0031], [0054].
Thus, Iagnemma et al. and Lee et al. teach or suggest determining a spatial location associated with a signaling device within a healthcare-facility environment and associating the determined spatial location with a position of interest.
Kim et al. discloses collecting three-dimensional LiDAR point clouds and processing a baseline point cloud and an input point cloud captured from a 3D scanner. Kim et al. computes spatial differences between the baseline and input data, including by comparing projection maps generated from the baseline and input point clouds. See Kim et al., col. 4, lines 28-45.
Kim et al. further discloses that the 3D motion data may include a series of input point clouds and that differences between baseline and input data are used to identify objects and spatial changes. See Kim et al., col. 7, lines 48-56.
Accordingly, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests the limitations of claim 19.
Fredrickson et al. expressly discloses the additional limitation of claim 29. Fredrickson et al. describes a robotic medical system in which a user can define one or more three-dimensional regions near a system model in which equipment is intended to be placed, thereby creating a keep-out zone. The system restricts robotic arms from entering the keep-out zones. See Fredrickson et al., para. [0143].
Fredrickson et al. further discloses that a medical accessory model can constitute a keep-out zone and that the keep-out zone may have various geometric configurations. See Fredrickson et al., para. [0144].
Fredrickson et al. further describes a method in which the system receives an indication of one or more keep-out zones based on user input, updates the system model to include the keep-out zones, and prevents movement of robotic arms into the keep-out zones. See Fredrickson et al., paras. [0145]-[0148].
Fredrickson et al. also discloses a medical robotic system using sensors to generate a dynamic model of the operating environment, including a dynamic model constructed from a LiDAR sensor. See Fredrickson et al., paras. [0150]-[0153].
Accordingly, Fredrickson et al. expressly teaches designating a spatial region associated with a medical environment as a keep-out zone and preventing equipment from entering that zone. This corresponds to the added limitation of flagging the first position of interest as a keep-out zone.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement the keep-out-zone technique expressly disclosed by Fredrickson et al. in the spatial position-of-interest system of Iagnemma et al., as applied within the healthcare-facility environment of Lee et al. and using the LiDAR baseline-versus-input processing of Kim et al.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Iagnemma et al. provides the signaling-device-based determination of a precise spatial location and corresponding goal location. Lee et al. provides a healthcare-facility environment in which spatial locations and points of interest are represented in three-dimensional space. Kim et al. provides baseline-versus-input LiDAR processing for identifying spatial differences. Fredrickson et al. expressly provides the use of a designated three-dimensional region as a keep-out zone in a medical robotic environment.
Applying the keep-out-zone technique of Fredrickson et al. to a position of interest established by the signaling-device and spatial-location techniques of Iagnemma et al., within the healthcare-facility spatial framework of Lee et al. and using the LiDAR difference processing of Kim et al., would allow a determined position of interest to be designated as a spatial region that equipment or another controlled object is prevented from entering.
The proposed combination would use the respective teachings of the references for their disclosed purposes. The signaling device would provide location information; the spatial-location system would determine and represent the corresponding location; the LiDAR processing would determine spatial differences; and the keep-out-zone functionality would designate the resulting spatial region as an area to be avoided.
A person of ordinary skill in the art would have had a reasonable expectation of success because Fredrickson et al. expressly demonstrates implementation of keep-out zones in a medical robotic environment, including three-dimensional keep-out regions and LiDAR-based environmental modeling, while Iagnemma et al., Lee et al., and Kim et al. expressly demonstrate the respective signaling, healthcare spatial-location, and LiDAR processing techniques.
Claim 15 and 33 are rejected under 35 U.S.C. § 103 as being unpatentable over Iagnemma et al., (U.S. 2018/0196417 A1), in view of Lee et al., (U.S. 2019/0108909 A1), further in view of Kim et al., (U.S. 8,948,501 B1), and further in view of Boyer et al., (U.S. 2020/0342026 A1).
Claim 15 depends from claim 14 and further recites “merging the first position of interest and the second position of interest.”
As discussed with respect to claim 14, Iagnemma et al. disclose a signaling device that transmits location indication signals to a receiver or stimulus detector, where the signals are processed to determine the precise location of the signaling device and a corresponding precise goal location. See Iagnemma et al., paras. [0033], [0054], [0076], and [0112]-[0113].
Iagnemma et al. further disclose that the rider may move while broadcasting a location indication signal and that an autonomous vehicle receiving the signal may receive a series of location indication signals and update the precise goal location with time. See Iagnemma et al., para. [0090]. Thus, Iagnemma et al. disclose successive signaling events associated with successive locations of the signaling device.
Lee et al. disclose applying location determination techniques in a healthcare-facility environment. Lee et al. disclose receiving wireless signals from a client device and determining the location of the client device, including using optical ranging and a LiDAR device. See Lee et al., para. [0031]. Lee et al. further disclose determining x, y, and z coordinates and correlating those position measurements with facility information identifying physical structures and points of interest within the healthcare facility. See Lee et al., para. [0054].
Kim et al. disclose processing LiDAR-derived three-dimensional data using a baseline point cloud and input point clouds. Kim et al. disclose generating voxels for the baseline and input point clouds and performing difference processing between the corresponding data. See Kim et al., col. 4, lines 28-45. Kim et al. further disclose a series of input point clouds forming a three-dimensional motion sequence and difference-map processing based on comparison with baseline data. See Kim et al., col. 7, lines 48-56.
Accordingly, for the limitations inherited from claim 14, the combination of Iagnemma et al., Lee et al., and Kim et al. teaches or suggests moving the signaling device to a second location, receiving a subsequent signaling event, obtaining a subsequent LiDAR scan, determining the changed spatial location of the signaling device using baseline-versus-input LiDAR processing, and determining a corresponding second position of interest.
With respect to the additional limitation of claim 15, Boyer et al., U.S. Patent Application Publication No. 2020/0342026 A1, specifically disclose methods and systems for merging point-of-interest data. Boyer et al. disclose identifying related point-of-interest data records, clustering related point-of-interest records into groups, and generating merged point-of-interest data records based on the clustered groups. See Boyer et al., paras. [0183]-[0194].
In particular, Boyer et al. disclose that a first cluster group may contain two point-of-interest data records and that a merge facility generates a merged point-of-interest data record based on the point-of-interest data records contained in that first cluster group. See Boyer et al., paras. [0207]-[0214]. Boyer et al. further disclose that data from both point-of-interest data records may be combined to form the merged point-of-interest data record. See Boyer et al., paras. [0267]-[0275].
Boyer et al. therefore specifically disclose combining two point-of-interest records into a single merged point-of-interest record. This provides specific documentary support for the claimed operation of merging a first position of interest and a second position of interest.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the point-of-interest merging technique of Boyer et al. to the first and second positions of interest generated by the location-signaling and healthcare-facility spatial system of Iagnemma et al. and Lee et al. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The reason for the combination is supported by the fact that Lee et al. expressly represent healthcare-facility locations as points of interest within a three-dimensional facility model. See Lee et al., para. [0054]. Boyer et al. expressly provide a technique for combining related point-of-interest records into a merged point-of-interest record. Applying that point-of-interest merging technique to two spatial positions of interest generated during successive location-signaling operations would provide the predictable result of representing the related positions of interest as a merged position-of-interest representation.
The combination would further have provided the benefit of reducing or consolidating multiple spatial position-of-interest representations when the first and second positions of interest correspond to the same or related location within the healthcare facility. Boyer et al. specifically teach identifying related point-of-interest records and combining them into a merged point-of-interest record.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to use the baseline-versus-input LiDAR difference processing of Kim et al. with the successive location determinations of Iagnemma et al. and the healthcare-facility spatial model of Lee et al. to generate the first and second spatial positions of interest before applying the point-of-interest merging operation of Boyer et al. It would be an implementation of use of know techniques to improve similar device in the same way.
The references are compatible because Iagnemma et al. provide successive signaling-device location determinations, Lee et al. provide a healthcare-facility three-dimensional spatial model containing points of interest, Kim et al. provide baseline-versus-input LiDAR difference processing, and Boyer et al. provide a specific technique for combining multiple point-of-interest records into a merged point-of-interest record.
Accordingly, the combined teachings of Iagnemma et al., Lee et al., Kim et al., and Boyer et al. teach or suggest each limitation of claim 15, including generating first and second positions of interest from successive signaling-device locations and merging the first and second positions of interest.
Claim 33 depends from claim 32 and further recites:
“wherein the processor is further configured to merge the first position of interest and the second position of interest.”
As set forth with respect to claims 19 and 32, Iagnemma et al. discloses a signaling-device location system in which a signaling device provides a location indication signal to a stimulus detector and the system determines a precise location of the signaling device and a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0053]-[0054], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further discloses successive location indication signals and corresponding successive updates of the precise goal location. In particular, Iagnemma et al. discloses that a rider may move while broadcasting a location indication signal and that the system may receive a series of location indication signals and update the precise goal location over time. See Iagnemma et al., para. [0090].
Lee et al. discloses a healthcare-facility location system in which locations are determined in three-dimensional space and correlated with facility information identifying physical structures and points of interest within the healthcare facility. Lee et al. further discloses LiDAR-based location determination and three-dimensional spatial coordinates. See Lee et al., paras. [0031], [0054].
Kim et al. discloses baseline and input three-dimensional point clouds obtained from a three-dimensional scanner and comparison of the input spatial data with baseline spatial data to generate difference information. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses a series of input point clouds forming a three-dimensional motion sequence. See Kim et al., col. 7, lines 48-56.
As explained with respect to claim 32, the combination of Iagnemma et al., Lee et al., and Kim et al. provides a specific technical basis for determining successive spatial locations and corresponding positions of interest from successive signaling events and LiDAR spatial data.
Boyer et al. specifically discloses computer-implemented systems for merging point-of-interest data. Boyer et al. discloses a system that identifies matching point-of-interest data records, clusters related point-of-interest data records into groups, and generates one or more merged point-of-interest data records based on the clustered groups. See Boyer et al., paras. [0002]-[0005], [0020]-[0025].
More specifically, Boyer et al. discloses a first cluster group containing two point-of-interest data records and a merge facility configured to generate a merged point-of-interest data record based on the two point-of-interest data records. See Boyer et al., paras. [0039]-[0041]. Boyer et al. further discloses that the merge facility may use data from both point-of-interest data records to generate a single merged point-of-interest data record. See Boyer et al., paras. [0048]-[0050].
Thus, Boyer et al. expressly teaches merging two related point-of-interest records into a single merged point-of-interest record.
Boyer et al. does not expressly disclose that the two point-of-interest records being merged are first and second positions of interest generated from successive signaling-device locations determined using LiDAR in a patient space. That particular combination is not disclosed by Boyer et al. Rather, Boyer et al. is relied upon for its express teaching of merging multiple related point-of-interest records into a single merged point-of-interest record.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the POI-merging technique of Boyer et al. to the successive positions of interest generated by the signaling-device and LiDAR location system of Iagnemma et al., Lee et al., and Kim et al., such that first and second positions of interest corresponding to successive spatial determinations could be combined into a merged position-of-interest representation.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The proposed modification would use each reference for its disclosed function. Iagnemma et al. provides successive signaling-device location determinations and corresponding precise goal locations; Lee et al. provides the healthcare-facility three-dimensional spatial and point-of-interest framework; Kim et al. provides baseline-versus-input LiDAR spatial-difference processing; and Boyer et al. provides the expressly disclosed technique of combining multiple related point-of-interest records into a single merged point-of-interest record.
A person of ordinary skill in the art would have had a reasonable expectation of success because the proposed modification would not require a new or unpredictable operating principle. The system would retain the successive spatial-location determination already provided by Iagnemma et al., Lee et al., and Kim et al., and would apply the disclosed POI-merging operation of Boyer et al. to the resulting POI records.
The motivation to perform the combination would include consolidating multiple related or successive point-of-interest records into a single spatial representation. Boyer et al. expressly identifies the purpose of its merging operation as combining related point-of-interest records and generating a merged point-of-interest record for use by a mapping system. See Boyer et al., paras. [0002]-[0005], [0020]-[0025].
Accordingly, applying Boyer et al.'s POI-merging technique to first and second positions of interest generated by the system of Iagnemma et al., Lee et al., and Kim et al. would have predictably produced a merged position-of-interest representation while retaining the underlying spatial-location functionality.
Claims 17 and 34 are rejected under 35 U.S.C. §103 as being unpatentable over Iagnemma et al., (US 2018/0196417 A1), in view of Lee et al., (US 2019/0108909 A1), further in view of Kim et al., (US 8,948,501 B1), and further in view of Kusens, (US 2017/0091562 A1).
Claim 17 depends from claim 16 and further recites:
“providing an alert if the patient location is determined to be within the first position of interest.”
As set forth above with respect to claim 16, Iagnemma et al. discloses a signaling device transmitting a location indication signal to a stimulus detector and determining a precise location associated with the signaling device and a corresponding precise goal location. See, e.g., Iagnemma et al., paras. [0047]-[0049], [0076], [0082]-[0083], [0087], [0112]-[0113]. Iagnemma et al. further discloses use of LiDAR for determining spatial information associated with the signaling device. See Iagnemma et al., paras. [0063], [0121].
Lee et al. discloses a healthcare-facility location system for determining locations of patients and other entities within a healthcare facility. Lee et al. discloses determining three-dimensional position information and correlating such position measurements with facility information identifying physical structures and points of interest in three-dimensional space. See Lee et al., paras. [0031], [0038], [0054].
Lee et al. further discloses a tracking component that monitors changes in location information to track movement of an entity about a healthcare facility. The tracking component includes a patient tracking component tailored to track information regarding the location and movement of a patient. See Lee et al., para. [0038]. Lee et al. further discloses regularly or continuously determining current location information identifying the current location of an entity relative to the physical space of the healthcare facility, including three-dimensional coordinate position information. See Lee et al., para. [0038]. Accordingly, Lee et al. teaches tracking a patient location within a healthcare facility relative to the spatial environment and locations represented therein.
Kim et al. discloses LiDAR-based processing using a baseline point cloud and input point clouds, including comparison/subtraction processing to identify differences between the baseline and input point-cloud data. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses processing a series of input point clouds forming a three-dimensional motion sequence and comparing input data with baseline data. See Kim et al., col. 4, lines 28-45; col. 7, lines 48-56.
Kusens further discloses a patient monitoring system in which one or more three-dimensional camera, motion, and sound sensors are used to monitor a patient in a patient room. Kusens discloses defining a virtual detection zone using three-dimensional coordinates and recognizing and tracking the patient's body as one or more three-dimensional blobs. See Kusens, US 2017/0091562 A1, description corresponding to steps F1a-F1e.
Kusens further discloses determining when the patient or a portion of the patient has crossed into the designated virtual detection zone and determining how long the patient's body has remained within the detection zone. See Kusens, US 2017/0091562 A1, steps F1e-F1g. Kusens expressly discloses that, when the patient's body remains within the detection zone for a configured period, the monitoring system alerts a computerized communication system. See Kusens, US 2017/0091562 A1, steps F1g-F1i. Kusens further discloses that the system may be programmed to generate an alert based solely on detecting a sufficiently large patient object within the detection zone for any period of time. Thus, Kusens expressly teaches providing an alert based on determining that a patient is within a defined spatial zone.
Accordingly, the combination of Iagnemma et al., Lee et al., Kim et al., and Kusens teaches or suggests the limitations of claim 16 and further teaches providing an alert when the tracked patient location is determined to be within a defined spatial position/zone.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the patient-zone alerting technique of Kusens to the patient-location tracking and healthcare-facility spatial-location system of Iagnemma et al. and Lee et al., as further implemented using the baseline-versus-input LiDAR processing of Kim et al., such that an alert would be provided when the tracked patient location is determined to be within the established first position of interest.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Kusens provides an established technique for monitoring a patient relative to a defined three-dimensional zone and generating an alert when the patient enters or remains within that zone. Lee et al. provides patient-location tracking within a healthcare facility and a spatial representation containing points of interest. Iagnemma et al. provides the signaling-device technique for establishing a precise spatial location/goal location, while Kim et al. provides baseline-versus-input LiDAR processing.
Applying the alerting technique of Kusens to the patient-location tracking system of Lee et al. would use the respective prior-art elements for their disclosed functions: the patient location would continue to be tracked by the healthcare-facility tracking system, the established first position of interest would constitute a spatial zone against which the patient location is evaluated, and an alert would be generated when the patient is determined to be within that zone.
The proposed combination would have provided the predictable result of notifying healthcare personnel when a tracked patient enters or remains within a defined position of interest. A reasonable expectation of success would have existed because Kusens expressly demonstrates the operation of a healthcare patient-monitoring system in which a patient's three-dimensional location is compared with a defined spatial zone and an alert is generated based on the patient's presence within that zone.
Claim 34 depends from claim 19 and further recites:
“wherein the processor is further configured to:
track a patient location relative to the first position of interest within the patient space;
provide an alert if the patient location is determined to be within the first position of interest.”
As set forth with respect to claim 19, Iagnemma et al. discloses a signaling device that provides a location indication signal to a stimulus detector and a system that analyzes the signal to determine the precise location of the signaling device and a corresponding precise goal location. See Iagnemma et al., paras. [0047]-[0049], [0053]-[0054], [0076], [0082]-[0083], [0112]-[0113].
Iagnemma et al. further discloses LiDAR-based spatial localization of the signaling device and determining a precise location of the signaling device. See Iagnemma et al., paras. [0063], [0121]. Iagnemma et al. also discloses that the precise goal location may correspond to the precise location of the signaling device.
Lee et al. discloses a healthcare-facility location system that determines three-dimensional locations of entities within a healthcare facility and correlates position measurements with facility information identifying physical structures, including points of interest, rooms, walls, floors, and other portions of the healthcare facility. See Lee et al., paras. [0031], [0054].
Lee et al. further expressly discloses tracking movement of an entity within the healthcare facility. In particular, Lee et al. discloses a tracking component configured to monitor changes in location information associated with an entity to track movement of the entity about a healthcare facility, and further discloses a patient tracking component configured to track information regarding the location and movement of a patient. See Lee et al., para. [0095]. Lee et al. also discloses regularly or continuously determining current location information of an entity relative to the physical space of the healthcare facility and providing three-dimensional coordinate information for the entity. See Lee et al., para. [0095].
Thus, Lee et al. specifically teaches tracking the location and movement of a patient within a healthcare facility relative to the facility's three-dimensional spatial representation and associated points of interest.
Kim et al. discloses obtaining baseline and input three-dimensional point clouds from a three-dimensional scanner and comparing the input spatial data with baseline spatial data to generate difference information. See Kim et al., col. 4, lines 28-45. Kim et al. further discloses a series of input point clouds forming a three-dimensional motion sequence. See Kim et al., col. 7, lines 48-56.
Accordingly, as explained with respect to claim 19, the combination of Iagnemma et al., Lee et al., and Kim et al. provides a technical basis for establishing a first position of interest in a healthcare patient space using signaling-device localization and LiDAR baseline-versus-input spatial processing, and for subsequently determining patient location within that spatial environment.
Kusens specifically discloses monitoring a patient in a patient room using a three-dimensional sensing system and defining virtual detection zones in three-dimensional space. See Kusens, US 2017/0091562 A1, paras. [0181]-[0189], [0219]-[0224].
Kusens further expressly discloses tracking the patient's body as one or more three-dimensional blobs and determining whether the patient or a portion of the patient has crossed into a designated virtual blob detection zone. See Kusens, paras. [0221]-[0228]. The system determines how long the patient's body remains within the virtual detection zone. See Kusens, para. [0228].
Kusens expressly further discloses generating an alert based on detection of the patient within the virtual detection zone. In particular, the monitoring system can generate an alert when a sufficiently large patient blob is detected within the detection zone, and the monitoring system alerts a computerized communication system. See Kusens, paras. [0229]-[0231]. Kusens further discloses visual and/or audio alerts and notification of caregivers or other designated persons. See Kusens, paras. [0234]-[0239].
Thus, Kusens specifically teaches the claimed functional relationship of determining a patient's location relative to a defined virtual zone and providing an alert when the patient is within that zone.
Kusens does not expressly disclose that the virtual detection zone is the particular first position of interest generated from a signaling-device location using the baseline-versus-input LiDAR processing of Iagnemma et al., Lee et al., and Kim et al. That particular combination is not disclosed by Kusens. Kusens is relied upon only for the disclosed patient-location monitoring and alert functionality.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to apply the patient virtual-zone monitoring and alerting technique of Kusens to the healthcare-facility spatial-location system of Iagnemma et al., Lee et al., and Kim et al., such that a patient whose tracked location is determined to be within an established position of interest would cause the system to provide an alert.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
The proposed modification uses the references for their respective disclosed functions. Iagnemma et al. provides signaling-device-based location determination and precise goal-location determination; Lee et al. provides the healthcare-facility environment, three-dimensional spatial representation, points of interest, and patient-location tracking; Kim et al. provides baseline-versus-input three-dimensional spatial-difference processing; and Kusens provides monitoring of a patient relative to a defined three-dimensional virtual zone and generation of an alert when the patient enters or remains within that zone.
The proposed modification would have provided a predictable improvement to the healthcare-facility location system by using an already-established spatial position of interest as a virtual monitoring zone for the patient. When the tracked patient location fell within that zone, the system would perform the alerting operation expressly taught by Kusens.
A person of ordinary skill in the art would have had a reasonable expectation of success because the modification would not require changing the underlying LiDAR localization, signaling, spatial-coordinate determination, or patient-tracking techniques. Instead, the modification would apply the disclosed patient virtual-zone alerting operation of Kusens to a spatial region already represented within the healthcare-facility location framework of Lee et al.
The combination would therefore predictably provide an alert when the tracked patient enters the established position of interest, thereby supplying the additional functionality expressly recited by claim 34.
Conclusion
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/HOI C LAU/Primary Examiner, Art Unit 2689
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