Notice of Pre-AIA or AIA Status
Claims 28-44 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election without traverse of Group II (claims 28-44) in the reply filed on 04/29/2026 is acknowledged.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Drawing number FIG 1-3 and FIG 6-15 are not shown as numbered in drawing which are mentioned in specification page 38-39. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 28-44 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 28 recites “similar/related in one or more ways” which is a relative term which renders the claim indefinite. The term “similar”, “related” and “one or more ways” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 28 recites the limitation "the computer" in an interface to the computer limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim 28 recites "storing the device measurements". The antecedent for "device measurements" is "a measurement" (singular) generated by the tool. It should be "the measurement" to maintain consistency.
Claim 28 recites the limitation "the status". There is insufficient antecedent basis for this limitation in the claim.
Claim 28 recites the limitation "wherein said at least one computer". The claim introduces "one or more computers" in the second element but later refers to "said at least one computer". There is insufficient antecedent basis for this limitation in the claim. It is unclear if this refers to the "one or more computers" or the "interface to the computer."
Claim 29 recites ("said at least one computer for storing is different from said at least one computer used for displaying"): Claim 29 refers to "said at least one computer for storing is different from said at least one computer used for displaying." There is insufficient antecedent basis for this limitation in the claim. This is a new term that was not clearly established in claim 28, which only mentioned one or more computers for "storing and displaying" combined, or an "interface to the computer for storing."
Claim 30 recites ("wherein said at least one computer for storing and displaying is different from said at least one computer used for deep learning."): There is insufficient antecedent basis for this limitation in the claim. Neither computer was clearly introduced as separate entities in claim 28.
Claim 42 recites “large number” and “higher” which is a relative term which renders the claim indefinite. The term “large number” and “higher” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 29-44 depend on claim 28 and do not cure the aforementioned deficiencies of claim 28, and thus, claims 29-44 are rejected for the reasons set forth above regarding claim 28 as a result.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 44 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Dependent claim 44 recites “the system of claim 44” which is improper dependent claim since it depends from itself rather than a proceeding claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
9. Claim(s) 28, 30-36 and 38-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Earthman et al. (PUB NO: US 20130174639 A1) in view of Scanlan et al. ("Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning." Bioengineering 5.4 (2018): 107.)
Regarding claim 28
Earthman teaches a system for detecting , (see para 195- The shape of the time versus percussion response profile for the abnormal implant structure indicates that defects, such as loose screws, a damaged internal structure, bone loss at the bone/implant interface, or poor osseointegration, are present) comprising:
at least one device having an energy application tool capable of applying energy to an object to generate a measurement and an interface to the computer for storing the device measurements; (see para 25-The measuring device is adapted for measuring the deceleration of the tapping rod upon impact with an object during operation, or any vibration caused by the tapping rod on the specimen. see para 60-62The hardware may include a computer for controlling the handpiece and for analyzing any data collected, for example, the deceleration of the energy applying tool, for example, the tapping rod, upon impact with a object. In one embodiment, the handpiece and hardware may communicate via a wire connection. In another embodiment, the handpiece and hardware may communicate via a wireless connection. In one aspect, the tapping rod may be programmed to strike an object a certain number of times per minute at substantially the same speed and the deceleration information is recorded or compiled for analysis by the system. See fig 1 and para 139- An energy application tool 120, for example, a tapping rod 120, may be mounted inside the housing 132 for axial movement, as noted above) and
at least one quantitative percussion device for capturing energy return data connected to one or more computers for storing and displaying energy return data, annotations, prediction results or combinations thereof; (see fig 1 and para 139-In one embodiment, the handpiece 104 may be, for example, as exemplified in FIGS. 1, 35 a, b and c, in the form of a percussion instrument. see para 187-For example, as illustrated also in FIG. 1, the computer 164 may further include memory registers, such that time versus percussion response, for example, the amount of energy reflected from the object 112 at several points over a discrete time period can be recorded. In such embodiments, the energy returned from the object 112 can be plotted as a function of time on a display attached to the computer 164. This configuration allows the user to view and analyze the time-energy profile of the energy reflected from the specimen 114. See also fig 18 and 18a, para 183)
Examiner note: Claim recites "energy return data, annotations, prediction results or combinations thereof" that indicates that the computer must display one, some, or all of these items. Energy return data alone is sufficient to meet this storing and displaying limitation which is mapped.
Earthman does not teach a system for detecting previously seen patterns/classes of defects in objects wherein said at least one computer utilize deep learning to mathematically identify patterns in energy return data sets annotated as being similar/related in one or more ways and to predict the status of new object samples.
In the related field of invention, Scanlan teaches a system for detecting previously seen patterns/classes of defects in objects (see abstract-the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. See page 2- Therefore, the aim of the project is to identify and use these parameters from the bone’s impulse Therefore, the aim of the project is to identify and use these parameters from the bone’s impulse response (IR) to classify individuals which likely have OP from the general population. The machine learning algorithm maps the individual impulse responses to a continuous output, which is then split into two classes: healthy (OK) and osteoporotic (OP))
wherein said at least one computer utilize deep learning to mathematically identify patterns in energy return data sets (see abstract and fig 1- the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. see section 2.4- Machine learning (ML) can be thought of as an error reduction algorithm which maps input data in a non-linear fashion to desired or statistically significant outputs. For this project, the ANN was chosen for its fast-learning capabilities and convenience. ANNs are made from a large number of interconnected neuron models which allow a learning capability) annotated as being similar/related in one or more ways (see section Introduction- The diagnostic decision-making mechanism of the proposed method is based on statistical machine learning from a large number of recordings. The machine learning algorithm maps the individual impulse responses to a continuous output, which is then split into two classes: healthy (OK) and osteoporotic (OP). This is based on the doctors’ diagnoses of the patients, taking into account DEXA t-scores and other physiological parameters and aspects. See fig 6 and section 2.4- The output of the network is compared with the doctor’s diagnosis (“teacher value”) and the error between them is calculated. The teacher values are: 0 for osteoporotic (OP) patients; 1 for healthy (OK) subjects.) and to predict the status of new object samples. (See section 2.6- The dataset is split between training dataset and validation dataset. The former teaches the ANN on examples which have teacher values in order to reduce the error. The latter tests the network on examples it has not “seen,” proving the ANN is able to be used on new data.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for determining structural characteristics of an object as disclosed by Earthman to include a system for detecting previously seen patterns/classes of defects in objects wherein said at least one computer utilize deep learning to mathematically identify patterns in energy return data sets annotated as being similar/related in one or more ways and to predict the status of new object samples as taught by Scanlan in the system of Earthman in order to use artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses, this it improves detection of osteoporosis in individuals. (See Abstract, Scanlan)
Regarding claim 30
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said at least one computer for storing and displaying is different from said at least one computer used for deep learning. (see para 179- If a familiar testing configuration is to be implemented, then the program loads previously determined calibration values stored in a file 312. A calibration file can be chosen from among the many previous calibration files stored in memory. See para 202-Still referring to FIG. 29, the system 100 is electronically connected to a computer 164 via an instrumentation interface 168. In such embodiments, the computer 164 may include a display 180 capable of graphically presenting data generated by the system 100, such as a time versus percussion response profile.)
Earthman does not teach at least one different computer used for deep learning.
In the related field of invention, Scanlan teaches at least one different computer used for deep learning. (see page 2-This paper details a new vibro-acoustic method with the potential to diagnose osteoporosis from the impulse responses of the tibia in vivo and presents an additional machine learning approach and results. The method is illustrated in Figure 1. A clinician taps on a patient’s proximal tibia bone with This paper details a new vibro-acoustic method with the potential to diagnose osteoporosis from the impulse responses of the tibia in vivo and presents an additional machine learning approach and results. The method is illustrated in Figure 1. A clinician taps on a patient’s proximal tibia bone with a Taylor reflex hammer and an electronic stethoscope picks up the induced sound at the midpoint and/or the distal end of the tibia. The signal is transmitted via a Bluetooth datalink to a computer for further signal processing and pattern recognition, leading eventually to a diagnostic decision. By utilizing common clinical devices and apparatus, the method has considerable potential)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for determining structural characteristics of an object as disclosed by Earthman to include at least one different computer used for deep learning as taught by Scanlan in the system of Earthman in order to use artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses, this it improves detection of osteoporosis in individuals. (See Abstract, Scanlan)
Regarding claim 31
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said object includes anatomical and non-anatomical objects. (see para 0009- For an anatomical object, such as a tooth structure, a natural tooth, a natural tooth that has a fracture due to wear or trauma, a natural tooth that has become at least partially abscessed, or a natural tooth that has undergone a bone augmentation procedure, a prosthetic dental implant structure, a dental structure, an orthopedic structure or an orthopedic implant. For objects in general, for example, polymeric composite structures including honeycombs or layered honeycombs or metallic composite structures; planes, automobiles, ships, bridges, buildings, industrial structures including, but not limited to power generation facilities, arch structures, or other similar physical structures; such measurements may also be correlated to any structural integrity, or structural stability, such as defects or cracks, even hairline fractures or microcracks, and so on.)
Regarding claim 32
The combination of Earthman and Scanlan teaches the system of claim 31. Earthman further teaches wherein said anatomical objects are teeth, teeth structures and implants. (see para 0009- For an anatomical object, such as a tooth structure, a natural tooth, a natural tooth that has a fracture due to wear or trauma, a natural tooth that has become at least partially abscessed, or a natural tooth that has undergone a bone augmentation procedure, a prosthetic dental implant structure, a dental structure, an orthopedic structure or an orthopedic implant)
Regarding claim 33
The combination of Earthman and Scanlan teaches the system of claim 31. Earthman further teaches wherein said non-anatomical objects includes physical structures. (see para 0009-For objects in general, for example, polymeric composite structures including honeycombs or layered honeycombs or metallic composite structures; planes, automobiles, ships, bridges, buildings, industrial structures including, but not limited to power generation facilities, arch structures, or other similar physical structures; such measurements may also be correlated to any structural integrity, or structural stability, such as defects or cracks, even hairline fractures or microcracks, and so on.)
Regarding claim 34
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said patterns/classes being detected are cracks. damage, defects, tissue decay or combinations thereof.(see para 0009- The structural characteristics as defined herein may include vibration damping capacities; acoustic damping capacities; defects including inherent defects in, for example, the bone or the material that made up the object; cracks, micro-cracks, fractures, microfractures; loss of cement seal of the object, for example, to the anchor and/or foundation; cement failure between, for example, the object and anchor and/or foundation; bond failure between, for example, the object and anchor and/or foundation; microleakage, for example, either from the objection and/or between the object and anchor and/or foundation; lesions; decay; structural integrity in general or structural stability in general. See also para 178- As noted above, the present invention has applications also in the detection of internal damage such as microcracking, fracture, microfracture and delamination in composite structures and other engineering materials.)
Regarding claim 35
The combination of Earthman and Scanlan teaches the system of claim 33. Earthman further teaches wherein the patterns/classes being detected are physical characteristics including size, material type, shapes or geometry. (See para 138- In addition, the present invention is useful in distinguishing between defects inherent in the material making up the structure or object, and cracks or fractures as discussed above due to trauma or wear or repeated loadings. Defects inherent in the bone or material construction of an implant, or a physical structure, for example, may include lesions in the bone, similar defects in the implant construction or polymer, polymer composites or alloys, any type of ceramics, or metallic composites or alloys.)
Regarding claim 36
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said energy return data is captured by said device at multiple anatomical locations on a tooth. (See para 32- In yet another aspect, for example, if the object is a tooth, and the feature is to rest against the back or front surface of the tooth, it may be of a dimension to cover a major portion of the back or front surface while the tab rests on the top surface of a tooth. see para 187- , the amount of energy reflected from the object 112 at several points over a discrete time period can be recorded.)
Regarding claim 38
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches simulated energy return data, (see para 108- FIG. 23 shows data from finite element analysis, using a glass rod to simulate a tooth and a curve created by impact in a finite element model. See para 175- The piezoelectric force sensor 160 a produces signals that correspond to the reflected mechanical energy resulting from the impact between the tapping rod 120 and the object 112.)
Earthman does not teach wherein said simulated energy return data is incorporated into the training process to strengthen the identification of specific patterns.
However, Scanlan further teaches wherein said simulated energy return data is incorporated into the training process to strengthen the identification of specific patterns. (See abstract- the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. see section 2.4- Machine learning (ML) can be thought of as an error reduction algorithm which maps input data in a non-linear fashion to desired or statistically significant outputs. For this project, the ANN was chosen for its fast-learning capabilities and convenience. ANNs are made from a large number of interconnected neuron models which allow a learning capability. See also section 2.6-Training and Validation)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for determining structural characteristics of an object as disclosed by Earthman to include wherein said simulated energy return data is incorporated into the training process to strengthen the identification of specific patterns as taught by Scanlan in the system of Earthman in order to use artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses, this it improves detection of osteoporosis in individuals. (See Abstract, Scanlan)
Regarding claim 39
The combination of Earthman and Scanlan teaches the system of claim 38. Earthman further teaches wherein said simulated energy return data comprises data from random energy return data pattern generators, varied Finite Element Models, or combinations thereof. (see para 256-257 and example 3 (Finite Element Analysis)-This analysis method involved the use of numerical models to simulate actual testing using the system and method of the present invention. Layered structures were used in the present experiment, one structure with no defect in the laminated composite layer (FIG. 24) and one with a defect in the center of the composite laminated)
Regarding claim 40
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said at least one computer collect repeated measurements on the same tooth by repeated application of energy on the object using the energy application tool. (See para 136-The present invention provides an effective and repeatable measurement of the structural characteristics of an object. see para 149-The sleeve 108 having a tab 110 and feature 111 may further aid in the repeatability of positioning the energy applying tool such as the tapping rod 120 on the object 112. See para 267-FIG. 18 e shows a normal ERG for the same tooth after the final restoration was completed. FIG. 18 f showed the same tooth shown in 18 b with the new restoration that was testing normally)
Regarding claim 41
The combination of Earthman and Scanlan teaches the system of claim 40. Earthman further teaches wherein said energy application tool is a tapping rod for tapping said object, (See para 149-The sleeve 108 having a tab 110 and feature 111 may further aid in the repeatability of positioning the energy applying tool such as the tapping rod 120 on the object 112.) and said repeated measurements comprises varying the number of taps, varying the force level of the taps or combinations thereof on the same object.(See para 53- For example, the drive mechanism may include a smaller drive coil to lessen the acceleration of the energy application tool, whether or not it is light weight, and/or smaller in length or diameter, and the impact force on the object during operation while maintaining sensitivity of measurement. See para 240- . In one embodiment, the energy application tool 120, for example, the tapping rod 120, may be made of lighter material to minimize the weight of the handpiece 104. The lighter tapping rod 120 may also reduce the impact force on the object 112 during measurement. In another embodiment, the energy application tool 120, for example, the tapping rod 120, may be made shorter and/or of smaller diameter such that the size of the handpiece 104 is minimized as well as the impact force on the object 112 during measurement. See also para 267 (same object))
Regarding claim 42
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches incorporating a physical mount and software automation to generate and collect a large number of energy return data or to generate or collect energy return data at higher throughput. (See para 11- In one exemplary embodiment, the device may include a handpiece having a housing with an open end and an energy application tool, for example, a tapping rod, or impact rod mounted inside the housing for movement at the open end. see para 60 and fig 9- As noted above, the handpiece may be part of a system that includes computerized hardware and instrumentation software that may be programmed to activate, input and track the action and response of the handpiece for determining the structural characteristics of the object. see para 181- If more measurements in the series of measurements are requested 357, the program loops back to the step where the program accepted the signal from the piezoelectric force sensor 324. If more measurements in the series of measurements are not requested, but instead a new series of measurements are requested, then program either discards or saves into a file the energy data depending upon the discretion of the operator 352 before looping back to the step where the program accepted the signal from the piezoelectric force sensor 324.)
Regarding claim 43
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches a force measurement device incorporated into the system to calibrate/adjust/normalize energy return data . (See para 25- In one aspect, the drive mechanism may include a measuring device, for example, a piezoelectric force sensor, located within the handpiece housing for coupling with the energy application tool, such as the tapping rod. The piezoelectric force sensor may detect changes in the properties of the object and may quantify objectively its internal characteristics. See para 188-In addition to generation of a time-energy profile, other analyses can also be performed on the signals returned from the piezoelectric force sensor 160 a. (see fig 10a-normalized energy return))
Earthman does not explicitly teach used for training.
However, Scanlan further teaches data used for training; (see abstract- the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. see section 2.4- Machine learning (ML) can be thought of as an error reduction algorithm which maps input data in a non-linear fashion to desired or statistically significant outputs. For this project, the ANN was chosen for its fast-learning capabilities and convenience. ANNs are made from a large number of interconnected neuron models which allow a learning capability. See also section 2.6-Training and Validation)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for determining structural characteristics of an object as disclosed by Earthman to include used for training as taught by Scanlan in the system of Earthman in order to use artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses, this it improves detection of osteoporosis in individuals. (See Abstract, Scanlan)
Regarding claim 44
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said force measurement device comprises a load cell. (See para 179 and fig 9-The program accepts the signal from the piezoelectric force sensor 324. See para 242-243 A force sensor may be included in the handpiece 104 for sensing this pressure application and may be accompanied by visual signal, voice or digital readout. This sensor may be employed also for assuring that proper alignment against the object during measurement is obtained. The sensor may include strain gauges or piezoelectric elements.-A mounting device may be utilized to mount strain gauges or other force measuring elements between the sleeve and the handpiece, such as, for example, the mounting device 900, shown in the top view of FIG. 38)
10. Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Earthman et al. (PUB NO: US 20130174639 A1) in view of Scanlan et al. ("Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning." Bioengineering 5.4 (2018): 107.) and further in view of Xue et al. (PUB NO: US20190180443A1)
Regarding claim 29
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said at least one computer for storing and displaying. (See para 179- If a familiar testing configuration is to be implemented, then the program loads previously determined calibration values stored in a file 312. A calibration file can be chosen from among the many previous calibration files stored in memory. See para 202-Still referring to FIG. 29, the system 100 is electronically connected to a computer 164 via an instrumentation interface 168. In such embodiments, the computer 164 may include a display 180 capable of graphically presenting data generated by the system 100, such as a time versus percussion response profile.)
The combination of Earthman and Scanlan does not teach wherein said at least one computer for storing and is different from said at least one computer used for displaying.
However, Xue teaches wherein said at least one computer for storing and is different from said at least one computer used for displaying. (see para 62-Returning again to FIG. 1, once the first machine learning model (deep learning model) is trained, that trained machine learning model is stored in model storage 145. See para 144-150- The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The term “machine” shall also be taken to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions. The computing device 1600 also may include a video display unit 1610. While the computer-readable storage medium 1624 is shown in an example embodiment to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for determining structural characteristics of an object as disclosed by Earthman to include wherein said at least one computer for storing and is different from said at least one computer used for displaying as taught by Xue in the system of Earthman and Scanlan in order to improve an accuracy and quality of the edge detection operations for image analysis and object detection, and reduce an amount of processing required to complete the edge detection, reduce a time that it takes to complete the edge detection, and increase the relevancy of detected edges for later processing (e.g., to train a machine learning model to classify edges relevant to dentistry and/or to make determinations about a patient's teeth from the edges). (See para 66, Xue)
11. Claim(s) 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Earthman et al. (PUB NO: US 20130174639 A1) in view of Scanlan et al. ("Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning." Bioengineering 5.4 (2018): 107.) and further in view of Sabina et al. (PUB NO: US20190231492A1)
Regarding claim 37
The combination of Earthman and Scanlan teaches the system of claim 28. Earthman further teaches wherein said objects are teeth (para 0009- For an anatomical object, such as a tooth structure, a natural tooth, a natural tooth that has a fracture due to wear or trauma) and
The combination of Earthman and Scanlan does not teach said classes of teeth are on a continuous scale of damage score, mobility score, or combinations thereof.
In the related field of invention, Sabina teaches said classes of teeth are on a continuous scale of damage score, mobility score, or combinations thereof. (See para 64-determining a confidence score for the one or more actionable dental features based on the corresponding region of the one or more records; and displaying the one or more actionable dental features when the confidence score of the one or more actionable dental features is above a threshold. As mentioned above, the one or more actionable dental feature comprises one or more of: cracks, gum recess, tartar, enamel thickness, pits, caries, pits, fissures, evidence of grinding, and interproximal voids. See para 228- In any of the methods and apparatuses described herein the confidence level indicated may be a quantitative and/or qualitative index. For example, a quantitative confidence level “score” may be provided (e.g., using a number between, for example, 0-100, 0 to 1.0, −100 to 100, or scaled to any range of numeric values). Qualitative indexes may include “high, medium high, medium, medium low, low”, etc. Both qualitative and quantitative confidence levels may be used.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for determining structural characteristics of an object as disclosed by Earthman to include classes of teeth are on a continuous scale of damage score, mobility score, or combinations thereof as taught by Sabina in the system of Earthman and Scanlan in order to use improved methods for displaying of internal tooth features using the one or more volumetric models of a patient's teeth. This information is used clinically to determine the need for, to help design and to help apply dental prosthetics, including veneers, crowns, and the like. Another motivation is to help prepare design a dental implant for a particular tooth or teeth. (See para 154, Sabina)
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stoppa et al. US20180211373A1
i. Discussing a method for detecting a defect in an object includes: capturing, by one or more depth cameras, a plurality of partial point clouds of the object from a plurality of different poses with respect to the object; merging, by a processor, the partial point clouds to generate a merged point cloud; computing, by the processor, a three-dimensional (3D) multi-view model of the object; detecting, by the processor, one or more defects of the object in the 3D multi-view model; and outputting, by the processor, an indication of the one or more defects of the object.
13. All claims 28-44 are rejected.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
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