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 .
Status
Claims 1-20 have been examined. Claims 1, 7-8, 11, 17-16 have been amended.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12059315. Although the claims at issue are not identical, they are not patentably distinct from each other because both claims of current application and claim 1 of US Patent No. 12059315 recite the same feature of receiving the plurality of oral images, aggregate a first aggregated oral image and aggregated second aggregated oral images.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim(s) 1 recite(s) a system for estimating a trend associated with dental tissue, which is a statutory category (i.e. machine). Claim(s) 11 recite(s) a method for estimating a trend associated with dental tissue, which is a statutory category (i.e. process). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A (MPEP 2106.04-.07), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes
The independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for estimating a trend associated with dental tissue, the system comprising:
a sensor configured to detect, a plurality of oral images representing a plurality of exposed tooth surfaces of a patient that include dental hard tissue; and
a computing device, in communication with the sensor, configured to:
receive the plurality of oral images from the sensor;
aggregate a first aggregated oral image as a function of a first plurality of oral images detected, at a first time;
aggregate a second aggregated oral image as a function of a second plurality of oral images detected, at a second time estimate a trend as a function of the first aggregated oral image and the second aggregated oral image;
estimate a trend as a function of the first aggregated oral image and the second
aggregated oral image, wherein the trend includes one or more of a future bite arrangement trend or an alignment trend
The Examiner submits that the foregoing underlined limitations constitute b) organizing human activity (e.g. which is a fundamental economic practice) by human mind to detect, aggregate the oral images and estimate the trend of multiple oral images. Accordingly, the claim is directed toward at least one abstract idea.
Furthermore, the abstract idea for claim 11 is identical as the abstract idea for claim 1, because the only difference between claim 1 and claim 11 is that claim 1 recites a system, whereas claim 11 recites a method.
Furthermore, dependent claims further define the at least one abstract idea as set forth below:
Dependent claims 2-10, 12-20 incorporate the abstract idea analysis set forth above expanding the abstract idea identified above. For example, claims 6-8. 16-18 further presents quantifying oral metric. Claims 9-10, 19-20 expand further estimating the trend. These additional information characteristics do not change the fundamental analogy to the abstract idea grouping of certain method of organizing human interactions, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in MPEP2106.04-.07, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
Claim 1 recites
A system for estimating a trend associated with dental tissue, the system comprising:
a sensor configured to detect a plurality of oral images representing a plurality of exposed tooth surfaces of a patient including dental hard tissue (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)(2)); and
a computing device in communication with the sensor, configured to (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)(2)):
receive the plurality of oral images from the sensor (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)(2));
aggregate a first aggregated oral image as a function of a first plurality of oral images detected at a first time (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)(2));
aggregate a second aggregated oral image as a function of a second plurality of oral images detected at a second time (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)(2)); and
estimate a trend as a function of the first aggregated oral image and the second aggregated oral image, wherein the trend includes one or more of a future bite arrangement trend or an alignment trend (merely data gathering steps as noted below, see MPEP 2106.05(g) and Symantec).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of “a sensor configured to detect a plurality of oral images representing a plurality of exposed tooth surfaces, as a function of a schedule”, this is pre-solution activity. The Examiner submits that these limitations are instructed to apply the above-noted abstract idea by merely invoking use of computer to perform the process, (see MPEP 2106.05(f)).
Regarding the additional limitations of “receive the plurality of oral images from the sensor; aggregate a first aggregated oral image as a function of a first plurality of oral images detected at a first time; aggregate a second aggregated oral image as a function of a second plurality of oral images detected at a second time ” this is pre-solution activity. The Examiner submits that these limitations are instructed to apply the above-noted abstract idea by merely invoking use of computer to perform the process, (see MPEP 2106.05(f)).
Regarding the additional limitations of “estimate a trend as a function of the first aggregated oral image and the second aggregated oral image.”, this post-solution activity. The Examiner submits that these additional limitations are instructed to apply the above-noted abstract idea by merely adds insignificant extra-solution activity of gathering data to the at least one abstract idea in a manner of post-solution activity that does not meaningfully limit the at least one abstract idea (merely data gathering steps as noted below, see MPEP 2106.05(g) and Symantec).
Particularly, the use of a processor, a sensor as in claims 1, 11 and dependent claims is not positively claimed in the claim as it defines the service but is claimed insufficient to a structure or apparatus that it represents mere instructions to implement an abstract idea MPEP 2106.05(f). The claims recite the additional elements, using computer, processor, memory …; for detecting, aggregating, estimating. The processor, sensors, computing device are recited at a high-level of generality (i.e. generic components).. The Specification describes the additional elements, i.e. processor, memory, etc… as in claims 1 and 11. However, the specification does not describe any specialized components of the computing environment, but only describes these suitable components. Furthermore, the use of computing components (i.e. processor, sensor, etc…) to detect, aggregate, estimate “add nothing when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception.
For these reasons, representative independent claim 1 and analogous independent claim 11do not recite additional elements that integrate the judicial exceptions into a practical application. The Examiner notes the mere recitation of a computing system, processor, does not take the claims out of the mental process grouping or organizing human activity. Thus, the claims recite an abstract idea.
The remaining dependent claim limitations are not addressed above fail to integrate the abstract idea into a practical application
Step 2B
Regarding Step 2B, independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Independent claims 1, 11 limit the use of a processor, a memory, a sensor...The Examiner submits that these limitations amount to merely using these computer devices as well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018).), and MPEP 2106.05(d)(I)(2). Further the use of generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patient-ineligible abstract idea into a patent-eligible invention”).
For the reasons stated, the claims fail the Subject Matter Eligibility Test and are
consequently rejected under 35 USC 101. Therefore, claims 1-20 have been held ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US20110287387A1) in view of Inam et al. (US20210343400A1 hereinafter Inam).
With respect claim 1, Chen teaches a system for estimating a trend associated with dental tissue, the system comprising:
a sensor configured to detect, a plurality of oral images representing a plurality of exposed tooth surfaces of a patient that include dental hard tissue (‘387; Para 0012: by disclosure, Chen describes medical imaging, particularly for intra-oral imaging applications, with particular interest in detection of cracks and other surface features of the tooth ; Para 0047: When the tooth is imaged with an imaging system and sensor, the light that is available to the sensor can be (i) light reflected from the tooth top surface; (ii) light scattered or reflected from the near surface volume of portion of the tooth; and (iii) light scattered inside the tooth); and
a computing device, in communication with the sensor, configured to:
receive the plurality of oral images from the sensor (‘387; Para 0047: When the tooth is imaged with an imaging system and sensor, the light that is available to the sensor can be (i) light reflected from the tooth top surface; (ii) light scattered or reflected from the near surface volume or portion of the tooth; and (iii) light scattered inside the tooth.);
aggregate a first aggregated oral image as a function of a first plurality of oral images detected, at a first time (‘387; Abstract: A method for imaging the surface of a tooth, the method executed at least in part on a computer records a first set of images of the tooth, wherein each image in the first set of images is illuminated according to a pattern oriented in a first direction );
Chen further disclose a first contour image is reconstructed according to the recorded first set of images and a second contour image according to the recorded second set of images. (‘387; Abstract; Paras 0042-0043).
Inam teaches
aggregate a second aggregated oral image as a function of a second plurality of oral images detected, at a second time longitudinally after the first time (‘387; Abstract: A second set of images of the tooth are recorded, wherein each image in the second set of images is illuminated according to a pattern oriented in a second direction that is shifted more than 10 degrees with respect to the first direction.); and
estimate a trend as a function of the first aggregated oral image and the second aggregated oral image, using a prediction machine learning model trained with a prediction training set comprising historical oral images longitudinally taken from one or more patients, (‘400; Para 0195: in FIG. 19, the flow 900 includes performing (at 960) an ongoing analysis of the treatment clinical data, based on additional obtained treatment clinical data for the individual. The ongoing analysis can be used to identify trends in the treatment clinical data), anomalies in the data, treatment “hotspots” or concentrations (where the concentrations can be associated with an outbreak of a disease), services provided by a provider, and so on. In some embodiments, the additional treatment clinical data for the individual comprises longitudinal treatment clinical data for the individual. The longitudinal treatment clinical data can be based on past treatments and current treatments. The longitudinal treatment data can include images associated with the individual. In embodiments, the dental images comprise current and historic radiography of the individual. In some embodiments, the ongoing analysis can be based on additional treatment clinical data for the individual that is performed in real-time or near real-time. The real-time or near real-time analysis can be used to quickly identify anomalies, outliers, manipulated images, and so on. In some embodiments, the ongoing analysis can be performed autonomously. The autonomous analysis can be accomplished using machine learning (which may also be implemented using machine learning processes similar to those performed at 952) where the machine learning implementation provides progressively improved output for the analysis over time.), wherein the trend includes one or more of a future bite arrangement trend or an alignment trend (‘400; Para 0153: calibration procedures may include the calibration processes discussed herein in relation to FIGS. 6-12, as well as other standardization processes such as image alignment processes; Para 0163: orientation and positioning information for the incoming input radiographic image data is available or can be derived (e.g., based on one or more of the measurement processes of FIGS. 6-12) to obtain or determine viewing angle and distance of an X-ray imaging apparatus from the imaged dental objects, the image data can be re-aligned to a common frame of reference. Such an alignment can further improve the accuracy and consistency of the images being analyzed, thus improving the robustness of the feature identification and decision making performed by the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Chen with the technique of integrity analysis of clinical data as taught by Inam and the motivation is to estimating the trend of the aggregated oral images.
Claim 11 is rejected as the same reason with claim 1.
With respect claim 2, the combined art teaches the system of claim 1, wherein estimating the trend further comprises: quantifying a first alignment metric as a function of the first plurality of oral images; quantifying a second alignment metric as a function of the second plurality of oral images; and estimating the one or more of the future bite arrangement trend or the alignment trend as a function of the first alignment metric and the second alignment metric (‘400; Para 0153: Calibration procedures may include the calibration processes discussed herein in relation to FIGS. 6-12, as well as other standardization processes such as image alignment processes. Accordingly, in some embodiments, the clinical data analysis approaches described herein may further include applying a calibration procedure to the source radiographic image data to perform one or more of, for example, the known sensor measurement process,).
Claim 12 is rejected as the same reason with claim 2.
With respect claim 3, the combined art teaches the system of claim 2, wherein quantifying the first alignment metric further comprises: inputting into an alignment metric machine learning model at least one image from the first plurality of oral images; and outputting the first alignment metric from the alignment metric machine learning model as a function of the at least one image from the first plurality of oral images (‘400; Para 0154: the system 700 includes a dental feature detector 710 (which may be implemented using a machine learning engine realized, for example, using neural networks or other types of learning machines, or implemented as an image processing/filtering engine) to identify objects in an image, label or mark such identified objects with).
Claim 13 is rejected as the same reason with claim 3.
With respect claim 4, the combined art teaches the system of claim 3, wherein quantifying the first alignment metric further comprises: receiving an alignment metric machine learning training set that correlates alignment metrics to oral images; and training the alignment metric machine learning model as a function of the alignment metric machine learning training set (‘400; Para 0154).
Claim 14 is rejected as the same reason with claim 4.
With respect claim 5, the combined art teaches the system of claim 2, wherein estimating the one or more of the future bite arrangement trend or the alignment trend further comprises: inputting the first alignment metric and the second alignment metric into an alignment prediction machine learning process; and outputting the trend as a function of the alignment prediction machine learning process, the first alignment metric, and the second alignment metric (‘400; Para 0155: the weight values may be determined, for example, according to a procedure minimizing a loss metric between predictions made by the neural network and labeled instances of the data (e.g., using a stochastic gradient descent procedure to minimize the loss metric)).
Claim 15 is rejected as the same reason with claim 5.
With respect claim 6, the combined art teaches the system of claim 5, wherein estimating the one or more of the future bite arrangement trend or the alignment trend further comprises: receiving a bite estimation training data comprising correlates oral images to subsequent bite arrangements; training the alignment prediction machine learning model as a function of the bite estimation training data and a machine learning algorithm; inputting the first alignment metric and the second alignment metric into the trained alignment prediction machine learning model; and outputting the one or more of the future bite arrangement trend or the alignment trend as a function of the trained alignment prediction machine learning model, the first alignment metric, and the second alignment metric (‘400; Para 0088).
Claim 16 is rejected as the same reason with claim 6.
Claims 7-10, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US20110287387A1) in view of Inam et al. (US20210343400A1 hereinafter Inam) and further in view of OUYANG, Congxing (US20210241885A1)
With respect claim 7, the combined art teaches the system of claim 1, wherein the first aggregated oral image comprises a 3D image and the first plurality of oral images comprise visible-spectrum 2D images representing the plurality of exposed tooth surfaces of the patient. However, OUYANG CONGXING discloses the aforementioned feature (‘885; Para 0052: The adults and children herein may also include adults and children in different age stages, 35 for example, adults may be classified as 18-28 years old, over 28 years old, and so on, and 7 minors may be classified as before 2 years old, over 2 years old, and so on, although not limited to the above classification.).
Claim 17 is rejected as the same reason with claim 7
Claims 8-10, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US20110287387A1) in view of Inam et al. (US20210343400A1 hereinafter Inam) and further in view of OUYANG, Congxing (US20210241885A1).
With respect claim 8, the combined art teaches the system of claim 6, wherein the patient includes a pediatric patient, and the pediatric patient has deciduous teeth. However, OUYANG, Congxing discloses the aforementioned feature (‘885; Para 0051: The scenarios include a normal healthy oral cavity scenario, a dirty oral cavity scenario, a pathological oral cavity scenario, a deformed oral cavity scenario, an injured oral cavity scenario, and a mixed dentition stage scenario in which deciduous teeth grow to replace permanent teeth, of adults, and further include a normal healthy oral cavity scenario, a dirty oral cavity scenario, a pathological oral cavity scenario, a deformed oral cavity scenario, an injured oral cavity scenario and a deciduous tooth eruption stage scenario of children).
It would have been obvious to one of ordinary skill in the art before the effective filing of claimed invention to modify the system of Chen/Inam with the technique of tooth virtual editing as taught by Congsing Ouyang and the motivation is to provide oral images for pediatrics patient
Claim 18 is rejected as the same reason with claim 8.
With respect claim 9.the combined teaches the system of claim 8, Congsing Ouyang teaches wherein the pediatric patient's bite arrangement changes between the first time and the second time (‘885; Para 0051)
Claim 19 is rejected as the same reason with claim 9.
With respect claim 10, the combined art teaches the system of claim 9, wherein the sensor comprises a camera further comprising: a lens; and an image sensor having a global shutter (‘400; Para 0053). Claim 20 is rejected as the same reason with claim 10.
Response to Arguments
Applicant's arguments filed 02/20/2026 have been fully considered but they are not persuasive.
In the Remark filed 02/20/2026, the Applicant argued that
For claim rejection on the ground of nonstatutory double patenting, the claims of the 869 Patent does not discloses the limitation of claim1, as amended (See Remark; page 1 out of 16). In response to the Applicant’s argument, the Examiner disagrees. As described in Double Patenting above, a nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
For claim rejection under 35USC101, the Applicant argued that detecting a plurality of oral images, analyzing them, and then predicting future changes to dental tissue is not a certain aspect of organizing human activity. Accordingly the Office fails to to demonstrate the claim recite a judicial exception under step 2A prong one analysis (Remark, page 4 out of 16).
In response to the Applicant’s argument, the Examiner disagrees. As described in claim rejection under 35USC101 above, and noted in Step 2A Prong 2, the claim limitation” of “a sensor configured to detect a plurality of oral images representing a plurality of exposed tooth surfaces, as a function of a schedule; receive the plurality of oral images from the sensor; aggregate a first aggregated oral image as a function of a first plurality of oral images detected at a first time; aggregate a second aggregated oral image as a function of a second plurality of oral images detected at a second time ”, this is pre-solution activity. The Examiner submits that these limitations are instructed to apply the above-noted abstract idea by merely invoking use of computer to perform the process, (see MPEP 2106.05(f)). Further, the limitation “estimate a trend as a function of the first aggregated oral image and the second aggregated oral image.”, this post-solution activity. The Examiner submits that these additional limitations are instructed to apply the above-noted abstract idea by merely adds insignificant extra-solution activity of gathering data to the at least one abstract idea in a manner of post-solution activity that does not meaningfully limit the at least one abstract idea (merely data gathering steps as noted below, see MPEP 2106.05(g) and Symantec).
Particularly, the use of a processor, a sensor as in claims 1, 11 and dependent claims is not positively claimed in the claim as it defines the service but is claimed insufficient to a structure or apparatus that it represents mere instructions to implement an abstract idea MPEP 2106.05(f). The claims recite the additional elements, using computer, processor, memory …; for detecting, aggregating, estimating. The processor, sensors, computing device are recited at a high-level of generality (i.e. generic components).. The Specification describes the additional elements, i.e. processor, memory, etc… as in claims 1 and 11. However, the specification does not describe any specialized components of the computing environment, but only describes these suitable components. Furthermore, the use of computing components (i.e. processor, sensor, etc…) to detect, aggregate, estimate “add nothing when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception.
For these reasons, representative independent claim 1 and analogous independent claim 11do not recite additional elements that integrate the judicial exceptions into a practical application. The Examiner notes the mere recitation of a computing system, processor, does not take the claims out of the mental process grouping or organizing human activity. Thus, the claims recite an abstract idea.
The remaining dependent claim limitations are not addressed above fail to integrate the abstract idea into a practical application.
For claim rejection under 35USC103, the Applicant argued that the combined art does not disclose estimate a trend as a function of the first aggregated oral image and the second aggregated oral image, using a prediction machine learning model trained with a prediction training set comprising historical oral images longitudinally taken from one or more patients. (Remark, page 12/16)
In response to the Applicant’s argument, Under broadest reasonable interpretation of the recited claim, Inam discloses, as in FIG. 19, the flow 900 includes performing (at 960) an ongoing analysis of the treatment clinical data, based on additional obtained treatment clinical data for the individual. The ongoing analysis can be used to identify trends in the treatment clinical data), anomalies in the data, treatment “hotspots” or concentrations (where the concentrations can be associated with an outbreak of a disease), services provided by a provider, and so on. In some embodiments, the additional treatment clinical data for the individual comprises longitudinal treatment clinical data for the individual. The longitudinal treatment clinical data can be based on past treatments and current treatments. The longitudinal treatment data can include images associated with the individual. In embodiments, the dental images comprise current and historic radiography of the individual. In some embodiments, the ongoing analysis can be based on additional treatment clinical data for the individual that is performed in real-time or near real-time. The real-time or near real-time analysis can be used to quickly identify anomalies, outliers, manipulated images, and so on. In some embodiments, the ongoing analysis can be performed autonomously. The autonomous analysis can be accomplished using machine learning (which may also be implemented using machine learning processes similar to those performed at 952) where the machine learning implementation provides progressively improved output for the analysis over time.( (‘400; Para 0195). Inam further discloses the trend includes one or more of a future bite arrangement trend or an alignment trend calibration procedures may include the calibration processes discussed herein in relation to FIGS. 6-12, as well as other standardization processes such as image alignment processes; orientation and positioning information for the incoming input radiographic image data is available or can be derived (e.g., based on one or more of the measurement processes of FIGS. 6-12) to obtain or determine viewing angle and distance of an X-ray imaging apparatus from the imaged dental objects, the image data can be re-aligned to a common frame of reference. Such an alignment can further improve the accuracy and consistency of the images being analyzed, thus improving the robustness of the feature identification and decision making performed by the system (‘400; Para 0153; Para 0163).
Given broadest reasonable interpretation of the recited claims, it is submitted that the longitudinal data before and after the first time of Iam’s reference is in a form as described in the invention.
Therefore, the Examiner maintain rejection of all claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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