DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In light of the amendments, the claims are objected to.
In light of the amendments, the claims are rejected under 35 U.S.C. 112(a).
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 05/18/2026, the following has occurred: claims 1 and 6 have been amended; claims 2-5 and 7-10 have been canceled; and no new claims have been added.
Claims 1 and 6 are pending.
Effective Filing Date: 03/29/2023
Response to Arguments
35 U.S.C. 101 Rejections:
Applicant argues that the claims are directed towards a specific technological improvement in medical image processing. Applicant further states that the spatial alignment and cropping, reducing positional variance and standardizing inputs is similar to McRO and Enfish. Examiner however respectfully disagrees as first, neither the specification nor the claims align with this statement. The specification recites in paragraph [0037] that there is an increase in the training data and accuracy of the model. The addition of more data is not necessarily a technical improvement, and therefore the claims are not similar to those in McRO and Enfish.
Additionally, the data augmentation step which Applicant states should be an additional element has been examined as an additional element. However, it adds routine activity to the claims as shown below in the updated 101 rejection section.
Lastly, Applicant argues with respect to a P.T.A.B. decision having a specific training data preparing method where the additional elements amount to significantly more than the judicial exception. Based on the determination in the updated 101 section however, the random scaling and rotating only add routine activity to the claims.
35 U.S.C. 103 Rejections:
Applicant argues that claims do not teach mapping of chest X-ray image features to BMD values of the same patient. The claims however do not specifically states this. Furthermore, Applicant argues that there is a specific selection of L1 and T12 and that it is clinically significant. Applicant also acknowledges that a previous reference did teach L1 and T12 but in combination with other bone segments. The claims however are broad enough to include a model that uses at least L1 and T12 data, therefore any reference which discloses at least these columns would meet an L1 and T12 requirement.
Claim Objections
Claims 1 and 6 are objected to because of the following informalities:
Claims 1 and 6 recite “L1” and “T12” without first establishing these terms in the claims.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 6 recite the terms “normalized anatomical images” and “normalized interference region images” where certain steps are using this data. The specification lacks support for these terms, leading Examiner to question whether Applicant had possession over the claimed invention at the time of filing.
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 and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is drawn to methods and claim 6 is drawn to system, each of which is within the four statutory categories. Claims 1 and 6 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a bone mineral density (BMD) model training method executed by a) a processing unit of an electronic computing device, comprising:
1) a training data retrieval step, obtaining a plurality of sets of chest X-ray images and BMD values of the same person and performing data augmentation including at least random scaling and/or rotation of the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images and the BMD values are taken as training data;
2) a first positioning step, locating and retrieving first X-ray images including a 1st segment of lumbar vertebrae and a 12th thoracic vertebra from the chest X-ray images using b) a Self-Cure-Network (SCN);
3) a training step, training a BMD AI model based on normalized anatomical region images and the BMD values of the 1st segment of lumbar vertebrae and a 12th thoracic vertebra;
4) an inference data retrieval step, obtaining a certain chest X-ray image as inference data;
5) a second positioning step, locating and retrieving second X-ray images of L1 and T12; and
6) an inference step, utilizing the trained BMD Al model and the normalized interference region images to generate an inferred BMD value;
7) a risk evaluation step, evaluating risk of BMD abnormality based on the inferred BMD value to generate an evaluation result; and
8) displaying the evaluation result on c) a display unit.
Claim 1 recites, in part, performing the steps of 2) a first positioning step, locating and retrieving first X-ray images including a 1st segment of lumbar vertebrae and a 12th thoracic vertebra from the chest X-ray images, 3) “creating” a BMD AI model based on normalized anatomical region images and the BMD values of the 1st segment of lumbar vertebrae and a 12th thoracic vertebra, 4) an inference data retrieval step, obtaining a certain chest X-ray image as inference data, 5) a second positioning step, locating and retrieving second X-ray images of L1 and T12, 6) an inference step, utilizing the trained BMD Al model and the normalized interference region images to generate an inferred BMD value, 7) a risk evaluation step, evaluating risk of BMD abnormality based on the inferred BMD value to generate an evaluation result, and 8) displaying the evaluation result. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, this claim describes a process of refining sourced data in order to make a determination. Independent claim 6 is similar to claim 1 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a processing unit of an electronic computing device, executing a BMD abnormality risk learning application to implement, b) a Self-Cure-Network (SCN), c) a display unit, d) a non-volatile memory, storing a BMD abnormality risk learning application (from claim 6), e) a training data input module (from claim 6), f) positioning module (from claim 6), g) a AI training module (from claim 6), h) an inference data input module (from claim 6), i) an inference module (from claim 6), and j) a risk evaluation module (from claim 6) to perform the claimed steps.
The claims also include the additional element steps of 1) “a training data retrieval step, obtaining a plurality of sets of chest X-ray images and BMD values of the same person and performing data augmentation including at least random scaling and/or rotation of the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images and the BMD values are taken as training data” and 3) “a training step, training a BMD AI model based on normalized anatomical region images and the BMD values of the 1st segment of lumbar vertebrae and a 12th thoracic vertebra.”
The a) processing unit, executing a BMD abnormality risk learning application to implement, d) non-volatile memory, storing a BMD abnormality risk learning application, e) training data input module, f) positioning module, g) AI training module, h) inference data input module, i) inference module, and j) risk evaluation module to perform the claimed steps and the additional element step of 3) “a training step, training a BMD AI model based on normalized anatomical region images and the BMD values of the 1st segment of lumbar vertebrae and a 12th thoracic vertebra” are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification for lack of description of something other than generic computing components for these components, see MPEP 2106.05(f)).
Additionally, the b) Self-Cure-Network (SCN) in these steps generally links the abstract idea to a particular technological environment or field of use (such as machine learning, see MPEP 2106.05(h)).
Lastly, the c) display unit in these steps and the additional element step of 1) “a training data retrieval step, obtaining a plurality of sets of chest X-ray images and BMD values of the same person and performing data augmentation including at least random scaling and/or rotation of the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images and the BMD values are taken as training data” adds insignificant extra-solution activity to the abstract idea which amounts to insignificant application, see MPEP 2106.05(g).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present 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. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) a processing unit, executing a BMD abnormality risk learning application to implement, b) a Self-Cure-Network (SCN), c) a display unit, d) a non-volatile memory, storing a BMD abnormality risk learning application, e) a training data input module, f) positioning module, g) a AI training module, h) an inference data input module, i) an inference module, and j) a risk evaluation module to perform the claimed steps and the additional element steps of 1) “a training data retrieval step, obtaining a plurality of sets of chest X-ray images and BMD values of the same person and performing data augmentation including at least random scaling and/or rotation of the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images and the BMD values are taken as training data” and 3) “a training step, training a BMD AI model based on normalized anatomical region images and the BMD values of the 1st segment of lumbar vertebrae and a 12th thoracic vertebra” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity), a general linking to a particular technological field, and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The current invention generates and outputs a result utilizing a) a processing unit, executing a BMD abnormality risk learning application to implement, d) a non-volatile memory, storing a BMD abnormality risk learning application, e) a training data input module, f) positioning module, g) a AI training module, h) an inference data input module, i) an inference module, and j) a risk evaluation module, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Furthermore, the invention trains using the step of 3) “a training step, training a BMD AI model based on normalized anatomical region images and the BMD values of the 1st segment of lumbar vertebrae and a 12th thoracic vertebra”, thus this training steps amount to adding the words “apply it” with mere instructions to implement the abstract idea using a generic recitation of machine learning.
Additionally, the b) Self-Cure-Network (SCN) generally links the abstract idea to a particular technological environment or field of use. The following represent an example that courts have identified as generally linking the abstract idea to a particular technological environment (e.g. see MPEP 2106.05(h)): Limiting the abstract idea data to an SCN, because limiting application of the abstract idea to machine learning is simply an attempt to limit the use of the abstract idea to a particular technological environment, e.g. see Electric Power Group, LLC v. Alstom S.A.
Furthermore, the c) display unit in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives evaluation result data, and transmits the data to a display unit over a network, for example the Internet.
The following State of the Art Publication demonstrates the well-understood, routine, and conventional nature of the additional elements: 1) “a training data retrieval step, obtaining a plurality of sets of chest X-ray images and BMD values of the same person and performing data augmentation including at least random scaling and/or rotation of the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images and the BMD values are taken as training data”, e.g. see paragraph [0085] of U.S. 2021/0056691 to Gernand et al., paragraph [0081] of U.S. 2022/0101489 to Nie et al., and paragraph [0054] of U.S. 2019/0122073 to Ozdemir et al. where random scaling and rotation to generate a larger data set are common.
Mere instructions to apply an exception using a generic computer component, a general linking to a particular technological field, or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1 and 6 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over T.W. 202131863A to Chen et al. in view of U.S. 2022/0083821 to Jiang et al. further in view of U.S. 2020/0054306 to Mehanian et al. and further in view of U.S. .
As per claim 1, Chen et al. teaches a bone mineral density (BMD) model training method executed by a processing unit of an electronic computing device, (see: FIGS. 1 and 2 and paragraph [0024] where there is a model executed by a computer) comprising:
--a training data retrieval step, obtaining a plurality of sets of chest X-ray images (see: paragraph [0024] and step S01 of FIG. 1 where multiple sets of patient data are being obtained) and BMD values of the same person, wherein the BMD values are taken as training data; (see: paragraph [0025] where bone mineral density is being determined and obtained as training data)
--a first positioning step, locating and retrieving first X-ray images including specific bone positions from the chest X-ray images using a network; (see: paragraph [0024] and step S01 of FIG. 1 where a predetermined bone is being located and retrieved from the obtained images using a network model)
--a training step, training a BMD AI model based on normalized anatomical region images and the BMD values of the specific bone positions; (see: paragraph [0028] and step S05 of FIG. 1 where there is training of a model based on the training image data (normalized region images) and the bone density data (BMD values))
--an inference data retrieval step, obtaining a certain chest X-ray image as inference data; (see: paragraph [0012] where there is obtaining of a certain X-ray of the target patient as inference data)
--an inference step, utilizing the trained BMD Al model and the normalized interference region images to generate an inferred BMD value; (see: paragraph [0034] and S14 of FIG. 3 where there is displaying of the evaluation result. An evaluation is occurring using the estimation model (trained BMD AI model) and the normalized interference region images (image data of the target patient) in order to generate an inferred BMD value of the specific bone positions (bone density estimation data))
--a risk evaluation step, evaluating risk of BMD abnormality based on the inferred BMD value to generate an evaluation result; (see: paragraphs [0035] and [0036] and where the bone density estimation data is evaluated using a predetermined warning condition in order to determine risk. Risk of a BMD abnormality such as a high risk of osteoporosis is being determined based on the inferred BMD value (bone density estimation data)) and
--displaying the evaluation result on a display unit (see: paragraph [0036] where there is output of the evaluation result (which may include a warning message)).
Chen et al. may not further, specifically teach:
1) --performing data augmentation including at least random scaling and/or rotation of the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images are taken as training data;
2) --a second positioning step, locating and retrieving second X-ray images of L1 and T12; and
3) --specific bone positions as 1st segment of lumbar vertebrae and a 12th thoracic vertebra; and
4) --network as a Self-Cure-Network (SCN).
Jiang et al. teaches:
2) --a second positioning step, locating and retrieving second X-ray images of L1 and T12; (see: paragraph [0040] where there is locating and retrieving of images of specific positions such as T12 and L1) and
3) --specific bone positions as 1st segment of lumbar vertebrae and a 12th thoracic vertebra (see: paragraph [0040] where there are such specific bone positions).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 2) a second positioning step, locating and retrieving second X-ray images of L1 and T12 as taught by Jiang et al. in the method as taught by Chen et al. with the motivation(s) of improving the quality and efficiency of analytical processing of source spinal images (see: paragraph [0030] of Jiang et al.).
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute 3) 1st segment of lumbar vertebrae and a 12th thoracic vertebra as taught by Jiang et al. for the specific bone positions as disclosed by Jiang et al. since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, Chen et al. already teaches of using specific bone positions thus one could replace these positions with other positions and achieve predictable results of evaluating these positions. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
Mehanian et al. teaches:
1) --performing data augmentation including at least random scaling and/or rotation of the chest X-ray images randomly scaling the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images are taken as training data (see: paragraph [0119] where there is augmentation occurring to obtain additional images to train with. The augmentation includes random image scaling. Also see: paragraph [0002] where there is a chest x-ray which the medical images are comprised of).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) perform data augmentation including at least random scaling and/or rotation of the chest X-ray images randomly scale the chest X-ray images to generate processed chest X-ray images, wherein the processed chest X-ray images are taken as training data as taught by Mehanian et al. in the method as taught by Chen et al. and Jiang et al. in combination with the motivation(s) of creating more data for the training of the model without requiring additional images (see: paragraph [0119] of Mehanian et al.).
Sahota et al. teaches:
4) --network as a Self-Cure-Network (SCN) (see: paragraph [0018] where there is a self-cure-network).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute 4) a Self-Cure-Network (SCN) as taught by Sahota et al. for the network as disclosed by Chen et al., Jiang et al., and Mehanian et al. in combination since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, the combination of Chen et al., Jiang et al., and Mehanian et al. teaches of using a network thus one can substitute that network for another network and achieve predictable results of using a network. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
As per claim 6, claim 6 is similar to claim 1 and is therefore rejected in a similar manner. Chen et al. further teaches a BMD abnormality risk learning system, comprising:
--a non-volatile memory, storing a BMD abnormality risk learning application and a BMD abnormality risk learning application; (see: 5 of FIG. 1 and paragraph [0023] where there is a computer which would include memory storing instructions (the BMD application))
--a display unit; (see: paragraph [0034] where there is a display unit) and
--a processing unit, executing a BMD abnormality risk learning application (see: paragraph [0023] and FIG. 1 where there is a processing unit 5 executing instructions (BMD application)) to implement features.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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