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 .
Claim Objections
Claims 3, 19-20 and 25 are objected to because of the following informalities:
In claim 3, Examiner suggests correction of “the first train data” to read as “the first training data”;
In claim 19, Examiner suggests correction of “”based on second trained parameter” to read as “based on a second trained parameter;
In claim 20, Examiner suggests correction of “second train data” to read as “second training data”; and
In claim 25, Examiner suggests correction of “A computer-readable non-transitory recording medium” to read as “A non-transitory computer-readable recording medium”.
Applicant is advised that should claim 1 be found allowable, claim 24 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). In the instant application, aside from recitation of “apparatus” instead of “system” in claim 24, the claims are substantial duplicates of one another.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an estimation unit configured to” in claim 1, “an encoder configured to”, “a decoder configured to” and “a converter configured to” in claim 4, and “a first estimation unit capable of” and “a second estimation unit capable of” in claim 7. Claims 1-22 are therefore being interpreted under 35 USC 112(f).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 5-8 and 22-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for estimating a bone density value of a bone of a patient present in a plain X-ray image, does not reasonably provide enablement for estimating any type of value from any type of image of a person. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims. For instance, as written, the features “an estimated value” and “a known value” recited in the independent claims could include any value capable of being derived from any type of image, such as a pixel intensity, an attenuation value, an area of a region, a distribution value, an outline or coordinates of a region of interest, and so on. Additionally, the claims do not specify the particulars of what is contained in the recited first image and the one or more second images of first training data upon which said estimation value is based. Therefore, the broadest reasonable interpretation of these limitations encompasses the determination of any type of estimated value from any type of image of a person or any part thereof, which would require undue experimentation on the part of one of ordinary skill in the art to make and use the claimed invention. Independent claims 1 and 23-25 are thus rejected. Dependent claims 5-8 and 22 are additionally rejected for failing to remedy the deficiencies of base claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, which recites the limitation “an encoder configured to extract a feature of a temporal change of the input information and location information”, it is unclear from the wording of the claim whether the recited encoder is configured to extract location information or to extract a feature of location information. The claim is additionally unclear with regard to the purpose of the recited location information, as the subsequent claim limitations do not specify how that information is further incorporated into the claimed invention. Appropriate clarification is requested. For purposes of examination, the limitation will be interpreted according to Examiner’s best understanding of Applicant’s specification.
Regarding claim 5, which recites “wherein a result of estimation…includes a second image”, Examiner notes that claim 1 recites “first training data comprising one or more second images”, rendering the recitation of “a second image” in claim 5 unclear. Claims 6-8 are rejected for inheriting this deficiency of claim 5, and it is recommended that “a second image” in claim 5 and subsequent claims be clearly distinguished from the ”one or more second images” of claim 1. For purposes of examination, the limitation will be interpreted according to Examiner’s best understanding of Applicant’s specification.
Regarding claim 7, the phrase "capable of" in each of “a first estimation unit capable of” and “a second estimation unit capable of” renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For purposes of examination, these limitations will be given their broadest reasonable interpretation in view of available prior art.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-8 and 22-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PG Pub. 2018/0247020A1 (hereinafter “Itu”).
Regarding claim 1, Itu discloses an estimation system comprising (Itu, Figs. 1-2, ¶0069-0077, 0134):
an estimation unit configured to estimate an estimated value of a first person at a future time based on a first trained parameter and input information including a first image in which the first person appears, the first trained parameter generated by a neural network based on first training data comprising one or more second images of one or more second persons and first supervised data comprising a known value associated with each second image, wherein the known value corresponds to the estimated value (Itu, ¶0069-0077; “one or more trained ML algorithms are used to predict/estimate one or more measures of interest including, without limitation, existence/severity of osteoporosis/osteopenia; fracture risk (score, percentile, etc.); biomechanical characteristic of interest, as extracted typically from FEA: whole-bone strength, load-to-strength ratio, the type of fracture to be expected, local/average stress, local/average strain, local/global stiffness; effect of a drug-treatment (e.g. increase in bone strength in time); and disease evolution (e.g. decrease in bone strength in time). The one or more ML algorithms may be used in a cascaded or parallel workflow, and may be trained on patient-specific and/or on synthetic data, generated in vitro or in silico. In general, any ML algorithm known in the art may be applied including, for example, algorithms based on artificial neural networks (ANN), deep learning, or learning classifier/regression systems… The most important aspect for the training phase is the existence of a large database 205 comprising patient-specific information (non-invasive data, medical images, bone turnover markers) and patient-specific outcome measures of interest for osteoporosis (e.g., fracture occurrence, fracture severity, quantity extracted from a biomechanical FEA, etc.). Once this database is established, at steps 210 and 215, features are extracted from patient-specific data and outcome measures of interest are extracted. Then, at step 220, the extracted data is used to train a data-driven surrogate model for predicting the outcome measures of interest using ML algorithms.”).
Regarding claim 5, claim 1 is incorporated, and Itu further discloses wherein a result of estimation by the estimation unit includes a second image (Itu, ¶0075; “the data is presented in a graphical way (e.g., overlaid on the medical images) and presented to the clinician.”)
Regarding claim 6, claim 5 is incorporated, and Itu further discloses wherein the second image includes an X-ray image-like image (Itu, ¶0070, 0075; “Non-invasive/invasive medical images of the patient are received at step 110. These images may generally be received from any source including, for example, DXA, CT, MRI, and/or Ultrasound” and “the data is presented in a graphical way (e.g., overlaid on the medical images) and presented to the clinician.”)
Regarding claim 7, claim 5 is incorporated, and Itu further discloses wherein the estimation unit includes: a first estimation unit capable of estimating the second image and a first value as the result of estimation; and a second estimation unit capable of estimating a second value as a result of estimation from the second image (Itu, ¶0096-0103; “the second machine learning model may predict the same quantity as the first machine learning, but the first machine learning model may use only synthetic data, and the second machine learning model may additionally include patient-specific data, and, thus, act as a corrector of the prediction generated by the first ML model” wherein computed results are visualized on an imaging workstation, with outputs of each machine learning model being a measure of biomechanical characteristic of interest overlaid on the image).
Regarding claim 8, claim 7 is incorporated, and Itu further discloses wherein the estimation unit outputs a third value as a result of estimation based on the first value and the second value (Itu, ¶0096-0103; “the second machine learning model may predict the same quantity as the first machine learning, but the first machine learning model may use only synthetic data, and the second machine learning model may additionally include patient-specific data, and, thus, act as a corrector of the prediction generated by the first ML model” – the corrected prediction (“final value” in ¶0099) is read here as the claimed “third value”).
Regarding claim 22, claim 1 is incorporated, and Itu further discloses further comprising a display configured to display a result of estimation of the estimation unit as an image (Itu, ¶0103; “Computed results can be visualized on the scanner, or on another device, such as an imaging workstation. In case the measure of interest is a biomechanical characteristic any point on the image can be queried (point & click) for the associated value of the measure of interest, and the corresponding value is shown overlaid to the image. ”)
Claim 23 recites a method having features corresponding to the features recited in system claim 1, the rejection of which is applicable here.
Claim 24 recites an apparatus having features corresponding to the features recited in system claim 1, the rejection of which is applicable here.
Claim 25 recites a computer-readable non-transitory recording medium having features corresponding to the features recited in system claim 1, the rejection of which is applicable here, and Itu further discloses a computer-readable non-transitory recording medium (Itu, ¶0134).
Claim Rejections - 35 USC § 103
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.
Claims 2-3 and 11-21 are rejected under 35 U.S.C. 103 as being unpatentable over Itu, as applied to claim 1 above, in view of U.S. Patent 6,064,716 A (hereinafter “Siffert”).
Regarding claim 2, claim 1 is incorporated, and although Itu further discloses wherein the first image is a medical image received from any source (Itu, ¶0070; “Non-invasive/invasive medical images of the patient are received at step 110. These images may generally be received from any source including, for example, DXA, CT, MRI, and/or Ultrasound”), he does not expressly teach the limitations as further claimed, but, in an analogous field of endeavor, Siffert does as follows.
Siffert teaches wherein the first image is a first plain X-ray image, and the estimated value is bone mass or bone density (Siffert, col.3, l.53-67, col. 6, l.53-56, col. 12, l.10-57, Fig. 5; “Another object is to meet the above object, such that bone-mineral density may be readily and more reliably quantitatively evaluated than heretofore” wherein “a neural network is used to numerically process the bony locale image data and the phantom image data in order to evaluate the status of the bone tissue. In this alternative embodiment, a neural network is configured with a set of training patterns, in which the tissue thicknesses are known…it should be understood that in general the training data can be produced also by use of actual plain radiographs, or by a combination of both simulated data and actual radiographic data. In the case of training data from actual radiographs, the actual tissue thicknesses are determined independently, as for example with a dual energy x-ray absorptiometry machine.”).
Siffert is considered analogous art because it pertains to bone health determination using machine learning. 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 system taught by Itu to receive a plain X-ray image and estimate a bone density from said image, as taught by Siffert, in order to more accurately evaluate bone tissue and assess for bone loss (Siffert, col.11, l.15-31).
Regarding claim 3, claim 2 is incorporated, and Siffert in the combination further teaches wherein the first train data includes a second plain X-ray image in which a second person appears, and the first supervised data includes bone mass or bone density of the second person (Siffert, col.3, l.53-67, col. 12, l.10-57, Fig. 5; “Another object is to meet the above object, such that bone-mineral density may be readily and more reliably quantitatively evaluated than heretofore” wherein “a neural network is used to numerically process the bony locale image data and the phantom image data in order to evaluate the status of the bone tissue. In this alternative embodiment, a neural network is configured with a set of training patterns, in which the tissue thicknesses are known…In the case of training data from actual radiographs, the actual tissue thicknesses are determined independently, as for example with a dual energy x-ray absorptiometry machine.”).
Regarding claim 11, claim 2 is incorporated, and Siffert in the combination further teaches wherein the estimated value is bone density, the bone density being represented by at least one of bone mineral density per unit area (g/cm2), bone mineral density per unit volume (g/cm3), YAM, a T-score, and/or a Z-score (Siffert, col.1, l.10-16; “The invention pertains to apparatus and method for non-invasively and quantitatively evaluating bone-mineral density in vivo at a given time, where the bone-mineral density is characterized in terms of…areal mineral density (i.e., grams of bone per square centimeter).”).
Regarding claim 12, claim 2 is incorporated, and Itu in the combination further teaches wherein the input information includes information related to therapy for the first person (Itu, ¶0100-0102; “An important application in the context of clinical osteoporosis management is therapy planning. A ML-based workflow may be used to estimate the effect of different treatment plans and chose the best possible treatment plan for each patient” – the assessment of effectiveness of a proposed treatment plan necessitates input of that treatment plan to the ML algorithm).
Regarding claim 13, claim 12 is incorporated, and Itu in the combination further teaches wherein the information related to therapy includes information related to physical therapy or drug therapy (Itu, ¶0100-0102; “Based on the data in this database 1320, ML models may be trained at step 1325 for different drugs (teriparatide, alendronate, denosumab, romosozumab, raloxifene), different combinations of drugs, assessing the effect of changes in treatment plans, and patients which have already suffered fractures, and patients without fractures.”).
Regarding claim 14, claim 12 is incorporated, and Itu in the combination further teaches wherein the information related to therapy includes information related to at least one of a calcium drug, a female hormone drug, a vitamin drug, a bisphosphonate drug, a selective estrogen receptor modulator (SERM) drug, a calcitonin drug, a thyroid hormone drug, and/or a denosumab drug (Itu, ¶0100-0102; “Based on the data in this database 1320, ML models may be trained at step 1325 for different drugs (teriparatide, alendronate, denosumab, romosozumab, raloxifene), different combinations of drugs, assessing the effect of changes in treatment plans, and patients which have already suffered fractures, and patients without fractures.”).
Regarding claim 15, claim 2 is incorporated, and Itu in the combination further teaches wherein the input information includes bone turnover information of the first person (Itu, ¶0071; “At step 115, patient-specific bone turnover markers value are received”).
Regarding claim 16, claim 2 is incorporated, Itu in the combination further teaches wherein the input information includes individual data of the first person (Itu, ¶0070; “At step 105, non-invasive patient data is received such as, for example, demographics and patient history (e.g., age, ethnicity, sex, weight, height, fracture history, family history, smoking, alcohol, glucocorticoids, rheumatoid arthritis, etc.).”).
Regarding claim 17, claim 16 is incorporated, and Itu in the combination further teaches wherein the individual data includes age information, gender information, height information, weight information, or fracture history (Itu, ¶0070; “At step 105, non-invasive patient data is received such as, for example, demographics and patient history (e.g., age, ethnicity, sex, weight, height, fracture history, family history, smoking, alcohol, glucocorticoids, rheumatoid arthritis, etc.).”).
Regarding claim 18, claim 16 is incorporated, and Itu in the combination further teaches wherein the individual data includes information on blood pressure, a lipid, cholesterol, neutral fats, or a blood sugar level (Itu, ¶0070; “At step 105, non-invasive patient data is received such as, for example, demographics and patient history (e.g., age, ethnicity, sex, weight, height, fracture history, family history, smoking, alcohol, glucocorticoids, rheumatoid arthritis, etc.).” Patient history includes measures of patient health).
Regarding claim 19, claim 2 is incorporated, and Siffert in the combination further teaches wherein the estimation unit estimates current bone mass or bone density of the first person based on second trained parameter (Siffert, col.3, l.53-67, col. 6, l.53-56, col. 12, l.10-57, Fig. 5; “Another object is to meet the above object, such that bone-mineral density may be readily and more reliably quantitatively evaluated than heretofore” wherein “a neural network is used to numerically process the bony locale image data and the phantom image data in order to evaluate the status of the bone tissue. In this alternative embodiment, a neural network is configured with a set of training patterns, in which the tissue thicknesses are known…”).
Regarding claim 20, claim 19 is incorporated, and Siffert in the combination further teaches wherein the second trained parameter is set based on second train data including a third image of a third person and second supervised data including bone mass or bone density of the third person (Siffert, col.3, l.53-67, col. 6, l.53-56, col. 12, l.10-57, Fig. 5; “Another object is to meet the above object, such that bone-mineral density may be readily and more reliably quantitatively evaluated than heretofore” wherein “a neural network is used to numerically process the bony locale image data and the phantom image data in order to evaluate the status of the bone tissue. In this alternative embodiment, a neural network is configured with a set of training patterns, in which the tissue thicknesses are known…”).
Regarding claim 21, claim 19 is incorporated, and Itu in the combination further teaches: a display configured to display future bone density and current bone density estimated by the estimation unit (Itu, ¶0102-0103; “the outcome measures of interest predicted by the ML model may be: optimal treatment plan (which drug, which quantity), improvement in bone strength after a certain period of time, decrease of fracture risk score, etc….Computed results can be visualized on the scanner, or on another device, such as an imaging workstation. In case the measure of interest is a biomechanical characteristic any point on the image can be queried (point & click) for the associated value of the measure of interest, and the corresponding value is shown overlaid to the image.” The display of Itu is configured to output results including improvement/change over time of a measure of interest.).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Itu in view of Siffert, as applied to claim 2 above, and further in view of “3D reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy X-ray absorptiometry” (hereinafter “Whitmarsh”; published 2013).
Regarding claim 9, claim 2 is incorporated, and the combination of Itu in view of Siffert does not expressly teach the limitations as further claimed, but, in an analogous field of endeavor, Whitmarsh does as follows.
Whitmarsh teaches wherein the first plain X-ray image is an anteroposterior image (Whitmarsh, Introduction, Section 2.1; “for 30 subjects also an AP and lateral DXA image of the lumbar spine was acquired.” The AP image is the anteroposterior image.).
Whitmarsh is considered analogous art because it pertains to bone density estimation from medical images, and in particular discloses that clinical images for bone density estimation are routinely acquired in an anteroposterior direction. 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 system taught by the combination of Itu and Siffert to accept an anteroposterior image as input to the neural network, such as taught by Whitmarsh, in order to obtain an accurate bone density estimate at the anatomical site of interest (Siffert, col.11, l.15-30).
Regarding claim 10, claim 2 is incorporated, and the combination of Itu in view of Siffert does not expressly teach the limitations as further claimed, but, in an analogous field of endeavor, Whitmarsh does as follows.
Whitmarsh teaches wherein the first plain X-ray image is a lateral image (Whitmarsh, Introduction, Section 2.1; “for 30 subjects also an AP and lateral DXA image of the lumbar spine was acquired.” The AP image is the anteroposterior image.).
Whitmarsh is considered analogous art because it pertains to bone density estimation from medical images, and in particular discloses that clinical images for bone density estimation are routinely acquired in a lateral direction. 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 system taught by the combination of Itu and Siffert to accept a lateral image as input to the neural network, such as taught by Whitmarsh, in order to obtain an accurate bone density estimate at the anatomical site of interest (Siffert, col.11, l.15-30).
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. 12,033,318 (hereinafter Patent ‘318). Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of the instant application is anticipated by claim 1 of the patent, which is narrower in scope, as shown in the table below.
Claim 1 of Patent ‘318
Claim 1 of instant application
1. An estimation apparatus comprising at least one processor communicatively coupled with at least one non-transitory computer readable medium, wherein the at least one processor is programmed to:
receive input information including a first image of a first portion of a skeleton of a first person, where the first image is a plain X-ray image obtained from X-rays transmitted through the first portion of the skeleton of the first person from only a single direction, and where the first image does not include a multiple-material phantom; and
estimate a bone density of the first person based on the first image of the first portion of the skeleton of the first person and a trained parameter,
wherein the trained parameter is generated by a neural network based on one or more second X-ray images of a skeleton of one or more second persons and supervised data comprising a known bone density corresponding to each second image.
An estimation system comprising:
an estimation unit configured to estimate an estimated value of a first person at a future time based on a first trained parameter and input information including a first image in which the first person appears,
the first trained parameter being generated by a neural network based on first training data comprising one or more second images of one or more second persons and first supervised data comprising a known value associated with each second image, wherein the known value corresponds to the estimated value.
Independent claims 23-25 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of Patent ‘318 for the same rationale as set forth above with respect to claim 1.
Dependent claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 29 Patent ‘318 for the same rationale as set forth above with respect to claim 1.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,511,748 (hereinafter Patent ‘748). Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of the instant application is anticipated by claim 1 of the patent, which is narrower in scope, as shown in the table below.
Claim 1 of Patent ‘748
Claim 1 of instant application
1. An estimation system comprising at least one processor communicatively coupled with at least one non-transitory computer readable medium, wherein the at least one processor is programmed to:
receive input information including a first image of a first person, where the first image is a plain X-ray image and where the first image does not include a multiple-material phantom; and
estimate an estimated value of the first person based on the first image and a first trained parameter, wherein the estimated value is of a first type,
wherein the first trained parameter is generated by a neural network based on first training data comprising one or more second images of one or more second persons and first supervised data comprising a measured value associated with each second image,
wherein the measured value is related to the first type,
wherein the estimated value is bone mass or bone density.
1. An estimation system comprising:
an estimation unit configured to estimate an estimated value of a first person at a future time based on a first trained parameter and input information including a first image in which the first person appears,
the first trained parameter being generated by a neural network based on first training data comprising one or more second images of one or more second persons and first supervised data comprising a known value associated with each second image, wherein the known value corresponds to the estimated value.
Independent Claims 23-25 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 29-31, respectively of Patent ‘748 for the same rationale as set forth above with respect to claim 1.
Dependent Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of Patent ‘748 for the same rationale as set forth above with respect to claim 1.
Dependent Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 26 of Patent ‘748 for the same rationale as set forth above with respect to claim 1.
Allowable Subject Matter
Claim 4 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: None of the cited prior art references, either alone or in combination, expressly teaches or suggests the particular combination of limitations as recited in claim 4. In particular, while Itu and Siffert individually and/or in combination each teach use of neural networks for bone density and bone disease estimation, neither expressly teaches or suggests an encoder configured to extract a feature of a temporal change of the input information and location information, a decoder which calculates a new feature based on the feature and temporal change and initial input information, and a converter that converts the new feature into bone density, as claimed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The additionally provided references pertain generally to bone density measurement and fracture risk prediction using various techniques, including machine learning.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMAH A BEG whose telephone number is (571)270-7912. The examiner can normally be reached M-F 9 AM - 5 PM.
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/SAMAH A BEG/Primary Examiner, Art Unit 2676
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