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 .
Preliminary Amendment
The amendment submitted 7/25/2024 has been accepted and entered. Claims 1-13 are amended. No claims are cancelled. No new claims are added. Thus, claims 1-13 are examined.
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: “big-data unit”, “angle measurement unit”, “artificial intelligence unit”, in claims 1-2, 4-13.
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 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “big-data unit”, “angle measurement unit”, “artificial intelligence unit”, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function in the claim. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
Claim(s) 1-8, 10, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schock et al “Artificial intelligence-based automatic assessment of lower limb torsion on MRI” in view of Goldberg et al (US 20230119098 A1)
Regarding claims 1, 13, Schock et al discloses a method for measuring a femoral anterior angle (torsion angle) (page 8 ) and a tibial torsion angle by using artificial intelligence (artificial intelligence-based automatic assessment of lower limb torsion) (See Fig. 5 and page 7), the method comprising the-steps of: (a) obtaining, by a MRI scanner, 2D MRI images of a femur, a tibia, and the a malleolus of a patient (See Table 1, Figs. 2, 5 and pages 3, 6); (b) storing, in a big-data unit (store of database query), the 2D MRI images transmitted from the MRI scanner; (c) setting, by an artificial intelligence unit, a first baseline related to the-femoral head and neck, a second baseline related to the a lower femur, a third baseline related to the-an upper tibia, and a fourth baseline related to the malleolus, based on the 2D MRI images (algorithm-based analysis of torsion) (page 7) and (d) measuring, by an angle measurement unit, the femoral anterior angle formed between the first baseline and the second baseline, and the tibial torsion angle formed between the third baseline and the fourth baseline (page 7). Schock et al teaches to reduce radiation exposure, MRI techniques to be equivalent with traditional CT techniques (page 1) however, Schock et al teaches primarily MRI imaging. Goldberg et al discloses method and apparatus (paragraph [0037]) for measuring transverse, frontal and bisection alignment of lower limb, comprising: a three-dimensional scan of lower limb of a patient by referring to first axis of symmetry of femoral neck and second axis of symmetry of a distal portion of a femur, wherein hip version is defined by an angle between the first axis of symmetry and the second the second axis of symmetry, identifying a level of tibial torsion by referring to a third axis of symmetry of a proximal portion of a tibia and a fourth axis of symmetry of a distal portion of the tibia, wherein tibial torsion is defined by an angle between the third axis of symmetry and the fourth axis of symmetry, and providing the level of hip version and the level of tibial torsion to a user; a processor trained using artificial intelligence (paragraph [0134]); 2D calculations my implement vector match to produce six individual angle values (paragraph [0133]); data storage unit (paragraph [0235]). Thus, it would have been obvious to modify Schock et al with the teaching of Goldberg et al, so as to enable accurate 3D reconstruction and faster imaging times.
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Regarding claim 2, Schock et al (See Table 1, Figs. 2, 5 and pages 3, 6-7) in view of Goldberg et al paragraph [0133]) discloses wherein the 2D CT images comprises a plurality of femur CT images, a plurality of tibia CT images, and a plurality of malleolus CT images, and the step (a) comprises: a plurality of femur CT images, a plurality of tibia CT images, and a plurality of malleolus CT images, and the step (a) comprises: (al) a step in which the CT scanner scans the patient's femur from a top to the a bottom of the patient's femur in the xy plane to obtain the plurality of femur CT images; (a2) a step in which the CT scanner scans the patient's tibia from a top to the a bottom of the patient's tibia in the xy plane to obtain the plurality of tibia CT images; (a3) a step in which the CT scanner scans the patient's malleolus from a top to the a bottom of the patient's malleolus in the xy plane to obtain the plurality of malleolus CT images; and (a4) a step in which the CT scanner transmits the plurality of femur CT images, the plurality of tibia CT images, and the plurality of malleolus CT images to the big-data unit. Thus, it would have been obvious to modify Schock et al with the teaching of Goldberg et al so as to enable comprehensive, high precision, 3D anatomical reconstruction and analysis of the lower limbs.
Regarding claim 3, Schock et al in view of Goldberg et al discloses wherein the step (a) further comprises (a5) a step in which the CT scanner obtains 3D CT images of the femur, the tibia, and the malleolus of the patient based on the plurality of femur CT images, the plurality of tibia CT images, and the plurality of malleolus CT images using an artificial neural network (paragraphs [0016], [0134]). Thus, it would have been obvious to modify Schock et al with the teaching of Goldberg et al, so as to enable accurate 3D reconstruction and faster imaging times.
Regarding claim 4, Schock et al in view of Goldberg et al discloses wherein the step (b) comprises: (b1) a step in which the big-data unit classifies and stores (paragraph [0237]) the plurality of femur CT images, the plurality of tibia CT images, and the plurality of malleolus CT images transmitted from the CT scanner, respectively; and (b2) a step in which the big-data unit transmits the plurality of femur CT images, the plurality of tibia CT images, and the plurality of malleolus CT images to the artificial intelligence unit (paragraphs [0123]-[0125]).
Regarding claim 5, Schock et al discloses wherein the step (c) comprises: (c1) a step in which the artificial intelligence unit sets the first baseline for measuring a femoral anterior angle based on the plurality of femur CT images transmitted from the big-data unit; (c2) a step in which the artificial intelligence unit sets the second baseline for measuring a femoral anterior angle based on the plurality of femur CT images transmitted from the big-data unit; (c3) a step in which the artificial intelligence unit sets the third baseline for measuring a tibial torsion angle based on the plurality of tibia CT images transmitted from the big-data unit; and (c4) a step in which the artificial intelligence unit sets the fourth baseline for measuring a tibial torsion angle based on the plurality of malleolus CT images transmitted from the big-data unit (See Table 1, Figs. 2, 5 and pages 3, 6-7);
Regarding claim 6, Schock et al discloses wherein the step (c1) comprises: (c11) a step in which the artificial intelligence unit receives a plurality of femoral head CT images from among the plurality of femur CT images transmitted from the big-data unit; (c12) a step in which the artificial intelligence unit finds a femoral head CT image in which the largest femoral head (largest diameter) (See Table 3 and Fig. 4, page 4) is captured among the plurality of femoral head CT images based on pre-learned femur CT images; and (c13) a step in which the artificial intelligence unit sets a first reference circle that contacts the largest femoral head in the femoral head CT image in which the largest femoral head is captured and the center of the first reference circle (See Table 3 and Fig. 4, page 4).
Regarding claim 7, Schock et al discloses wherein the step (c) further comprises: (c14) a step in which the artificial intelligence unit finds a femur CT image in which the largest femoral neck (Table 3, pages 4-5) is captured among the plurality of femur CT images based on the pre- learned femur CT images; (c15) a step in which the artificial intelligence unit sets a first reference rectangle that contacts the largest femoral neck in the femur CT image in which the largest femoral neck is captured; and (c16) a step in which the artificial intelligence unit sets the first baseline parallel to the long axis of the first reference rectangle from the center of the first reference circle (Fig. 4, page 6).
Regarding claim 8, Schock et al discloses wherein the step (c2) comprises: (c21) a step in which the artificial intelligence unit receives a plurality of lower femur (lower limbs) (See Table 2, Section Segmentation performance, pages 2-3) CT images among the plurality of femur CT images transmitted from the big-data unit; (c22) a step in which the artificial intelligence unit finds a lower femur CT image in which the largest lower femur is captured among the plurality of lower femur CT images based on pre-learned femur CT images; and (c23) a step in which the artificial intelligence unit sets a second reference rectangle that contacts the largest lower femur in the lower femur CT image in which the largest lower femur is captured, and a first contact point in which the second reference rectangle contacts the largest lower femur (See Table 3 and Fig. 4, page 4).
Regarding claim 10, Schock et al discloses wherein the step (c3) comprises: (c31) a step in which the artificial intelligence unit receives a plurality of upper tibia CT images among the plurality of tibia CT images transmitted from the big-data unit; (c32) a step in which the artificial intelligence unit finds an upper tibia CT image in which the largest upper tibia is captured among the plurality of upper tibia CT images based on pre-learned tibia CT images; and (c33) a step in which the artificial intelligence unit sets a third reference rectangle that contacts the largest upper tibia in the upper tibia CT image in which the largest upper tibia is captured and a third contact point in which the third reference rectangle contacts the largest upper tibia (automatic segmentation and wide diameter detection) (See Fig. 5 and pages 6-7).
Regarding claim 12, Schock et al in view of Goldberg et al discloses wherein the step (c4) comprises: (c41) a step in which the artificial intelligence unit receives the plurality of malleolus CT images transmitted from the big-data unit; (c42) a step in which the artificial intelligence unit finds a malleolus CT image in which the largest malleolus is captured among the plurality of malleolus CT images based on pre- learned malleolus CT images; (c43) a step in which the artificial intelligence unit (paragraph [0134]) sets a fourth reference rectangle that contacts the largest malleolus in the malleolus CT image in which the largest malleolus is captured; and (c44) a step in which the artificial intelligence unit sets a fourth baseline that bisects the fourth reference rectangle while being parallel to the long axis of the fourth reference rectangle (paragraphs [0030], [0125], [0226]).
Allowable Subject Matter
Claims 9, 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 9, the prior art fails to disclose or reasonably suggest wherein the step (c2) further comprises: (c24) a step in which the artificial intelligence unit sets a plurality of first diagonal lines that connect from a vertex located on another side of a lower part among vertices of the second reference rectangle to an edge of the largest lower femur; (c25) a step in which the artificial intelligence unit sets a second contact point in which the shortest first diagonal line among the plurality of first diagonal lines contacts the largest lower femur; and (c26) a step in which the artificial intelligence unit sets the second baseline by connecting the first contact point and the second contact point.
Regarding claim 11, the prior art fails to disclose or reasonably suggest wherein the step (c3) further comprises: (c24) a step in which the artificial intelligence unit sets a plurality of first diagonal lines that connect from a vertex located on another side of a lower part among vertices of the second reference rectangle to the an edge of the largest lower femur; (c25) a step in which the artificial intelligence unit sets a second contact point in which the shortest first diagonal line among the plurality of first diagonal lines contacts the largest lower femur; and (c26) a step in which the artificial intelligence unit sets the second baseline by connecting the first contact point and the second contact point.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rahman et al (“A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction”) discloses 3D deep learning improves CT image quality and reduce radiation dose by using neural networks to reconstruct 3D volumes.
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/F.P.B./Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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