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
Applicant submitted a preliminary amendment on 3/20/2025. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Claim Objections
Claim 7 is objected to because of the following informalities:
“a prediction module, being signal-connected to the imaging analysis module, configured of a body composition prediction model” is incorrect verbiage. Appropriate correction is required.
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.
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 imaging analysis module, configured to in claim 7 and 9.
an prediction module, configured in claim 7, 8, 9, and 10.
a training module, configured to in claim 9.
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
Claims 6-7 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.
Claims 6-7 recite the limitation " the n sub-variables of the individual variable is extracted from an individual information corresponding to the medical image, and comprises a gender variable, an age variable or a combination thereof." There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Valsan (U.S. Patent Pub. No. 2023/0065288) in view of Kodama (U.S. Patent Pub. No. 2021/0027880).
Regarding Claim 1, Valsan teaches a method for evaluating body composition, comprising:
inputting a body composition index and an individual variable to a body composition prediction model so as to produce a body composition evaluation index (Fig. 6; Fig. 7; FIG. 6 is a schematic diagram of body composition analysis circuitry 58 being used to determine body composition from face and/or neck images; ¶55 User demographic information may be received from the user as part of a dedicated body composition analysis questionnaire and/or may be received from the user as part of some other health-related application)
wherein the body composition evaluation index comprises a body composition quantile value or a body age evaluated value, and (¶90 other actions may be taken based on the results of the sensor processing operations of block 108. For example, display 14 may display the estimated body fat percentage value, body mass index value, bone mass value, and/or other information determined by body composition analysis circuitry 58)
wherein the body composition index is measured based on a medical image (¶89 Body composition analysis circuitry 58 may also use images of the user's face (e.g., previously gathered face images such as face images that are gathered during user identification operations and/or face images that are captured specifically for body composition analysis) to scale full body images (e.g., body dimensions may be determined based on a fully body image and a face image, using the face image for scale),) and comprises a muscle index, a fat index or a comprehensive index, and
the individual variable corresponds to an individual variable of the medical image; and (¶81 body composition analysis circuitry 58 may analyze the images captured during block 100 and may identify which regions of the captured images are relevant for body composition analysis. This may include identifying which regions of the image data correspond to regions that strongly correlate with body composition (e.g., regions 62 of FIGS. 8, 9, 10, 11, and 12))
outputting the body composition evaluation index (¶90 other actions may be taken based on the results of the sensor processing operations of block 108. For example, display 14 may display the estimated body fat percentage value, body mass index value, bone mass value, and/or other information determined by body composition analysis circuitry 58)
Valsan does not explicitly disclose wherein the body composition index comprises a muscle index, a fat index or a comprehensive index.
Kodama is in the same field of art of image analysis. Further, Kodama teaches wherein the body composition index comprises a muscle index, a fat index or a comprehensive index (¶43 The body composition information obtaining unit 120 of the present embodiment constitutes an obtaining means for obtaining, among the body compositions of the user, a fat index indicating the degree of the user's fat and a muscle index indicating the degree of the user's muscle mass.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Valsan by using an index that comprises a muscle or fat index that is taught by Kodama; thus, one of ordinary skilled in the art would be motivated to combine the references to identify health conditions in the body (Kodama ¶103).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 6, Valsan in view of Kodama discloses the method according to any one of claim 1, wherein:
the muscle index comprises a muscle mass index, a muscle labelling index, and a muscle area index
the muscle mass index comprises SMI (Skeletal Muscle Index), SMD (Skeletal Muscle Density), ImatA (Intramuscular Adipose Tissue Area), ImatD (Intramuscular Adipose Tissue Density), NamaA (Normal Attenuation Muscle Area), LamaA (Low Attenuation Muscle Area) or a combination thereof;
the muscle labelling index comprises LWM (Lean Whole-body Mass), Total (Total Muscle Mass) or an assigned muscle group selected from a group consisting of lumbar vertebrae side muscle group, leg muscle group, chest muscle group, dorsal muscle group, ventral muscle group, biceps muscle group, triceps muscle group, and core muscle group;
the muscle area index comprises VBA (Vertebral Body Area);the fat index comprises VatA (Visceral Adipose Tissue Area), VatD (Visceral Adipose Tissue Density), SatA (Subcutaneous Adipose Tissue Area), SatD (Subcutaneous Adipose Tissue Density) or any combination thereof,
the comprehensive index comprises a muscle volume comprehensive index, a muscle quality comprehensive index, a muscle comprehensive index, a visceral fat index or a body comprehensive index (Kodama, ¶32 The body composition of the user includes fat rate, fat mass, fat free mass, visceral fat mass, visceral fat level, visceral fat area, subcutaneous fat mass, muscle mass, bone mass, body water percentage, body water content, intracellular fluid volume, and extracellular fluid volume of the whole body and various parts; Since the limitation states “wherein the body composition index comprises a muscle index, a fat index or a comprehensive index” only one of the indexes must be mapped correspondingly.)
the n sub-variables of the individual variable is extracted from an individual information corresponding to the medical image, and comprises a gender variable, an age variable or a combination thereof (Valsan, ¶86 If desired, body composition analysis circuitry 58 may also take into account any available user demographic information (e.g., gender, height, weight, age, ethnicity, and/or other user data stored in device 10 and/or otherwise provided to circuitry 58) to determine the body composition of the user.)
The reasons for combining Valsan and Kodama are similar to that stated in the rejection of claim 1.
Regarding Claim 7, Valsan teaches a system for evaluating body composition (Fig. 1,) comprising:
an imaging analysis module, configured to measure and output a body composition index based on a medical image, and to extract an individual variable corresponding to the medical image (Fig. 6; Fig. 7; FIG. 6 is a schematic diagram of body composition analysis circuitry 58 being used to determine body composition from face and/or neck images; ¶55 User demographic information may be received from the user as part of a dedicated body composition analysis questionnaire and/or may be received from the user as part of some other health-related application,) wherein the body composition index comprises a muscle index, a fat index or a comprehensive index; and
a prediction module, being signal-connected to the imaging analysis module, configured of a body composition prediction model (Fig. 1,) wherein the body composition index and the individual variable are input to the body composition prediction model so as to produce a body composition evaluation index, wherein (Fig. 6; Fig. 7; FIG. 6 is a schematic diagram of body composition analysis circuitry 58 being used to determine body composition from face and/or neck images; ¶55 User demographic information may be received from the user as part of a dedicated body composition analysis questionnaire and/or may be received from the user as part of some other health-related application):
the body composition evaluation index comprises a body composition quantile value or a body age evaluated value (¶90 other actions may be taken based on the results of the sensor processing operations of block 108. For example, display 14 may display the estimated body fat percentage value, body mass index value, bone mass value, and/or other information determined by body composition analysis circuitry 58)
the n sub-variables of the individual variable are extracted from an individual information corresponding to the medical image, and comprises a gender variable, an age variable or a combination thereof (¶86 If desired, body composition analysis circuitry 58 may also take into account any available user demographic information (e.g., gender, height, weight, age, ethnicity, and/or other user data stored in device 10 and/or otherwise provided to circuitry 58) to determine the body composition of the user.)
Valsan does not explicitly disclose wherein the body composition index comprises a muscle index, a fat index or a comprehensive index
the muscle index comprises a muscle mass index, a muscle labelling index, and a muscle area index;
the muscle mass index comprises SMI (Skeletal Muscle Index), SMD (Skeletal Muscle Density), ImatA (Intramuscular Adipose Tissue Area), ImatD (Intramuscular Adipose Tissue Density), NamaA (Normal Attenuation Muscle Area), LamaA (Low Attenuation Muscle Area) or a combination thereof,
the muscle labelling index comprises LWM (Lean Whole-body Mass), Total (Total Muscle Mass) or an assigned muscle group selected from a group consisting of lumbar vertebrae side muscle group, leg muscle group, chest muscle group, dorsal muscle group, ventral muscle group, biceps muscle group, triceps muscle group, and core muscle group;
the muscle area index comprises VBA (Vertebral Body Area);
the fat index comprises VatA (Visceral Adipose Tissue Area), VatD (Visceral Adipose Tissue Density), SatA (Subcutaneous Adipose Tissue Area), SatD (Subcutaneous Adipose Tissue Density) or any combination thereof,
the comprehensive index comprises a muscle volume comprehensive index, a muscle quality comprehensive index, a muscle comprehensive index, a visceral fat index or a body comprehensive index.
Kodama is in the same field of art of image analysis. Further, Kodama teaches wherein the body composition index comprises a muscle index, a fat index or a comprehensive index (¶43 The body composition information obtaining unit 120 of the present embodiment constitutes an obtaining means for obtaining, among the body compositions of the user, a fat index indicating the degree of the user's fat and a muscle index indicating the degree of the user's muscle mass)
the muscle index comprises a muscle mass index, a muscle labelling index, and a muscle area index;
the muscle mass index comprises SMI (Skeletal Muscle Index), SMD (Skeletal Muscle Density), ImatA (Intramuscular Adipose Tissue Area), ImatD (Intramuscular Adipose Tissue Density), NamaA (Normal Attenuation Muscle Area), LamaA (Low Attenuation Muscle Area) or a combination thereof,
the muscle labelling index comprises LWM (Lean Whole-body Mass), Total (Total Muscle Mass) or an assigned muscle group selected from a group consisting of lumbar vertebrae side muscle group, leg muscle group, chest muscle group, dorsal muscle group, ventral muscle group, biceps muscle group, triceps muscle group, and core muscle group;
the muscle area index comprises VBA (Vertebral Body Area);
the fat index comprises VatA (Visceral Adipose Tissue Area), VatD (Visceral Adipose Tissue Density), SatA (Subcutaneous Adipose Tissue Area), SatD (Subcutaneous Adipose Tissue Density) or any combination thereof,
the comprehensive index comprises a muscle volume comprehensive index, a muscle quality comprehensive index, a muscle comprehensive index, a visceral fat index or a body comprehensive index (¶32 The body composition of the user includes fat rate, fat mass, fat free mass, visceral fat mass, visceral fat level, visceral fat area, subcutaneous fat mass, muscle mass, bone mass, body water percentage, body water content, intracellular fluid volume, and extracellular fluid volume of the whole body and various parts; Since the limitation states “wherein the body composition index comprises a muscle index, a fat index or a comprehensive index” only one of the indexes must be mapped correspondingly.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Valsan by using an index that comprises a muscle or fat index that is taught by Kodama; thus, one of ordinary skilled in the art would be motivated to combine the references to identify health conditions in the body (Kodama ¶103).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claims 2-5 and 8-10 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 claims 2 and 8, no prior art teaches wherein the body composition prediction model is defaulted to contain m prediction quantile values and a prediction index matrix, wherein the prediction index matrix is a matrix of dimension m*1 comprising m prediction body composition index corresponding to the prediction quantile values one on one, and m is any one of positive integers larger or equal to 1, wherein the method further comprises:
comparing the body composition index and the prediction body composition index based on the prediction index matrix so as to obtain a corresponding predicted quantile value outputted to be the body composition quantile value, wherein:
the prediction index matrix is derived from a multiplication of a prediction coefficient matrix and an individual variable matrix, and the prediction coefficient matrix is a matrix of dimension m*(n+1) comprising a prediction constant corresponding to each one of the prediction quantile values and a prediction coefficient set corresponding to each one of the prediction quantile values, and wherein the prediction coefficient set comprises n prediction coefficients, and n is any one of positive integers larger or equal to 1;
the individual variable matrix is a matrix of dimension (n+1)*1 comprising a prediction variable corresponding to the prediction constant and n sub-variables corresponding to the individual variable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
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