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 .
PNG
media_image1.png
200
400
media_image1.png
Greyscale
This action is responsive to the Application filed on 03/23/2026
Claims 1-24 are pending in the case. Claims 1, 12 and 14 are independent claims. Claims 21-22 have been canceled. Claims 23-24 have been newly added.
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.
Claims 1-2, 10, 14 and 25-26 are rejected under 35 U.S.C 103 as being unpatentable over Dale et al. (US Patent No. 7,680,308 B2), hereinafter referred to as Dale in view of “Managing Repeat Digital Radiography Images—A Systematic Approach and Improvement”, https://link.springer.com/article/10.1007/s10916-011-9744-8, Tzeng, 05/31/2011, hereinafter referred to as Tzeng and further in view of Hsieh (US Pub No.: 20180144214 A1), hereinafter referred to as Hsieh .
With respect to claim 1, Dale disclose:
A computer-implemented method for providing error information in respect of a plurality of individual measurements, comprising: providing the plurality of individual measurements in a database, each individual measurement among the plurality of individual measurements being assigned to a corresponding examination (In Fig. 1 and Col. 10, lines 20–36, Dale discloses one or more parameters identified to generate a numerical measure of dissatisfaction associated with said one or more parameters within said set of medical imaging exams.)
Receiving check information via an interface in respect of a cohort of examinations, the cohort of examinations including a plurality of examinations, performing first operations for each respective examination among the cohort of examinations based on the receiving, and the first operations including(In Fig. 1 and Col. 10, lines 20–36, Dale discloses that QAISys also receives data entry from a point-of-exam input and radiologist interface, including an exam dissatisfaction value.)
Extracting examination information corresponding to first individual measurements assigned to the respective examination from among the plurality of individual measurements (In Col. 10, lines 20-56, Dale discloses QAISys database 91 interfaces with HIS, RIS and PACS databases, it receives and stores exam-specific information, including patient information, image information, and exam dissatisfaction values. The database then manipulates and analyzes that information on a per-exam basis to generate quality ratings and reports. The reports retrieve the data associated with a particular examination from the database before presenting or analyzing it.)
Determining whether the first individual measurements include at least one incorrect measurement based on the examination information to obtain a determining result, each of the plurality of individual measurements being obtained based on an individual measuring process performed with a medical device(In Fig. 7 and Cols.16–17, Dale discloses assigning dissatisfaction values to said unsatisfactory images, each unsatisfactory exam based upon the radiologist's degree of dissatisfaction.)
Ascertaining corresponding examination error information for the respective examination based on the determining result (In Fig. 10B and Col.19, lines 41–53, Dale discloses QAISys stores and derives exam dissatisfaction values, quality rating, and quality information for that examination. Once the examination is identified as unsatisfactory, QAISys generates/stores the associated quality information.)
Providing the compiled examination error information via the interface (In Col. 10, 20-36, Dale discloses a QAISys database interface that provides baseline information associated with each examination.)
Performing a further examination using a medical imaging system or a laboratory diagnostic device, measurement settings of the medical imaging system or the laboratory diagnostic device used during the further examination being configured based on the compiled examination error information (In Fig. 7 and Col. 7, lines 7-17, Dale disclose a QUALITY ASSESSMENT and IMPROVEMENT SYSTEM (QAISys) in MEDICAL IMAGING)
With respect to claim 1, Dale does not explicitly disclose:
Compiling the corresponding examination error information for each respective examination among the cohort of examinations to obtain compiled examination error information, the compiled examination error information including information corresponding to one or more incorrect measurements included in each of two or more examinations among the cohort of examinations, each of the two or more examinations including a successful measurement and a respective incorrect measurement among the one or more incorrect measurements, the successful measurement and the respective incorrect measurement being among the plurality of individual measurements
Successful measurement being obtained by repeating the respective incorrect measurement
However, it is known by Tzeng to disclose:
Compiling the corresponding examination error information for each respective examination among the cohort of examinations to obtain compiled examination error information, the compiled examination error information including information corresponding to one or more incorrect measurements included in each of two or more examinations among the cohort of examinations, each of the two or more examinations including a successful measurement and a respective incorrect measurement among the one or more incorrect measurements, the successful measurement and the respective incorrect measurement being among the plurality of individual measurements (On page 3-4 (Reason for repeated image), Tzeng discloses, based on the premise, that if an image is repeated, the first acquisition was not acceptable for diagnostic use. Furthermore, Tzeng discloses an initial radiographic acquisition that is determined to be unacceptable due to imaging errors (e.g., positioning or exposure errors) and is therefore being repeated.)
Dale in view of Tzeng are analogous pieces of art because both references concern assessing anatomy and physiology in patients. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dale, with collecting data in the medical information systems as taught by Dale, while repeating images, and the collected analysis as taught by Tzeng. The motivation for doing so would have been to improve equipment maintenance in order to improve quality of the image (In Col. 3, lines 27-28, of Dale.)
With respect to claim 1, Dale in view of Tzeng do not explicitly disclose:
Successful measurement being obtained by repeating the respective incorrect measurement
However, it is known by Hsieh to disclose:
Successful measurement being obtained by repeating the respective incorrect measurement (In paragraph [0185], Hsieh discloses the substance of a successful measurement: an image acquisition having sufficient or acceptable quality after the acquisition settings are adjusted and the acquisition process is repeated. )
Dale in view of Tzeng and Hsieh are analogous pieces of art because both references concern assessing anatomy and physiology in patients. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Hsieh, with identify acceptable or unacceptable resolution in the image with respect to a range, threshold, etc., that is defined and/or learned by the DDLD as taught by Hsieh. The motivation for doing so would have been to improvement in imaging device (See [0192] of Hsieh.)
Regarding claim 2, Dale in view of Tzeng and Hsieh disclose the elements of claim 1. In addition, Dale disclose:
The computer-implemented method of claim 1, wherein the compiling the corresponding examination error information comprises a statistical evaluation of the corresponding examination error information for each respective examination among the cohort of examinations (In Fig. 7 and Col. 12, Dale discloses a dissatisfaction value for each test image. The dissatisfaction value 90 is a key input to the QAISys database 91 and is used by the Quality Rating formulation means 92 to establish a quality rating (QR) for each test image.)
Regarding claim 10, Dale in view of Tzeng and Hsieh disclose the elements of claim 1. In addition, Hsieh disclose:
The computer-implemented method of claim 12, wherein the trained function is continuously trained further via feedback, the feedback being provided via an external source (In paragraph [0093], Hsieh discloses that a feedback collector 808 monitors the output (and input) and gathers feedback based on operation of the deployed deep learning device 703. The network of the training device 701 is then updated/re-trained using the feedback until the model evaluator 802 is satisfied that the training network model is complete.)
With respect to claim 14, Dale disclose:
A system for providing error information in respect of a plurality of individual measurements, comprising: a medical imaging system or a laboratory diagnostic device (In Fig. 7 and Col. 7, lines 7-17, Dale disclose a QUALITY ASSESSMENT and IMPROVEMENT SYSTEM (QAISys) in MEDICAL IMAGING.)
An interface, at least one of the processing circuitry or the interface being configured to provide the plurality of individual measurements in a database, each individual measurement among the plurality of individual measurements being assigned to a corresponding examination, wherein the interface is configured to receive check information in respect of a cohort of examinations, the cohort of examinations including a plurality of examinations for each respective examination among the cohort of examinations, the processing circuitry is configured to: (In Col. 10, 20-36, Dale discloses a QAISys database interface that provides baseline information associated with each examination.)
Perform first operations for each respective examination among the cohort of examinations based on the received check information, the first operations including (In Fig. 1 and Col. 10, lines 20–36, Dale discloses that QAISys also receives data entry from a point-of-exam input and radiologist interface, including an exam dissatisfaction value.)
Extracting examination information corresponding to first individual measurements assigned to the respective examination from among the plurality of individual measurements, each of the plurality of individual measurements being obtained based on an individual measuring process performed with a medical device (In Col. 10, lines 20-56, Dale discloses QAISys database 91 interfaces with HIS, RIS and PACS databases, it receives and stores exam-specific information, including patient information, image information, and exam dissatisfaction values. The database then manipulates and analyzes that information on a per-exam basis to generate quality ratings and reports. The reports retrieve the data associated with a particular examination from the database before presenting or analyzing it.)
Determining whether the first individual measurements include at least one incorrect measurement based on the examination information to obtain a determining result (In Fig. 7 and Cols.16–17, Dale discloses assigning dissatisfaction values to said unsatisfactory images, each unsatisfactory exam based upon the radiologist's degree of dissatisfaction.)
Ascertaining corresponding examination error information for the respective examination based on the determining (In Fig. 10B and Col.19, lines 41–53, Dale discloses QAISys stores and derives exam dissatisfaction values, quality rating, and quality information for that examination. Once the examination is identified as unsatisfactory, QAISys generates/stores the associated quality information.)
The medical imaging system or the laboratory diagnostic device is configured to perform a further examination, measurement settings of the medical imaging system or the laboratory diagnostic device used during the further examination being configured based on the compiled examination error information (In Col. 12, lines 25-39, Dale disclose a QAISys system to assess the performance capability and determine an action plan to improve the rating, collect and present medical images but stops short of teaching its use of data collected.)
With respect to claim 14, Dale does not explicitly disclose:
Processing circuitry
Compile the corresponding examination error information for each respective examination among the cohort of examinations to obtain compiled examination error information, the compiled examination error information including information corresponding to one or more incorrect measurements included in each of two or more examinations among the cohort of examinations, each of the two or more examinations including a successful measurement and a respective incorrect measurement among the one or more incorrect measurements the successful measurement and the respective incorrect measurement being among the plurality of individual measurements
The successful measurement being obtained by repeating the respective incorrect measurement
However, it is known by Hsieh to disclose:
Processing circuitry (In paragraph [0112], Hsieh disclose a one or more processors, a component of a healthcare system)
The successful measurement being obtained by repeating the respective incorrect measurement (In paragraph [0185], Hsieh discloses the substance of a successful measurement: an image acquisition having sufficient or acceptable quality after the acquisition settings are adjusted and the acquisition process is repeated. )
Dale in view of Hsieh are analogous pieces of art because both references concern assessing anatomy and physiology in patients. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dale, with collecting data in the medical information systems as taught by Dale, with identify acceptable or unacceptable resolution in the image with respect to a range, threshold, etc., that is defined and/or learned by the DDLD as taught by Hsieh.. The motivation for doing so would have been to improve equipment maintenance in order to improve quality of the image (In Col. 3, lines 27-28, of Dale.)
With respect to claim 14, Dale in view of Hsieh do not explicitly disclose:
Compile the corresponding examination error information for each respective examination among the cohort of examinations to obtain compiled examination error information, the compiled examination error information including information corresponding to one or more incorrect measurements included in each of two or more examinations among the cohort of examinations, each of the two or more examinations including a successful measurement and a respective incorrect measurement among the one or more incorrect measurements the successful measurement and the respective incorrect measurement being among the plurality of individual measurements
However, it is known by Tzeng to disclose:
Compile the corresponding examination error information for each respective examination among the cohort of examinations to obtain compiled examination error information, the compiled examination error information including information corresponding to one or more incorrect measurements included in each of two or more examinations among the cohort of examinations, each of the two or more examinations including a successful measurement and a respective incorrect measurement among the one or more incorrect measurements the successful measurement and the respective incorrect measurement being among the plurality of individual measurements (On page 3-4 (Reason for repeated image), Tzeng discloses, based on the premise, that if an image is repeated, the first acquisition was not acceptable for diagnostic use. Furthermore, Tzeng discloses an initial radiographic acquisition that is determined to be unacceptable due to imaging errors (e.g., positioning or exposure errors) and is therefore being repeated.)
Dale in view of Hsieh and Tzeng are analogous pieces of art because both references concern assessing anatomy and physiology in patients. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tzeng, while repeating images, and the collected analysis as taught by Tzeng.. The motivation for doing so would have been to improvement in imaging device (See [0192] of Hsieh.)
Regarding claim 25, Dale in view of Tzeng and Hsieh disclose the elements of claim 1. In addition, Tzeng disclose:
The computer-implemented method of claim 1, wherein the measurement settings of the medical imaging system or the laboratory diagnostic device include at least one of: a body region or a scan region; a measurement instant; an image contrast; a sample type; a reagent; an exposure time; an exposure length; or an energy of X-ray radiation (Examiner selects: exposure time, On page 3 (Image count generated through DR), Tzeng disclose Other important information (e.g., exposure times), which can be used to calculate the number of images generated is usually stored on a file in QC stations of DR systems.)
Regarding claim 26, Dale in view of Tzeng and Hsieh disclose the elements of claim 1. In addition, Tzeng disclose:
The computer-implemented method of claim 1, wherein the providing the compiled examination error information via the interface includes displaying the compiled examination error information in a graphical user interface on a screen (In Col. 10, 20-36, Dale discloses a QAISys database interface that provides baseline information associated with each examination.)
Claims 3 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Dale in view of Tzeng, Hsieh in view of SATO et al. (US Pub No.: 20230033495 A1), hereinafter referred to as SATO.
Regarding claim 3, Dale in view of Tzeng and Hsieh disclose elements of claim 1. Dale in view of Tzeng and Heish do not explicitly disclose:
The computer-implemented method of claim 1, wherein a first set of individual measurements and a second set of individual measurements are assigned to a first examination among the cohort of examinations
the first set of individual measurements includes one or more successful individual measurements that occurred during the first examination
the second set of individual measurements includes the at least one incorrect measurement that occurred during the first examination
the examination information includes at least one item of first information on the first set of individual measurements
and the examination information includes at least one item of second information on a subset of the second set of individual measurements
However, SATO disclose the limitations:
The computer-implemented method of claim 1, wherein a first set of individual measurements and a second set of individual measurements are assigned to a first examination among the cohort of examinations (In paragraph [0074], SATO discloses the first evaluation data with respect to the second evaluation data.)
The first set of individual measurements includes one or more successful individual measurements that occurred during the first examination (In paragraph [0074], SATO discloses the first evaluation data being correct.)
The second set of individual measurements includes the at least one incorrect measurement that occurred during the first examination (In paragraph [0074], SATO discloses a second evaluation data that was incorrect.)
The examination information includes at least one item of first information on the first set of individual measurements (In paragraph [0027], SATO discloses the first evaluation, including the processing of generating image data including non-defective bead B1 or image data including defective bead B1, without using learning data D1 as original learning data by computer graphics (CG) technology.)
The examination information includes at least one item of second information on a subset of the second set of individual measurements (In paragraph [0028], SATO discloses the second evaluation that having a projection on the surface is set as processing target data. The parameters of the data extension processing may include the movement amount of the projection, the size of the projection, and the rotation amount of the projection.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh to modify SATO, a generation method for learning data, a generation method for learned model, and an evaluation system for learning data as taught by SATO. The motivation for doing so would have been to increase the recognition rate at the time of estimation concerning the recognition target (See [0045] of SATO).
Regarding claim 18, Dale in view of Tzeng and Hsieh disclose elements of claim 2. Dale in view of Tzeng and Heish do not explicitly disclose:
The computer-implemented method of claim 2, wherein a first set of individual measurements and a second set of individual measurements are assigned to a first examination among the cohort of examinations
the first set of individual measurements includes one or more successful individual measurements that occurred during the first examination
the second set of individual measurements includes the at least one incorrect measurement that occurred during the first examination
the examination information includes at least one item of first information on the first set of individual measurements
and the examination information includes at least one item of second information on a subset of the second set of individual measurements
However, SATO disclose the limitations:
The computer-implemented method of claim 2, wherein a first set of individual measurements and a second set of individual measurements are assigned to a first examination among the cohort of examinations (In paragraph [0074], SATO discloses the first evaluation data with respect to the second evaluation data.)
The first set of individual measurements includes one or more successful individual measurements that occurred during the first examination (In paragraph [0074], SATO discloses the first evaluation data being correct.)
The second set of individual measurements includes the at least one incorrect measurement that occurred during the first examination (In paragraph [0074], SATO discloses a second evaluation data that was incorrect.)
The examination information includes at least one item of first information on the first set of individual measurements (In paragraph [0027], SATO discloses the first evaluation, including the processing of generating image data including non-defective bead B1 or image data including defective bead B1, without using learning data D1 as original learning data by computer graphics (CG) technology.)
The examination information includes at least one item of second information on a subset of the second set of individual measurements (In paragraph [0028], SATO discloses the second evaluation that having a projection on the surface is set as processing target data. The parameters of the data extension processing may include the movement amount of the projection, the size of the projection, and the rotation amount of the projection.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh to modify SATO, a generation method for learning data, a generation method for learned model, and an evaluation system for learning data as taught by SATO. The motivation for doing so would have been to increase the recognition rate at the time of estimation concerning the recognition target (See [0045] of SATO).
Claims 4, 7-9, 19 and 24 are rejected under 35 U.S.C 103 as being unpatentable over Dale in view of Tzeng, Hsieh, SATO in view of Kano et al. (US Pub No.: 20200105388 A1), hereinafter referred to as Kano
Regarding claim 4, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 3. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 3, wherein the first set of individual measurements is specified in an examination protocol
The computer-implemented method further comprises providing the examination protocol
The determining is based on the examination protocol
However, Kano disclose the limitation:
The computer-implemented method of claim 3, wherein the first set of individual measurements is specified in an examination protocol (In Fig. 23 and paragraphs [0213-0216], Kano discloses the first group, which includes the designated medical examination data item, displaying instructions to display a display screen concerning medical examinations on a specific patient is input via the input interface.)
The computer-implemented method further comprises providing the examination protocol (In Fig. 23 and paragraphs [0213-0216], Kano discloses a set of steps followed to display a screen concerning medical examinations on a specific patient.)
The determining is based on the examination protocol (In Fig. 23 and paragraphs [0213-0216], Kano discloses displaying the classified medical examination data items.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Kano, extracting attributes concerning a plurality of medical examination data items of a patient as taught by Kano. The motivation for doing so would have been to improve the collect medical exam information from environments (See [0226] of Kano).
Regarding claim 7, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 3. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 3, wherein a consecutive number is assigned to each individual measurement among both the first set of individual measurements and the second set of individual measurements to obtain a plurality of numbers, the examination information includes the first numbers assigned to the individual measurements among both the first set of individual measurements and the subset of the second set of individual measurements, the first numbers being among the plurality of numbers
However, Kano disclose the limitation:
The computer-implemented method of claim 3, wherein a consecutive number is assigned to each individual measurement among both the first set of individual measurements and the second set of individual measurements to obtain a plurality of numbers, the examination information includes the first numbers assigned to the individual measurements among both the first set of individual measurements and the subset of the second set of individual measurements, the first numbers being among the plurality of numbers (In paragraph [0028], Kano discloses a first group which includes the designated medical examination data item and a second group which includes at least one of the other medical examination data items. In paragraph [0068], Kano discloses the medical examination data item is data including at least one measurement, medical image data item, or the like obtained from the patient in, for example, a laboratory test or an imaging test.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Kano, extracting attributes concerning a plurality of medical examination data items of a patient as taught by Kano. The motivation for doing so would have been to improve the collect medical exam information from environments (See [0226] of Kano).
Regarding claim 8, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 3. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 3, wherein an incorrect measurement of the second set of individual measurements corresponds to a successful individual measurement of the first set of individual measurements
and the corresponding examination error information includes information indicating which successful individual measurements among the first set of individual measurements has a corresponding incorrect measurement among the subset of the second set of individual measurements
However, Kano disclose the limitation:
The computer-implemented method of claim 3, wherein an incorrect measurement of the second set of individual measurements corresponds to a successful individual measurement of the first set of individual measurements (In paragraph [0028], Kano discloses a first group which includes the designated medical examination data item and a second group which includes at least one of the other medical examination data items.)
and the corresponding examination error information includes information indicating which successful individual measurements among the first set of individual measurements has a corresponding incorrect measurement among the subset of the second set of individual measurements (In Fig. 3 and paragraph [0028], Kano discloses extracting attributes concerning a plurality of medical examination data items of a patient, classifies the medical examination data items into a first group and a second group based on the attributes.)
Regarding claim 9, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 3. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 3, wherein a respective designation is assigned to each individual measurement among both the first set of individual measurements and the second set of individual measurements to obtain a plurality of designations
the first designations assigned to the successful individual measurements of the first set of individual measurements are unique, the first designations being among the plurality of designations
a second designation assigned to an incorrect individual measurement of the second set of individual measurements corresponds to the designation of a corresponding successful individual measurement of the first set of individual measurements, the second designation being among the plurality of designations
the examination information includes the third designations assigned to the individual measurements of both the first set of individual measurements and the subset of the second set of individual measurements, the third designations being among the plurality of designations
and the determining includes determining identical designations among the third designations included in the examination information
However, Kano disclose the limitation:
The computer-implemented method of claim 3, wherein a respective designation is assigned to each individual measurement among both the first set of individual measurements and the second set of individual measurements to obtain a plurality of designations (In paragraph [0028], Kano discloses a first group which includes the designated medical examination data item and a second group which includes at least one of the other medical examination data items.)
the first designations assigned to the successful individual measurements of the first set of individual measurements are unique, the first designations being among the plurality of designations (In paragraph [0085], Kano, the first embodiment, the successful individual measurements in the first group are unique, and these names are part of a larger set of names.)
a second designation assigned to an incorrect individual measurement of the second set of individual measurements corresponds to the designation of a corresponding successful individual measurement of the first set of individual measurements, the second designation being among the plurality of designations (In paragraph [0085], Kano, the second embodiment, the successful individual measurements in the first group are unique, and these names are part of a larger set of names.)
the examination information includes the third designations assigned to the individual measurements of both the first set of individual measurements and the subset of the second set of individual measurements, the third designations being among the plurality of designations (In paragraph [0145], Kano discloses the medical examination data items classified under the third display style are plotted on one line segment of “+” with filled circles. The medical examination data items classified under the third display style are plotted on one line segment of “−” by filled circles.)
and the determining includes determining identical designations among the third designations included in the examination information (In paragraph [0148], Kano disclosure is determined based on the item name (item ID) and unit of the data attributes; however, the configuration is not limited to this. In the second modification, the similarity is calculated, and the display method is determined based on, for example, the “measurement” and data attributes “tester” and “apparatus name” of the measurement of the imaging test (echo) as well as the item name (item ID) and unit.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Kano, extracting attributes concerning a plurality of medical examination data items of a patient as taught by Kano. The motivation for doing so would have been to improve the collect medical exam information from environments (See [0226] of Kano).
Regarding claim 19, Dale in view of Tzeng, Hsieh, SATO and Kano disclose elements of claim 18. In addition, Kano disclose:
The computer-implemented method of The computer-implemented method of wherein the first set of individual measurements is specified in an examination protocol (In Fig. 23 and paragraphs [0213-0216], Kano discloses the first group, which includes the designated medical examination data item, displaying instructions to display a display screen concerning medical examinations on a specific patient is input via the input interface.)
The computer-implemented method further comprises providing the examination protocol (In Fig. 23 and paragraphs [0213-0216], Kano discloses a set of steps followed to display a screen concerning medical examinations on a specific patient.)
Determining is based on the examination protocol (In Fig. 23 and paragraphs [0213 0216], Kano discloses displaying the classified medical examination data items.)
Regarding claim 24, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 1. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 1, wherein the performing performs the further examination using the medical imaging system, the medical imaging system including at least one of: an X-ray device; a Computed Tomography (CT) device
However, Kano disclose the limitation:
The computer-implemented method of claim 1, wherein the performing performs the further examination using the medical imaging system, the medical imaging system including at least one of: an X-ray device; a Computed Tomography (CT) device (Examiner selects: Computed Tomography In paragraph [0035], Kano disclose The medical image diagnosis apparatus includes, for example, an X-ray computed tomography apparatus, an X-ray diagnostic apparatus, a magnetic resonance imaging apparatus, a nuclear medicine diagnostic apparatus, and an ultrasound diagnostic apparatus.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Kano, extracting attributes concerning a plurality of medical examination data items of a patient as taught by Kano. The motivation for doing so would have been to improve the collect medical exam information from environments (See [0226] of Kano).
Claim 5-6, 17, 20 and 23 are rejected under 35 U.S.C 103 as being unpatentable over Dale in view of Tzeng, Hsieh, SATO in view of Lane et al. (US Pub No.: 20090275805 A1), hereinafter referred to as Lane.
Regarding claim 5, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 3. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 3, wherein the at least one item of first information and the at least one item of second information include at least one of: a designation of a respective individual measurement included in the first set of individual measurements or the subset of the second set of individual measurements; a type of the respective individual measurement; a body region measured during the respective individual measurement; a dose-length product of the respective individual measurement; an X-ray voltage of the respective individual measurement; a name of an examination protocol of the first examination; and a reason for the first examination
However, Lane disclose the limitation:
The computer-implemented method of claim 3, wherein the at least one item of first information and the at least one item of second information include at least one of: a designation of a respective individual measurement included in the first set of individual measurements or the subset of the second set of individual measurements; a type of the respective individual measurement; a body region measured during the respective individual measurement; a dose-length product of the respective individual measurement; an X-ray voltage of the respective individual measurement; a name of an examination protocol of the first examination; and a reason for the first examination (Examiner selects: a type of the respective individual measurement. In paragraph [0090], Lane discloses evaluating measurement parameters (the actual physiological data being monitored) using medical equipment to identify potential errors as they happen. The system can also monitor other metrics (secondary parameter) that might indicate measurement errors, supporting real-time error detection. )
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Lane, with automatically detecting an operator error and provide assistance to the operator using medical equipment as taught by Lane. The motivation for doing so would have been to detect and provide erroneous measurement in respect to physiological data (See [0094] of Lane.)
Regarding claim 6, Dale in view of Tzeng, Hsieh, SATO and Lane disclose elements of claim 5. In addition, Hsieh disclose:
The computer-implemented method of claim 5, wherein the determining comprises: determining a difference between a protocol number of individual measurements and an examination number of individual measurements, the protocol number corresponds to a number of individual measurements among the first set of individual measurements, the examination number corresponds to a number of individual measurements among both the first set of individual measurements and the subset of the second set of individual measurements, and determining a difference between the protocol number and the examination number (In paragraph [0150], Hsieh disclose the DDLD can help determine what protocol is the best selection to provide image data set output. Patient behavior, such as movement during scans, how their body handles contrast, the timing of the scan, perceived dose, etc., can be gathered as input by the DDLD .)
Regarding claim 17, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 2. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of wherein the statistical evaluation of the corresponding examination error information for each respective examination among the cohort of examinations is in relation to at least one of: a frequency of incorrect measurements on a-the medical device; a frequency of incorrect measurements due to an operator; or a frequency of incorrect measurements during an examination of a particular disease
However, Lane disclose the limitation
The computer-implemented method of wherein the statistical evaluation of the corresponding examination error information for each respective examination among the cohort of examinations is in relation to at least one of: a frequency of incorrect measurements on a-the medical device; a frequency of incorrect measurements due to an operator; or a frequency of incorrect measurements during an examination of a particular disease (Examiner selects: a frequency of incorrect measurements on a-the medical
device. In paragraph [0088], Lane discloses medical equipment to detect operator errors
and to positively intervene with concise directions to correct an operator error.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Lane, with automatically detecting an operator error and provide assistance to the operator using medical equipment as taught by Lane. The motivation for doing so would have been to detect and provide erroneous measurement in respect to physiological data (See [0094] of Lane.)
Regarding claim 20, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 1. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of wherein the at least one item of first information the at least one item of second information include at least one of: a designation of a respective individual measurement included in the first set of individual measurements or the subset of the second set of individual measurements; a type of the respective individual measurement; a body region measured during the respective individual measurement; a dose-length product of the respective individual measurement; an X-ray voltage of the respective individual measurement; a name of an examination protocol of the first examination; and a reason for the first examination.
However, Lane disclose the limitation:
The computer-implemented method of wherein the at least one item of first information the at least one item of second information include at least one of: a designation of a respective individual measurement included in the first set of individual measurements or the subset of the second set of individual measurements; a type of the respective individual measurement; a body region measured during the respective individual measurement; a dose-length product of the respective individual measurement; an X-ray voltage of the respective individual measurement; a name of an examination protocol of the first examination; and a reason for the first examination (Examiner selects: a type of the respective individual measurement. In paragraph [0090], Lane discloses evaluating measurement parameters (the actual physiological data being monitored) using medical equipment to identify potential errors as they happen. The system can also monitor other metrics (secondary parameter) that might indicate measurement errors, supporting real time error detection.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Lane, with automatically detecting an operator error and provide assistance to the operator using medical equipment as taught by Lane. The motivation for doing so would have been to detect and provide erroneous measurement in respect to physiological data (See [0094] of Lane.)
Regarding claim 23, Dale in view of Tzeng, Hsieh and SATO disclose elements of claim 1. Dale in view of Tzeng, Hsieh and SATO does not disclose:
The computer-implemented method of claim 1, wherein each respective incorrect measurement among the one or more incorrect measurements is performed with at least one incorrect measurement setting
and the successful measurement is obtained by repeating the respective incorrect measurement without the at least one incorrect measurement setting
However, Lane disclose the limitation:
The computer-implemented method of claim 1, wherein each respective incorrect measurement among the one or more incorrect measurements is performed with at least one incorrect measurement setting (In Fig. 2C and paragraph [0098], Lane discloses that the medical equipment begins to receive and monitor physiological data and other measurements from a person. These measurements can include information about the position of a sensor or probe, which can be gathered using small devices called accelerometers or gyros. )
and the successful measurement is obtained by repeating the respective incorrect measurement without the at least one incorrect measurement setting (The medical equipment checks if the physiological data and other measurements seem right for what the operator wants to measure. If the data looks good, the equipment keeps monitoring. If the data seems wrong, the equipment can help fix possible mistakes by the operator. If the operator fixes the problem successfully, the equipment will notice and continue monitoring. If not, the equipment suggests again what the problem could be and how to fix it, either by prompting or showing more advice.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, SATO to modify Lane, with automatically detecting an operator error and provide assistance to the operator using medical equipment as taught by Lane. The motivation for doing so would have been to detect and provide erroneous measurement in respect to physiological data (See [0094] of Lane.)
Claim 11 is rejected under 35 U.S.C 103 as being unpatentable over Dale in view of Tzeng, Hsieh and further in view of Garnavi et al. (US Patent No. 9,779,492 B1), hereinafter referred to as Garnavi.
Regarding claim 11, Dale in view of Tzeng and Hsieh disclose elements of claim 10. Dale in view of Tzeng and Hsieh do not explicitly disclose:
The computer-implemented method of wherein the trained function includes a first trained sub-function and a second trained sub-function, the first trained sub-function including an unsupervised learning algorithm, and the second trained sub-function including a classification algorithm
and the second trained sub-function is configured to receive a result of the first trained sub-function as input
However, Garnavi disclose the limitation:
The computer-implemented method of wherein the trained function includes a first trained sub-function and a second trained sub-function, the first trained sub-function including an unsupervised learning algorithm, and the second trained sub-function including a classification algorithm (In Col. 14, lines 31–36, Garnavi discloses a first decision on whether the gradability of the image is predicted with a first confidence score, by training a first classifier based on a combined set of unsupervised features and a complementary set of supervised features, e.g., as described above.)
and the second trained sub-function is configured to receive a result of the first trained sub-function as input (In Col. 14, lines 37–42, Garnavi discloses a second decision on whether the gradability of the image is predicted with a second confidence score, by training a second classifier based on the set of supervised features, e.g., as described above. In one embodiment, the first classifier and the second classifier may include a random forest classifier that predicts the label of the image.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh, to modify Garnavi, with determining image quality of a machine generated image as taught by Garnavi. The motivation for doing so would have been to improve image quality, providing an automatic approach to improve image quality based on the output of quality of assessment algorithms (See[0029] of Garnavi)).
Claims 12-13 are rejected under 35 U.S.C 103 as being unpatentable over Mavroeidis et al. (US Pub No.: 20200372344 A1), hereinafter referred to as Mavroeidis in view of “Managing Repeat Digital Radiography Images—A Systematic Approach and Improvement”, https://link.springer.com/article/10.1007/s10916-011-9744-8, Tzeng, 05/31/2011, hereinafter referred to as Tzeng and further in view of Dale et al. (US Patent No. 7,680,308 B2), hereinafter referred to as Dale.
With respect to claim 12, Mavroeidis disclose:
Training the trained function by iteratively performing first operations, the first operations including applying the trained function to a subset of the training input data to obtain output data (In paragraph [0064], Mavroeidis discloses training a neural network using training data and then iteratively performing additional training operations. After an initial training phase, the patent repeatedly tests the neural network. Adjusts a training parameter (the regularization parameter), and re-trains the neural network using the training data. These steps are repeated until training and testing loss function converge, thereby teaching iterative training of the trained function.)
adjusting parameters of the trained function such that the output data of a subsequent iteration of the applying more closely matches a subset of the training output data corresponding to the subset of the training input data (In paragraph [0064], Mavroeidis discloses training a neural network using training data and then iteratively performing additional training operations. The system adjusts the regularization parameter, re-trains the neural network. The network weights are updated during training so that iterative adjustment and retraining continue until the loss functions converge.)
providing the trained function in response to the training (In paragraph [0088], Mavroeidis discloses iteratively training a neural network by repeatedly updating model parameters until the training and testing loss function converge.)
With respect to claim 12, Mavroeidis do not explicitly disclose:
A computer-implemented method for providing a trained function, comprising: providing training input data, the training input data including at least one item of examination information corresponding to an examination, the examination information including a plurality of individual measurements assigned to the examination, the plurality of individual measurements including one or more incorrect measurements and one or more successful measurements, each respective incorrect measurement among the one or more incorrect measurements corresponding to a first successful measurement among the one or more successful measurements
The first successful measurement being obtained by repeating the respective incorrect measurement
Generating training output data identifying at least one of incorrect measurements or successful measurements among the training input data, the training output data including at least one item of examination error information corresponding to the examination, and the training output data and the training input data being related
However, Tzeng is known to disclose:
A computer-implemented method for providing a trained function, comprising: providing training input data, the training input data including at least one item of examination information corresponding to an examination, the examination information including a plurality of individual measurements assigned to the examination, the plurality of individual measurements including one or more incorrect measurements and one or more successful measurements, each respective incorrect measurement among the one or more incorrect measurements corresponding to a first successful measurement among the one or more successful measurements (On page 3-4 (Reason for repeated image), Tzeng discloses, based on the premise, that if an image is repeated, the first acquisition was not acceptable for diagnostic use. Furthermore, Tzeng discloses an initial radiographic acquisition that is determined to be unacceptable due to imaging errors (e.g., positioning or exposure errors) and is therefore being repeated.)
Mavroeidis in view of Tzeng are analogous pieces of art because both references concern receiving training data comprising a set of annotated images . Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mavroeidis, with testing the trained neural network model using the test data to determine a loss function of the trained neural network mode as taught by Mavroeidis, while repeating images, and the collected analysis as taught by Tzeng. The motivation for doing so would have been to classifying the contents of images, each neuron in the neural network may comprise a mathematical operation comprising a weighted linear sum of the pixel (See [0038] of Mavroeidis.)
With respect to claim 12, Mavroeidis in view of Tzeng do not explicitly disclose:
The first successful measurement being obtained by repeating the respective incorrect measurement
Generating training output data identifying at least one of incorrect measurements or successful measurements among the training input data, the training output data including at least one item of examination error information corresponding to the examination, and the training output data and the training input data being related
However, Dale is known to disclose:
The first successful measurement being obtained by repeating the respective incorrect measurement (In paragraph [0185], Hsieh discloses the substance of a successful measurement: an image acquisition having sufficient or acceptable quality after the acquisition settings are adjusted and the acquisition process is repeated. )
Generating training output data identifying at least one of incorrect measurements or successful measurements among the training input data, the training output data including at least one item of examination error information corresponding to the examination, and the training output data and the training input data being related (In Col. 2, lines 30–44, Dale discloses generating quality assessment information for each examination based on the entered quality evaluation. Specifically, the radiologist enters an exam dissatisfaction value and, optionally, comments identifying quality deficiencies, which constitute examination error information associated with the corresponding examination. )
Mavroeidis in view of Tzeng and Dale are analogous pieces of art because both references concern receiving training data comprising a set of annotated images . Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Dale, with collecting data in the medical information systems as taught by Dale. The motivation for doing so would have been to improve equipment maintenance in order to improve quality of the image (In Col. 3, lines 27-28, of Dale.)
Regarding claim 13, Mavroeidis in view of Tzeng and Dale disclose the elements of claim 12. In addition, Dale disclose:
The computer-implemented method of claim 12, wherein the trained function is continuously trained further via feedback, the feedback being provided via an external source (In Col.13, lines 6-14, Dale disclose a system conveys feedback from the radiologist, This feedback enables medical radiologists, technologists.)
Claims 15-16 are rejected under 35 U.S.C 103 as being unpatentable over Dale in view of Tzeng, Hsieh and further in view of Semba et al. (US Pub No.: 20180049713 A1), hereinafter referred to as Semba
Regarding claim 15, Dale in view of Tzeng and Hsieh disclose elements of claim 1. Dale in view of Tzeng and Hsieh do not explicitly disclose:
A non-transitory computer program product storing a computer program, directly loadable into a memory of a first system, including program segments to carry out the computer-implemented method of claim 1 upon the program segments being run by the first system
However, Semba discloses the limitation (In paragraph [0119], Semba disclose the present invention can also be implemented using a computer system that reads and executes instructions stored on a non-transitory computer-readable storage medium.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh to modify Semba, with assigning information representing an imaging success/failure to the image based on information of an imaging protocol of the image and information representing whether the image is the rejected image instructed by the instruction unit as taught by Semba. The motivation for doing so would have been to determine whether to output the rejected image to a rejected image statistic terminal (See [0013] of Semba).
Regarding claim 16, Dale in view of Tzeng and Hsieh disclose elements of claim 1. Dale in view of Tzeng and Hsieh do not explicitly disclose:
A non-transitory computer-readable storage medium storing program segments, readable and runnable by a first system, to carry out the computer-implemented method of claim 1 upon the program segments being run by the first system
However, Semba discloses the limitation (In paragraph [0119], Semba disclose the present invention can also be implemented using a computer system that reads and executes instructions stored on a non-transitory computer-readable storage medium.)
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teaching of Dale in view of Tzeng and Hsieh to modify Semba, with assigning information representing an imaging success/failure to the image based on information of an imaging protocol of the image and information representing whether the image is the rejected image instructed by the instruction unit as taught by Semba. The motivation for doing so would have been to determine whether to output the rejected image to a rejected image statistic terminal (See [0013] of Semba.)
Response to Arguments
Applicant's arguments filed on 03/23/2026 have been fully considered, and in part are persuasive
Pertaining to Rejection under 101
Rejections for claims 1-24 are withdrawn under 35 USC § 101.
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
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 EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
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, Mariela D Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights