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 .
This communication is responsive to the correspondence filled on 09/03/2026.
Claims 1-12 are presented for examination.
IDS Considerations
The information disclosure statement (IDS) submitted on 09/26/2024 is/are being considered by the examiner as the submission is in compliance with the provisions of 37 CFR 1.97.
Response to Arguments
Applicant's arguments filed 09/03/2026 with respect to claims 1-12 have been considered but are moot in view of the new ground(s) of rejection.
Claim amendments of “the quality control information storing whether imaging for the imaging apparatus to perform is capable of imaging an image with a quality necessary for performing the dynamic analysis processing.” recited in independent claims is rejected using a new reference Zanca (U.S. Pub. No. 20190159747 A1) because of scope change. Examiner notes that few other limitations of dependent and independent claims are also taught by Zanca. As such those limitations are remapped using Zanca to reduce the number of references required for rejection.
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 1-3 and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zanca (U.S. Pub. No. 20190159747 A1), in view of Nagatsuka (U.S. Pub. No. 20210404974).
Regarding to claim 1 and 10-11:
1. Zanca teach a management apparatus, comprising: (Zanca Fig. 1 [0017] a quality assurance system 120. Although the quality assurance system 120 can be located remote from the imaging system 100 of FIG. 1, the quality assurance system 120 can otherwise be integrated with or located adjacent to the imaging system 100 of FIG. 1) an image DB in which an image is stored; (Zanca [0037] The results storage 208 stores the results of the analysis of a received data set 108. For example, the results storage 208 can store data in conjunction with the data set received from the imaging system 100. The image data can include the image, timestamps of when the image(s) was generated, measured performance indicator values of the image(s), any alerts that may have been triggered, any corresponding phantom types, data related to whether the image(s) was correctly performed, and/or any other data related to the image(s). The criteria storage 210 stores various criteria that can be used by the processor 202. For example, the criteria storage 210 can store performance indicators and corresponding threshold ranges, specific validity criteria, and/or general validity criteria)
an image management information DB in which an imaging apparatus is recorded, (Zanca [0033] The processor 202 processes the received data set 108. In some examples, the data set 108 includes data of multiple images from the imaging system 100. In some examples, the processor 202 determines if the data set 108 corresponds to a test exam(s) by processing the metadata of the received data set 108. For example, the metadata may include an identifier or code that corresponds to a test image(s). Accordingly, the example data processor 202 may process the metadata of the data set 108 to determine is the test image identifier(s) is/are present. Different hospitals and/or imaging systems can identify the data set 108 as a test exam(s) using different techniques. Accordingly, the hospital information system 114 transmits the data key 116 that identifies how to process the metadata of the data set 108 to determine whether the data set 108 corresponds to a test exam(s).)
the imaging apparatus having imaged each of a plurality of the images; (Zanca [0018] The imaging system 100 can be any a device that captures one or more images of the subject … the data set 108 can be a data set or a data structure(s) include multiple images generated by the imaging system 100. The protocol for including the information in the header/metadata can be defined by the hospital information system 114, as further described below.)
and a hardware processor that determines, based on quality control information, (Zanca [0058] If the processor 202 determines that the data set 108 does not satisfy the validity criterion (block 508: NO), the processor 202 determines that the test exam was incorrectly performed (block 516). If the processor 202 determines that the data set 108 satisfies the validity criterion (block 508: YES), the processor 202 determines if all validity criteria have been satisfied (block 510). For example, if there are four different validity criteria to be satisfied, and only two criteria have been tested, the process returns to block 510 to process the data set 108 based on the two remaining criteria. If the processor 202 determines that all validity criteria have not been satisfied (block 510: NO), the processor 202 selects a subsequent validity criterion (block 512) and the process returns to block 508 until all the criteria have been analyzed. If the processor 202 determines that all validity criteria have been satisfied (block 510: YES), the processor 202 determines that the test exam was correctly performed (block 514).)
the quality control information storing (Zanca Fig. 3 and claim 1 a storage configured to store the performance indicator in conjunction with the data.) whether imaging for the imaging apparatus to perform is capable of imaging an image (Zanca Fig. 3 and claim 1 determine whether the test exam was correctly performed based on a comparison of the data to a validity criterion; and when the test exam was correctly performed, determine that the imaging system satisfies [capable of] a performance criteria based on a comparison of a characteristic of the data corresponding to the performance indicator with a performance threshold [image quality]. [0037] The results storage 208 stores the results of the analysis of a received data set 108. For example, the results storage 208 can store data in conjunction with the data set received from the imaging system 100. The image data can include the image, timestamps of when the image(s) was generated, measured performance indicator values of the image(s), any alerts that may have been triggered, any corresponding phantom types, data related to whether the image(s) was correctly performed, and/or any other data related to the image(s). The criteria storage 210 stores various criteria that can be used by the processor 202. For example, the criteria storage 210 can store performance indicators and corresponding threshold ranges, specific validity criteria, and/or general validity criteria. As described above in conjunction with FIG. 1, validity criteria may correspond to characteristic(s) of the data set 108, including a shift from an isocenter, differences in the levels of grey between two sides of the image(s), field uniformity values, AEC values, consistency of Hounsfield units, a tube current modulation, etc.) with a quality. (Zanca
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Zanca do not explicitly teach whether dynamic analysis processing is executable on the plurality of images stored in the image DB, quality necessary for performing the dynamic analysis processing.
However Nagatsuka teach whether dynamic analysis processing (Nagatsuka [0119] In the case where dynamic imaging of the phantom F is performed, the controller 21 compares the action of the phantom F in a dynamic image with the action of the phantom F on an optical image(s) obtained beforehand by optical imaging of the phantom F performing the accurate actions, and determines that the imaging is appropriate if the actions match each other and that it is not appropriate if the actions do not match each other. [0096] The information on the quality control includes the first quality information on a dynamic image obtained by dynamic imaging or the second quality information on functions or operations of at least one of the devices used in dynamic imaging.) is executable (Nagatsuka [0094] After having obtained the image data, the controller 21 executes an analyzing process for quality control concerning dynamic imaging (Step S2). Claim: 6. The dynamic imaging quality control device according to claim 5, wherein the analysis result includes an adequacy analysis result about whether each content of the information on the quality control is adequate.
7. The dynamic imaging quality control device according to claim 6, wherein the hardware processor presents that the dynamic imaging is ready in response to the adequacy analysis result indicating that a predetermined content of the information on the quality control is adequate) on the plurality of images stored in the image DB, (Nagatsuka [0102] The second quality information includes information on at least one of: generation of a dynamic image; transfer of a dynamic image; analysis of a dynamic image; and storage of a dynamic image. [0154] The check items may be extracted by referring to a table stored in the storage 22 in advance showing relations between analysis results and check items (possible causes) or by inputting the present analysis results in a machine learning model that is trained on analysis results as input objects and check items as output objects. [0055] “Dynamic imaging” indicates a series of operations (execution of program(s)) of the system 100 including: emission of radiation R by the radiation generating device 3; generation and transfer of a dynamic image by the radiation detector 1; transfer of the dynamic image between the devices; analysis by the dynamics analysis device; and storage of the dynamic image in a data storage (a cloud server, PACS, etc.). The analysis by the dynamics analysis device may be analysis of, for example, ventilation, blood flow, and structures (motions of structures such as bones and internal organs). Nagatsuka [0067] FIG. 2 is a block diagram showing the control device 2. FIG. 3 is a flowchart of the dynamic imaging quality control process executed by the control device 2. FIG. 4 is a table showing information on quality control presented by the control device 2. FIG. 5 to FIGS. 7A-FIG. 7D show exemplary errors which may appear in dynamic images. FIG. 8 shows an exemplary screen displayed by the control device 2. [0101] The first quality information described above includes information specific to dynamic images (not applicable to static images), but may include information common to static images (at least one of, for example, a size, sharpness, linearity, dynamic range, low contrast resolution, system sensitivity, uniformity, and erasability). [0102] The second quality information includes information on at least one of: generation of a dynamic image; transfer of a dynamic image; analysis of a dynamic image; and storage of a dynamic image)
quality necessary for performing the dynamic analysis processing. (Nagatsuka Claim: 6. The dynamic imaging quality control device according to claim 5, wherein the analysis result includes an adequacy analysis result about whether each content of the information on the quality control is adequate. 7. The dynamic imaging quality control device according to claim 6, wherein the hardware processor presents that the dynamic imaging is ready in response to the adequacy analysis result indicating that a predetermined content of the information on the quality control is adequate)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zanca, further incorporating Nagatsuka in video/camera technology. One would be motivated to do so, to incorporate dynamic analysis processing is executable on the plurality of images stored in the image DB, quality necessary for performing the dynamic analysis processing. This functionality will improve efficiency with predictable results.
Regarding to claim 2:
2. Zanca teach the management apparatus according to claim 1, (Zanca [0068] In the illustrated example, one or more input devices 722 are connected to the interface circuit 720. The input device(s) 722 permit(s) a user to enter data and commands into the processor 712. The input device(s) can be implemented by, for example, a sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. [0069] One or more output devices 724 are also connected to the interface circuit 720 of the illustrated example. The output devices 724 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, and/or speakers). The interface circuit 720 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor)
Zanca do not explicitly teach further comprising an inputter/outputter to which a dynamic analysis processing request for each of the plurality of images is inputted and which outputs a determination result of the hardware processor.
However Nagatsuka teach further comprising an inputter/outputter to which a dynamic analysis processing request for each of the plurality of images is inputted and which outputs a determination result of the hardware processor. (Nagatsuka [0154] The check items may be extracted by referring to a table stored in the storage 22 in advance showing relations between analysis results and check items (possible causes) or by inputting the present analysis results in a machine learning model that is trained on analysis results as input objects and check items as output objects. [0156] After having analyzed the quality of dynamic imaging, the controller 21 executes the quality control presenting process (Step S3). [0157] In the quality control presenting process, the controller 21 presents information on the quality control to users. [0158] In the quality control presenting process according to this embodiment, the controller 21 displays the information on the quality control on the display 24 to present the information. [0159] The controller 21 may send the information on the quality control to other device(s) (for example, the console 4, etc.) and display the information on the display of the other device. [0160] In the quality control presenting process according to this embodiment, the controller 21 presents the analysis results in the above-described analyzing process (values, relations of values, determination on whether the values or relations are appropriate, etc.). [0161] Specifically, the information is presented in a form of the table shown in FIG. 4.)
Regarding to claim 3:
3. Zanca teach the management apparatus according to claim 2, Zanca do not explicitly teach wherein the dynamic analysis processing request includes one image among the plurality of images and a type of the dynamic analysis processing to be executed, the one image being an image on which the dynamic analysis processing is executed.
However Nagatsuka teach wherein the dynamic analysis processing request includes one image among the plurality of images and a type of the dynamic analysis processing to be executed, the one image being an image on which the dynamic analysis processing is executed. (Nagatsuka [0055] “Dynamic imaging” indicates a series of operations (execution of program(s)) of the system 100 including: emission of radiation R by the radiation generating device 3; generation and transfer of a dynamic image by the radiation detector 1; transfer of the dynamic image between the devices; analysis by the dynamics analysis device; and storage of the dynamic image in a data storage (a cloud server, PACS, etc.). The analysis by the dynamics analysis device may be analysis of, for example, ventilation, blood flow [type], and structures (motions of structures such as bones and internal organs).)
Regarding to claim 9:
9. Zanca teach an apparatus, Zanca do not explicitly teach dynamic analysis comprising the management apparatus according to claim 1.
However Nagatsuka teach dynamic analysis comprising the management apparatus according to claim 1. (Nagatsuka [0055] “Dynamic imaging” indicates a series of operations (execution of program(s)) of the system 100 including: emission of radiation R by the radiation generating device 3; generation and transfer of a dynamic image by the radiation detector 1; transfer of the dynamic image between the devices; analysis by the dynamics analysis device; and storage of the dynamic image in a data storage (a cloud server, PACS, etc.). The analysis by the dynamics analysis device may be analysis of, for example, ventilation, blood flow, and structures (motions of structures such as bones and internal organs).)
Claims 4 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zanca (U.S. Pub. No. 20190159747 A1), in view of Nagatsuka (U.S. Pub. No. 20210404974), further in view of Hahn (U.S. Pub. No. 20120185292).
Regarding to claim 4:
4. Zanca teach the management apparatus according to claim 3, Zanca do not explicitly teach wherein the inputter/ outputter outputs whether the dynamic analysis processing is performable in response to the dynamic analysis processing request having been inputted.
However Hahn teach wherein the inputter/ outputter outputs whether the dynamic analysis processing is performable in response to the dynamic analysis processing request having been inputted. (Hahn [0172] A QC test plan setup screen 3900 in accordance with one embodiment of the present invention is shown in FIG. 39. The user can indicate that a test start on a specific date and set a recommended occurrence frequency, and the user may define worksheets to capture specified field values for each test. The system also provides a dashboard view that displays all tests due to occur at a specified facility. The system records the dates that QC tests were conducted for a specific device and the test output and values within a worksheet for specific QC test. Finally, the system stores and generates reports of a specified device's QC Test history.)
The motivation for combining Zanca and Nagatsuka as set forth in claim 1 is equally applicable to claim 4. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zanca, further incorporating Nagatsuka and Hahn in video/camera technology. One would be motivated to do so, to incorporate the inputter/ outputter outputs whether the dynamic analysis processing is performable in response to the dynamic analysis processing request having been inputted. This functionality will improve quality with predictable results.
Regarding to claim 8:
8. Zanca teach nformation system, Zanca do not explicitly teach a radiology information system, comprising the management apparatus according to claim 1.
However Hahn teach a radiology information system, comprising the management apparatus according to claim 1. (Hahn [0060] The mammography management system and method of the present invention is readily integrated with Picture Archive and Communications Systems (PACS), Hospital Information Systems (HIS), and Radiology Information Systems (RIS), allowing for existing systems to continue to function with the additional benefits of the mammography management system of the present invention.)
Claims 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zanca (U.S. Pub. No. 20190159747 A1), in view of Nagatsuka (U.S. Pub. No. 20210404974), further in view of Solanki (U.S. Pub. No. 20170039412 A1).
Regarding to claim 5:
5. Zanca teach the management apparatus according to claim 1, and which outputs whether each of a plurality of pieces of the dynamic analysis processing is executable on the each of the plurality of images, (Please the rejection of claim 1)
Zanca do not explicitly teach further comprising an inputter/outputter to which information for identifying each of the plurality of images is inputted, the each of the plurality of images having been identified by the inputted information for identifying the each of the plurality of images.
However Solanki teach further comprising an inputter/outputter to which information for identifying each of the plurality of images is inputted, the each of the plurality of images having been identified by the inputted information for identifying the each of the plurality of images. (Solanki [0353] e. For each image identified in (a), computing a vector of numbers (“image-level descriptors”) by using one or least two or more types from: [0354] i. histogram of pixel-level classifier decision statistics computed in (d); [0355] ii. descriptors based on dictionary of codewords of pixel-level descriptors (primary descriptors) computed in (c) aggregated at image level; or [0356] iii. histogram of blob-level decision statistic numbers (one number per blob) computed as mean, median, maximum, or minimum of pixel-level classifier decision statistics computed in (d) for all pixels belonging to the blob; [0357] f. Combining the image-level descriptors computed in (e) with or without further processing for the subset of images identified in (a) to obtain encounter-level descriptors. [0389] In one embodiment, the system allows screening of patients to identify signs of diabetic retinopathy (DR). A similar system can be applied for screening of other retinal diseases such as macular degeneration, hypertensive retinopathy, retinopathy or prematurity, glaucoma, as well as many others. [0391] FIG. 38 depicts one embodiment of a pipeline used for DR screening. The illustrated blocks may be implemented either on the cloud 19014, a computer or computing device 19004, a mobile device 19008, or the like, as shown in FIG. 1. The image 100 refers in general to the retinal data, single or multidimensional, that has been captured using a retinal imaging device, such as cameras for color image capture, fluorescein angiography (FA), adaptive optics, optical coherence tomography (OCT), hyperspectral imaging, scanning laser ophthalmoscope (SLO), wide-field imaging or ultra-wide-field imaging. Image 100 is input to fundus mask generation block 102 and image gradability computation block 104 and image enhancement module 106 if the image is of sufficient quality. Interest region identification block 108 and descriptor set computation block 110 feed into lesion localization block 112 which determines the most likely label and/or class of the lesion and extent of the lesion. This output can be used for multiple purposes such as abnormality screening, diagnosis, or the like. DR screening block 114 determines whether a particular fundus image includes abnormalities indicative of diabetic retinopathy such that the patient should be referred to an expert. [0410] FIG. 42 depicts a process for one embodiment of CMVR screening. The illustrated blocks may be implemented either on the cloud 19014, or a computer or computing device 19004, a mobile device 19008 or the like, as shown in FIG. 1. The image 100 refers in general to the retinal data, single or multidimensional, that has been captured using a retinal imaging device, such as cameras for color image capture, fluorescein angiography (FA), adaptive optics, optical coherence tomography (OCT), hyperspectral imaging, scanning laser ophthalmoscope (SLO), wide-field imaging or ultra-wide-field imaging. Image 100 is input to the image enhancement module 106 and then input to interest region identification block 108 and descriptor set computation block 110. The descriptors are input to CMVR screening block 900 to determine whether a particular fundus image includes abnormalities indicative of Cytomegalovirus infection and that the patient needs to be referred to an expert.)
The motivation for combining Zanca and Nagatsuka as set forth in claim 1 is equally applicable to claim 5. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zanca, further incorporating Nagatsuka and Solanki in video/camera technology. One would be motivated to do so, to incorporate an inputter/outputter to which information for identifying each of the plurality of images is inputted, the each of the plurality of images having been identified by the inputted information for identifying the each of the plurality of images. This functionality will improve quality with predictable results.
Regarding to claim 6:
6. Zanca teach the management apparatus according to claim 1, dynamic analysis processing is executable on each image of the patient, the each image having been inputted. (Please see the rejection of claim 1)
Zanca do not explicitly teach further comprising an inputter/outputter to which information for identifying a patient is inputted and which outputs whether each of a plurality of pieces of the dynamic analysis processing is executable on each image of the patient, the each image having been inputted.
However Solanki teach further comprising an inputter/outputter to which information for identifying a patient is inputted and (Solanki [0014] In a further embodiment, a computer implemented method for automated processing of retinal images for screening of diseases or abnormalities is disclosed. The method may include: accessing retinal images related to a patient.)
which outputs whether each of a plurality of pieces of the dynamic analysis processing is executable on each image of the patient, the each image having been inputted. (Solanki [0411] By choosing an SVM classifier that produces an ROC curve with an AUROC close to the average of 0.93 obtained during the 40 experiments, an additional 29 images from the cannot determine category were tested. None of these images were included during training phase. The system recommended that 27 of 29 images (patients) be referred, which is acceptable given that experts too did not have consensus on CMVR status of those two images. [0412] In one embodiment, the quality of the image is first analyzed using a “gradability assessment” module. This module will flag blurry, saturated or under exposed images to be of poor quality and unsuitable for reliable screening. The actual CMVR screening would then be performed on images that have passed this quality module. Both system could use the same descriptors, but one can use a support vector regressor engine trained to assess quality, and the other a support vector classifier trained to screen for CMVR. In another embodiment, additional descriptors are included, such as texture, color layout, and/or other descriptors to the CMVR screening setup to help distinguish the lesions better)
Regarding to claim 7:
7. Zanca teach the management apparatus according to claim 1, Zanca do not explicitly teach wherein in a case where the hardware processor determines that the dynamic analysis processing is impossible, the hardware processor requests new dynamic imaging.
However Solanki teach wherein in a case where the hardware processor determines that the dynamic analysis processing is impossible, (Solanki [0440] Posting results to PACS: Once the processing is done, for images of gradable quality, the corresponding screening results are embedded as JSON data and sent as a response to the authenticated PACS server AJAX request. If images are ungradable they are indicated as such in the response. This response, triggers the corresponding callback (set during the request) at the PACS server, which could process the results and add them to the patient database, for example block 1304.) the hardware processor requests new dynamic imaging. (Solanki [0247] FIG. 17 Poor quality images are rejected and the operator is asked to retake the image. [0426] In one embodiment, the system includes a flexible application programming interface (API) for integration with existing or new telemedicine, systems, programs, or software. The Picture Archival and Communication System (PACS) is used as an example telemedicine service to enable such an integration. Block diagram of one embodiment of such a system is shown in FIGS. 43A and 43B. The system includes an API allowing coding of one or more of patients' metadata 1306, de-identifying images 1307 to anonymize patients for analysis and protect privacy, analyzing image quality 1312, initiating reimaging as needed 1316, updating patient metadata, storing images and analysis results in database 1314, inputting 1310, and/or outputting 1308 transmission interfaces.)
Claims 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zanca (U.S. Pub. No. 20190159747 A1) Hahn (U.S. Pub. No. 20120185292), in view of Nagatsuka (U.S. Pub. No. 20210404974), further in view of Pelissier (U.S. Pub. No. 20220327691 A1).
Regarding to claim 12:
12. Zanca teach the management apparatus according to claim 1, wherein the quality control information stores whether the imaging apparatus is capable of imaging an image with a quality. (Please see the rejection of claim 1)
Zanca do not explicitly teach quality necessary for each of a plurality of different pieces of dynamic image processing.
However Pelissier teach quality necessary for each of a plurality of different pieces of dynamic image processing. (Pelissier [0061] It has been appreciated that an assessment of the quality of a medical image is often contingent on the clinical application for which the image is being captured for. For example, some clinical applications may require higher quality image captures than other clinical applications. For instance, diagnosing kidney stones in abdominal imaging can often demand near perfect image quality acquisition owing to the granular detail required to effectively assess kidney stone condition. In contrast, gallstones in the gallbladder are more easily observable (e.g., identifiable) in images because of the recognizable bright structures (stones) with black shadows, and accordingly, can be evaluated using lesser quality images. [0097] As noted, in at least some embodiments, the quality value threshold may be different for different target clinical applications (e.g., feature identification and/or diagnostic interpretation). For example, the quality value threshold may be higher for clinical applications that require identifying smaller features (e.g., kidney stones), and lower for clinical applications that only require identifying relatively larger features (e.g., gallstones in the gallbladder). Accordingly, the image interpretation program 214 may store a look-up table and/or database of clinical applications and corresponding quality value thresholds. In various cases, image interpretation program 214 can receive input that indicates the relevant clinical application—for the purpose of accessing the look-up table—either directly from a user input, or from the IQ program (if received there and transmitted for storage on storage component 216). Based on this input, the appropriate quality threshold may be used during act 414. claim 7. The method of claim 1, wherein the method further comprises: determining whether the quality value meets another quality threshold associated with another target clinical application; and when the quality value meets the another quality threshold associated with the another target clinical application, permitting clinical interpretation of the medical image for the another target clinical application.)
The motivation for combining Zanca and Nagatsuka as set forth in claim 1 is equally applicable to claim 12. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zanca, further incorporating Nagatsuka and Pelissier in video/camera technology. One would be motivated to do so, to incorporate quality necessary for each of a plurality of different pieces of dynamic image processing. This functionality will improve user experience with predictable results.
Closely related prior art
Examiner notes teaching of U.S. Pub. No. 20240415386 A1 is/are pertinent to the independent claim(s), however is not used because dependent claims are better covered by cited references.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 NASIM N NIRJHAR whose telephone number is (571) 272-3792. The examiner can normally be reached on Monday - Friday, 8 am to 5 pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William F Kraig can be reached on (571) 272-8660. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NASIM N NIRJHAR/Primary Examiner, Art Unit 2896
Prosecution Insights