CTNF 18/730,735 CTNF 100770 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The Information Disclosure Statement filed 07/19/2024 has been considered by examiner. Response to Amendment The applicant has made preliminary amendments to the drawings. According to MPEP 714.01(e) paragraph 1 , “For applications filed on or after September 21, 2004 (the effective date of 37 CFR 1.115(a)(1) ), a preliminary amendment that is present on the filing date of the application is part of the original disclosure of the application.” . Since applicant’s amendments meet this condition, the application has been examined accordingly. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because it is directed towards a program not in a physical or tangible form and thus is software per se. See MPEP 2106.03 I. The Four Categories “Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category.” Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-2, 5-8, 10-13, 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Babic (WO 2015091015 A2) in view of Receveur (US 20210219873 A1) . With respect to claim 1 , Babic teaches an information processing device comprising: an acquisition unit that acquires at least video data from an imaging device that images a patient and video data from an imaging device that images an instrument monitoring the patient (“According to the present invention, a medical imaging system arranged to track a predetermined movable object is presented. The predetermined movable object may be a medical instrument and/or a patient. The object tracking device comprises a primary imaging unit and a secondary imaging unit. The primary imaging unit is configured to provide first image data of a patient's body. It might further be configured to provide image data of the interior of a patient's body.” Page 2 paragraph 3) , but does not teach a detection unit that detects an abnormality of a condition of the patient by analyzing the video data acquired by the acquisition unit. Receveur teaches a detection unit that detects an abnormality of a condition of the patient by analyzing the video data acquired by the acquisition unit (“The condition assessor 144 analyzes the features identified by the computer vision processor 142 to determine patient postures and movements that indicate clinical patient parameters.” Paragraph 0057) . Receveur is analogous art in the same field of endeavor as the claimed invention. Receveur is directed towards video based clinical need assessment (“Embodiments of the disclosure are directed to systems and methods for predicting clinical patient parameters. More particularly, a system including a camera and a computer vision processor are utilized to analyze real-time video feeds of patients in bed to determine if clinical patient parameters are present. In instances where the clinical patient parameters indicate that the patient needs assistance, an alert can be automatically issued to caregivers.” Paragraph 0003) . A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Babic and Receveur by utilizing Receveur’s computer vision processor inside of the camera system of Babic, with the expectation that doing so would lead to better patient outcomes (“Analyses performed by the computer vision processor 142 can include object identification (is that the patient?), object verification (is there someone at the bed?), object landmark detection (where is the patient in relation to the bed?), object segmentation (which pixels belong to an object in the image), and recognition (is that the patient and where are they?).” paragraph 0055 and “However, visual observation of a patient can be the best way to detect some health indications. Visual monitoring of patients can provide information about patient mobility, fall risk, sleep patterns, and risk for developing various medical conditions.” Paragraph 0002) . With respect to claim 2 , Babic and Receveur teach the information processing device according to claim 1, Babic further teaches wherein a plurality of the imaging devices that images a plurality of the instruments monitoring the patient is grouped by the patient (“According to the present invention, a medical imaging system arranged to track a predetermined movable object is presented. The predetermined movable object may be a medical instrument and/or a patient. The object tracking device comprises a primary imaging unit and a secondary imaging unit. The primary imaging unit is configured to provide first image data of a patient's body. It might further be configured to provide image data of the interior of a patient's body.” Page 2 paragraph 3) . Receveur further teaches wherein a plurality of the imaging devices that images a plurality of the instruments monitoring the patient is grouped by reading a patient ID assigned to the patient using the imaging device (“Analyses performed by the computer vision processor 142 can include object identification (is that the patient?), object verification (is there someone at the bed?), object landmark detection (where is the patient in relation to the bed?), object segmentation (which pixels belong to an object in the image), and recognition (is that the patient and where are they?)“ paragraph 0055 and “The condition assessor 144 analyzes the features identified by the computer vision processor 142 to determine patient postures and movements that indicate clinical patient parameters. The condition assessor 144 can also draw upon patient information from the data store 146 and/or the patient bed 102. In some embodiments, the condition assessor 144 directly draws upon information from the EMR system 114” paragraph 0057) . With respect to claim 5 , Babic and Receveur teach the information processing device according to claim 1. Babic further teachers wherein the imaging devices of which a distance is close to each other are grouped (“The system, in particular the primary imaging unit, is further arranged to determine a position of the secondary imaging unit relative to the position of a reference point. The reference point is preferably the position of the primary imaging unit” page 2 paragraph 5 and “For the preferred merging of the first image data from e.g. the X-ray system and the second image data from e.g. the video camera, the position of the secondary imaging unit relative to the primary imaging unit needs to be known and therefore to be determined and monitored.” Page 4 paragraph 6) . With respect to claim 6 , Babic and Receveur teach the information processing device according to claim 1. Babic further teaches wherein a monitoring target of the instrument imaged by the imaging device is estimated (“According to an example, the primary and secondary imaging units are configured to determine a position of a determined characteristic or a fiducial on the object.” Page 5 paragraph 3) . With respect to claim 7 , Babic and Receveur teach the information processing device according to claim 6. Receveur further teaches wherein an analysis method suitable for the estimated target is set (“The computer vision processor 142 operates to analyze the images to extract features needed for the condition assessor 144 to determine if a clinical patient parameter is present. In some embodiments, features relate to patient postures, presence, and movements indicative of medical conditions, patient mobility, sleep patterns, and the like. As described in more detail above, some examples of features include amount of time a patient spends in one position on the bed, sequences of movements indicating impending bed exit, and presence of a caregiver near the bed. The computer vision processor 142 utilizes algorithms and models trained on sample data by the computer vision trainer 150.” Paragraph 0054 and Analyses performed by the computer vision processor 142 can include object identification (is that the patient?), object verification (is there someone at the bed?), object landmark detection (where is the patient in relation to the bed?), object segmentation (which pixels belong to an object in the image), and recognition (is that the patient and where are they?)” paragraph 0055) , and the video data is analyzed on a basis of the set analysis method (“Analyses performed by the computer vision processor 142 can include object identification (is that the patient?), object verification (is there someone at the bed?), object landmark detection (where is the patient in relation to the bed?), object segmentation (which pixels belong to an object in the image), and recognition (is that the patient and where are they?)” paragraph 0055) . With respect to claim 8 , Babic and Receveur teach the information processing device according to claim 6. Babic further teaches wherein a storage method of data suitable for the estimated target is set (“In a further example, in the parking mode, only the secondary imaging unit provides current second image data of the exterior of a patient's body. However, these second image data can preferably be merged with previously captured and stored image data of the primary imaging unit. If a necessity for imaging guidance/update/check up of the first image data occurs, the primary imaging unit is positioned back from the parking mode into the imaging mode and new first image data of the interior of the body are acquired. The merge of the first and second imaging data is then automatically updated with the new information.” Page 4 paragraph 5) , and the video data is stored on a basis of the set storage method (“In a further example, in the parking mode, only the secondary imaging unit provides current second image data of the exterior of a patient's body. However, these second image data can preferably be merged with previously captured and stored image data of the primary imaging unit. If a necessity for imaging guidance/update/check up of the first image data occurs, the primary imaging unit is positioned back from the parking mode into the imaging mode and new first image data of the interior of the body are acquired. The merge of the first and second imaging data is then automatically updated with the new information.” Page 4 paragraph 5) . With respect to claim 10 , Babic and Receveur teach the information processing device according to claim 8. Babic teaches video data is stored on the basis of the storage method (“In a further example, in the parking mode, only the secondary imaging unit provides current second image data of the exterior of a patient's body. However, these second image data can preferably be merged with previously captured and stored image data of the primary imaging unit. If a necessity for imaging guidance/update/check up of the first image data occurs, the primary imaging unit is positioned back from the parking mode into the imaging mode and new first image data of the interior of the body are acquired. The merge of the first and second imaging data is then automatically updated with the new information.” Page 4 paragraph 5) . Receveur further teaches wherein in a case where the abnormality of the condition of the patient is detected by the detection unit, the video is stored (“The data store 146 operates to store data processed by the image processor 140 and computer vision processor 142.” Paragraph 0059) . With respect to claim 11 , Babic and Receveur teach the information processing device according to claim 8. Babic teaches video data is stored on the basis of the storage method (“In a further example, in the parking mode, only the secondary imaging unit provides current second image data of the exterior of a patient's body. However, these second image data can preferably be merged with previously captured and stored image data of the primary imaging unit. If a necessity for imaging guidance/update/check up of the first image data occurs, the primary imaging unit is positioned back from the parking mode into the imaging mode and new first image data of the interior of the body are acquired. The merge of the first and second imaging data is then automatically updated with the new information.” Page 4 paragraph 5) . Receveur further teaches wherein in a case where the abnormality of the condition of the patient is detected by the detection unit, video data imaged by the grouped imaging devices and an analysis result obtained by analyzing the video data are stored ( “The data store 146 operates to store data processed by the image processor 140 and computer vision processor 142.” Paragraph 0059 and “At operation 310, any conditions detected by the computer vision processor are recorded in the patient's EMR. Generally, any conditions detected could be recorded in the EMR system 114 for future reference by caregivers. In some embodiments, the conditions are at least temporarily stored in the data store 146 before being communicated to the EMR system 114.” Paragraph 0078 ) With respect to claim 12 , Babic and Receveur teach the information processing device according to claim 1. Receveur further teaches wherein at least a posture of the patient is analyzed using video data from the imaging device that images the patient (“Those operations include: receiving a video feed of a patient at a bed in real-time; extracting, with a computer vision processor, features from the video feed, the features including one or more of patient presence, patient body posture, patient movement over time, and presence of other individuals; and analyzing the features to determine one or more clinical parameters of the patient.” Paragraph 0005) . With respect to claim 13 , Babic and Receveur teach the information processing device according to claim 1. Babic teaches the imaging device that images the instrument measuring the vital signs of the patient (“The secondary imaging unit is configured to provide second image data of a patient's body. It might further be configured to provide optical image data of the exterior of a patient's body, e.g. body shape, vital signs” page 2 paragraph 4) . Receveur teaches wherein a numerical value of vital signs is recognized for condition assessment (“The condition of a patient sleeping can be detected by assessing the patient body posture and how often the patient moves. This could be aided by information such as respiration rate and heart rate” paragraph 0023) . With respect to claim 15 , Babic and Receveur teach the information processing device according to claim 1. Receveur teaches wherein the condition of the patient is estimated using a plurality of analysis results obtained by analyzing video data (“Analyses performed by the computer vision processor 142 can include object identification (is that the patient?), object verification (is there someone at the bed?), object landmark detection (where is the patient in relation to the bed?), object segmentation (which pixels belong to an object in the image), and recognition (is that the patient and where are they?)” paragraph 0055 and “The condition assessor 144 analyzes the features identified by the computer vision processor 142 to determine patient postures and movements that indicate clinical patient parameters.” Paragraph 0057 and “The computer vision trainer 150 operates to train machine learning algorithms that are employed by the computer vision processor 142. Training data comprises video feed labeled with corresponding clinical patient parameters.” Paragraph 0058) acquired from each of a plurality of the imaging devices (“The image processor 140 operates to receive video feeds from one or more cameras 108. Initial processing of the images is performed by the image processor 140 to prepare the images for further analysis by the computer vision processor 142.” Paragraph 0053) . With respect to claim 16 , Babic and Receveur teach all claim limitations in consideration of claim 1, due to the substantial similarity of claim 1 and claim 16. Claim 1 is directed towards a device that does the method of claim 16. With respect to claim 17 , Babic and Receveur teach all claim limitations in consideration of claim 1 due to the substantial similarity of claim 1 and claim 16. Claim 1 is directed towards a device that executes the processing steps of claim 17. Babic additionally teaches a program for causing a computer that controls an information processing device to execute processing (“The present invention relates to an object tracking device for a medical imaging system, a medical imaging system, an object tracking method for a medical imagin system, a computer program element for controlling such device and a computer readable medium having stored such computer program element.” Page 1 field of invention) . 07-22-aia AIA Claim s 2-3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Babic and Receveur as applied to claim s 2 and 8 above, and further in view of Li (WO 2020029921 A1) . With respect to claim 3 , Babic and Receveur teach the information processing device according to claim 2, Babic further teaches the imaging device set as the master unit and the imaging device set as a slave unit are paired (“The system, in particular the primary imaging unit, is further arranged to determine a position of the secondary imaging unit relative to the position of a reference point. The reference point is preferably the position of the primary imaging unit” page 2 paragraph 5) , but does not teach wherein reading the patient ID is performed by the imaging device set as a master unit among the plurality of imaging devices. Li teaches wherein reading the ID is performed by the imaging device set as a master unit among the plurality of imaging devices (“Multi-eye snapshot optimization unit 1042: For the images saved in the previous module, according to the image mapping relationship (spatial correspondence) between the calibrated primary camera and each secondary camera, and according to the time stamp (time dimension) recorded by the monitoring system , And the target location (spatial dimension) and other information for correlation matching, matching the same object in the saved image as the same target, and unified ID value (subject to the ID number in the image collected by the main camera)” page 11 last paragraph) . Li is analogous art reasonably pertinent to the problem faced by the inventor. Li is directed towards video monitoring (“The present application relates to the field of video technology, and in particular, to a monitoring method and device.” Page 2 Technical field) . A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Babic and Receveur by utilizing Li’s video techniques and teachings applying them to the multi-camera video system of Babic, with the expectation that doing so would lead to better quality imaging (“The embodiments of the present application provide a monitoring method and device, which can solve the problems of blurred captured images, low image brightness, large noise, and small captured target size in monitoring application scenarios such as low illumination, long target distance, and backlighting / wide dynamics. Performance indicators for subsequent intelligent processing of the graph (face recognition, license plate recognition, vehicle model / model recognition, etc.).” page 3 Summary of the invention) . With respect to claim 4 , Babic, Receveur, and Li teach the information processing device according to claim 3. Li further teaches wherein, by the imaging device set as the master unit, a face of the patient is imaged (“Multi-eye target detection and tracking unit 1041: receives YUV data obtained by the multi-channel ISP processing unit 103, including YUV image data corresponding to the main camera and YUV image data corresponding to the sub-camera; for these images to perform target detection and tracking, the target here Including but not limited to: motor vehicles, non-motor vehicles, pedestrians, faces, important objects, etc., are related to monitoring needs.” Page 11 paragraph 4) , the imaged face of the patient is collated with a face of the patient stored in a database (“In summary, through the multi-eye target detection and tracking unit 1041, multiple saved images can be obtained; at the same time, the unit records information such as the ID of each target and the position of each target in these images.” Page 11 Paragraph 7) , and the imaging device and the patient are associated with each other (“Multi-eye snapshot optimization unit 1042: For the images saved in the previous module, according to the image mapping relationship (spatial correspondence) between the calibrated primary camera and each secondary camera, and according to the time stamp (time dimension) recorded by the monitoring system , And the target location (spatial dimension) and other information for correlation matching, matching the same object in the saved image as the same target, and unified ID value (subject to the ID number in the image collected by the main camera)” page 11 last paragraph) . With respect to claim 9 , Babic and Receveur teach the information processing device according to claim 8, but don’t teach wherein the storage method is to set a frame rate and resolution suitable for the target. Li further teaches wherein the storage method is to set a frame rate and resolution suitable for the target (“For any one of the secondary cameras 102, real-time image data is collected at a second frame rate (lower than the first frame rate), processed by the multi-channel ISP processing unit 103, and the processed image data is subjected to target detection by the multi-eye target detection and tracking unit 1041.” Page 13 paragraph 9) . Li is analogous art reasonably pertinent to the problem faced by the inventor. Li is directed towards video monitoring (“The present application relates to the field of video technology, and in particular, to a monitoring method and device.” Page 2 Technical field) . A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Babic and Receveur by utilizing Li’s video techniques and teachings applying them to the multi-camera video system of Babic, with the expectation that doing so would lead to better quality imaging (“The embodiments of the present application provide a monitoring method and device, which can solve the problems of blurred captured images, low image brightness, large noise, and small captured target size in monitoring application scenarios such as low illumination, long target distance, and backlighting / wide dynamics. Performance indicators for subsequent intelligent processing of the graph (face recognition, license plate recognition, vehicle model / model recognition, etc.).” page 3 Summary of the invention) . 07-22-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Babic and Receveur as applied to claim 1 above, and further in view of Liu (CN 109498857 A) . With respect to claim 14 , Babic and Receveur teach the information processing device according to claim 1, but do not teach wherein a color of drainage is analyzed using video data from the imaging device that images a drainage drain. Liu teaches wherein a color of drainage is analyzed (“In order to solve the above technical problem, the present invention provides a monitoring system and method of medical drainage, it can realize drainage of important information data flow, flow speed, colour and turbidity drainage process for unified monitoring, effectively collecting, recording the relative data, and when there is abnormality to alarm.” Page 2 summary of the invention paragraph 1) using video data from the imaging device that images a drainage drain (“According to one aspect of the present invention, there is provided a image recognition-based drainage monitoring system, comprising: at least one video collecting device for acquiring video stream data of drainage, an image processing module, which is connected with said at least one video collecting device for receiving the video stream data and the video stream data is processed to obtain the drainage guide data, and monitoring module, which is connected with the image processing module and used for receiving and monitoring the drainage data.” Page 2 summary of the invention paragraph 2) . Liu is analogous art in the same field of endeavor as the claimed invention. Liu is directed towards a video-based monitoring of patient condition (“In order to solve the above technical problem, the present invention provides a monitoring system and method of medical drainage, it can realize drainage of important information data flow, flow speed, colour and turbidity drainage process for unified monitoring, effectively collecting, recording the relative data, and when there is abnormality to alarm.” Page 2 summary of the invention paragraph 1) . A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Babic and Receveur by utilizing the drainage based condition assessment teachings of Liu with the condition assessment and alarm practices of Receveur with the expectation that doing so would better patient outcomes by reducing medical risk and medical staff workload (“Therefore, it is desirable to provide a monitoring device for drainage, can perform centralized monitoring comprises color, etc. of the flow, flow speed and drainage drainage data, reduce the medical risk to the utmost extent, and discovering, alarming, recording and processing, so as to reduce the work load of the medical staff and reduces the manual monitoring vulnerability and may cause medical risk.” Page 2 paragraph 4) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Zaleski (US 20070271122 A1) is directed to a patient monitoring system that specifically targets an identified patient via a video feed Meschisen (JP 2010537672 A) is directed towards a patient monitoring system that utilizes an imaging device to monitor patient condition and scan patient ID Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm. 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, Andrew W Bee can be reached at (571)270-5183. 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. /REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677 /ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677 Application/Control Number: 18/730,735 Page 2 Art Unit: 2677 Application/Control Number: 18/730,735 Page 3 Art Unit: 2677 Application/Control Number: 18/730,735 Page 4 Art Unit: 2677 Application/Control Number: 18/730,735 Page 5 Art Unit: 2677 Application/Control Number: 18/730,735 Page 6 Art Unit: 2677 Application/Control Number: 18/730,735 Page 7 Art Unit: 2677 Application/Control Number: 18/730,735 Page 8 Art Unit: 2677 Application/Control Number: 18/730,735 Page 9 Art Unit: 2677 Application/Control Number: 18/730,735 Page 10 Art Unit: 2677 Application/Control Number: 18/730,735 Page 11 Art Unit: 2677 Application/Control Number: 18/730,735 Page 12 Art Unit: 2677 Application/Control Number: 18/730,735 Page 13 Art Unit: 2677 Application/Control Number: 18/730,735 Page 14 Art Unit: 2677 Application/Control Number: 18/730,735 Page 15 Art Unit: 2677
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