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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/05/2025 has been entered.
Status of Claims
This action is in reply to an amendment filed on 12/05/2025. Claims 1, 13, and 14 have been amended. Claims 2 and 4 have been cancelled. No claims have been added. Therefore, claims 1, 3 and 5-14 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
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.
Claims 1, 3 and 5-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a method, computer readable non-transitory storage media, and system which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea of:
A method comprising: receiving a request to view two or more images associated with a patient, wherein the two or more images each comprise Digital Imaging and Communications in Medicine ("DICOM") images having DICOM tag information; receiving a request to perform Detection and Diagnosis on the two or more images; performing on the two or more images, wherein performing includes analyzes an image property using the DICOM tag information and identifying at least one characteristic of each of the two or more images, respectively, and wherein the respective characteristic of each of the two or more images comprises a disease comprising a plurality of abnormalities grouped as the disease; sorting the two or more images to generate an image order, the image order determined based at least in part on each of the two or more images; transferring the two or more images; and displaying at least one of the two or more images, wherein the displayed images are displayed in the image order.
The claims, as illustrated by the limitations of Claim 1 above, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite sorting and displaying patient images in order based on characteristics determined from detection and diagnosis of the images. Sorting and displaying patient images in order based on characteristics determined from detection and diagnosis of the images is a process that merely organizes human activity, as it involves following rules and instructions to receive images, requesting detection and diagnosis of images, performing detection and diagnosis of images, identifying characteristics of the images, sorting images in order based on characteristics, transferring images, displaying images. It also involves an interaction between a person and a computer. Interaction between a person and computer qualifies as interaction under certain methods of organizing human activity. See MPEP 2106.04(a)(2)(II). As such, the claims recite an abstract idea within the category of certain methods of organizing human activity.
The dependent claims 3-12 recite further abstract concepts of organizing human activity because they recite following rules and instructions, such as 3 the one or more computing devices comprise a picture archiving and communication system (PACS); 5 the disease comprises a plurality of lesions; 6 classifying each respective disease as benign or malignant; wherein sorting is based at least in part on the classifications; 7 the respective characteristic of each of the two or more images comprises an anatomy type; 8 the image order is based on a plurality of characteristics of each of the two or more images; 9 filtering the two or more images, the filtering determined based at least in part on the at least one characteristic of each of the two or more images; wherein the displayed images are displayed based on the filtering; 10 grouping the two or more images, the grouping determined based at least in part on the at least one characteristic of each of the two or more images; wherein the displayed images are displayed based on the grouping; 11 identifying at least one image associated with the characteristic of at least one of the two or more images; 12 sorting is based on a preference of the user.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claim 1:
by one or more computing devices and from a client device of a first user
by the one or more computing devices and from the client device of the first user
by the one or more computing devices
from the one or more computing devices to the client device
by the client user device
Computer Aided
(CAD)
using a processor that
the CAD of
Dependent Claim 9:
by the one or more computing devices
Dependent Claim 10:
by the one or more computing devices
Independent claim 13:
One or more computer readable non-transitory storage media embodying software that is operable when executed to
at one or more computing devices and from a client device of a first user
by the one or more computing devices and from the client device of the first user
by the one or more computing devices
from the one or more computing devices to the client device
by the client user device
Computer Aided
(CAD)
using a processor that
the CAD of
Independent claim 14:
one or more hardware processors; a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to
at one or more computing devices and from a client device of a first user
by the one or more computing devices and from the client device of the first user
by the one or more computing devices
from the one or more computing devices to the client device
by the client user device
Computer Aided
(CAD)
using a processor that
the CAD of
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The computing device, client device, computer aided, processor, memory, and computer-readable non-transitory storage media are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved computing device, client device, computer aided, processor, memory, and computer-readable non-transitory storage media. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract image ordering process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract image ordering process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, limitations 6 classifying each respective disease as benign or malignant; wherein sorting is based at least in part on the classifications; 9 filtering the two or more images, the filtering determined based at least in part on the at least one characteristic of each of the two or more images; wherein the displayed images are displayed based on the filtering; 10 grouping the two or more images, the grouping determined based at least in part on the at least one characteristic of each of the two or more images; wherein the displayed images are displayed based on the grouping; 11 identifying at least one image associated with the characteristic of at least one of the two or more images are directed to the abstract ideas of organizing human activity without integrating into a practical application or amounting to significantly more. Limitations 3 the one or more computing devices comprise a picture archiving and communication system (PACS); 5 the disease comprises a plurality of lesions; 7 the respective characteristic of each of the two or more images comprises an anatomy type; 8 the image order is based on a plurality of characteristics of each of the two or more image; 12 sorting is based on a preference of the user merely serve to further narrow the abstract idea above. As such, the additional elements do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3 and 5-14 are rejected under 35 U.S.C. 103 as being unpatentable over Reicher, et al. (US 10,127,662 B1) in view of Podilchuk, et al. (US 2017/0200268 A1) in further view of Kanada (US 2017/0032089 A1).
With regards to claim 1, Reicher teaches a method, comprising: receiving, by one or more computing devices and from a client device of a first user, a request to view two or more images associated with a patient (see at least column 12, lines 23-25, user interfaces with computer device to view medical images; column 21, lines 38-45, user requests comparison exam images (previous exam images) and new exam images), wherein the two or more images each comprise Digital Imaging and Communications in Medicine ("DICOM") images having DICOM tag information (see at least figure 13, column 14, line 62 – column 15, line 3, Attribute: Any characteristic associated with a data item (e.g., a data item such as a medical exam, an image series, a medical image, and/or the like). Attributes may be inherited in a hierarchical manner. For example, a medical image may inherit attributes of an image series of which it is a part, and an image series may inherit attributes of a medical exam of which it is a part. Attributes may be stored as part of an associated data item (e.g., as metadata, DICOM header data [both DICOM tag information], etc.; column 15, line 51 – column 16, line 4, Annotation: Any notes, measurements, links, assessments, graphics, and/or the like, associated with a data item, either automatically (e.g., by one or more CAP, described below) or manually (e.g., by a user). For example, when used in reference to a medical image, annotations include, without limitation, any added information that may be associated with the image, whether incorporated into an image file directly, comprising metadata [DICOM tag information] associated with the image file, and/or stored in a separate location but linked to the image file in some way. Examples of annotations include measurements by using linear dimensions, area, density in Hounsfield units, optical density, standard uptake value (e.g., for positron emission tomography), volume, curved lines (such as the length of a curved vessel), stenosis (e.g., percent narrowing of a vessel at a certain location relative to a reference location), or other parameters. Additional examples of annotations include arrows to indicate specific locations or anatomy, circles, polygons, irregularly shaped areas, notes, and/or the like. Further examples of annotations include graphics that, for example, outline lesions, lumbar discs, and/or other anatomical features; column 27, lines 27-30, image annotation information may be incorporated into an image file directly, comprise metadata [DICOM tag information] associated with the image file (for example, DICOM metadata information)); receiving, by the one or more computing devices…, a request to perform Computer Aided Detection and Diagnosis (CAD) on the two or more images (see at least column 38, line 60 – column 39, line 1, attached to the network 190 may be one or more Computer Aided Diagnosis Systems (CAD) systems that are generally used to perform Computer-Aided Processing (CAP) such as, for example, CAD processes. For example, in an embodiment the system may automatically process new 2D images that are associated with measurements and/or annotations from other previous 2D images, and adjust aspects of the measurements and/or annotations in view of new medical information from the new 2D images); performing, by the one or more computing devices, CAD on the two or more images, wherein performing CAD includes using a processor that analyzes an image property using the DICOM tag information (see at least column 27, lines 37-57, the system may execute one or more CAP on a displayed image in combination with identifying matching images and annotations. For example, the system may determine that one or more annotations in matching images indicate the presence of a lesion in a current image. Accordingly, the system may automatically process the current image to reassess the lesion. For example, the lesion may be automatically measured by the CAP, and the updated measurements may be automatically added to the current image (such as in the pop-up graphic 602 of FIG. 6). In other examples, the system may automatically find the volume, area, and/or stenosis of new lesions in a current image, and/or re-measure prior lesions from matched images. The CAP process may utilize the previous annotation information to more efficiently identify the presence, location, and type of a lesion so as to speed up, or make more accurate, the CAP. Such automatic CAP processing of the current image, combined with the indications of previous annotations and/or labels, may help the user more efficiently and accurately detect new findings in the current image) and identifying at least one characteristic of each of the two or more images, respectively (see at least column 38, line 60 – column 39, line 1, attached to the network 190 may be one or more Computer Aided Diagnosis Systems (CAD) systems that are generally used to perform Computer-Aided Processing (CAP) such as, for example, CAD processes. For example, in an embodiment the system may automatically process new 2D images that are associated with measurements and/or annotations from other previous 2D images, and adjust aspects of the measurements and/or annotations in view of new medical information from the new 2D images); sorting, by the one or more computing devices, the two or more images to generate an image order, the image order determined based at least in part on the at least one characteristic of each of the two or more images (see at least column 15, lines 11-26, the process of sorting images from multiple image series may include generating a resultant “sorted” image series. While in some embodiments a sorted image series (including images from multiple image series) is generated, generation of a sorted image series is not necessary. Rather, in various embodiments, the process of sorting images may include determining an order of the images, which order may then be referenced when, for example, the images are displayed and/or viewed. For example, the system may simply reference pointers to images from multiple image series; the system may generate a “sorting metadata” file associated with the sorted series that indicates how images from multiple image series are sorted; and/or pointers to images from multiple image series may be determined in real-time as images are viewed in a sorted order); transferring the two or more images from the one or more computing devices to the client device (see at least column 21, lines 50-52, in response to the user selection, the previous exam, the matching 2D images and the new exam are transmitted to the computing system); and displaying, by the client user device, at least one of the two or more images, wherein the displayed images are displayed in the image order (see at least column 21, lines 50-53, in response to the user selection the previous exam, the matching 2D images and the new exam are displayed to the user for review).
Reicher does not explicitly teach …and from the client device of the first user …and wherein the respective characteristic of each of the two or more images comprises a disease comprising a plurality of abnormalities grouped as the disease.
Podilchuk teaches …and from the client device of the first user (see at least ¶ 0120, a remote client, e.g., a computing device associated with a radiology workstation 22, may initiate a new session with the user interface 104, in order to access and interact with aspects of the CAD lesion application 12 and CAD web and processing server 28. The remote client may initiate a new session, and a UI presentation may be transmitted to the remote client. The remote client may then request a PACS search. In order to respond to the request, the CAD lesion application 12 may interpret the request and pass that request to backend portions of the CAD system 20. More specifically, the CAD lesion application 12 may initiate queries to the PACS database 110 to response to the request of the remote client). It would have been obvious to one of ordinary skill in the art at the time of invention to combine the remote device image CAD request of Podilchuk with the image matching system of Reicher with the motivation of improving the quality of patient care (Podilchuk, ¶ 0006).
Kanada teaches …and wherein the respective characteristic of each of the two or more images comprises a disease comprising a plurality of abnormalities grouped as the disease (see at least figure 35, ¶ 0167-0168, lesions in the respective groups are further grouped (sub-grouped) according to the organs of interest and lesion types, the lesions are grouped according to the organs of interest and the lesion types). It would have been obvious to one of ordinary skill in the art at the time of invention to combine the imaged lesion grouping of Kanada with the image matching system of Reicher with the motivation of providing reliable information and strong support for clinical decisions (Kanada, ¶ 0003).
Claims 13 and 14 recite similar limitations and are rejected for the same reasons.
With regards to claim 3, Reicher teaches the method of claim 1, wherein the one or more computing devices comprise a picture archiving and communication system (PACS) (see at least column 38, lines 29-30).
With regards to claim 5, Reicher teaches the method of claim 4, wherein the disease comprises a plurality of lesions (see at least column 27, lines 47-50, the system may automatically find the volume, area, and/or stenosis of new lesions in a current image, and/or re-measure prior lesions from matched images).
With regards to claim 6, Reicher teaches the method of claim 4, further comprising classifying each respective disease; wherein sorting is based at least in part on the classifications (see at least figure 5, column 23, lines 13-61, rules recognize image sets that have a diagnosis of hydrocephalus or tumor and sort out those exams to compare or match).
Furthermore, Podilchuk teaches …as benign or malignant (see at least ¶ 0019). It would have been obvious to one of ordinary skill in the art at the time of invention to combine the remote device image CAD request of Podilchuk with the image matching system of Reicher with the motivation of improving the quality of patient care (Podilchuk, ¶ 0006).
With regards to claim 7, Reicher teaches the method of claim 1, wherein the respective characteristic of each of the two or more images comprises an anatomy type (see at least column 14, lines 11-15, “image characteristics” is used herein in reference to 2D medical images to refer to the various characteristics of the images with reference to the physical anatomy of a patient from which they were obtained).
With regards to claim 8, Reicher teaches the method of claim 1, wherein the image order is based on a plurality of characteristics of each of the two or more images (see at least column 15, lines 17-18, 44-45, sorting of images includes determining order of images, images are sorted based on multiple attributes).
With regards to claim 9, Reicher teaches the method of claim 1, further comprising filtering, by the one or more computing devices, the two or more images, the filtering determined based at least in part on the at least one characteristic of each of the two or more images (see at least column 15, lines 17-18, 44-45, sorting of images includes determining order of images, images are sorted based on multiple attributes); wherein the displayed images are displayed based on the filtering (see at least column 15, lines 16-19, the order is referenced when the images are displayed).
With regards to claim 10, Reicher teaches the method of claim 1, further comprising grouping, by the one or more computing devices, the two or more images, the grouping determined based at least in part on the at least one characteristic of each of the two or more images (see at least column 15, lines 17-45, sorting of images includes grouping images based on multiple attributes); wherein the displayed images are displayed based on the grouping (see at least column 15, lines 16-19, the order of the grouped images is referenced when the images are displayed).
With regards to claim 11, Reicher teaches the method of claim 1, further comprising identifying at least one image associated with the characteristic of at least one of the two or more images (see at least column 5, lines 44-47).
With regards to claim 12, Reicher teaches the method of claim 1, wherein sorting is based on a preference of the user (see at least column 5, lines 29-33, identify a first rule in the rules database associated with one or more characteristics of the first medical exam, the first rule including one or more first criteria for identifying a second medical exam for comparison with the first medical exam; column 18, lines 19-21, rules may be specific to users, user groups, and/or sites, and are also referred to herein as user preferences)
Response to Arguments
Applicant's arguments with respect to the 35 USC § 101 rejections set forth in the previous office action have been considered, but are not persuasive. In an effort to advance prosecution, the Examiner has provided a response to applicant's arguments. Applicant argues:
Applicant argues the limitations are not an abstract idea and are subject matter eligible for similar reasons as claim 2 of USPTO Example 37.
In response to Applicant’s argument the limitations are not an abstract idea and are subject matter eligible for similar reasons as claim 2 of USPTO Example 37, the Examiner respectfully disagrees. In claim 2 of Example 37, the claims were determined to not recite a judicial exception “because the claim, under its broadest reasonable interpretation, does not cover performance in the mind but for the recitation of generic computer components. For example, the “determining step” now requires action by a processor that cannot be practically applied in the mind. . In particular, the claimed step of determining the amount of use of each icon by tracking how much memory has been allocated to each application associated with each icon over a predetermined period of time is not practically performed in the human mind, at least because it requires a processor accessing computer memory indicative of application usage. Further, the claim does not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claim does not recite a mathematical relationship, formula, or calculation. Thus, the claim is eligible because it does not recite a judicial exception.”
In contrast, the claims in the instant application recite receiving requests and receiving and transferring images. Collecting information has been treated as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011); Electric Power, LLC v. Alstom S.A., (Fed. Cir. 2016). The claims further recite analyzing image properties, detecting/diagnosing image characteristics, grouping abnormalities and sorting images in an order based on characteristics. Analyzing information has been treated as within the abstract-idea category. See, e.g., TLI Commc’ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App’x 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); Electric Power, LLC v. Alstom S.A., (Fed. Cir. 2016); see also Mayo, 132 S. Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589–90 (1978); Gottschalk v. Benson, 409 U.S. 63, 67 (1972). The invention further discloses displaying images in the determined order. Merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014); Electric Power, LLC v. Alstom S.A., (Fed. Cir. 2016). As such, the claims recite an abstract idea.
Applicant's arguments with respect to the 35 USC § 103 rejections set forth in the previous office action have been considered, but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Golden, et al. (US 2020/0380675 A1) which discloses Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.
Min, et al. (US 2022/0392065 A1) which discloses systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.
R. B. Dubey and M. Hanmandlu, "Integration of CAD into PACS," 2012 2nd International Conference on Power, Control and Embedded Systems, Allahabad, India, 2012, pp. 1-6, doi: 10.1109/ICPCES.2012.6508034 which discloses Picture archiving and communication system (PACS) has become a mature technology over the past few years and has been widely implemented in several developed countries, in health care delivery for daily clinical imaging service and data management. A PACS is an integrated workflow system for managing images and related data which is designed to streamline operations throughout the whole patient care delivery process. Use of PACS has changed the medical image interpretation from conventional hard copy images to soft-copy studies viewed on the systems workstations. Computer-aided detection and diagnosis (CAD) utilizes computer methods to obtain quantitative measurements from medical images and clinical information to assist clinicians. The CAD needs image input and related information from PACS to improve its accuracy and PACS benefits from CAD results online and available at the PACS workstation (WS) as a second reader to assist physicians in the decision making process. Although yet these two technologies remain as two separate independent systems with only minimal system integration. The use of the CAD-PACS software toolkit package has revolutionized PACS WS and thus clinical workflow from single event patient-based queries to longitudinal-based queries. The advantages of query/retrieving content-based imaging data can be a great benefit for medical imaging research and clinical practice. This paper describes a general method to integrate CAD results with PACS in clinical environment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joey Burgess whose telephone number is (571)270-5547. The examiner can normally be reached Monday through Friday 9-6.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3681