DETAILED ACTION
Status of Claims
This action is in reply to the Request for Continued Examination filed on 12/23/2025.
Claims 1, 8 and 14-15 have been amended.
Claims 2-7, 9-13, 16-20 and 22 have been cancelled.
Claims 1, 8, 14-15, 21 and 23-24 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/23/2025 has been entered.
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, 8, 14-15, 21 and 23-24 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1, 21 and 23-24 are directed to a method (i.e., a process), claims 8 and 14 are directed to a system (i.e., a machine) and claim 15 is directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1, 8, 14-15, 21 and 23-24 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 8 includes limitations that recite an abstract idea. Note that independent claim 8 is the system claim, while claim 1 covers a method claim and claim 12 covers the matching computer readable medium.
Specifically, independent claim 8 recites:
A device comprising:
a memory configured to store instructions; and
one or more processors configured to execute the instructions to:
receive information related to a data ingestion of Digital Imaging and Communications in Medicine (DICOM) data from a radiology device to a database;
perform a first data validation using the DICOM data and a first data validation rule that is generic to a plurality of imaging modalities of radiology devices;
perform a second data validation using the DICOM data and a second data validation rule that is specific to an imaging modality of the radiology device;
perform a third data validation using the DICOM data and a third data validation rule that is specific to a data ingestion application associated with the data ingestion;
determine, using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database, respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion, wherein the data validation rules include the first data validation rule that is generic to the plurality of imaging modalities of the radiology devices, the second data validation rule that is specific to the imaging modality of the radiology device, and the third data validation rule that is specific to the data ingestion application associated with the data ingestion;
determine that a data quality, of the respective data qualities, does not satisfy a threshold;
perform backward data lineage based on determining that the data quality does not satisfy the threshold;
determine, using an artificial intelligence (AI) model, a root cause of the data quality not satisfying the threshold based on performing the backward data lineage;
perform forward data lineage to determine one or more data ingestion applications that might be affected by the data quality not satisfying the threshold;
prevent the one or more data ingestion applications from accessing the DICOM data;
generate a user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion based on determining the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion and that displays the root cause of the data quality not satisfying the threshold,
wherein the user interface displays the first data validation rule and a corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because using a set of validation rules to ingesting DICOM data from the radiology device generic to multiple imaging modalities is a part of a medical workflow, and displaying the root cause of data quality not satisfying the threshold which are ways managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mental process” because determining that a data quality, of the respective data qualities, does not satisfy a threshold and determining the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion is an observation/evaluation/analysis that can be performed in the human mind or with a pen and paper. The foregoing underlined limitations also relate to claim 8 (similarly to claims 1 and 15).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 14, 21 and 23-24, these claims merely recite determining steps such as: claim 5, 14 –the information related to the respective data qualities of the DICOM data includes a DICOM tag of a data element of the DICOM data that contributed to the data quality not satisfying the threshold, claims 6, 13 and 20 - generating the user interface to display information identifying the root cause, claim 21 - performing anomaly detection using the DICOM data, claim 23 - generating a graph data structure based on the information related to the data ingestion, wherein the graph data structure includes a set of nodes corresponding to the DICOM data at the set of data ingestion stages of the data ingestion, and edges that connect the set of nodes and that identify relationships between the set of nodes and claim 24 - generating the user interface comprises generating the user interface to display information that permits an operator to select to improve the data quality of the DICOM data.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 8 and 15, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a database, a device, a user interface, a memory configured to store instructions, one or more processors, and a non-transitory computer-readable medium storing instructions to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the database, device, user interface, memory configured to store instructions, one or more processors, and non-transitory computer-readable medium storing instructions are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitation of “using an artificial intelligence (AI) model”, “using a set of data validation rules” and “imaging modalities of a radiology device,” which the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)), the Examiner further submits that these limitations do no more than generally link use of the abstract idea to a particular field of use because they merely specify the type of input which does not alter or affect how the abstract idea is performed (see MPEP § 2106.05(e)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 8, regarding the additional limitations of the database, device, user interface, memory configured to store instructions, one or more processors, and non-transitory computer-readable medium storing instructions, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 8 and analogous independent claims 1 and 15 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1, 8, 14-15, 21 and 23-24 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Paik (US 2024/0177836 A1) in view Gendron (US 2002/0023172 A1).
Claim 1:
Paik discloses A method comprising:
receiving information related to a data ingestion of Digital Imaging and Communications in Medicine (DICOM) data from a radiology device to a database (See [P0020-P0021] optimization of the hanging protocol comprises obtaining information from at least one of imaging order, clinical text, metadata (e.g., DICOM metadata), or image data (e.g., DICOM pixel data) for the image study. Also, see sharing same DICOM framed images in P0099, P0168, P0229, and database to store medical images and medical imaging data in P0252.);
determining, using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database (See P0020-P0021, P0099 and P0168 hanging protocol of medical images include DICOM metadata and pixel data.), respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion (See identifying attributes in P0020-P0021, P0152.), wherein the data validation rules include the first data validation rule that is generic to the plurality of imaging modalities of the radiology devices, the second data validation rule that is specific to the imaging modality of the radiology device, and the third data validation rule that is specific to the data ingestion application associated with the data ingestion (See modalities in P0016, the segmentation and labeling of vertebra in an MRI or CT image in P0110, matching criteria of hanging protocol in P0225 and study attributes in P0230-P0231 using a machine learning classifier where DICOM metadata and pixel data are taken into consideration.);
determining that a data quality, of the respective data qualities, does not satisfy a threshold (Regarding medical images, besides quality metrics (P0010), tracking quality measures (P0080-P0081) and [P0220] AI algorithm analyzes the findings to generate one or more performance or quality measures/metrics, see keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.);
performing backward data lineage based on determining that the data quality does not satisfy the threshold (See Fig. 26 backpropagating across complexity levels, analyzing medical images where easy, medium and difficult construe threshold ranges in [P0108] this could be determined quantitatively by ranking segmentation accuracy given a neural network that does not use progressive reasoning whereby lower accuracy anatomic regions would be considered more difficult. Also, see encrypting image data files when transmitted in P0190, P0303.);
determining, using an artificial intelligence (AI) model, a root cause of the data quality not satisfying the threshold based on performing the backward data lineage (Taught as AI-assisted models in P0081, P0101 including convolutional neural networks (CNN) when gradients are backpropagated across complexity levels, analyzing medical images where easy, medium and difficult.);
performing forward data lineage to determine one or more data ingestion applications that might be affected by the data quality not satisfying the threshold (See keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.);
preventing the one or more data ingestion applications from accessing the DICOM data (See P0199 locking case from review of imaging studies as hanging protocol of DICOM metadata (P0020, P0224, P0230-P0231).);
generating a user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion based on determining the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion (See exemplary ReadLex Playbook and other attributes in P0230 such as modality modifier (e.g., angiography), procedure modifier (e.g., transjugular), population (e.g., pediatric), body region (e.g., neck), anatomic focus (e.g., spine), laterality (e.g., left), reason for exam (e.g., screening), technique (e.g., dual energy CT), pharmaceutical (e.g., with IV contrast), view (e.g., lateral), or any combination.), and that displays the root cause of the data quality not satisfying the threshold (See detecting candidate abnormality categories in [P0138-P0139] the image position can be defined either using the mouse or by other computer input devices such as an eye-tracking device. In some embodiments, when a candidate abnormality category (e.g., a candidate lesion) is close enough to the designated image location and has a probability or score above a given threshold, this result is presented to the user and a full text sentence describing this finding is generated (e.g., a computer-finding). Also, see the keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.).
Although Paik discloses a method for ingesting of Digital Imaging and Communications in Medicine (DICOM) data as mentioned above, Paik does not explicitly teach a user interface displaying a first data validation rule, corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule. Gendron teaches:
based on performing the first data validation, the second data validation, and the third data validation, performing a first data validation using the DICOM data and a first data validation rule that is generic to a plurality of imaging modalities of radiology devices (See Fig. 1- 4, using DICOM protocol exchanging data among medical modalities and other devices in P0030-P0031, [P0058-P0059] automatically reformat patient identifiers as received from a medical imaging modality. Furthermore, the rules may be used to selectively propagate or filter messages or particular commands, such as DICOM commands, along one or more specific routes. Also, see Fig. 16, P0154 where user interface 128 displays exemplary imaging modality.);
performing a second data validation using the DICOM data and a second data validation rule that is specific to an imaging modality of the radiology device (See Fig. 1- 4, using DICOM protocol exchanging data among medical modalities and other devices in P0030-P0031 and Fig. 16, P0154 where user interface 128 displays exemplary imaging modality data include capture device ID, DICOM UID, manufacturer information and whether contrast bolus agent was applicable or not.);
performing a third data validation using the DICOM data and a third data validation rule that is specific to a data ingestion application associated with the data ingestion (See Fig. 3, Fig. 12, P0064, P0067 where the verification module extracting pixel data during transmission and domain identifier according to DICOM protocol as ingesting data in P0081-P0082, P0152, Fig. 7-8.); and
wherein the user interface displays the first data validation rule and a corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule (See Fig. 12 user interface display DICOM UID, modality, manufacturing equipment type and pixel representation when user edits and searches DICOM database in P0152.).
Therefore, it would have been obvious to one of ordinary skill in the art of routing medical images before the effective filing date of the claimed invention to modify the method, system and software of Paik to include a user interface displaying a first data validation rule, corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule as taught by Gendron to define vendor-independent data formats and data transfer services for digital medical images mentioned in Gendron’s P0003.
Claim 8:
Paik discloses A device comprising:
a memory configured to store instructions (See memory in P0306-P0307.); and
one or more processors configured to execute the instructions (See Fig. 9 processors, non-transitory computer-readable storage medium, software is configured to cause the processor to carry out function in P0306-P0307.) to:
receive information related to a data ingestion of Digital Imaging and Communications in Medicine (DICOM) data from a radiology device to a database (See [P0020-P0021] optimization of the hanging protocol comprises obtaining information from at least one of imaging order, clinical text, metadata (e.g., DICOM metadata), or image data (e.g., DICOM pixel data) for the image study. Also, see sharing same DICOM framed images in P0099, P0168, P0229 and database to store medical images and medical imaging data in P0252.);
determine, using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database (See P0020-P0021, P0099 and P0168 hanging protocol of medical images include DICOM metadata and pixel data.), respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion (See identifying attributes in P0020-P0021, P0152.), wherein the data validation rules include the first data validation rule that is generic to the plurality of imaging modalities of the radiology devices, the second data validation rule that is specific to the imaging modality of the radiology device, and the third data validation rule that is specific to the data ingestion application associated with the data ingestion (See modalities in P0016, the segmentation and labeling of vertebra in an MRI or CT image in P0110, matching criteria of hanging protocol in P0225 and study attributes in P0230-P0231 using a machine learning classifier where DICOM metadata and pixel data are taken into consideration.).);
determine that a data quality, of the respective data qualities, does not satisfy a threshold (Regarding medical images, besides quality metrics (P0010), tracking quality measures (P0080-P0081) and [P0220] AI algorithm analyzes the findings to generate one or more performance or quality measures/metrics, see keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.);
perform backward data lineage based on determining that the data quality does not satisfy the threshold (See Fig. 26 backpropagating across complexity levels, analyzing medical images where easy, medium and difficult construe threshold ranges in [P0108] this could be determined quantitatively by ranking segmentation accuracy given a neural network that does not use progressive reasoning whereby lower accuracy anatomic regions would be considered more difficult. Also, see encrypting image data files when transmitted in P0190, P0303.);
determine, using an artificial intelligence (AI) model, a root cause of the data quality not satisfying the threshold based on performing the backward data lineage (Taught as AI-assisted models in P0081, P0101 including convolutional neural networks (CNN) when gradients are backpropagated across complexity levels, analyzing medical images where easy, medium and difficult.);
perform forward data lineage to determine one or more data ingestion applications that might be affected by the data quality not satisfying the threshold (See keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.);
prevent the one or more data ingestion applications from accessing the DICOM data (See P0199 locking case from review of imaging studies as hanging protocol of DICOM metadata (P0020, P0224, P0230-P0231).); and
generate a user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion based on determining the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion (See Fig. 1, [column 7, line 59 to column 8, line 7] DCDS 32 can be configured to determine whether the identified output device or address is capable of displaying the converted image with the requisite image resolution and quality before sending the image.) and that displays the root cause of the data quality not satisfying the threshold (See detecting candidate abnormality categories in [P0138-P0139] the image position can be defined either using the mouse or by other computer input devices such as an eye-tracking device. In some embodiments, when a candidate abnormality category (e.g., a candidate lesion) is close enough to the designated image location and has a probability or score above a given threshold, this result is presented to the user and a full text sentence describing this finding is generated (e.g., a computer-finding). Also, see the keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.).
Although Paik discloses a method for ingesting of Digital Imaging and Communications in Medicine (DICOM) data as mentioned above, Paik does not explicitly teach a user interface displaying a first data validation rule, corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule. Gendron teaches:
perform a first data validation using the DICOM data and a first data validation rule that is generic to a plurality of imaging modalities of radiology devices (See Fig. 1- 4, using DICOM protocol exchanging data among medical modalities and other devices in P0030-P0031, [P0058-P0059] automatically reformat patient identifiers as received from a medical imaging modality. Furthermore, the rules may be used to selectively propagate or filter messages or particular commands, such as DICOM commands, along one or more specific routes. Also, see Fig. 16, P0154 where user interface 128 displays exemplary imaging modality.);
perform a second data validation using the DICOM data and a second data validation rule that is specific to an imaging modality of the radiology device (See Fig. 1- 4, using DICOM protocol exchanging data among medical modalities and other devices in P0030-P0031 and Fig. 16, P0154 where user interface 128 displays exemplary imaging modality data include capture device ID, DICOM UID, manufacturer information and whether contrast bolus agent was applicable or not.);
perform a third data validation using the DICOM data and a third data validation rule that is specific to a data ingestion application associated with the data ingestion (See Fig. 3, Fig. 12, P0064, P0067 where the verification module extracting pixel data during transmission and domain identifier according to DICOM protocol as ingesting data in P0081-P0082, P0152, Fig. 7-8.); and
based on performing the first data validation, the second data validation, and the third data validation,
wherein the user interface displays the first data validation rule and a corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule (See Fig. 12 user interface display DICOM UID, modality, manufacturing equipment type and pixel representation when user edits and searches DICOM database in P0152.).
Therefore, it would have been obvious to one of ordinary skill in the art of routing medical images before the effective filing date of the claimed invention to modify the method, system and software of Paik to include a user interface displaying a first data validation rule, corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule as taught by Gendron to define vendor-independent data formats and data transfer services for digital medical images mentioned in Gendron’s P0003.
Regarding claim 14, although Paik and Gendron teaches the device of claim 8, Paik discloses wherein the information related to the respective data qualities of the DICOM data includes a DICOM tag of a data element of the DICOM data that contributed to the data quality not satisfying the threshold (See reporting using tags (P0014-P0015, P0080, P0086-P0087) and detecting candidate abnormality categories in [P0138-P0139] the image position can be defined either using the mouse or by other computer input devices such as an eye-tracking device. In some embodiments, when a candidate abnormality category (e.g., a candidate lesion) is close enough to the designated image location and has a probability or score above a given threshold, this result is presented to the user and a full text sentence describing this finding is generated (e.g., a computer-finding). Also, see the keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.).
Claim 15:
Paik discloses A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors (See non-transitory computer readable storage medium and processor in P0020, P0086, P0088.) to:
receive information related to a data ingestion of Digital Imaging and Communications in Medicine (DICOM) data from a radiology device to a database (See [P0020-P0021] optimization of the hanging protocol comprises obtaining information from at least one of imaging order, clinical text, metadata (e.g., DICOM metadata), or image data (e.g., DICOM pixel data) for the image study. Also, see sharing same DICOM framed images in P0099, P0168, P0229 and database to store medical images and medical imaging data in P0252.);
determine, using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database (See P0020-P0021, P0099 and P0168 hanging protocol of medical images include DICOM metadata and pixel data.), respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion (See identifying attributes in P0020-P0021, P0152.), wherein the data validation rules include the first data validation rule that is generic to the plurality of imaging modalities of the radiology devices, the second data validation rule that is specific to the imaging modality of the radiology device, and the third data validation rule that is specific to the data ingestion application associated with the data ingestion (See modalities in P0016, the segmentation and labeling of vertebra in an MRI or CT image in P0110, matching criteria of hanging protocol in P0225 and study attributes in P0230-P0231 using a machine learning classifier where DICOM metadata and pixel data are taken into consideration.);
determine that a data quality, of the respective data qualities, does not satisfy a threshold (Regarding medical images, besides quality metrics (P0010), tracking quality measures (P0080-P0081) and [P0220] AI algorithm analyzes the findings to generate one or more performance or quality measures/metrics, see keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.);
perform backward data lineage based on determining that the data quality does not satisfy the threshold (See Fig. 26 backpropagating across complexity levels, analyzing medical images where easy, medium and difficult construe threshold ranges in [P0108] this could be determined quantitatively by ranking segmentation accuracy given a neural network that does not use progressive reasoning whereby lower accuracy anatomic regions would be considered more difficult. Also, see encrypting image data files when transmitted in P0190, P0303.);
determine, using an artificial intelligence (AI) model, a root cause of the data quality not satisfying the threshold based on performing the backward data lineage (Taught as AI-assisted models in P0081, P0101 including convolutional neural networks (CNN) when gradients are backpropagated across complexity levels, analyzing medical images where easy, medium and difficult.);
perform forward data lineage to determine one or more data ingestion applications that might be affected by the data quality not satisfying the threshold (See keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.);
prevent the one or more data ingestion applications from accessing the DICOM data (See P0199 locking case from review of imaging studies as hanging protocol of DICOM metadata (P0020, P0224, P0230-P0231).); and
generate a user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion based on determining the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion (See Fig. 1, [column 7, line 59 to column 8, line 7] DCDS 32 can be configured to determine whether the identified output device or address is capable of displaying the converted image with the requisite image resolution and quality before sending the image.), and that displays the root cause of the data quality not satisfying the threshold (See detecting candidate abnormality categories in [P0138-P0139] the image position can be defined either using the mouse or by other computer input devices such as an eye-tracking device. In some embodiments, when a candidate abnormality category (e.g., a candidate lesion) is close enough to the designated image location and has a probability or score above a given threshold, this result is presented to the user and a full text sentence describing this finding is generated (e.g., a computer-finding). Also, see the keyword meeting a predetermined threshold in P0327 as clinically relevant data in radiology reporting.).
Although Paik discloses a method for ingesting of Digital Imaging and Communications in Medicine (DICOM) data as mentioned above, Paik does not explicitly teach a user interface displaying a first data validation rule, corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule. Gendron teaches:
perform a first data validation using the DICOM data and a first data validation rule that is generic to a plurality of imaging modalities of radiology devices (See Fig. 1- 4, using DICOM protocol exchanging data among medical modalities and other devices in P0030-P0031, [P0058-P0059] automatically reformat patient identifiers as received from a medical imaging modality. Furthermore, the rules may be used to selectively propagate or filter messages or particular commands, such as DICOM commands, along one or more specific routes. Also, see Fig. 16, P0154 where user interface 128 displays exemplary imaging modality.);
perform a second data validation using the DICOM data and a second data validation rule that is specific to an imaging modality of the radiology device (See Fig. 1- 4, using DICOM protocol exchanging data among medical modalities and other devices in P0030-P0031 and Fig. 16, P0154 where user interface 128 displays exemplary imaging modality data include capture device ID, DICOM UID, manufacturer information and whether contrast bolus agent was applicable or not.);
perform a third data validation using the DICOM data and a third data validation rule that is specific to a data ingestion application associated with the data ingestion (See Fig. 3, Fig. 12, P0064, P0067 where the verification module extracting pixel data during transmission and domain identifier according to DICOM protocol as ingesting data in P0081-P0082, P0152, Fig. 7-8.); and
based on performing the first data validation, the second data validation, and the third data validation, wherein the user interface displays the first data validation rule and a corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule (See Fig. 12 user interface display DICOM UID, modality, manufacturing equipment type and pixel representation when user edits and searches DICOM database in P0152.).
Therefore, it would have been obvious to one of ordinary skill in the art of routing medical images before the effective filing date of the claimed invention to modify the method, system and software of Paik to include a user interface displaying a first data validation rule, corresponding first status of the DICOM data with respect to the first data validation rule, the second data validation rule and a corresponding second status of the DICOM data with respect to the second data validation rule, and the third data validation rule and a corresponding third status of the DICOM data with respect to the third data validation rule as taught by Gendron to define vendor-independent data formats and data transfer services for digital medical images mentioned in Gendron’s P0003.
Claims 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Paik (US 2024/0177836 A1) in view Gendron (US 2002/0023172 A1) further in view of Vilnai (US 2020/0365252 A1).
Regarding claim 21, although Paik and Gendron teach the method of claim 1 mentioned above, Paik and Gendron do not explicitly teach performing anomaly detection using the DICOM data. Vilnai teaches further comprising: performing anomaly detection using the DICOM data (See P0042 and [P0047] the technology of security medical imaging network nodes (that implement the DICOM® protocol), in particular providing real time security. For example, against malicious attack (e.g., by malware and/or by a malicious human entity), against malicious actions, against anomalies (e.g., errors not necessarily due to malicious intent, against theft of medical imaging devices, and/or against incorrect installation of new medical imaging devices.).
Therefore, it would have been obvious to one of ordinary skill in the art of DICOM protocol management before the effective filing date of the claimed invention to modify the method, system and software of Paik and Gendron to include performing anomaly detection using the DICOM data as taught by Vilnai to maintain DICOM as an international standard to transmit, store, retrieve, print, process, and display medical imaging information mentioned in Vilnai’s P0002.
Regarding claim 23, although Paik and Gendron teach the method of claim 1 mentioned above, Paik and Gendron do not explicitly teach generating a graph data structure based on the information related to the data ingestion, including a set of nodes corresponding to the DICOM data at the set of data ingestion stages of the data ingestion, and edges that connect the set of nodes and that identify relationships between the set of nodes. Vilnai teaches further comprising:
generating a graph data structure based on the information related to the data ingestion,
wherein the graph data structure includes a set of nodes corresponding to the DICOM data at the set of data ingestion stages of the data ingestion, and edges that connect the set of nodes and that identify relationships between the set of nodes (See graph data structures with nodal relationships in Fig. 4-5D, [P0100] Edges 402 are directed from client nodes 404A-D to server nodes 406A-B. Each server node 406 is labeled with respective server ports (e.g., 104 and 106 as shown as examples, pointed to by arrows 408 410 respectively). Within each client node 404A-D and server node 406A-B is presented the IP address of the respective node, and the DICOM AE title. Also, see P0128-P0131.).
Therefore, it would have been obvious to one of ordinary skill in the art of DICOM protocol management before the effective filing date of the claimed invention to modify the method, system and software of Paik and Gendron to include generating a graph data structure based on the information related to the data ingestion, including a set of nodes corresponding to the DICOM data at the set of data ingestion stages of the data ingestion, and edges that connect the set of nodes and that identify relationships between the set of nodes as taught by Vilnai to maintain DICOM as an international standard to transmit, store, retrieve, print, process, and display medical imaging information mentioned in Vilnai’s P0002.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Paik (US 2024/0177836 A1) in view Gendron (US 2002/0023172 A1) further in view of Arroyo Camejo (US 2024/0070856 A1).
Regarding claim 24, although Paik and Gendron teach the method of claim 1 mentioned above, Paik and Gendron do not explicitly generating the user interface to display information that permits an operator to select to improve the data quality of the DICOM data. Arroyo Camejo teaches further comprising:
wherein generating the user interface comprises generating the user interface to display information that permits an operator to select to improve the data quality of the DICOM data (See automated image quality assessment algorithms per DICOM series of images in P0047-P049, P0061-P0063] for a user to determine a quality metric for the entire file for each input DICOM file, Fig. 2, and 4-5.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical image management before the effective filing date of the claimed invention to modify the method, system and software of Paik and Gendron to include generating a graph data structure based on the information related to the data ingestion, including a set of nodes corresponding to the DICOM data at the set of data ingestion stages of the data ingestion, and edges that connect the set of nodes and that identify relationships between the set of nodes as taught by Arroyo Camejo for providing quality feedback for the user of a diagnostic scanner mentioned in Arroyo Camejo’s P0002.
Response to Arguments
Applicant alleges that claims are directed to operations associated with determining data quality of DICOM data at a set of data ingestion stages of a data ingestion of DICOM data from a radiology device to a database, and do not recite concepts related to “fundamental economic principles or practices,” “commercial or legal interactions” between people, or “managing personal behavior or relationships or interactions between people”. See pgs. 11-12 of Remarks – Examiner disagrees.
Not only is corresponding the status of DICOM data with respect to validation rules insignificant pre-solution activity as noted according to MPEP § 2106.05(g), but Digital Imaging and Communications in Medicine (DICOM) is a standard protocol for storing and exchanging medical images and related information that healthcare providers use to ensure medical equipment can communicate within the healthcare industry. The healthcare providers are expected to use DICOM in workflow processes and medical imaging practices.
Applicant alleges that the claims provide of radiology and a technical improvement to devices associated with the acquisition, ingestion, and usage of DICOM data of radiology devices by determining data quality of DICOM data at a set of data ingestion stages of a data ingestion of the DICOM data. See pgs. 12-13 and 15-16 of Remarks – Examiner disagrees.
Neither technology is claimed in the instant case, nor is DICOM data being used in a technical way to acquire, ingest or determine data quality. For example, “generating the user interface comprises generating the user interface to display information that permits an operator to select to improve the data quality of the DICOM data” recited in newly added claim 24, is a limitation that is not affecting upon the speed or components of increasing processor and memory efficiency, but is rather performing basic data processing operations that any generic computer would be expected to do.
Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied new art and art already of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Cooke Jr. (US 6,57,629 B1) and Lyman (US 11,922,348 B2).
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/T.S.W./Examiner, Art Unit 3687 06/10/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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