DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 12/30/2025.
Claims 1, 6, 14-, 16, 18 and 20 have been amended.
Claims 1-20 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 .
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-13 are directed to a system (i.e., a machine), claims 14-17 are directed to a method (i.e., a process), and claim 18-20 are directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1-20 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 1 includes limitations that recite an abstract idea. Note that independent claim 1 is the system claim, while claim 14 covers a method claim and claim 18 covers the matching computer readable medium.
Specifically, independent claim 1 recites:
A computing system, comprising:
a computing device comprising at least one processor, at least one memory and at least one output component;
a server comprising a processor, a memory and a data store, the data store comprising:
templates, wherein each template in the templates comprises a tag and at least one rule that differ from one another among the templates, wherein the at least one rule comprises an evaluation criteria and a comparison value, the comparison value comprising a value field of a Digital Imaging and Communication in Medicine (DICOM) attribute; and
a knowledge graph, wherein the knowledge graph comprises nodes and edges connecting the nodes to be displayed in the at least one output component of the computing device, wherein the nodes represent the templates and the edges represent relationships between the templates, wherein each node comprises a respective tag corresponding to a respective template in the templates; and
one or more data sources comprising clinical data items, the one or more data sources communicatively coupled to the computing device and the server via a network connection;
the memory of the server storing instructions that, when executed by the processor, cause the processor to perform acts comprising:
receiving, via the computing device, an identifier of a patient;
obtaining clinical data items of the patient from the one or more data sources based on the identifier of the patient;
matching each clinical data item in the clinical data items to one or more templates in the templates of the data store, based upon the at least one rule included in the one or more templates in the data store;
generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched;
receiving a keyword and a non-negative integer from the computing device;
upon receiving the keyword and the non-negative integer, identifying a seed node in the knowledge graph based upon the keyword;
upon receiving the keyword and the non-negative integer, identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer, wherein the subset of nodes includes the seed node and first nodes;
identifying a subset of the tagged clinical data items based upon first tags of the subset of nodes; and
providing graphical data corresponding to the subset of the tagged clinical data items to the computer device to be presented via the at least one output component on a display.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because matching clinical data in a template, comparing Digital Imaging and Communication in Medicine (DICOM) values and attributes, tagging clinical data items, identifying a subset of nodes and graphing clinical items for display are managing human behavior/interactions between people. For example, a healthcare provider is capable of gathering clinical data highlighted within a patient’s medical records, physiologic measurements, lab work, image studies, medical devices, medical reports, and arrange the clinical data into a nodal network. These limitations constitute (b) “a mental process” because matching clinical data, identifying a subset of nodes and identifying a subset of tagged clinical data items are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper. For example, a nodal network can be hand-sketched by a human. Furthermore, these limitations constitute (c) “mathematical concepts” because a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer and connecting a seed node to a first node by no more than a number of edges equal to a non-integer is using math to perform. The foregoing underlined limitations also relate to claims 14 and 18 (similarly to claim 1).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 3, 5-8, 10, 12 and 16-17, these claims merely recite specific kinds data, such as: claim 3 - the clinical data items include Digital Imaging and Communications in Medicine (DICOM) clinical data items and non-DICOM clinical data items, claim 5 – the clinical data items include one or more of: a historical imaging study, a clinical report, an admission form, a radiation report, a result obtained as output of an algorithm or artificial intelligence (AI) model, a measurement, a video or a clinical portable document format (PDF) document, claim 6 - the first DICOM attributes of the clinical data items are matched to second DICOM attributes specified by the rules of the templates, claim 7 – the graphical data comprises identifiers for each of the subset of the tagged clinical data items, claim 8 – the subset of the tagged clinical data items are images of the patient, wherein the graphical data includes the images, claim 10 – the edges of the knowledge graph are directed edges, and claim 12 - the subset of the tagged clinical data items includes an associated date, wherein the graphical data comprises a timeline that includes identifiers for each of the subset of the tagged clinical data items, wherein the timeline is chronologically arranged based upon the associated date of each of the subset of the tagged clinical data items, claim 16 – the subset of the tagged clinical data items includes an image of the patient and a clinical report about the patient, wherein the graphical data includes an identifier for the image and an identifier for the clinical report and claim 17 - the templates represents: a body part, a medical facility, a medical department within the medical facility an imaging modality, a piece of medical equipment or a disease.
In relation to claims 4, 9, 15 and 19-20, these claims merely recite determining steps such as: claim 4 - the acts further comprising: prior to matching each clinical data item in the clinical data items to the one or more templates in the templates, converting the non-DICOM clinical data items to DICOM attributes by way of an adapter, claim 9 - the subset of nodes include the seed node, a first node, and a second node, wherein the seed node and the first node are connected by a first edge, wherein the first node and the second node are connected by a second edge, wherein the first node represents a body part template corresponding to a body part, wherein the second node represents a disease template corresponding to a disease that affects the body part, claim 15 - a rule of a first template comprises evaluation criteria and a comparison value, wherein the first tagged clinical data item is generated by evaluating a first clinical data item against the evaluation criteria and the comparison value, claim 19 - generate a set of predictor-parameters for the machine learning algorithm, claim 19 - the acts further comprising: prior to obtaining the clinical data items, receiving a user-defined template as input from a user, wherein the user-defined template comprises a user-defined tag and at least one user-defined rule; receiving a selection of a node in the nodes of the knowledge graph, and generating a user-defined node in the knowledge graph based upon the user-defined template, wherein the user-defined node is connected to the node in the knowledge graph and claim 20 - the acts further comprising: subsequent to generating the tagged clinical data items and prior to receiving the keyword, causing identifiers for the tagged clinical data items to be presented on the display, based on a selection of an identifier for a first tagged clinical data item is selected by the healthcare worker, wherein the identifier for the first tagged clinical data item is the keyword.
In relation to claims 11 and 13, these claims merely gathering data, such as: claim 11 - receiving an identifier for the patient from a computing device operated by a healthcare worker and claim 13 - receiving a medical record number (MRN) of the patient, wherein the clinical data items of the patient are obtained based upon the MRN of the patient.
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, 14 and 18, 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 system, a processor, a data store, memory storing instructions that, when executed by the processor, cause the processor to perform acts, a display, a network, a plurality of electronic sources and a non-transitory computer-readable storage medium comprising instructions, 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 system, processor, data store, memory storing instructions that, when executed by the processor, cause the processor to perform acts, display, network, plurality of electronic sources and non-transitory computer-readable storage medium comprising 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 limitations “templates, wherein each template in the templates comprises a tag and at least one rule”, “templates, wherein each template in the templates comprises a tag and at least one rule”, and “the knowledge graph” 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)). Regarding the additional limitation “obtaining clinical data items of a patient” and “upon receiving a keyword”, the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding claim 2, the additional limitation “the clinical data items are obtained over a network connection from a plurality of electronic source”, the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
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 2019 PEG and 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 1, regarding the additional limitations of the system, processor, data store, memory storing instructions that, when executed by the processor, cause the processor to perform acts, display, network, plurality of electronic sources and non-transitory computer-readable storage medium comprising 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)). Furthermore, obtaining clinical data and receiving data liken to a keyword or patient identifiers input entry is nothing more than that recognized as well-understood, routine and conventional computer function because transmitting or receiving data over a network is well-understood, routine and conventional as noted in the MPEP 2106.05(d)(II) and thus doesn’t provide “significantly more.”
Thus, representative independent claim 1 and analogous independent claims 14 and 18 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 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5 and 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Shankar (US 2017/0277841 A1) in view of Schrempf (US 2022/0270721 A1) further in view of Hao (US 2018/0165415 A1) and Solis (US 2022/0172824 A1).
Claim 1:
Shankar discloses a computing system, comprising:
a computing device comprising at least one processor, at least one memory and at least one output component (See GUI on display in P0071, Fig. 1-Fig. 16 items 1630 and 1640 in P0081, Fig. 18, processors 1824, 1834 1844, 1854 in P0083, P0085 and memory.);
a server comprising a processor, a memory and a data store, the data store (See server in P0037, P0052 and a database in P0068, P0095.) comprising:
a knowledge graph, wherein the knowledge graph comprises nodes and edges connecting the nodes to be displayed in the at least one output component of the computing device (See Fig. 3, P0048-P0049 where the analysis of graph algorithms and inference use rules ordered into nodes and edges is delivered as diagnosis and recommended treatments (Fig. 5) displayed in Fig. 12, Item 1250 mentioned in P0072.), wherein the nodes represent the templates and the edges represent relationships between the templates, wherein each node comprises a respective tag corresponding to a respective template in the templates (See Fig. 1 build medical rule graph 130, using knowledge representation 160, 170 and personalized template mentioned in [P0038-P0039] represented as a graph due to the nature of the relationships between the medical entities, or nodes, which are the various medical knowledge factors, and the edges, which are the connecting possibilities between nodes of the graph.); and
one or more data sources comprising clinical data items (Besides collecting biological information from sensors, vital signs and clinical records in P0041, see biological data, electronic medical records, clinical records and image data in P0035 and Fig. 4A-4C, Fig. 7, P0065 patterns recognized in text.), the one or more data sources communicatively coupled to the computing device and the server via a network connection (See Fig. 7 user interface 740 mentioned in P0065 downloaded from internet and [P0058] FIG. 4C, or portions thereof, can be implemented using a mobile device, a server, a web interface into a cloud processor, and so on.); and
the memory of the server storing instructions that, when executed by the processor, cause the processor to perform acts (See P0007 a memory which stores instructions one or more processors attached to the memory wherein the one or more processors, when executing the instructions which are stored and server in P0058.) comprising:
receiving, via the computing device, an identifier of a patient (With identifiers as tagged clinical data in a clinical study, see established analysis of collected medical and clinical data from various studies in P0003 and presenting results of latest diagnosing and treatment plan studies in P0074, according to demographic data shown in Fig. 8 item 810 mentioned in P0032, P0066.);
obtaining clinical data items of the patient from the one or more data sources based on the identifier of the patient (See P0041 collecting biological information from sensors, vital signs and clinical records and demographic data shown in Fig. 8 item 810 mentioned in P0032, P0066.);
receiving a keyword and a non-negative integer from the computing device (See portable, network-enabled electronic devices 1630, 1630 in Fig. 16, P0081, non-negative integers 2 and 4 shown in Fig. 5 and P0059 where there are 2 medical interventions 540 (nodes 13 and 1014) and 4 diseases and disorders 530 (nodes 5, 6, 1011 and 1112). Besides using keywords to enter the medical interventions 540, diseases and disorders 530, see exemplary keywords as ability to assemble medical vocabulary terms in P0031 and arbitrary terms in [P0051] actual acronym terms such as ASCVD, which stands for atherosclerotic cardiovascular diseases.);
identifying a subset of the tagged clinical data items based upon first tags of the subset of nodes (See exemplary subset of medial ordering rules in P0034-P0035 and see Fig. 4A patient attributes, diagnosis, treatment, P0035 clinical records, image data, sensor data and ailments as tagged data.); and
providing graphical data corresponding to the subset of the tagged clinical data items to the computing device to be presented via the at least one output component on a display (See P0071, P0077 displaying clinal data and in [P0034] The medical probabilistic rule graph can apply rules within the subset of the medical rules in a specific order based on the ordering the medical rules.).
Although Shankar discloses a knowledge graph comprising nodes and edges connecting the nodes as mentioned above, Shankar does not explicitly teach tagging to distinguish templates, matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched. Schrempf teaches:
templates, wherein each template in the templates comprises a tag and at least one rule that differ from one another among the templates (See Fig. 8-11, Fig. 13-14 exemplary templates mentioned in P0067, P0136-P0138.); and
matching each clinical data item in the clinical data items to one or more templates in the templates of the data store, based upon the at least one rule included in the one or more templates in the data store (See set of synonyms as exemplary knowledge source in P0171-P0172 and rule of populating templates with knowledge from suitable database in P0209-P0210.);
generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched (See Fig. 6 and P0170-P0174, where labels serve as tagging clinical data being matched.);
Therefore, it would have been obvious to one of ordinary skill in the art of processing medical text before the effective filing date of the claimed invention to modify the system, method and software of Shankar to include tagging to distinguish templates, matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched as taught by Schrempf for processing text data, for training models to process text data using templates and/or synthesized text data mentioned in Schrempf’s P0001.
Although Shankar and Schrempf teach a computing system for receiving the keyword and a non-negative integer from a user and matching clinical data items to templates by identifying a seed node in the knowledge graph when identifying a subset of nodes mentioned above. Shankar and Schrempf do not explicitly teach identifying a seed node in the knowledge graph based upon the keyword and identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer based upon receiving the keyword and a non-negative integer. Hao teaches:
upon receiving the keyword, identifying a seed node in the knowledge graph based upon the keyword (See exemplary word “patient” as nodal root of knowledge tree in P0043, and concept node as root node P0062, where the root node construes identifying a seed node.).
upon receiving the keyword and the non-negative integer, identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer, wherein the subset of nodes includes the seed node and first nodes (With the keyword as a concept and an odd number as less than or equal to a non-negative, see selecting a single-concept subtree of the knowledge tree shown in Fig. 3, Fig. 4A-4B, Fig. 6A-6B, P0044, P0056-P0065 where the single concept subtree nodes represent odd number 1.).
Therefore, it would have been obvious to one of ordinary skill in the knowledge-based management of medical records arts before the effective filing date of the claimed invention to modify the system, method and software of Shankar and Schrempf to include identifying a seed node in the knowledge graph based upon the keyword and identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer based upon receiving the keyword and a non-negative integer as taught by Hao when reducing human efforts required on knowledge-based feature engineering, that are very useful for data mining in clinical knowledge in the literatures and the heterogeneous EMR datasets mentioned in Hao’s P0007.
Although Shankar, Schrempf and Hao teach a computing system for receiving the keyword and a non-negative integer from a user and matching clinical data items to templates by identifying a seed node in the knowledge graph when identifying a subset of nodes, when identifying a seed node in the knowledge graph based upon the keyword and identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer based upon receiving the keyword and the non-negative integer. Shankar, Schrempf and Hao do not explicitly teach evaluation criteria when comparing Digital Imaging and Communication in Medicine (DICOM) attributes. Solis teaches:
wherein the at least one rule comprises an evaluation criteria and a comparison value, the comparison value comprising a value field of a Digital Imaging and Communication in Medicine (DICOM) attribute (See established conversion of DICOM formats in P0031, DICOM tags as comparison values used for matching metadata in P0089 and image identifiers when determining the DICOM tags in P0102. Also, see Fig. 7A-7B and [P0079-P0080] header 710 may include information about acquisition date referenced by tag (0008,0022), type or format of data format utilized, e.g., video image format acquired by tag (0008, 1022) or digital image format acquired by tag (0008, 0123), and attributes or operating parameters of different types of image acquisition devices 110, e.g., whether MRI, CT, X-ray, or tomosynthesis. The DICOM standard uses hundreds of tags 571 referencing respective metadata 572 about the patient, images, image acquisition device, operating parameters, demographics, etc. While the types of DICOM tags 571 are comprehensive, the user of the image review workstation 120A is typically interested in only a subset, often a small subset, of available header 710 data. Image file 310 in form of a DICOM file also include a data set for image pixel intensity data 573, which can be used to reconstruct an image.).
Therefore, it would have been obvious to one of ordinary skill in the medical breast imaging arts before the effective filing date of the claimed invention to modify the system, method and software of Shankar, Schrempf and Hao to include evaluation criteria when comparing Digital Imaging and Communication in Medicine (DICOM) attributes as taught by Hao to avoid inconveniences and shortcomings compounded when more images are reviewed as part of the radiologist's workflow mentioned in Solis’ P0006.
Regarding claims 2, Shankar discloses wherein the clinical data items are obtained over a network connection from a plurality of electronic sources (See Fig. 16, P0081 network-enabled electronic devices 1630 and 1640.).
Regarding claims 5, Shankar discloses wherein the clinical data items include one or more of: a historical imaging study; a clinical report; an admission form; a radiation report; a result obtained as output of an algorithm or artificial intelligence (AI) model; a measurement; a video; or a clinical portable document format (PDF) document (See study results for a given treatment plan for a given diagnosis (P0074), indicate radiation therapy (P0058), video capture of patient (P0081), and measurements (P0041).).
Regarding claims 7, Shankar discloses wherein the graphical data comprises identifiers for each of the subset of the tagged clinical data items (With the tag as a name of a body part, a body region or system, a name of a medical facility, a name of a department within the medical facility, a name of an imaging modality, a name of a piece of medical equipment, or a name of a disease or a name of a clinical observation, see Fig. 4A patient attributes, diagnosis, treatment, P0035 clinical records, image data, sensor data and ailments.).
Regarding claims 8, Shankar discloses wherein the subset of the tagged clinical data items are images of the patient, wherein the graphical data includes the images (See image data in P0035.).
Regarding claims 9, Shankar discloses wherein the subset of nodes include the seed node, a first node, and a second node, wherein the seed node and the first node are connected by a first edge, wherein the first node and the second node are connected by a second edge, wherein the first node represents a body part template corresponding to a body part, wherein the second node represents a disease template corresponding to a disease that affects the body part (Taught in P0035,where traversing an edge from one node to another with patients having aliments such as cardiovascular diseases, diabetes, cancer and the cardiovascular diseases construe the heart as a body part, the graph is used as the template and utilizing cardiology module mentioned in P0039.).
Regarding claims 10, Shankar discloses wherein the edges of the knowledge graph are directed edges (See directed acyclic graph (DAG) in [P0048] The models can be updated by evaluating treatment results and being fed back into machine learning/deep learning 322 to update risk models and DAG nodes and edges.).
Regarding claims 11, Shankar discloses wherein the acts occur responsive to receiving an identifier for the patient from a computing device operated by a healthcare worker (See P0046 healthcare provider interactions.).
Regarding claims 12, Shankar discloses wherein each of the subset of the tagged clinical data items includes an associated date, wherein the graphical data comprises a timeline that includes identifiers for each of the subset of the tagged clinical data items, wherein the timeline is chronologically arranged based upon the associated date of each of the subset of the tagged clinical data items (See Fig. 13, P0075 exemplary blood pressure over time 1350 and time range 1322.).
Claims 3-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Shankar (US 2017/0277841 A1) in view of Schrempf (US 2022/0270721 A1) further in view of Hao (US 2018/0165415 A1) and Aaron (WO 2022/212771 A2).
Regarding claims 3, although Shankar, Schrempf and Hao teach the computing system of claim 1 as mentioned above, Shankar, Schrempf and Hao do not explicitly teach DICOM and non-DICOM clinical data items. Aaron teaches wherein the clinical data items include Digital Imaging and Communications in Medicine (DICOM) clinical data items and non-DICOM clinical data items (See DICOM metadata, DICOM pixel data in P0021. See camera in P00141, P00192 video-based communication with screen sharing serve as non-DICOM.).
Therefore, it would have been obvious to one of ordinary skill in the art of AI medical image assistance before the effective filing date of the claimed invention to modify the system, method and software of Shankar, Schrempf and Hao to include DICOM and non-DICOM clinical data items as taught by Aaron to enables more accurate labeling, which is important for successfully shifting the workload of medical image interpretation from the clinician onto the computing system mentioned in Aaron’s P0006.
Regarding claims 4, although Shankar, Schrempf and Hao teach the computing system of claim 1 as mentioned above, Shankar, Schrempf and Hao do not explicitly teach converting the non-DICOM clinical data items to DICOM attributes by way of an adapter. Aaron teaches prior to matching each clinical data item in the clinical data items to the one or more templates in the templates, converting the non-DICOM clinical data items to DICOM attributes by way of an adapter (See P0155-P0156 where using speech-to-text input and dictaphone-based template field navigation to enter tagged radiological imaging findings serve as entering tagged clinical data items from a plurality of electronic sources being matched to templates stored. See P0020, where DICOM and non-DICOM clinical data items as relevant features and attributes can be extracted from various sources then optimized.).
Therefore, it would have been obvious to one of ordinary skill in the art of AI medical image assistance before the effective filing date of the claimed invention to modify the system, method and software of Shankar, Schrempf and Hao to include converting the non-DICOM clinical data items to DICOM attributes by way of an adapter as taught by Aaron to enables more accurate labeling, which is important for successfully shifting the workload of medical image interpretation from the clinician onto the computing system mentioned in Aaron’s P0006.
Regarding claims 6, although Shankar, Schrempf and Hao teach the computing system of claim 1 as mentioned above, Shankar, Schrempf and Hao do not explicitly teach the first DICOM attributes of the clinical data items are matched to second DICOM attributes specified by the rules of the templates. Aaron teaches wherein the clinical data items comprise first Digital Imaging and Communications in Medicine (DICOM) attributes, wherein the first DICOM attributes of the clinical data items are matched to second DICOM attributes specified by the rules of the templates (See P0099, where a series of image sharing the same DICOM frame construe matching DICOM attributes. See P0020, where DICOM and non-DICOM clinical data items as relevant features and attributes can be extracted from various sources then optimized. Also taught in [P00169] the user clicks on the image and the 3D coordinates (e.g., in DICOM Reference Coordinates System within a given DICOM Frame of Reference) are mapped to an anatomic descriptor by a direct pixel lookup in the labeled segmentation map.).
Therefore, it would have been obvious to one of ordinary skill in the art of AI medical image assistance before the effective filing date of the claimed invention to modify the system, method and software of Shankar, Schrempf and Hao to include the first DICOM attributes of the clinical data items are matched to second DICOM attributes specified by the rules of the templates as taught by Aaron to enables more accurate labeling, which is important for successfully shifting the workload of medical image interpretation from the clinician onto the computing system mentioned in Aaron’s P0006.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Shankar (US 2017/0277841 A1) in view of Schrempf (US 2022/0270721 A1) further in view of Hao (US 2018/0165415 A1) and Draelos (US 12,086, 563 B1).
Regarding claim 13, although Shankar, Schrempf and Hao teach the computing system of claim 1 as mentioned above, Shankar, Schrempf and Hao do not explicitly teach receiving a medical record number (MRN) of the patient. Draelos teaches the acts further comprising: receiving a medical record number (MRN) of the patient, wherein the clinical data items of the patient are obtained based upon the MRN of the patient (See column 35, lines 1-13 and 30-44 MRN’s of the patients are used when assigning patients to a training set.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical narrative management before the effective filing date of the claimed invention to modify the system, method and software of Shankar, Schrempf and Hao to include receiving a medical record number (MRN) of the patient as taught by Draelos to enable the user to record information that can be used as the label when training machine learning models mentioned in Draelos’ column 3, lines 25-32.
Claims 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Aaron (WO 2022/212771 A2) in view of Schrempf (US 2022/0270721 A1) further in view of Shankar (US 2017/0277841 A1), Hao (US 2018/0165415 A1) and Solis (US 2022/0172824 A1).
Claim 14:
Aaron discloses a method executed by a processor of a computing system (See Fig. 15, digital processing device mentioned in P0316-P0317.), the method comprising:
receiving, via a computing device communicatively coupled to a server, an identifier of a patient (See P0243, P0253 database on server and patient data for a clinical study.);
obtaining clinical data items of the patient from one or more data sources based on the identifier of the patient (See obtained images and image study attributes in P0020.);
receiving a selection of a first tagged clinical data item of a patient from amongst tagged clinical data items of the patient (See P0155-P0156 where using speech-to-text input and dictaphone-based template field navigation to enter tagged radiological imaging findings serve as entering tagged clinical data items from a plurality of electronic sources being matched to templates stored. See P0125 tagging relevant to parts of the anatomy. Also, see P0297 where existing reporting templates construe storing the templates in a data store.);
identifying a seed node in a knowledge graph stored in the data store based upon a first tag of the first tagged clinical data item, wherein the knowledge graph comprises nodes and edges connecting the nodes, wherein the nodes represent the templates and the edges represent relationships between the templates, wherein each node comprises a respective tag corresponding to a respective template in the templates (See exemplary denoted tags in [P0127-P0128] In the knowledge graph, each concept is a node and a directed arc between two nodes denotes a relationship. For instance, “C2-C3 foramen” has_observation “stenosis” and “stenosis” has_severity “mild”. See Fig. 28A-C calculating an edge potential map of an image (P0129-P0133). See P0155-P0156 where using speech-to-text input and dictaphone-based template field navigation to enter tagged radiological imaging findings serve as entering tagged clinical data items. Also, see P00292-P00293.).
Although Aaron discloses a method identifying a knowledge graph comprising nodes and edges connecting the nodes as mentioned above, Aaron does not explicitly teach matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched. Schrempf teaches:
matching each clinical data item in the clinical data items to one or more templates from templates stored in a data store of the server, based upon at least one rule included in the one or more templates in the data store (See Fig. 8-11, Fig. 13-14 exemplary templates mentioned in P0067, P0136-P0138. See set of synonyms as exemplary knowledge source in P0171-P0172 and rule of populating templates with knowledge from suitable database in P0209-P0210.);
generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched (See Fig. 6 and P0170-P0174, where labels serve as tagging clinical data being matched.);
Therefore, it would have been obvious to one of ordinary skill in the art of processing medical text before the effective filing date of the claimed invention to modify the system, method and software of Shankar to include matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched as taught by Schrempf for processing text data, for training models to process text data using templates and/or synthesized text data mentioned in Schrempf’s P0001.
Although Aaron and Schrempf teach knowledge graph for matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched as mentioned above, Aaron and Schrempf do not explicitly teach receiving a keyword and a non-negative integer from a user, a knowledge graph based on the first nodes connected to the seed node by no more than a number of edges equal to the non-negative integer and displaying a subset of the tagged clinical data. Shankar teaches:
receiving a keyword and a non-negative integer from the computing device (See non-negative integers 2 and 4 shown in Fig. 5 and P0059 where there are 2 medical interventions 540 (nodes 13 and 1014) and 4 diseases and disorders 530 (nodes 5, 6, 1011 and 1112). Besides using keywords to enter the medical interventions 540, diseases and disorders 530, see exemplary keywords as ability to assemble medical vocabulary terms in P0031 and arbitrary terms in [P0051] actual acronym terms such as ASCVD, which stands for atherosclerotic cardiovascular diseases.);
identifying a subset of nodes in the knowledge graph based upon the seed node and a non-negative integer, wherein the subset of nodes includes the seed node and first nodes, wherein each of the first nodes are connected to the seed node by no more than a number of edges equal to the non-negative integer (See nodal relationship in Fig. 5, P0059-P0062 where node 1 under medical knowledge information is connected to exemplary node 3 for high blood pressure and node 4 for low blood pressure serve as non-negative integers. See P0039, where edges connecting possibilities between nodes of the graph allow for a number of edges equal to the non-negative integer such as lipid levels, measurements of inflammatory state, family history, diagnoses of hypertension, and dyslipidemia.);
identifying a subset of the tagged clinical data items of the patient based upon first tags of the subset of nodes (See exemplary subset of medial ordering rules in P0034-P0035 and see Fig. 4A patient attributes, diagnosis, treatment, P0035 clinical records, image data, sensor data and ailments as tagged data.); and
causing providing graphical data corresponding to the subset of the tagged clinical data items to the computer device to be presented on a display of the computing device (See P0071, P0077 displaying clinal data and in [P0034] The medical probabilistic rule graph can apply rules within the subset of the medical rules in a specific order based on the ordering the medical rules.).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical intelligence before the effective filing date of the claimed invention to modify the system, method and software of Aaron and Schrempf to include receiving a keyword and a non-negative integer from a user, a knowledge graph based on the first nodes connected to the seed node by no more than a number of edges equal to the non-negative integer and displaying a subset of the tagged clinical data as taught by Shankar for effectiveness of the treatments wherein the other individuals are associated with specific characteristics of the individual. mentioned in Shankar’s P0036.
Although Aaron, Schrempf and Shankar teach a method tagging clinical data items for identifying a seed node in a knowledge graph that receives a keyword and a non-negative integer from a user, the knowledge graph being based on the first nodes connected to the seed node by no more than a number of edges equal to the non-negative integer and displaying a subset of the tagged clinical data as mentioned above. Aaron, Schrempf and Shankar do not explicitly teach identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer based upon receiving the keyword and a non-negative integer. Hao teaches:
upon receiving the keyword and the non-negative integer, identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer, wherein the subset of nodes includes the seed node and first nodes (With the keyword as a concept and an odd number as less than or equal to a non-negative, see selecting a single-concept subtree of the knowledge tree shown in Fig. 3, Fig. 4A-4B, Fig. 6A-6B, P0044, P0056-P0065 where the single concept subtree nodes represent odd number 1.).
Therefore, it would have been obvious to one of ordinary skill in the knowledge-based management of medical records arts before the effective filing date of the claimed invention to modify the system, method and software of Aaron, Schrempf and Shankar to include identifying a subset of nodes in the knowledge graph such that a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer based upon receiving the keyword and a non-negative integer as taught by Hao when reducing human efforts required on knowledge-based feature engineering, that are very useful for data mining in clinical knowledge in the literatures and the heterogeneous EMR datasets mentioned in Hao’s P0007.
Although Aaron, Schrempf, Shankar and Hao teach a method for knowledge graph for matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched and tagging clinical data items for identifying a seed node in a knowledge graph that receives a keyword and a non-negative integer from a user, the knowledge graph being based on the first nodes connected to the seed node by no more than a number of edges equal to the non-negative integer and displaying a subset of the tagged clinical data mentioned above, Aaron, Schrempf, Shankar and Hao do not explicitly teach evaluation criteria when comparing Digital Imaging and Communication in Medicine (DICOM) attributes. Solis teaches:
wherein the at least one rule comprises an evaluation criteria and a comparison value, the comparison value comprising a value field of a Digital Imaging and Communication in Medicine (DICOM) attribute (See established conversion of DICOM formats in P0031, DICOM tags as comparison values used for matching metadata in P0089 and image identifiers when determining the DICOM tags in P0102. Also, see Fig. 7A-7B and [P0079-P0080] header 710 may include information about acquisition date referenced by tag (0008,0022), type or format of data format utilized, e.g., video image format acquired by tag (0008, 1022) or digital image format acquired by tag (0008, 0123), and attributes or operating parameters of different types of image acquisition devices 110, e.g., whether MRI, CT, X-ray, or tomosynthesis. The DICOM standard uses hundreds of tags 571 referencing respective metadata 572 about the patient, images, image acquisition device, operating parameters, demographics, etc. While the types of DICOM tags 571 are comprehensive, the user of the image review workstation 120A is typically interested in only a subset, often a small subset, of available header 710 data. Image file 310 in form of a DICOM file also include a data set for image pixel intensity data 573, which can be used to reconstruct an image.).
Therefore, it would have been obvious to one of ordinary skill in the medical breast imaging arts before the effective filing date of the claimed invention to modify the system, method and software of Aaron, Schrempf, Shankar and Hao to include evaluation criteria when comparing Digital Imaging and Communication in Medicine (DICOM) attributes as taught by Hao to avoid inconveniences and shortcomings compounded when more images are reviewed as part of the radiologist's workflow mentioned in Solis’ P0006.
Regarding claim 15, although Aaron discloses tagged clinical data items matched to templates, a seed node in a knowledge graph, where nodes represent the templates and the edges represent relationships between the templates as mentioned above, Aaron Schrempf and Hao do not explicitly teach a rule of a first template comprises evaluation criteria and a comparison value, where tagged clinical data item is tagged and generated by evaluating the clinical data item against the evaluation criteria and the comparison value. Shankar teaches:
wherein a rule of a first template comprises a first evaluation criteria and a first comparison value, wherein the first tagged clinical data item is generated by evaluating a first clinical data item against the first evaluation criteria and the first comparison value (See rules in Fig. 8, P0066, using rules to evaluating a patient’s atherosclerotic cardiovascular disease (ASCVD) >= 7.5 (recommended Statin therapy) and TG/HDL >= 3.0 (insulin resistance).).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical intelligence before the effective filing date of the claimed invention to modify the system, method and software of Aaron Schrempf and Hao to include a rule of a first template comprises evaluation criteria and a comparison value, where tagged clinical data item is tagged and generated by evaluating the clinical data item against the evaluation criteria and the comparison value as taught by Shankar for effectiveness of the treatments wherein the other individuals are associated with specific characteristics of the individual. mentioned in Shankar’s P0036.
Regarding claim 16, Aaron discloses wherein the subset of the tagged clinical data items includes an image of the patient and a clinical report about the patient, wherein the graphical data includes an identifier for the image and an identifier for the clinical report (See presenting a medical report (P0090) and in [P00125] each piece of anatomy in the system has a tag which describes the type of anatomy. This is a unique identifier for each type of anatomy in the software’s ontology.).
Regarding claim 17, Aaron discloses wherein each template in the templates represents: a body part; a medical facility; a medical department within the medical facility; an imaging modality; a piece of medical equipment; or a disease (See P00156-P0157 reporting template when the user selects an anatomic region (e.g., L2-L3 intervertebral disc). Also, see modality in P00226, and eye tracking equipment in P0080.).
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shankar (US 2017/0277841 A1) in view of Schrempf (US 2022/0270721 A1) further in view of Baldwin (US 2019/0198138 A1) and Solis (US 2022/0172824 A1).
Claim 18:
Shankar discloses a non-transitory computer-readable storage medium comprising instructions that, when executed by a processor of a computing system, cause the processor (See non-transitory computer readable medium that includes code executable by one or more processors in P0046, Fig. 14, P0085 processors 1824, 1834 1844, 1854.) to perform acts comprising:
receiving, via a computing device communicatively coupled to a server, an identifier of a patient (With identifiers as tagged clinical data in a clinical study, see established analysis of collected medical and clinical data from various studies in P0003 and presenting results of latest diagnosing and treatment plan studies in P0074, according to demographic data shown in Fig. 8 item 810 mentioned in P0032, P0066.);
obtaining clinical data items of the patient from one or more data sources based on the identifier of the patient (See P0041 collecting biological information from sensors, vital signs and clinical records and demographic data shown in Fig. 8 item 810 mentioned in P0032, P0066.);
receiving a keyword from the computing device (See P0051, P0057, where patient attributes entered by a medical practitioner (P0041) include arbitrary terms, acronyms and industry terms.);
upon receiving the keyword identifying at least one node in a knowledge graph stored in the data store based upon the keyword, wherein the knowledge graph comprises nodes and edges connecting the nodes, where the nodes represent the templates and the edges represent relationships between the templates, wherein each node comprises a respective tag corresponding to a respective template in the templates (See nodal relationship in Fig. 5, P0059-P0062 where node 1 under medical knowledge information is connected to exemplary node 3 for high blood pressure and node 4 for low blood pressure serve as non-negative integers. See P0039, where edges connecting possibilities between nodes of the graph allow for a number of edges equal to the non-negative integer such as lipid levels, measurements of inflammatory state, family history, diagnoses of hypertension, and dyslipidemia.);
upon receiving the keyword identifying a first subset of the tagged clinical data items based upon a tag of the at least one seed node, wherein each of the first subset of the tagged clinical data items includes the tag of the at least one seed node (See exemplary subset of medial ordering rules in P0034-P0035 and see Fig. 4A patient attributes, diagnosis, treatment, P0035 clinical records, image data, sensor data and ailments as tagged data.);
causing first graphical data corresponding to the first subset of the tagged clinical data items to be presented on a display of the computing device (See P0071, P0077 displaying clinal data and in [P0034] The medical probabilistic rule graph can apply rules within the subset of the medical rules in a specific order based on the ordering the medical rules.);
identifying a second subset of the tagged clinical data items based upon the subset of nodes (See exemplary subset of medial ordering rules in P0034-P0035 and see Fig. 4A patient attributes, diagnosis, treatment, P0035 clinical records, image data, sensor data and ailments as tagged data.); and
causing second graphical data corresponding to the second subset of the tagged clinical data items to be presented on the display concurrently with the first graphical data (See P0071, P0077 displaying clinal data and in [P0034] The medical probabilistic rule graph can apply rules within the subset of the medical rules in a specific order based on the ordering the medical rules.).
Although Shankar discloses a knowledge graph comprising nodes, edges connecting the nodes and multiple subsets of nodes as mentioned above, Shankar does not explicitly teach matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched. Schrempf teaches:
matching each clinical data item in the clinical data items to one or more templates in templates stored in a data store of the server based upon rules included in the templates (See Fig. 8-11, Fig. 13-14 exemplary templates mentioned in P0067, P0136-P0138. See set of synonyms as exemplary knowledge source in P0171-P0172 and rule of populating templates with knowledge from suitable database in P0209-P0210.);
generating tagged clinical data items based upon the clinical data items and tags included in the templates upon the clinical data items being matched (See Fig. 6 and P0170-P0174, where labels serve as tagging clinical data being matched.);
Therefore, it would have been obvious to one of ordinary skill in the art of processing medical text before the effective filing date of the claimed invention to modify the system, method and software of Shankar to include matching clinical data items to templates of a template data store, generating tagged clinical data items based upon the clinical data items and tags of the templates for the clinical data items being matched as taught by Schrempf for processing text data, for training models to process text data using templates and/or synthesized text data mentioned in Schrempf’s P0001.
Although Shankar and Schrempf teach a knowledge graph comprising nodes, edges connecting the nodes and multiple subsets of nodes as mentioned above, Shankar and Schrempf do not explicitly teach identifying a seed node in the knowledge graph when identifying a subset of nodes. Baldwin teaches:
upon receiving an indication from the computing device and the causing of the first graphical data to be presented in the display of the computing device, identifying a subset of nodes in the knowledge graph, wherein each node in the subset of nodes is connected to the at least one seed node via an edge (See seed concepts serve as a node in [P0105] The mechanisms expand the seed concepts and terms by identifying medical variants and related concepts based on an ontological hierarchy and biomedical knowledge graph. In identifying the medical variants and related concepts of the seed concepts and terms duplicates concepts may be identified. Also, see words, phrases and expression in P0048.);
Therefore, it would have been obvious to one of ordinary skill in the art of clinical intelligence before the effective filing date of the claimed invention to modify the system, method and software of Shankar and Schrempf to include identifying a seed node in the knowledge graph when identifying a subset of nodes as taught by Baldwin to help medical practitioners avoid missing important diagnoses, and can assist medical practitioners with determining appropriate treatments for specific diseases mentioned in Baldwin’s P0017.
Although Schrempf, Shankar and Baldwin teach a method applying a knowledge graph comprising nodes, edges connecting the nodes and multiple subsets of nodes, identifying a seed node in the knowledge graph when identifying a subset of nodes mentioned above, Schrempf, Shankar and Baldwin do not explicitly teach evaluation criteria when comparing Digital Imaging and Communication in Medicine (DICOM) attributes. Solis teaches:
wherein the at least one rule comprises an evaluation criteria and a comparison value, the comparison value comprising a value field of a Digital Imaging and Communication in Medicine (DICOM) attribute (See established conversion of DICOM formats in P0031, DICOM tags as comparison values used for matching metadata in P0089 and image identifiers when determining the DICOM tags in P0102. Also, see Fig. 7A-7B and [P0079-P0080] header 710 may include information about acquisition date referenced by tag (0008,0022), type or format of data format utilized, e.g., video image format acquired by tag (0008, 1022) or digital image format acquired by tag (0008, 0123), and attributes or operating parameters of different types of image acquisition devices 110, e.g., whether MRI, CT, X-ray, or tomosynthesis. The DICOM standard uses hundreds of tags 571 referencing respective metadata 572 about the patient, images, image acquisition device, operating parameters, demographics, etc. While the types of DICOM tags 571 are comprehensive, the user of the image review workstation 120A is typically interested in only a subset, often a small subset, of available header 710 data. Image file 310 in form of a DICOM file also include a data set for image pixel intensity data 573, which can be used to reconstruct an image.).
Therefore, it would have been obvious to one of ordinary skill in the medical breast imaging arts before the effective filing date of the claimed invention to modify the system, method and software of Schrempf, Shankar and Baldwin to include evaluation criteria when comparing Digital Imaging and Communication in Medicine (DICOM) attributes as taught by Hao to avoid inconveniences and shortcomings compounded when more images are reviewed as part of the radiologist's workflow mentioned in Solis’ P0006.
Regarding claim 19, Shankar discloses the non-transitory computer-readable storage medium of claim 18 (See non-transitory computer readable medium that includes code executable by one or more processors in P0046, Fig. 14), the acts further comprising:
prior to obtaining the clinical data items, receiving a user-defined template as input from a user, wherein the user-defined template comprises a user-defined tag and at least one user-defined rule (Taught in P0039, where the graph used template is used to enter patient specific conditions and attributes such as collected biological information, a diagnosis, obtained from electronic medical records, clinical records, image data, sensor data mentioned in P0035.);
receiving a selection of a node in the nodes of the knowledge graph (See Fig. 1 build medical rule graph 130, using knowledge representation 160, 170 and personalized template mentioned in [P0038-P0039] represented as a graph due to the nature of the relationships between the medical entities, or nodes, which are the various medical knowledge factors, and the edges, which are the connecting possibilities between nodes of the graph.); and
generating a user-defined node in the knowledge graph based upon the user-defined template, wherein the user-defined node is connected to the node in the knowledge graph (See Fig. 1, generated medical rules 120 mentioned in P0037 and graphed ruled in [P0039] This graph is used as the template for personalization for each individual, i.e. modified for each patient with specific conditions and patient attributes. With the tag as a name of a body part, a body region or system, a name of a medical facility, a name of a department within the medical facility, a name of an imaging modality, a name of a piece of medical equipment, or a name of a disease or a name of a clinical observation, see Fig. 4A patient attributes, diagnosis, treatment, P0035 clinical records, image data, sensor data and ailments.).
Regarding claim 20, although Shankar discloses the non-transitory computer-readable storage medium as mentioned above, Baldwin further teaches the acts further comprising:
subsequent to generating the tagged clinical data items and prior to receiving the keyword, causing identifiers for the tagged clinical data items to be presented on the display, based on a selection of an identifier for a first tagged clinical data item is selected by the healthcare worker, wherein the identifier for the first tagged clinical data item is the keyword (See Fig. 43A-D, P00328 an abdominal MRI and liver where a healthcare worker presents tagged clinical data as words such as “liver” and “kidney”, which are keywords.).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical intelligence before the effective filing date of the claimed invention to modify the system, method and software of Shankar to include an identifier for a first tagged clinical data item is selected by the healthcare worker, where the identifier for the first tagged clinical data item is the keyword presented on the display as taught by Baldwin to help medical practitioners avoid missing important diagnoses, and can assist medical practitioners with determining appropriate treatments for specific diseases mentioned in Baldwin’s P0017.
Response to Arguments
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 and applied new art.
Applicant argues that claims 1-20 implement an improvement to the technical field of identifying and displaying relevant patient data to a healthcare worker, as a practical application detailed in paragraphs 3 and 21 of Applicant’s specification, and improves an existing technological process. see pgs. 20-21 of Remarks – Examiner disagrees.
In explaining how the invention is applied in a meaningful way, the specification merely provides conclusive statements such as “accurately identify relevant clinical data items for display without being limited by overly inclusive or overly exclusive matching rules”, “enable the automatic surfacing of clinical data items, even when the clinical data items correspond to different types of data” and “efficiently utilize limited display space …. while not overcrowding available display space with irrelevant clinical data items”, without describing any technology. For example, no speech recognition software, gesture recognition software, machine learning processes or robotics is used to build the knowledge graph, match and tag clinical data. The recited improvements are nonetheless directed towards improving the abstract idea and not the computer itself – that is, the recited invention may improve the process of matching clinical data in a template, tagging clinical data items, identifying a subset of nodes and graphing clinical items for display, identifying a subset of nodes and identifying a subset of tagged clinical data items and using math to determine a number of edges between the seed node and each node in the subset of nodes is less than or equal to the non-negative integer and connecting a seed node to a first node by no more than a number of edges equal to a non-integer (i.e. the abstract idea), but there is no evidence to show that it improves the structural or functional properties of the computer itself, outside of improving the computer specifically for implementing the abstract idea.
Applicant argues that claims 1-20 implement an improvement to an existing technological process, as a similar case found by the courts to be patent eligible. see pgs. 21-22 of Remarks – Examiner disagrees.
Regarding McRO, the memo says “The McRO court thus relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated when determining that the claims were directed to improvements in computer animation instead of an abstract idea. The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” Regarding this application, a human (see Applicant’s figure 1, Healthcare worker 222) incorporating knowledge of inputting patient clinical data, identifying relevant patient data and using math to contribute to a knowledge graph with a subset of nodes such that has a number of edges between the seed node and each node in the subset of nodes that is less than or equal to a non-negative integer, using a processor are done mentally and problems that have already been solved.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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, Mamon Obeid can be reached at (571) 270-1813. 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.
/T.S.W./Examiner, Art Unit 3687 04/29/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
Prosecution Insights