DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) filed 2/17/26. Claims 1, 6-8, 10, and 16 have been amended. Claims 9, 11-15, and 17-19 are canceled. Claims 21-23 are newly added. Claims 1-8, 10, 16, and 20-23 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/17/26 has been entered.
Claim Objections
Claim 10 is objected to because of the following informalities: it is unclear whether or not the “patient data” in claim 10 is the same “patient data” recited in independent claim 1, or if it is different patient data. Appropriate correction is required.
Claims 7 and 22 are objected to because of the following informalities: it is unclear whether or not the “learned choices” in the claims are the same “learned choices” in independent claim 1, or if they are different “choices.” Appropriate correction is required.
Claim 23 is objected to because of the following informalities: it is unclear whether or not the “one or more learned choices” are from the “plurality of learned choices,” or if they are different “choices.” Appropriate correction is required.
Claim 23 is objected to because of the following informalities: change “a diagnosis” to “the diagnosis” at line 27. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: change “the current patient’s” to “the patient’s.” Appropriate correction is required.
Claims 1, 5, and 23 are objected to because of the following informalities: change “will generate” to “generates” in claims 1 and 23 and change “will filter” to “filters” in claim 5. Appropriate correction is required.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1 and 3 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Langan et al. (US 2004/0064341 A1) in view of Zeiger et al. (US 8,860,717 B1), in view of Walker et al. (US 2004/0122706 A1), in view of Dekel et al. (US 8,923,580 B2), and further in view of Cane et al. (US 2012/0035956 A1).
(A) Referring to claim 1, Langan discloses a medical information system comprising (abstract and Fig. 3 of Langan):
one or more processors (para. 17-20 and Fig. 2 of Langan);
an engine executing on the one or more processors (para. 24 & 27 and Fig. 3 of Langan; note the analysis engine);
a graphical user interface in communication with the engine (para. 5, 18, and 24 of Langan; note the interactive interface and that each terminal includes a GUI),
non-transitory electronic memory (para. 20 & 21 and Fig. 3 of Langan); and
a data warehouse in communication with the engine, the data warehouse stored in the non-transitory electronic memory (para. 42 and Fig. 3 of Langan; In step 404, data from steps 406, 408 is for example standardized into a normalized relational database, data-mart or data warehouse),
wherein the engine will generate any of a number of reports relating to the patient based in response to the patient's diagnostic data, and the patient's demographics and patient data and demographics of other patients' data in the warehouse, wherein patient trend data is displayed on the graphical user interface (Figures 2-4, para. 3, 20, 21, 27-29, 30, 39 and 35 of Langan; note that database section 204 utilizes a common data element: a patient electronic medical record number ("EMR#") and the standard medical codes (e.g., DRG, CPT, ICD-9 and HCPCS). The common data element is processed by analysis engine 214 to generate reports and analyses requested by management entities 216. Also, analysis engine 214 "de-identifies" specific patient information from any of its aggregated reports or analyses, to protect particular patient information while maintaining demographic and systemic information for aggregated analysis, benchmarking, trending and/or prediction of data from databases 210, 212. Also, the patient leaves the hospital system and is billed. The negative outcomes are exemplified by block 18, which, for example, includes generating an incident report (step 20), engaging in legal actions (step 22), managing risk (step 24), and administrative actions (step 26).);
wherein the patient data comprises identifiable patient data, wherein the identifiable patient data is accessible only by the patient's caregivers (para. 24, 28, 30, 32, and 36 of Langan; Firewall 222 can facilitate patient record privacy, such as governed by the Healthcare Insurance Portability and Accountability Act ("HIPAA"). In one example, firewall 222 permits access to defined information within relational database 210 and/or defined features of analysis engine 214 by authorized persons possessing passwords for such features and information. In step 410, patient information is optionally de-identified, so as to remove particularity of person-specific information.).
Langan does not expressly disclose that the engine is a learning engine and the learning engine configured to learn from choices made by a physician attending to a patient to generate a plurality of learned choices, wherein the plurality of learned choices comprises a medicine prescribed for a diagnosis; wherein the engine comprises an interactive 3-D body atlas, the interactive 3-D body atlas displayed using the graphical user interface, wherein the interactive 3-D body atlas is annotatable through the graphical user interface; wherein the learning engine generates a report on a patient based in response to the plurality of learned choices of the physician attending to the patient, the diagnosis, and annotations to the interactive 3-D body atlas, the 3-D body atlas comprising one or more images; wherein upon entry of the physician's name into the graphical user interface, the plurality of learned choices are accessed, wherein when the diagnosis is entered into the graphical user interface, the diagnosis is linked to one or more morphology entries, wherein the diagnosis links to the one or more morphology entries in the data warehouse, wherein the one or more morphology entries link to the atlas through coordinates on the one or more images.
Zeiger discloses an interactive 3-D body atlas, the interactive 3-D body atlas displayed using the graphical user interface, wherein the interactive 3-D body atlas is annotatable through the graphical user interface and generates a report on a patient based in response to annotations to the interactive 3-D body atlas, the 3-D body atlas comprising one or more images (col. 9, line 65 – col. 10, line 14, col. 19, lines 10-46, col. 21, lines 44-57, Fig. 2, and Fig. 4A of Zeiger; Each of the clients 110 is configured to receive part or all of the searchable data of the 3D object and display the searchable data to a user of the client 110 for the user to view in a 3D space, search, edit, and annotate and users can also provide additional 3D objects for display near, at, or within the 3D object 402. For example, a physician seeking to educate a patient on a surgical procedure involving a knee injection can use the disclosed system to integrate a 3D needle at an injection position with respect to a knee on a 3D human body. The physician can then send the bookmark for that view to the patient so that the patient can explore, in 3D, the injection procedure on the 3D human body that includes the displayed 3D needle.); wherein when the diagnosis is entered into the graphical user interface, the diagnosis is linked to one or more morphology entries (col. 11, lines 20-52 and col. 20, line 47 - col. 21, line 13 of Zeiger; The output device 114 can be a computer display, such as a touch screen display. The query can be an alphanumeric input, such as "liver" or "2" (for cervical spinal nerve 2), or input from an input device 116. Similarly, the query can be an alphanumeric input indirectly related to a portion of the 3D object (e.g., for a human body, the entry "cirrhosis" can map to the liver) based on, for example, pre-defined mappings, user-generated contents, or implicit connections, such as prior user history.), wherein the diagnosis links to the one or more morphology entries in the data warehouse (col. 11, lines 20-52 and col. 20, line 47 - col. 21, line 13 of Zeiger; The user has generated the annotation "my cirrhotic liver" 489 to label the 3D cirrhotic liver 487. The 3D generated content can be shared amongst users such that a library of editable entries (e.g., anatomic entries) can be made available to users for editing and placement in the 3D object 402. For example, generated 3D content can be uploaded to a shared location and tagged for later indexing and retrieval.),wherein the one or more morphology entries link to the atlas through coordinates on the one or more images (col. 21, line 59- col. 22, line 38, col. 21, lines 2-13, and col. 11, lines 20-52 of Zeiger; The content can be located in respect to (or "registered against") one or more portions of multiple 3D objects 402 by automated scaling and orientation to match portions of each 3D object 402, by text or image matching to labeled portions of each 3D object 402, by surface coordinates of each 3D object 402, and by 3D object specifications ("reference volumes") for each 3D object 402 that are provided for authoring purposes. A reference volume can be either a detailed 3D shape, a 3D shape that has been significantly lowered in resolution while still retaining key shape characteristics, a set of attachment points to other known entities, or any combination thereof. For example, the user who generated the cirrhotic liver 487 can request the disclosed system to place the liver as a replacement at the BML coordinates (329, 112, 983), which define the position for integrating the replacement human liver in the 3D human body according to BML. Any other user seeking to appropriately replace the displayed liver in the 3D human body would use the same coordinates.).
Walker discloses wherein the learning engine generates a report on a patient based in response to the diagnosis (para. 13, 292, 381, and 392 of Walker; A set of data from the integrated knowledge base is analyzed to identify a possible diagnosis of a medical condition of the patient. The set of data includes data derived from patient-specific data from at least one controllable and prescribable data resource, and non-patient specific data. A patient-specific reports summarizing at least one possible diagnosis is then generated.)
Dekel discloses a learning engine configured to learn from choices made by a physician attending to a patient to generate a plurality of learned choices, and wherein the learning engine generates a report on a patient based in response to the plurality of learned choices of the physician attending to the patient (col. 14, lines 6-22 and col. 16, line 56 – col. 17, line 10 of Dekel; a learning engine 570 is provided with a variety of information in the form of features from which to learn preference(s), priority(-ies), requirement(s), etc., for one or more hanging protocols and/or one or more user(s). Information, such as DICOM data 510, user selection 520, medical report(s) 530, knowledge base 540, etc., is provided for feature extraction 550. DICOM data 510 can include patient information, scanning information, etc., for one or more studies, image series, patient exams, etc. User selection 520 can include viewport(s) information, prior(s) displayed, contrast selected, etc. Medical reports 530 can include procedure, history, etc. The knowledge base 540 can include information such as ontologies, atlas images, prior studies, related studies, best practices, etc.); wherein upon entry of the physician's name into the graphical user interface, the plurality of learned choices are accessed (col. 7, lines 14-35 and col. 13, lines 6-51 of Dekel; a default protocol may be selected based on a user identity. For example, a user may have a preferred DDP. The DDP may have been customized to meet the user's preferences for a particular temporal and/or spatial layout of images. Once a user gains access to a PACS workstation 140 (for example, by entering a correct login and password combination or some other type of user identification procedure), the preferred DDP may be communicated to the PACS workstation 140, for example. Once the "learn this setup" is used, the system creates a snapshot of the setup and associated parameter(s).).
Cane discloses wherein the plurality of learned choices comprises a medicine prescribed for a diagnosis (para. 38 of Cane; The system keeps track of the physician's preferences automatically and will propose those preferences first to the clinician in the parameter windows. For example, if the clinician most frequently prescribes the drug Adoxa.RTM. for the treatment of acne, the system will place Adoxa.RTM. at the top of the prescription list in the prescription window. Similarly, if the clinician typically requests a biopsy when a papiloma is diagnosed, then biopsy is placed at the top of the procedures list. Further, another window is the opened so that the type of biopsy can be specified. The specific details (metadata) of the biopsy performed are collected with the information or choices provided defaulting to the clinician's anticipated selections. Various artificial intelligence, genetic or other learning algorithms can be used to accomplish this process.).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Zeiger, Walker, Dekel, and Cane within Langan. It would have been obvious to modify Langan to include an interactive 3-D body atlas, as taught by Zeiger and a report on a patient based in response to a diagnosis, as taught by Walker, and to include the learning engine of Dekel and the choices in Cane. The motivation for doing so would have been to provide explanations (col. 19, lines 13-18 of Zeiger), to provide a summary of the patient’s medical condition during a medical contact session (para. 13 of Walker), to speed up and/or increase efficiency in a user’s workflow (col. 11, line 66- col. 12, line 16 of Dekel), and so that accuracy of the anticipated selections rapidly increases as the clinician uses the system (para. 38 of Cane).
(B) Referring to claim 3, Langan discloses wherein the generated report comprises one or more of a measure of popularity and a measure of efficacy of treatment and a determination of reimbursement on billing (para. 28 & 29 of Langan).
Claims 16 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Langan et al. (US 2004/0064341 A1) in view of Zeiger et al. (US 8,860,717 B1), in view of Walker et al. (US 2004/0122706 A1), in view of Dekel et al. (US 8,923,580 B2), in view of Cane et al. (US 2012/0035956 A1), in view of Grigorian (US 2010/0185588 A1), and further in view of Harnick (US 2009/0248445 A1).
(A) Referring to claim 16, Langan, Zeiger, Walker, Dekel, Cane and Grigorian do not disclose wherein the learning engine correlates treatment popularity with the current patient's demographics and displays one or more correlated treatments associated with statistically similar cases and efficacy of the one or more corelated treatments.
Harnick discloses wherein the engine correlates treatment popularity with the current patient's demographics and displays one or more correlated treatments associated with statistically similar cases and efficacy of the one or more corelated treatments (para. 9, 104, 111, 122 & 123 of Harnick).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Harnick within Langan, Zeiger, Walker, Dekel, Cane, and Grigorian. The motivation for doing so would have been to provide trend or other useful information (para. 122 of Harnick).
Claims 2, 5, 10, and 20-22 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Langan et al. (US 2004/0064341 A1) in view of Zeiger et al. (US 8,860,717 B1), in view of Walker et al. (US 2004/0122706 A1), in view of Dekel et al. (US 8,923,580 B2), in view of Cane et al. (US 2012/0035956 A1), and further in view of Grigorian (US 2010/0185588 A1).
(A) Referring to claim 2, Langan discloses wherein the data warehouse includes at least one data mart comprising summarized and indexed aggregated data (para. 42 & 25 of Langan)
Langan and Zeiger do not expressly disclose wherein the learning engine is an artificial intelligence engine.
Walker discloses wherein the learning engine is an artificial intelligence engine (para. 292 & 436 of Walker).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Walker within Langan and Zeiger. The motivation for doing so would have been to emulate human expertise in well-defined problem domains (para. 306 of Walker).
Langan, Zeiger, Walker, Dekel, and Cane do not expressly disclose that the data are pre-calculated and pre-joined.
Grigorian discloses pre-calculated and pre-joined data (para. 35, 125, and 134 of Grigorian).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Grigorian within Langan, Zeiger, Walker, Dekel, and Cane. The motivation for doing so would have been to improve performance (para. 125 of Grigorian).
(B) Referring to claim 5, Langan discloses wherein the engine will filter requests for the aggregated data of the data warehouse based on parameters supplied by the graphical user interface (para. 18-21 of Langan).
Langan and Zeiger do not expressly disclose that the engine is a learning engine.
Walker discloses a learning engine (para. 292 of Walker).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Walker within Langan and Zeiger. The motivation for doing so would have been to coordinate the various functions (para. 292 of Walker).
(C) Referring to claim 10, Langan does not disclose wherein an annotated body atlas with patient data is linked to other aggregated data in a database, wherein the 3-D body atlas comprises a plurality of tissue levels.
Zeiger discloses wherein an annotated body atlas with patient data is linked to other aggregated data in a database, wherein the 3-D body atlas comprises a plurality of tissue levels (col. 9, line 65 – col. 10, line 14, col. 19, lines 10-12, col. 21, lines 44-57, col. 16, lines 60-64, Fig. 2, and Fig. 4A of Zeiger).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Zeiger within Langan. The motivation for doing so would have been to provide explanations (col. 19, lines 13-18 of Zeiger).
(D) Referring to claim 20, Langan does not disclose wherein the tissue levels are selected from the group consisting of subcutaneous, muscular, and skeletal.
Zeiger discloses wherein the tissue levels are selected from the group consisting of subcutaneous, muscular, and skeletal (col. 10, lines 9-14 and col. 14, line 55- col. 15, line 26 of Zeiger).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Zeiger within Langan. The motivation for doing so would have been to provide explanations (col. 19, lines 13-18 of Zeiger).
(E) Referring to claim 21, Langan does not disclose wherein the annotations to the interactive 3-D body atlas are linked to other aggregated data in the data warehouse.
Zeiger discloses wherein the annotations to the interactive 3-D body atlas are linked to other aggregated data in the data warehouse (col. 9, line 65 – col. 10, line 14, col. 19, lines 10-12, col. 21, lines 44-57, col. 16, lines 60-64, Fig. 2, and Fig. 4A of Zeiger).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Zeiger within Langan. The motivation for doing so would have been to provide explanations (col. 19, lines 13-18 of Zeiger).
(F) Referring to claim 22, Langan, Zeiger, Walker, and Dekel do not expressly disclose wherein the graphical user interface provides one or more learned choices to the physician for incorporation into a record for the patient.
Cane discloses wherein the graphical user interface provides one or more learned choices to the physician for incorporation into a record for the patient (para. 36-38, 51, 10 of Cane).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Cane within Langan, Zeiger, Walker, and Dekel. The motivation for doing so would have been to increase accuracy of selections (para. 38 of Cane).
Claim 4 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Langan et al. (US 2004/0064341 A1) in view of Zeiger et al. (US 8,860,717 B1), in view of Walker et al. (US 2004/0122706 A1), in view of Dekel et al. (US 8,923,580 B2), in view of Cane et al. (US 2012/0035956 A1), in view of Grigorian (US 2010/0185588 A1), and further in view of Curran et al. (US 2012/0253842 A1).
(A) Referring to claim 4, Langan, Zeiger, Walker, Dekel, Cane, and Grigorian do not disclose wherein a plurality medicines are aggregated by their FDB/RxNorm drug name.
Curran discloses a plurality of medicines aggregated by their FDB/RxNorm drug name (para. 75 & 78 of Curran).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Curran within Langan, Zeiger, Walker, Dekel, Cane and Grigorian. The motivation for doing so would have been to provide better communication through the use of predefined code terminology (para. 75 of Curran).
Claim 6-8 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Langan et al. (US 2004/0064341 A1) in view of Zeiger et al. (US 8,860,717 B1), in view of Walker et al. (US 2004/0122706 A1), in view of Dekel et al. (US 8,923,580 B2), in view of Cane et al. (US 2012/0035956 A1), in view of Grigorian (US 2010/0185588 A1), and further in view of Jung et al. (US 2009/0094053 A1).
(A) Referring to claim 6, Langan discloses wherein a user may query medicine treatment statistics based in response to diagnostics, the patient's data, and the aggregated data of the data warehouse (para. 20 & 41 of Langan).
Langan, Zeiger, Walker, Dekel, Cane, and Grigorian do not disclose wherein the graphical user interface is configured to output a visual display of the diagnosis over time, plotting severity and morphology against different treatment plans.
Jung discloses wherein the graphical user interface is configured to output a visual display of the diagnosis over time, plotting severity and morphology against different treatment plans (para. 47-51, 56, 87, 130, and 131 of Jung).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Jung within Langan, Zeiger, Walker, Dekel, Cane, and Grigorian. The motivation for doing so would have been to determine an efficacy of a particular medication or treatment (para. 36 of Jung).
(B) Referring to claims 7 and 8, Langan, Zeiger, and Walker do not disclose wherein some of a plurality of learned choices of the physician are predetermined by selection by the physician, wherein some of a plurality of learned preferences of the physician are predetermined by actions taken by the physician.
Dekel discloses wherein some of a plurality of learned choices of the physician are predetermined by selection by the physician, wherein some of a plurality of learned preferences of the physician are predetermined by actions taken by the physician (col. 14, lines 6-22 and col. 16, line 56 – col. 17, line 10 of Dekel).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Dekel within Langan, Zeiger, and Walker. The motivation for doing so would have been to speed up and/or increase efficiency in a user’s workflow (col. 11, line 66- col. 12, line 16 of Dekel).
Claims 23 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Langan et al. (US 2004/0064341 A1) in view of Zeiger et al. (US 8,860,717 B1), in view of Walker et al. (US 2004/0122706 A1), in view of Dekel et al. (US 8,923,580 B2), in view of Cane et al. (US 2012/0035956 A1), and further in view of Harnick (US 2009/0248445 A1).
(A) Referring to claim 23, Langan discloses a medical information system comprising (abstract and Fig. 3 of Langan):
one or more processors (para. 17-20 and Fig. 2 of Langan);
an engine executing on the one or more processors (para. 24 & 27 and Fig. 3 of Langan; note the analysis engine);
a graphical user interface in communication with the engine (para. 5, 18, and 24 of Langan; note the interactive interface and that each terminal includes a GUI),
non-transitory electronic memory (para. 20 & 21 and Fig. 3 of Langan); and
a data warehouse in communication with the engine, the data warehouse stored in the non-transitory electronic memory (para. 42 and Fig. 3 of Langan; In step 404, data from steps 406, 408 is for example standardized into a normalized relational database, data-mart or data warehouse),
wherein the engine will generate any of a number of reports relating to the patient based in response to the patient's diagnostic data, and the patient's demographics and patient data and demographics of other patients' data in the warehouse, wherein patient trend data is displayed on the graphical user interface (Figures 2-4, para. 3, 20, 21, 27-29, 30, 39 and 35 of Langan; note that database section 204 utilizes a common data element: a patient electronic medical record number ("EMR#") and the standard medical codes (e.g., DRG, CPT, ICD-9 and HCPCS). The common data element is processed by analysis engine 214 to generate reports and analyses requested by management entities 216. Also, analysis engine 214 "de-identifies" specific patient information from any of its aggregated reports or analyses, to protect particular patient information while maintaining demographic and systemic information for aggregated analysis, benchmarking, trending and/or prediction of data from databases 210, 212. Also, the patient leaves the hospital system and is billed. The negative outcomes are exemplified by block 18, which, for example, includes generating an incident report (step 20), engaging in legal actions (step 22), managing risk (step 24), and administrative actions (step 26).);
wherein the patient data comprises identifiable patient data, wherein the identifiable patient data is accessible only by the patient's caregivers (para. 24, 28, 30, 32, and 36 of Langan; Firewall 222 can facilitate patient record privacy, such as governed by the Healthcare Insurance Portability and Accountability Act ("HIPAA"). In one example, firewall 222 permits access to defined information within relational database 210 and/or defined features of analysis engine 214 by authorized persons possessing passwords for such features and information. In step 410, patient information is optionally de-identified, so as to remove particularity of person-specific information.).
Langan does not expressly disclose that the engine is a learning engine and the learning engine configured to learn from choices made by a physician attending to a patient to generate a plurality of learned choices, wherein the plurality of learned choices comprises a medicine prescribed for a diagnosis; wherein the engine comprises an interactive 3-D body atlas, the interactive 3-D body atlas displayed using the graphical user interface, wherein the interactive 3-D body atlas is annotatable through the graphical user interface; wherein the learning engine generates a report on a patient based in response to a plurality of learned preferences of the physician attending to the patient, the diagnosis, and annotations to the interactive 3-D body atlas, the 3-D body atlas comprising one or more images; wherein upon entry of the physician's name into the graphical user interface, the plurality of learned preferences are accessed, wherein when the diagnosis is entered into the graphical user interface, the diagnosis is linked to one or more morphology entries, wherein the diagnosis links to the one or more morphology entries in the data warehouse, wherein the one or more morphology entries link to the atlas through coordinates on the one or more images; wherein the report on the patient comprises a measure of popularity and a measure of efficacy of one or more treatments associated with a diagnosis, wherein the graphical user interface provides one or more learned choices to the physician for incorporation into a record for the patient.
Zeiger discloses an interactive 3-D body atlas, the interactive 3-D body atlas displayed using the graphical user interface, wherein the interactive 3-D body atlas is annotatable through the graphical user interface and generates a report on a patient based in response to annotations to the interactive 3-D body atlas, the 3-D body atlas comprising one or more images (col. 9, line 65 – col. 10, line 14, col. 19, lines 10-46, col. 21, lines 44-57, Fig. 2, and Fig. 4A of Zeiger; Each of the clients 110 is configured to receive part or all of the searchable data of the 3D object and display the searchable data to a user of the client 110 for the user to view in a 3D space, search, edit, and annotate and users can also provide additional 3D objects for display near, at, or within the 3D object 402. For example, a physician seeking to educate a patient on a surgical procedure involving a knee injection can use the disclosed system to integrate a 3D needle at an injection position with respect to a knee on a 3D human body. The physician can then send the bookmark for that view to the patient so that the patient can explore, in 3D, the injection procedure on the 3D human body that includes the displayed 3D needle.); wherein when the diagnosis is entered into the graphical user interface, the diagnosis is linked to one or more morphology entries (col. 11, lines 20-52 and col. 20, line 47 - col. 21, line 13 of Zeiger; The output device 114 can be a computer display, such as a touch screen display. The query can be an alphanumeric input, such as "liver" or "2" (for cervical spinal nerve 2), or input from an input device 116. Similarly, the query can be an alphanumeric input indirectly related to a portion of the 3D object (e.g., for a human body, the entry "cirrhosis" can map to the liver) based on, for example, pre-defined mappings, user-generated contents, or implicit connections, such as prior user history.), wherein the diagnosis links to the one or more morphology entries in the data warehouse (col. 11, lines 20-52 and col. 20, line 47 - col. 21, line 13 of Zeiger; The user has generated the annotation "my cirrhotic liver" 489 to label the 3D cirrhotic liver 487. The 3D generated content can be shared amongst users such that a library of editable entries (e.g., anatomic entries) can be made available to users for editing and placement in the 3D object 402. For example, generated 3D content can be uploaded to a shared location and tagged for later indexing and retrieval.),wherein the one or more morphology entries link to the atlas through coordinates on the one or more images (col. 21, line 59- col. 22, line 38, col. 21, lines 2-13, and col. 11, lines 20-52 of Zeiger; The content can be located in respect to (or "registered against") one or more portions of multiple 3D objects 402 by automated scaling and orientation to match portions of each 3D object 402, by text or image matching to labeled portions of each 3D object 402, by surface coordinates of each 3D object 402, and by 3D object specifications ("reference volumes") for each 3D object 402 that are provided for authoring purposes. A reference volume can be either a detailed 3D shape, a 3D shape that has been significantly lowered in resolution while still retaining key shape characteristics, a set of attachment points to other known entities, or any combination thereof. For example, the user who generated the cirrhotic liver 487 can request the disclosed system to place the liver as a replacement at the BML coordinates (329, 112, 983), which define the position for integrating the replacement human liver in the 3D human body according to BML. Any other user seeking to appropriately replace the displayed liver in the 3D human body would use the same coordinates.).
Walker discloses wherein the learning engine generates a report on a patient based in response to the diagnosis (para. 13, 292, 381, and 392 of Walker; A set of data from the integrated knowledge base is analyzed to identify a possible diagnosis of a medical condition of the patient. The set of data includes data derived from patient-specific data from at least one controllable and prescribable data resource, and non-patient specific data. A patient-specific reports summarizing at least one possible diagnosis is then generated.)
Dekel discloses a learning engine configured to learn from choices made by a physician attending to a patient to generate a plurality of learned choices, and wherein the learning engine generates a report on a patient based in response to a plurality of learned preferences of the physician attending to the patient (col. 14, lines 6-22 and col. 16, line 56 – col. 17, line 10 of Dekel; a learning engine 570 is provided with a variety of information in the form of features from which to learn preference(s), priority(-ies), requirement(s), etc., for one or more hanging protocols and/or one or more user(s). Information, such as DICOM data 510, user selection 520, medical report(s) 530, knowledge base 540, etc., is provided for feature extraction 550. DICOM data 510 can include patient information, scanning information, etc., for one or more studies, image series, patient exams, etc. User selection 520 can include viewport(s) information, prior(s) displayed, contrast selected, etc. Medical reports 530 can include procedure, history, etc. The knowledge base 540 can include information such as ontologies, atlas images, prior studies, related studies, best practices, etc.); wherein upon entry of the physician's name into the graphical user interface, the plurality of learned preferences are accessed (col. 7, lines 14-35 and col. 13, lines 6-51 of Dekel; a default protocol may be selected based on a user identity. For example, a user may have a preferred DDP. The DDP may have been customized to meet the user's preferences for a particular temporal and/or spatial layout of images. Once a user gains access to a PACS workstation 140 (for example, by entering a correct login and password combination or some other type of user identification procedure), the preferred DDP may be communicated to the PACS workstation 140, for example. Once the "learn this setup" is used, the system creates a snapshot of the setup and associated parameter(s).).
Cane discloses wherein the plurality of learned choices comprises a medicine prescribed for a diagnosis and wherein the graphical user interface provides one or more learned choices to the physician for incorporation into a record for the patient (para. 36-38, 51, and 10 of Cane; The system keeps track of the physician's preferences automatically and will propose those preferences first to the clinician in the parameter windows. For example, if the clinician most frequently prescribes the drug Adoxa.RTM. for the treatment of acne, the system will place Adoxa.RTM. at the top of the prescription list in the prescription window. Similarly, if the clinician typically requests a biopsy when a papiloma is diagnosed, then biopsy is placed at the top of the procedures list. Further, another window is the opened so that the type of biopsy can be specified. The specific details (metadata) of the biopsy performed are collected with the information or choices provided defaulting to the clinician's anticipated selections. Various artificial intelligence, genetic or other learning algorithms can be used to accomplish this process.).
Harnick discloses wherein the report on the patient comprises a measure of popularity and a measure of efficacy of one or more treatments associated with a diagnosis (para. 9, 104, 111, 122 & 123 of Harnick; If the diagnosis has been made before in the system, the system sends the top five treatment plans to the provider at step 2108. The top five plans may be based on simple frequency of times prescribed, by ranking the efficacy of different treatment plans during feedback operations, or some combination of the two. After step 2108 or if there are no matches at step 2107, the system proceeds to step 2109 and the provider prescribes a treatment plan.).
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Zeiger, Walker, Dekel, Cane, and Harnick within Langan. It would have been obvious to modify Langan to include an interactive 3-D body atlas, as taught by Zeiger and a report on a patient based in response to a diagnosis, as taught by Walker, and to include the learning engine of Dekel, the choices in Cane, and the report content in Harnick. The motivation for doing so would have been to provide explanations (col. 19, lines 13-18 of Zeiger), to provide a summary of the patient’s medical condition during a medical contact session (para. 13 of Walker), to speed up and/or increase efficiency in a user’s workflow (col. 11, line 66- col. 12, line 16 of Dekel), so that accuracy of the anticipated selections rapidly increases as the clinician uses the system (para. 38 of Cane), and to provide trend or other useful information (para. 122 of Harnick).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's additional arguments filed 2/17/26 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 2/17/26.
(1) Applicant disagrees with the 103 rejections in light of the amendments and arguments presented.
(A) As per the first argument, in response to applicant's argument Zeiger and Walker do not teach certain features, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Zeiger discloses an interactive 3-D body atlas, the interactive 3-D body atlas displayed using the graphical user interface, wherein the interactive 3-D body atlas is annotatable through the graphical user interface and generates a report on a patient based in response to annotations to the interactive 3-D body atlas, the 3-D body atlas comprising one or more images (see col. 9, line 65 – col. 10, line 14, col. 19, lines 10-46, col. 21, lines 44-57, Fig. 2, and Fig. 4A of Zeiger; Each of the clients 110 is configured to receive part or all of the searchable data of the 3D object and display the searchable data to a user of the client 110 for the user to view in a 3D space, search, edit, and annotate and users can also provide additional 3D objects for display near, at, or within the 3D object 402. For example, a physician seeking to educate a patient on a surgical procedure involving a knee injection can use the disclosed system to integrate a 3D needle at an injection position with respect to a knee on a 3D human body. The physician can then send the bookmark for that view to the patient so that the patient can explore, in 3D, the injection procedure on the 3D human body that includes the displayed 3D needle.). Regarding the “report,” see at least col. 19, lines 13-18 of Zeiger which discloses “The annotations can include text describing the associated body portion, such as a definition of the associated body portion or an explanation of a medical procedure or condition related to the associated body portion. The annotations can be used to teach students, such as by providing annotations with definitions or annotations that are examination questions.”
Walker discloses wherein the learning engine generates a report on a patient based in response to the diagnosis (see para. 13, 292, 381, and 392 of Walker; A set of data from the integrated knowledge base is analyzed to identify a possible diagnosis of a medical condition of the patient. The set of data includes data derived from patient-specific data from at least one controllable and prescribable data resource, and non-patient specific data. A patient-specific reports summarizing at least one possible diagnosis is then generated.)
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motivations to combine came directly from the references.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687