DETAILED ACTION
Response to Amendment
This action is in response to the amendment filed on March 26, 2026. Claims 1 and 6-10 have been amended. Claims 1-10 have been examined and are currently pending.
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 .
Inventorship
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.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 6 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent claim 6 recites the limitation, “(ii)process the patient data using an artificial intelligence (AI) model”, which is not fully supported within the applicant’s specification. The applicant’s specification discloses, “The data management module 2202 can perform one or more operations on the data, such as checking the data for quality, anonymizing the data, normalizing the data, transforming the data, removing redundant data, and so on. The data management module 2202 can integrate the data with the semantic ontology framework. This data can be provided to the data analytics module 2203 and the inference engine 2204. The data analytics module 2203 can perform multimodal analysis of the data, as depicted in FIG. 22D. The inference engine 2204 can receive inputs from the data management module 2202 and the data analytics module 2203, which can draw one or more inferences from the inputs. Examples of the inferences can be, metastasis probability, prediction of adverse events, providing therapeutic targets, disease prognosis (survival probability), identifying subtypes of the cohorts, and omics signatures. These inferences can be provided to the recommendation engine 2205.” (paragraph 0088 of the originally-filed specification). Paragraph 0088 does not disclose an artificial intelligence model processing patient data.
Claims 6 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent claim 6 recites the limitation, “(iv) generate at least one recommendation for a clinician user based on outputs of the AI model and the semantic ontology framework”, which is not fully supported within the applicant’s specification. The applicant’s specification discloses, “The data management module 2202 can perform one or more operations on the data, such as checking the data for quality, anonymizing the data, normalizing the data, transforming the data, removing redundant data, and so on. The data management module 2202 can integrate the data with the semantic ontology framework. This data can be provided to the data analytics module 2203 and the inference engine 2204. The data analytics module 2203 can perform multimodal analysis of the data, as depicted in FIG. 22D. The inference engine 2204 can receive inputs from the data management module 2202 and the data analytics module 2203, which can draw one or more inferences from the inputs. Examples of the inferences can be, metastasis probability, prediction of adverse events, providing therapeutic targets, disease prognosis (survival probability), identifying subtypes of the cohorts, and omics signatures. These inferences can be provided to the recommendation engine 2205.” (paragraph 0088 of the originally-filed specification) and “The recommendation engine 2205 can use data such as, NCCN guidelines, chemoinformatics, modelling mutations, bioinformatics, clinical trial recommendation, immunotherapy recommendations, biomarkers, prognostic, diagnostic, and so on, for determining one or more recommendations. The recommendations can be in terms of, for example, precision medicine, prescription, prioritization, and so on. In this example, the recommendation is in the form of a prescription. These recommendations from the recommendation engine 2205 and inferences from the inference engine 2204 can be provided to the user.” (paragraph 0089 of the originally-filed specification). Paragraphs 0088-0089 do not disclose generating at least one recommendation for a clinician based on outputs of the AI model and the semantic ontology framework.
Claims 6 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent claim 6 recites the limitation, “a communication interface configured to exchange data with at least one external server, mobile device, sensor, or electronic medical record system;”, which is not fully supported within the applicant’s specification. The examiner cannot find support for the limitation within the applicant’s specification.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tal et al. US Patent 12,080,388 B1.
Claim 1:
As per claims 1, Tal teaches a method comprises:
providing at least one recommendation to a user using an Artificial Intelligence (AI) model (“FIG. 4 illustrates an exemplary panomics ontology in accordance with one or more preferred implementations that relates clinical concepts with panomics concepts to create a semantic neighborhood for cancer treatment and for a preventive treatment approach. Although FIG. 4 illustrates one exemplary panomics ontology in accordance with one or more preferred implementations, other potential panomics ontologies and variations thereof are contemplated as well. One or more preferred embodiments relate to a precision medicine system that introduces an integrated, evidence-based, personalized approach to healthcare solutions that includes actionable clinical data and insights, enabling physicians to make informed decisions from complex genomic and proteomic analysis. In accordance with one or more preferred implementations, targeted treatments can be improved, for example, by applying genome sequencing and RNA sequencing that might uncover previously unknown mutations for a specific patient.” (column 6, lines 34-50), “In accordance with one or more preferred implementations, a cancer population management system is provided to allow entities to better manage their cancer patients though tools for primary care, care navigator, and oncologists to stratify and prioritize populations based on their cancer conditions, omics information, and protocol based care plan information. Preferably, such a solution represents an end-to-end solution to manage cancer patients, increase omics aware treatment plans and increase omics based cancer recommendations. FIG. 13 illustrates an exemplary dashboard for such a solution.” (column 11, lines 55-65) and “In accordance with one or more preferred implementations, a cancer population management system includes functionality to identify and stratify cancer patients population or patients at risk, provide recommendations for sequencing, provide scores (e.g. prediction scores or a severity scoring of cancer patients), provide comorbidities management, monitor relevant clinical data, events, gaps, protocol violation, care plan status and deviations, provide actionable ordering and tasking, provide patient tasking, providing messaging (e.g. provider-provider and patient-provider), track indicators (e.g. Myelotoxicity), identify gaps in care, identify deviations from protocol, track treatment results, identify unwanted events.” (column 12, lines 8-20)),
and a semantic ontology recommender framework (“Accordingly, one aspect of the present invention relates to a clinical semantic ontology related to precision medicine for providing a new taxonomy for cancer terminology.” (column 1, lines 65-67) and “Ontologies are commonly used throughout healthcare provision. For example, FIG. 1 illustrates a traditional ontology related to a diabetes semantic neighborhood. One or more preferred embodiments relate to a practical ontology for the panomics domain that is utilized to support use of patient panomics indications. In accordance with one or more preferred implementations, a panomics ontology includes both cancer and non-cancer panomics concepts, with relations to related insights, traditional cancer types, and clinical concepts.” (column 6, lines 6-12) and “FIG. 4 illustrates an exemplary panomics ontology in accordance with one or more preferred implementations that relates clinical concepts with panomics concepts to create a semantic neighborhood for cancer treatment and for a preventive treatment approach. Although FIG. 4 illustrates one exemplary panomics ontology in accordance with one or more preferred implementations, other potential panomics ontologies and variations thereof are contemplated as well. One or more preferred embodiments relate to a precision medicine system that introduces an integrated, evidence-based, personalized approach to healthcare solutions that includes actionable clinical data and insights, enabling physicians to make informed decisions from complex genomic and proteomic analysis. In accordance with one or more preferred implementations, targeted treatments can be improved, for example, by applying genome sequencing and RNA sequencing that might uncover previously unknown mutations for a specific patient.” (column 6, lines 34-50).)
Claim 2:
As per claim 2, Tal teaches the method of claims 1 as described above and further teaches wherein the semantic ontology recommender framework is a graph based semantic ontology recommender framework (Figures 1-2).
Claim 3:
As per claim 3, Tal teaches the method of claim 1 as described above and further teaches wherein the method further comprises viewing the recommendation in a workspace (Figure 14 and column 13, lines 19-26).
Claim 4:
As per claim 4, Tal teaches the method of claim 1 as described above further teaches wherein the method further comprises viewing linked patient data and clinical journey of the patient in at least one of a text and a graphical format (Figure 6 and column 8, lines 23-29).
Claim 5:
As per claim 5, Tal teaches the method of claim 1 as described above and further teaches wherein the method further comprises viewing, managing and analyzing cohort data using at least one of a text and a graphical format (Figure 13 and column 11, lines 55-65).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Harley et al. US Publication 20210319907 A1 in view of Stumpe et al. US Publication 20220261668 A1 further in view of Tal et al. US Patent 12,080,388 B1.
Claim 6:
As per claim 6, Harley teaches a system comprising:
a processor (paragraph 0207 “In various embodiments of the present teachings, computer system 1000 can include a bus 1002 or other communication mechanism for communicating information, and a processor 1004 coupled with bus 1002 for processing information.”);
a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the system to (paragraph 0207 “In various embodiments, computer system 1000 can also include a memory 1006, which can be a random access memory (RAM) or other dynamic storage device, coupled to bus 1002 for determining instructions to be executed by processor 1004. Memory 1006 also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 1004. In various embodiments, computer system 1000 can further include a read only memory (ROM) 1008 or other static storage device coupled to bus 1002 for storing static information and instructions for processor 1004. A storage device 1010, such as a magnetic disk or optical disk, can be provided and coupled to bus 1002 for storing information and instructions.”):
(i) receive multi-modal and multi-omic patient data (paragraphs 0074, 0104, and 0194 “In various embodiments, the user query can include, for example, a patient/individual ID number, a cohort name/ID number, a certain gene name or gene symbol, a particular annotation source, a variant, and/or a phenotype. In various embodiments, the input can be a check box or clickable button that restricts or filters the output to sequence, for example, variants, genes, phenotypic data, a particular combination of multi-omic data stream, and statistically significant variants, genes, pathways.” And “In accordance with various embodiments, the methods (and systems) discussed or contemplated herein can operate to centralize a vast amount of cancer multi-omic data to provide a platform for oncologists, medical practitioners, research scientists, and other non-programmers to interrogate cancer bioinformatics pipelines at any level of detail and obtain clinical and biological insights into cancer biology and potential clinical treatment of cancer. Data types can include, for example, genomic (single nucleotide variations, indels in tumor and normal, structural rearrangements, copy number variations, gene fusions, and expressed variants for tumor genomes), transcriptomic, epigenetic, chromatin accessibility, microbiomic, proteomic abundance and localization, medical literature data (publications, treatment guidelines, clinical trials inclusion/exclusion criteria), phenotypic data (functional, clinical, electronic medical records, histopathology and radiology reports), imaging data (histopathology slides, MRI scans, X-rays, mammograms, ultrasounds, PET images, CT scans), cancer annotation sources (variants, genes, pathways, drugs), derived cancer analytics (tumor mutation burden, mutational signatures, microsatellite instability status, RNA sequence confirmed variants, differentially expressed genes, spatial omics lineage representations, neo-antigen binding affinities for MHC class I and class II molecule).”);
wherein the system is implemented using at least one hardware device comprising a microprocessor, a server, a personal computer, a mobile device, an FPGA, an ASIC, or any combination thereof (paragraph 0216 “In accordance with various embodiments, suitable digital processing devices can include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, and personal digital assistants. Those of ordinary skill in the art will recognize that many smartphones are suitable for use in the system described herein. Those of ordinary skill in the art will also recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of ordinary skill in the art.”).
Harley does not teach (ii) process the patient data using an artificial intelligence (AI) model. However, Stumpe teaches Artificial Intelligence Engine for Directed Hypothesis Generation and Ranking and further teaches, “Given the large sizes of the input components, including two or more of the number of records being evaluated, the number of patients corresponding to those records, the number of different sources of data, the total number of possible features to evaluate from among all records, the number of features within the set of features being evaluated, and, when the features comprise genomic data, the numbers of whole exome sequencing, whole genome sequencing, or RNA sequencing, it can be seen that the complexity of the present systems and methods may quickly expand exponentially beyond the point by which human mental or manual activity may be able to implement those systems or carry out those methods. Thus, the present systems and methods also may implement an artificial engine applying one or more machine learning models (for example, Autoencoders, Variational Autoencoders, Sparse Vector Analysis, Principal Component Analysis, Independent Component Analysis, Tensor Factorization), neural networks (including, for example, autoencoders, variational autoencoders, deep belief networks, restricted Boltzman machines and generative adversarial networks), regression techniques, graphing techniques, inductive reasoning approaches, or other artificial intelligence evaluations to identify the records to analyze, normalize those records, identify the features to be analyzed, generate values for one or more of those features, analyzing those features or the interrelationships between features or one or more features and one or more other variables, and/or implement one or more steps of those methods…” (paragraph 0149). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley to include process the patient data using an artificial intelligence (AI) model as taught by Stumpe in order to analyze patient data using artificial intelligence.
Harley does not teach and a communication interface configured to exchange data with at least one external server, mobile device, sensor, or electronic medical record system. However, Stumpe teaches Artificial Intelligence Engine for Directed Hypothesis Generation and Ranking and further teaches, “Patient records exist in numerous formats ranging from physical papers sitting in folders on a shelf at a physician's office to electronic health/medical records to even structured formats residing in a database of patient information. The present systems and methods may rely on data extracted from one or more of these sources, as well as from large numbers of these records, regardless of source. For example, the systems and methods may ingest data from at least about 500 distinct records, or at least about 1,000 records, or at least about 5,000 records, or at least about 10,000 records. Certain records may be associated with a common patient or each record may be associated with a distinct patient. In either case, the data may relate to substantially the same number of patents, namely at least about 500 patients, or at least about 1,000 patients, or at least about 5,000 patients, or at least about 10,000 patients.” (paragraph 0144) and “These patient records may be associated with multitudes of identified, structured components unique to the recording system that stores the data, for example, when the records are sourced from different healthcare providers or other institutions that use different electronic health or medical record software systems and/or different database solutions for storing the data used by those software systems. Searching these individual recording systems, including those that are mere physical storage solutions, requires substantial initial preparation to tie the information in each storage solution to a commonly referenceable structured format. Such structuring may result in the data being used by the disclosed systems and methods, regardless of the format in which that data was originally stored.” (paragraph 0145). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley to include a communication interface configured to exchange data with at least one external server, mobile device, sensor, or electronic medical record system as taught by Stumpe in order to receive a variety of different data associated with a patient.
Harley and Stumpe do not teach (iii) apply a semantic ontology-based recommender framework to derive context- aware clinical insights. However, Tal teaches Panomics Ontology and further teaches, “Accordingly, one aspect of the present invention relates to a clinical semantic ontology related to precision medicine for providing a new taxonomy for cancer terminology.” (column 1, lines 65-67) and “Ontologies are commonly used throughout healthcare provision. For example, FIG. 1 illustrates a traditional ontology related to a diabetes semantic neighborhood. One or more preferred embodiments relate to a practical ontology for the panomics domain that is utilized to support use of patient panomics indications. In accordance with one or more preferred implementations, a panomics ontology includes both cancer and non-cancer panomics concepts, with relations to related insights, traditional cancer types, and clinical concepts.” (column 6, lines 6-12) and “FIG. 4 illustrates an exemplary panomics ontology in accordance with one or more preferred implementations that relates clinical concepts with panomics concepts to create a semantic neighborhood for cancer treatment and for a preventive treatment approach. Although FIG. 4 illustrates one exemplary panomics ontology in accordance with one or more preferred implementations, other potential panomics ontologies and variations thereof are contemplated as well. One or more preferred embodiments relate to a precision medicine system that introduces an integrated, evidence-based, personalized approach to healthcare solutions that includes actionable clinical data and insights, enabling physicians to make informed decisions from complex genomic and proteomic analysis. In accordance with one or more preferred implementations, targeted treatments can be improved, for example, by applying genome sequencing and RNA sequencing that might uncover previously unknown mutations for a specific patient.” (column 6, lines 34-50). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley and Stumpe to include (iii) apply a semantic ontology-based recommender framework to derive context- aware clinical insights as taught by Tal in order to generate an analysis of the clinical data.
Harley and Stumpe do not teach and (iv) generate at least one recommendation for a clinician user based on outputs of the AI model and the semantic ontology framework. However, Tal teaches Panomics Ontology and further teaches, “FIG. 4 illustrates an exemplary panomics ontology in accordance with one or more preferred implementations that relates clinical concepts with panomics concepts to create a semantic neighborhood for cancer treatment and for a preventive treatment approach. Although FIG. 4 illustrates one exemplary panomics ontology in accordance with one or more preferred implementations, other potential panomics ontologies and variations thereof are contemplated as well. One or more preferred embodiments relate to a precision medicine system that introduces an integrated, evidence-based, personalized approach to healthcare solutions that includes actionable clinical data and insights, enabling physicians to make informed decisions from complex genomic and proteomic analysis. In accordance with one or more preferred implementations, targeted treatments can be improved, for example, by applying genome sequencing and RNA sequencing that might uncover previously unknown mutations for a specific patient.” (column 6, lines 34-50), “In accordance with one or more preferred implementations, a cancer population management system is provided to allow entities to better manage their cancer patients though tools for primary care, care navigator, and oncologists to stratify and prioritize populations based on their cancer conditions, omics information, and protocol based care plan information. Preferably, such a solution represents an end-to-end solution to manage cancer patients, increase omics aware treatment plans and increase omics based cancer recommendations. FIG. 13 illustrates an exemplary dashboard for such a solution.” (column 11, lines 55-65) and “In accordance with one or more preferred implementations, a cancer population management system includes functionality to identify and stratify cancer patients population or patients at risk, provide recommendations for sequencing, provide scores (e.g. prediction scores or a severity scoring of cancer patients), provide comorbidities management, monitor relevant clinical data, events, gaps, protocol violation, care plan status and deviations, provide actionable ordering and tasking, provide patient tasking, providing messaging (e.g. provider-provider and patient-provider), track indicators (e.g. Myelotoxicity), identify gaps in care, identify deviations from protocol, track treatment results, identify unwanted events.” (column 12, lines 8-20). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley and Stumpe to include (iv) generate at least one recommendation for a clinician user based on outputs of the AI model and the semantic ontology framework as taught Tal in order to provide relevant information to the medical professional.
Claim 7:
As per claim 7, Harley, Stumpe, and Tal teach the system of claim 6 as described above and Tal further teaches wherein the semantic ontology recommender framework comprises a graph based semantic ontology recommender framework (Figures 1-2). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley and Stumpe to include wherein the semantic ontology recommender framework comprises a graph based semantic ontology recommender framework as taught by Tal in order to generate a visual display of the relationships associated with terms or keywords.
Claim 8:
As per claim 8, Harley, Stumpe, and Tal teach the system of claim 6 as described above and Tal further teaches wherein the system is configured to enable a user to view at least one the recommendation in a workspace via a graphical user interface (Figure 14 and column 13, lines 19-26). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley and Stumpe to include wherein the system is configured to enable a user to view at least one the recommendation in a workspace via a graphical user interface as taught by Tan in order to provide the medical professional one or more suggestions based on a patient’s condition.
Claim 9:
As per claim 9, Harley, Stumpe, and Tal teach the system of claim 6 as described above and Tal further teaches wherein the system is configured to enable a user to view linked patient data and clinical journey of the patient in at least one of a text and a graphical format (Figure 6 and column 8, lines 23-29). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Harley and Stumpe to include wherein the system is configured to enable a user to view linked patient data and clinical journey of the patient in at least one of a text and a graphical format as taught by Tal in order to provide the medical professional a visual representation of the patient’s data.
Claim 10:
As per claim 10, Harley, Stumpe, and Tal teach the system of claim 6 as described above and Tal further teaches wherein the system is configured to enable a user to view, manage, and analyze cohort data using at least one of a text and a graphical format (Figure 13 and column 11, lines 55-65).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-10 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.
Objection to claim 1 has been withdrawn.
Claims 6-10 rejected under 35 U.S.C. 101 has been withdrawn
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gome et al. US Publication 20130218596 A1 Method and System for Facilitating User Navigation Through a Computerized Medical Information
Gome discloses a system and method for facilitating clinician-user navigation through a computerized medical information repository including utilizing a clinically ontological hierarchy of clinical semantic elements, the system/method comprising generating an ontology of suggested data requests defined in terms of said ontological hierarchy of clinical semantic elements; and, responsive to an individual clinician-user's navigation through the medical information repository, presenting suggested data requests to the clinician-user based on pre-defined rules defined over the ontology of suggested data requests.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571)270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3682
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