DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 10/21/2025.
Claims 46-48, 50-53, 55-57 and 60-65 have been amended.
Claims 1-45 have been cancelled.
Claims 46-65 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 46-65 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 46-54 are directed to a system (i.e., a machine), claims 55-62 are directed to a method (i.e., a process), and claims 63-65 fit within the non-transitory computer readable medium (i.e., a manufacture) statutory category. Accordingly, claims 46-62 are all within at least one of the four statutory categories, with exception to claims 63-65 mentioned above.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. 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 46 includes limitations that recite an abstract idea. Note that independent claim 46 is the system claim, while claim 55 covers a method claim and claim 63 covers the matching computer readable medium.
Specifically, independent claim 46 recites:
A device comprising:
a processor configured to:
obtain illness state data that indicates an illness occurrence based on a first taxonomy;
obtain healthcare provider data that indicates an illness event based on a second taxonomy, and a geographic region associated with the illness event;
determine, based on the illness state data and the healthcare provider data, that the illness occurrence occurred within the geographic region;
process the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy;
determine, based at least on the correlation between the first taxonomy and the second taxonomy, that the illness occurrence is related to the illness event;
assign a severity level to the illness occurrence based at least on the geographic region and the determination that the illness occurrence is related to the illness event;
compare the severity level to a severity threshold to determine that the illness occurrence is verified by the illness event; and
based on the determination that the device is located in the geographic region and the determination that the illness occurrence is verified:
generate a first visual indication indicating that the illness occurrence is verified by the illness event;
determine a medication based on the illness occurrence being verified, wherein the medication is associated with a treatment of an illness associated with the illness occurrence;
generate a second visual indication indicating the determined medication associated with treating the illness; and
display, on the device, a digital map including an overlay of the first visual indication and the second visual indication in the geographic region.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain
methods of organizing human activity” because assigning a severity level to the illness occurrence based at least on the geographic region and the determination that the illness occurrence is related to the illness event, displaying a geographic map indicating confirmed illnesses by a healthcare provider and determining a medication based on the reported illness occurrence being verified, when the medication is associated with a treatment of an illness associated with the reported illness occurrence all related to managing human behavior/interactions between people. Note that the Centers for Disease Control and Prevention (CDC) is an organization that tracks disease outbreaks in the U.S. in collaboration with state and local territorial health departments. Furthermore, the foregoing underlined limitations constitute (b) “a mental process” because verifying a reported illness, comparing illness occurrences and events, processing the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy and determining, based at least on the correlation between the first taxonomy and the second taxonomy, that the illness occurrence is related to the illness event and comparing the severity level to a severity threshold are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper.
Accordingly, the claim describes at least one abstract idea.
Furthermore, dependent claims 47-54 (similarly for dependent claims 56-62 and 64-65) further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below.
Turning to the dependent claims, claims 47-53, 58-62 and 64-65 recite determining steps such illness occurrence deriving from social media content and indicates a symptom in the geographic region, and wherein the severity level indicates that the illness event corroborates the symptom, applying based on the determination that the first taxonomy and the second taxonomy are correlated, a weighting factor to the illness occurrence to obtain a weighted illness occurrence, wherein the illness occurrence is determined to be verified further based on the weighted illness occurrence, generating, based on the obtained illness state data, a third visual indication of an unverified illness occurrence, wherein the third visual indication is overlaid on the digital map, and wherein the third visual indication indicates that the unverified illness occurrence has not been verified by the healthcare provider data, identifying the illness occurrence, a corresponding illness occurrence location, and whether the illness occurrence has been verified by the illness event, indicating the illness occurrence based on the first taxonomy is obtained from social media content, generating a third visual indication that indicates the determined severity level in the geographic region, wherein the third visual indication is overlaid on the digital map, determine an illness trend for an illness in the geographic region based on the healthcare provider data and the illness state dat
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. 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.”
Regarding the additional limitations of the computer system that includes a device, a processor computer-readable medium comprising executable instructions, memory and display, 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)) and are mere instructions to apply the above-noted at least one abstract idea (Id.).
Regarding the additional limitation “obtain … a geographic region,” 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)).
Claim 46 (similar to claims 55 and 63) does not have any additional elements.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds 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 improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the 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 other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception . Thus, claims 46-65 as a whole do not integrate the above-noted at least one abstract idea into a practical application.
For these reasons, representative independent claim 46 with its dependent claims 47-54 and analogous independent claim 55 with its dependent claims 56-62, analogous independent claim 63 with its dependent claims 64-65 do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B:
Regarding Step 2B, in representative independent claim 46, regarding the additional limitations of the device, processor computer-readable medium comprising executable instructions, memory and display, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 46 and analogous independent claims 55 and 63 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.
In dependent claims 47-54 and analogous dependent claims 56-62 and 64-65, there is no additional elements.
Therefore, claims 46-65 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 46-49, 54-58 and 63-64 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2009/0216747 A1) in view of Fuerst (US 2006/0036619 A1).
Claim 46:
a processor configured to:
obtain illness state data that indicates an illness occurrence based on a first taxonomy (See exemplary reports of increased hospital visits in Asia mentioned in P0142. Also, see P0165-P0166: Table 2, page 15, col. 2, lines 22-44 demand for medical services and medications when reporting collected data. Also, see P0178-P0180, taxonomy in P0244, P0327 and taxonomy examples in P0329-P0330.);
obtain healthcare provider data that indicates an illness event based on a second taxonomy, and a geographic region associated with the illness event(See keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents. See the demand for medical services P0165-P0166: Table 2, page 15, col. 2, lines 22-44 such as increased hospital visits and admissions in Asia (P0142).).
determine, based on the illness state data and the healthcare provider data, that the illness occurrence occurred within the geographic region (See exemplary reports of increased hospital visits in Asia mentioned in P0142. Also, see P0165-P0166: Table 2, page 15, col. 2, lines 22-44 demand for medical services and medications when reporting collected data. Also, see P0178-P0180.);
process the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy (Besides exemplary normal or abnormal validity of taxonomy in P0234, compared event information correlated to biological taxonomy in P0244, see [P0329] a specific taxonomy having 114 parameters has been developed to help distinguish anomalous influenza seasons from those recorded during pandemics. The taxonomy includes the relationships (i.e. hierarchy) between parameters, as well as specific I&W examples from both seasonal and pandemic years. Finally, the taxonomy will continue to evolve as the nature of the data is increased and novel I&Ws are identified.);
determine, based at least on the correlation between the first taxonomy and the second taxonomy, that the illness occurrence is related to the illness event (Taught in, P0244, P0329 as distinguishing anomalous influenza seasons, alert worthy detection and warnings during pandemics and population studies in P0338.);
assign a severity level to the illness occurrence based at least on the geographic region and the determination that the illness occurrence is related to the illness event (Besides analysts reporting by entering staging value mentioned in P0286, P0291-P0292, shown in Fig. 3D in country’s report, see P0161-P0162: Table 1A, page 13, where disease events include groups Priority 1-4 as severity levels.);
compare the severity level to a severity threshold to determine that the illness occurrence is verified by the illness event (See measles as exemplary disease with a population threshold number surpassed as mentioned in P0017. Also, see P0220 number of keywords as a threshold value in email alerts if anomalies are detected for a given location.); and
based on the determination that the device is located in the geographic region and the determination that the illness occurrence is verified (See P0219-P0223 where illnesses for a given location can be verified using decision node, anomaly number of keywords in email alerts and documents reaching a threshold value. Also, see exemplary ground verification of Ebola epidemic in Kasai, Democratic Republic of the Congo in P0323):
generate a first visual indication indicating that the illness occurrence is verified by the illness event (See Fig. 16, Fig. 20, Fig. 23-27 as exemplary visual indicating reported illnesses.);
generate a second visual indication indicating the determined medication associated with treating the illness (See update event report list and map (Fig. 24, P0302-P0303) where events include demand for medical services and medications when reporting collected data in P0165-P0166: Table 2, page 15, col. 2, lines 22-44. Also, see P0178-P0180 demand for pharmaceuticals and supplies include increased purchase or use of medications and vaccines.); and
display, on the device, a digital map including an overlay of the first visual indication and the second visual indication in the geographic region (See launch GIS overlay viewer in Fig. 24, P0298-P0303.).
Although Lin discloses a system, method and software for reporting an illness event and healthcare data associated with a geographic location, identifying correlations and medications for treating the illness event and an overlay visual indication on a digital map as mentioned above, Lin does not explicitly teach determining a medication associated with a treatment of an illness associated with the reported illness occurrence. Fuerst teaches:
determine a medication based on the illness occurrence being verified, wherein the medication is associated with a treatment of an illness associated with the illness occurrence (Besides tracking mass vaccination procedures and medication delivery (P0098), captured drug dispensing (P0104-P0105) and exemplary monitoring medication inventory levels (P0147), see [P0081] Modeling can be used to both verify outpatient diagnostic data and to independently track fluctuations in drug usage that could provide early alerting for disease outbreaks.).
Therefore, it would have been obvious to one of ordinary skill in the art of infectious disease management before the effective filing date of the claimed invention to modify the system, method and software of Lin to include determining a medication associated with a treatment of an illness associated with the reported illness occurrence as taught by Fuerst for recommending drug sequencing and drug regimens based on medical information about a patient as mentioned in Fuerst’s P0012.
Regarding claim 47, Li and Fuerst teach the device of claim 46 mentioned above, and Li further teaches wherein the illness occurrence is derived from social media content and indicates a symptom in the geographic region, and wherein the severity level indicates that the illness event corroborates the symptom (See keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents.).
Regarding claim 48, Li and Fuerst teach the device of claim 46 mentioned above, and Li further teaches wherein the processor is further configured to: apply, based on the determination that the first taxonomy and the second taxonomy are correlated, a weighting factor to the illness occurrence to obtain a weighted illness occurrence, wherein the illness occurrence is determined to be verified further based on the weighted illness occurrence (Besides exemplary normal or abnormal validity of taxonomy in P0234, compared event information correlated to biological taxonomy in P0244, see [P0329] a specific taxonomy having 114 parameters has been developed to help distinguish anomalous influenza seasons from those recorded during pandemics. The taxonomy includes the relationships (i.e. hierarchy) between parameters, as well as specific I&W examples from both seasonal and pandemic years. Finally, the taxonomy will continue to evolve as the nature of the data is increased and novel I&Ws are identified. Also, see alert worthy detection and warnings during pandemics and population studies in P0338.).
Regarding claim 49, Li and Fuerst teach the device of claim 46 mentioned above, and Li further teaches wherein processor is further configured to:
generate, based on the obtained illness state data, a third visual indication of an unverified illness occurrence, wherein the third visual indication is overlaid on the digital map, and wherein the third visual indication indicates that the unverified illness occurrence has not been verified by the healthcare provider data (See launch GIS overlay viewer in Fig. 24, P0298-P0303, P0085 where the unverified illness occurrence can be determined by the subsystem for verification of information. Also, see P0167-P0168] organizations such as GOARN and ProMED that follow the standard epidemiological practice of initial surveillance followed by event verification. Also, see rapid verification of exemplary diseases and pandemics in P0278, listed in Table for comparing.).
Regarding claim 54, Lin discloses further comprising a memory and a display (See exemplary databases in P0095, P0149 and Fig. 27 map displayed on screen interface in P0301.).
Claim 55:
Li discloses A method comprising:
obtaining illness state data that indicates an illness occurrence based on a first taxonomy (See exemplary reports of increased hospital visits in Asia mentioned in P0142. Also, see P0165-P0166: Table 2, page 15, col. 2, lines 22-44 demand for medical services and medications when reporting collected data. Also, see P0178-P0180, taxonomy in P0244, P0327 and taxonomy examples in P0329-P0330.);
obtaining healthcare provider data that indicates an illness event based on a second taxonomy, and a geographic region associated with the illness event(See keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents. See the demand for medical services P0165-P0166: Table 2, page 15, col. 2, lines 22-44 such as increased hospital visits and admissions in Asia (P0142).).
determining, based on the illness state data and the healthcare provider data, that the illness occurrence occurred within the geographic region (See exemplary reports of increased hospital visits in Asia mentioned in P0142. Also, see P0165-P0166: Table 2, page 15, col. 2, lines 22-44 demand for medical services and medications when reporting collected data. Also, see P0178-P0180.);
processing the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy (Besides exemplary normal or abnormal validity of taxonomy in P0234, compared event information correlated to biological taxonomy in P0244, see [P0329] a specific taxonomy having 114 parameters has been developed to help distinguish anomalous influenza seasons from those recorded during pandemics. The taxonomy includes the relationships (i.e. hierarchy) between parameters, as well as specific I&W examples from both seasonal and pandemic years. Finally, the taxonomy will continue to evolve as the nature of the data is increased and novel I&Ws are identified.);
determining, based at least on the correlation between the first taxonomy and the second taxonomy, that the illness occurrence is related to the illness event (Taught in, P0244, P0329 as distinguishing anomalous influenza seasons, alert worthy detection and warnings during pandemics and population studies in P0338.);
assigning a severity level to the illness occurrence based at least on the geographic region and the determination that the illness occurrence is related to the illness event (Besides analysts reporting by entering staging value mentioned in P0286, P0291-P0292, shown in Fig. 3D in country’s report, see P0161-P0162: Table 1A, page 13, where disease events include groups Priority 1-4 as severity levels.);
comparing the severity level to a severity threshold to determine that the illness occurrence is verified by the illness event (See measles as exemplary disease with a population threshold number surpassed as mentioned in P0017. Also, see P0220 number of keywords as a threshold value in email alerts if anomalies are detected for a given location.); and
based on the determination that the device is located in the geographic region and the determination that the illness occurrence is verified (See P0219-P0223 where illnesses for a given location can be verified using decision node, anomaly number of keywords in email alerts and documents reaching a threshold value. Also, see exemplary ground verification of Ebola epidemic in Kasai, Democratic Republic of the Congo in P0323):
generating a first visual indication indicating that the illness occurrence is verified by the illness event (See Fig. 16, Fig. 20, Fig. 23-27 as exemplary visual indicating reported illnesses.);
generating a second visual indication indicating the determined medication associated with treating the illness (See update event report list and map (Fig. 24, P0302-P0303) where events include demand for medical services and medications when reporting collected data in P0165-P0166: Table 2, page 15, col. 2, lines 22-44. Also, see P0178-P0180 demand for pharmaceuticals and supplies include increased purchase or use of medications and vaccines.); and
displaying, on a device, a digital map including an overlay of the first visual indication and the second visual indication in the geographic region (See launch GIS overlay viewer in Fig. 24, P0298-P0303.).
Although Lin discloses a system, method and software for reporting an illness event and healthcare data associated with a geographic location, identifying correlations and medications for treating the illness event and an overlay visual indication on a digital map as mentioned above, Lin does not explicitly teach determining a medication associated with a treatment of an illness associated with the reported illness occurrence. Fuerst teaches:
determining a medication based on the illness occurrence being verified, wherein the medication is associated with a treatment of an illness associated with the illness occurrence (Besides tracking mass vaccination procedures and medication delivery (P0098), captured drug dispensing (P0104-P0105) and exemplary monitoring medication inventory levels (P0147), see [P0081] Modeling can be used to both verify outpatient diagnostic data and to independently track fluctuations in drug usage that could provide early alerting for disease outbreaks.).
Therefore, it would have been obvious to one of ordinary skill in the art of infectious disease management before the effective filing date of the claimed invention to modify the system, method and software of Lin to include determining a medication associated with a treatment of an illness associated with the reported illness occurrence as taught by Fuerst for recommending drug sequencing and drug regimens based on medical information about a patient as mentioned in Fuerst’s P0012.
Regarding claim 56, Li and Fuerst teach the method of claim 55 mentioned above, and Li further teaches wherein the illness occurrence is derived from social media content and indicates a symptom in the geographic region, and wherein the severity level indicates that the illness event corroborates the symptom (See keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents.).
Regarding claim 57, Li and Fuerst teach method of claim 55 mentioned above, and Li further teaches wherein the method further comprises: applying, based on the determination that the first taxonomy and the second taxonomy are correlated, a weighting factor to the illness occurrence to obtain a weighted illness occurrence, wherein the illness occurrence is determined to be verified further based on the weighted illness occurrence (Besides exemplary normal or abnormal validity of taxonomy in P0234, compared event information correlated to biological taxonomy in P0244, see [P0329] a specific taxonomy having 114 parameters has been developed to help distinguish anomalous influenza seasons from those recorded during pandemics. The taxonomy includes the relationships (i.e. hierarchy) between parameters, as well as specific I&W examples from both seasonal and pandemic years. Finally, the taxonomy will continue to evolve as the nature of the data is increased and novel I&Ws are identified. Also, see alert worthy detection and warnings during pandemics and population studies in P0338.).
Regarding claim 58, Li and Fuerst teach the method of claim 55mentioned above, and Li further teaches wherein processor is further configured to:
generating, based on the obtained illness state data, a third visual indication of an unverified illness occurrence, wherein the third visual indication is overlaid on the digital map, and wherein the third visual indication indicates that the unverified illness occurrence has not been verified by the healthcare provider data (See launch GIS overlay viewer in Fig. 24, P0298-P0303, P0085 where the unverified illness occurrence can be determined by the subsystem for verification of information. Also, see P0167-P0168] organizations such as GOARN and ProMED that follow the standard epidemiological practice of initial surveillance followed by event verification. Also, see rapid verification of exemplary diseases and pandemics in P0278, listed in Table for comparing.).
Claim 63:
Li discloses A non-transitory, computer-readable medium comprising executable instructions that, when executed by a computer system (See computing devices and reporting module as computer-readable medium comprising executable instructions in P0153-P0154.), cause the computer system to:
obtain illness state data that indicates an illness occurrence based on a first taxonomy (See exemplary reports of increased hospital visits in Asia mentioned in P0142. Also, see P0165-P0166: Table 2, page 15, col. 2, lines 22-44 demand for medical services and medications when reporting collected data. Also, see P0178-P0180, taxonomy in P0244, P0327 and taxonomy examples in P0329-P0330.);
obtain healthcare provider data that indicates an illness event based on a second taxonomy, and a geographic region associated with the illness event(See keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents. See the demand for medical services P0165-P0166: Table 2, page 15, col. 2, lines 22-44 such as increased hospital visits and admissions in Asia (P0142).);
determine, based on the illness state data and the healthcare provider data, that the illness occurrence occurred within the geographic region (See exemplary reports of increased hospital visits in Asia mentioned in P0142. Also, see P0165-P0166: Table 2, page 15, col. 2, lines 22-44 demand for medical services and medications when reporting collected data. Also, see P0178-P0180.);
process the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy (Besides exemplary normal or abnormal validity of taxonomy in P0234, compared event information correlated to biological taxonomy in P0244, see [P0329] a specific taxonomy having 114 parameters has been developed to help distinguish anomalous influenza seasons from those recorded during pandemics. The taxonomy includes the relationships (i.e. hierarchy) between parameters, as well as specific I&W examples from both seasonal and pandemic years. Finally, the taxonomy will continue to evolve as the nature of the data is increased and novel I&Ws are identified.);
determine, based at least on the correlation between the first taxonomy and the second taxonomy, that the illness occurrence is related to the illness event (Taught in, P0244, P0329 as distinguishing anomalous influenza seasons, alert worthy detection and warnings during pandemics and population studies in P0338.);
assign a severity level to the illness occurrence based at least on the geographic region and the determination that the illness occurrence is related to the illness event (Besides analysts reporting by entering staging value mentioned in P0286, P0291-P0292, shown in Fig. 3D in country’s report, see P0161-P0162: Table 1A, page 13, where disease events include groups Priority 1-4 as severity levels.);
compare the severity level to a severity threshold to determine that the illness occurrence is verified by the illness event (See measles as exemplary disease with a population threshold number surpassed as mentioned in P0017. Also, see P0220 number of keywords as a threshold value in email alerts if anomalies are detected for a given location.); and
based on the determination that the device is located in the geographic region and the determination that the illness occurrence is verified (See P0219-P0223 where illnesses for a given location can be verified using decision node, anomaly number of keywords in email alerts and documents reaching a threshold value. Also, see exemplary ground verification of Ebola epidemic in Kasai, Democratic Republic of the Congo in P0323):
generate a first visual indication indicating that the illness occurrence is verified by the illness event (See Fig. 16, Fig. 20, Fig. 23-27 as exemplary visual indicating reported illnesses.);
generate a second visual indication indicating the determined medication associated with treating the illness (See update event report list and map (Fig. 24, P0302-P0303) where events include demand for medical services and medications when reporting collected data in P0165-P0166: Table 2, page 15, col. 2, lines 22-44. Also, see P0178-P0180 demand for pharmaceuticals and supplies include increased purchase or use of medications and vaccines.); and
display, on the device, a digital map including an overlay of the first visual indication and the second visual indication in the geographic region (See launch GIS overlay viewer in Fig. 24, P0298-P0303.).
Although Lin discloses a system, method and software for reporting an illness event and healthcare data associated with a geographic location, identifying correlations and medications for treating the illness event and an overlay visual indication on a digital map as mentioned above, Lin does not explicitly teach determining a medication associated with a treatment of an illness associated with the reported illness occurrence. Fuerst teaches:
determine a medication based on the illness occurrence being verified, wherein the medication is associated with a treatment of an illness associated with the illness occurrence (Besides tracking mass vaccination procedures and medication delivery (P0098), captured drug dispensing (P0104-P0105) and exemplary monitoring medication inventory levels (P0147), see [P0081] Modeling can be used to both verify outpatient diagnostic data and to independently track fluctuations in drug usage that could provide early alerting for disease outbreaks.).
Therefore, it would have been obvious to one of ordinary skill in the art of infectious disease management before the effective filing date of the claimed invention to modify the system, method and software of Lin to include determining a medication associated with a treatment of an illness associated with the reported illness occurrence as taught by Fuerst for recommending drug sequencing and drug regimens based on medical information about a patient as mentioned in Fuerst’s P0012.
Regarding claim 64, Li and Fuerst teach the non-transitory, computer-readable medium of claim 63 mentioned above, and Li further teaches wherein the illness occurrence is derived from social media content and indicates a symptom in the geographic region, and wherein the severity level indicates that the illness event corroborates the symptom (See keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents.).
Claims 50, 59 and 65 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2009/0216747 A1) in view of Fuerst (US 2006/0036619 A1) further in view of Kahn (US 7,705,723 B2).
Regarding claims 50 and 59, although Lin and Fuerst teach the device of claim 46 and the method of claim of 55 mentioned above, Lin and Fuerst do not explicitly teach using records, identified reporting of illnesses occurred, location of the illnesses event verification. Khan teaches wherein the verified illness state data comprises a plurality of records, a record being configured to identify the illness occurrence, a corresponding illness occurrence location, and whether the illness occurrence has been verified by the illness event (See This backtracking, in one embodiment includes checking for corresponding/related illnesses in [column 2, line 1-28], where a doctor’s records and CDC reporting serve as a plurality of records and identifying reported illness occurrence. Also, see column 3, lines 39-49, column 5, lines 5-8 and 35-40 where mapping the spread of an outbreak provides technology to verify an illness event.).
Therefore, it would have been obvious to one of ordinary skill in the art of outbreak notification management before the effective filing date of the claimed invention to modify the system, method and software of Lin and Fuerst to include using records, identified reporting of illnesses occurred, location of the illnesses event verification as taught by Khan in order to better prepare healthcare providers when an influx of patients need urgent care and treatment on both a local and global scale.
Regarding claim 65, although Lin and Fuerst teach the non-transitory computer-readable medium of claim 66 mentioned above, Lin and Fuerst do not explicitly teach using records, identified reporting of illnesses occurred, location of the illnesses and healthcare provider verification. Khan teaches wherein the verified illness state data comprises a plurality of records, a record being configured to identify the illness occurrence, a corresponding illness occurrence location, and whether the illness occurrence has been verified by the healthcare provider data (See This backtracking, in one embodiment includes checking for corresponding/related illnesses in [column 2, line 1-28], where a doctor’s records and CDC reporting serve as a plurality of records and identifying reported illness occurrence.).
Therefore, it would have been obvious to one of ordinary skill in the art of outbreak notification management before the effective filing date of the claimed invention to modify the system, method and software of Lin and Fuerst to include using records, identified reporting of illnesses occurred, location of the illnesses and healthcare provider verification as taught by Khan in order to better prepare healthcare providers when an influx of patients need urgent care and treatment on both a local and global scale.
Claims 51 and 60 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2009/0216747 A1) in view of Fuerst (US 2006/0036619 A1) further in view of Li (2) (US 8,881,040 B2).
Regarding claims 51 and 60, although Li and Fuerst teach the device of claim 46 as mentioned above, Li and Fuerst do not teach indicating whether the reported illness occurrence has been verified in social media content data. Li (2) teaches wherein the illness state data that indicates the illness occurrence based on the first taxonomy is obtained from social media content (See verifiable ways a healthcare provider can indicate reported illness occurrences in column 35, lines 20-47 and column 43, line 63 to column 44, line 6. See exemplary social media content data is used (column 37, lines 33-61, column 57, lines 20-27). See column 41, line 45-58, media-produced web documents when identifying events of interest. When analyzing raw media and case file summaries (column24, lines 33-47), see Fig. 5, step 514, Examine Social Network to Match Information Sources to Reporting Requirement mentioned in column 36, lines 51-61.).
Therefore, it would have been obvious to one of ordinary skill in the art of Biosurveillance management before the effective filing date of the claimed invention to modify the system of Li and Fuerst to include indicating whether the reported illness occurrence has been verified in social media content data as taught by Li (2) to use as a tool to facilitate more informed decision making and thus would help mitigate the international spread of infectious disease mentioned in Li (2)’s column 8, line 63 to column 9, line 4.
Claims 52 and 61 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2009/0216747 A1) in view of Fuerst (US 2006/0036619 A1) further in view of McMillan (US 2015/0371006 A1).
Regarding claim 52, although Lin and Fuerst teach the device of claim 46 mentioned above and Lin discloses generate a third visual indication that indicates the determined severity level in the geographic region, wherein the third visual indication is overlaid on the digital map (See Fig. 27 and prioritizing health conditions according to severity in P0187. See launch GIS overlay viewer with advisory data populated on a map in Fig. 24, P0298-P0303.).
Regarding claim 61, although Lin and Fuerst teach the method of claim 55 mentioned above and Lin discloses generating a third visual indication that indicates the determined severity level in the geographic region, wherein the third visual indication is overlaid on the digital map (See Fig. 27 and prioritizing health conditions according to severity in P0187. See launch GIS overlay viewer with advisory data populated on a map in Fig. 24, P0298-P0303.).
Claims 53 and 62 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2009/0216747 A1) in view of Fuerst (US 2006/0036619 A1) further in view of Dodge (WO 2012/174230 A1).
Regarding claim 53, although Lin and Fuerst teach the device of claim 46 as mentioned above, Lin and Fuerst do not explicitly teach the healthcare provider data and the illness state data and generating a visual indication that determined illness trend for the illness in the geographic region, wherein the third visual indication is overlaid on the digital map. Dodge teaches:
determine an illness trend for an illness in the geographic region based on the healthcare provider data and the illness state data; and generate a visual indication that indicates the determined illness trend for the illness in the geographic region, wherein the third visual indication is overlaid on the digital map (See Exemplary state health departments, GOOGLE FLU TRENDS, HEALTH MAP or GLOBAL PUBLIC HEALTH INTELLIGENCE NETWORK in P0003 and in [P0009] "user broadcasting" platforms such as FACEBOOK , TWITTER, GOOGLE PLUS, GOOGLE OPENSOCIAL. Also, see exemplary overlaying map in P0019-P0020.).
Therefore, it would have been obvious to one of ordinary skill in the art of tracking illnesses with social networking before the effective filing date of the claimed invention to modify the system, method and software of Lin and Fuerst to include the healthcare provider data and the illness state data and generating a visual indication that determined illness trend for the illness in the geographic region, wherein the third visual indication is overlaid on the digital map as taught by Dodge to provide the user information about the location and spread of illnesses and health-related dangers mentioned in Dodge’s P0004.
Regarding claim 62, although Lin and Fuerst teach the method of claim 55 as mentioned above, Lin and Fuerst do not explicitly teach the healthcare provider data and the illness state data and generating a visual indication that determined illness trend for the illness in the geographic region, wherein the third visual indication is overlaid on the digital map. Dodge teaches:
determining an illness trend for an illness in the geographic region based on the healthcare provider data and the illness state data; and generating a third visual indication that indicates the determined illness trend for the illness in the geographic region, wherein the third visual indication is overlaid on the digital map (See Exemplary state health departments, GOOGLE FLU TRENDS, HEALTH MAP or GLOBAL PUBLIC HEALTH INTELLIGENCE NETWORK in P0003 and in [P0009] "user broadcasting" platforms such as FACEBOOK , TWITTER, GOOGLE PLUS, GOOGLE OPENSOCIAL. Also, see exemplary overlaying map in P0019-P0020.).
Therefore, it would have been obvious to one of ordinary skill in the art of tracking illnesses with social networking before the effective filing date of the claimed invention to modify the system, method and software of Lin and Fuerst to include using healthcare provider data and the illness state data and generating a visual indication that determined illness trend for the illness in the geographic region, wherein the third visual indication is overlaid on the digital map as taught by Dodge to provide the user information about the location and spread of illnesses and health-related dangers mentioned in Dodge’s P0004.
Response to Arguments
Applicant argues that amended claim 46 is not directed to an abstract idea because it recites at least a feature that cannot be practically performed in the human mind, e.g. see pgs. 11-12 of Remarks – Examiner disagrees.
Recited limitations, “processing the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy," and "determining, based at least on the correlation between the first taxonomy and the second taxonomy, that the illness occurrence is related to the illness event”, is equivalent to Scientists, specialized in the taxonomy of classifying diseases based on data collected from healthcare providers and social media, which can be done when the Scientists analyze, make observations and interact with other humans.
Applicant argues that amended claim 46 is not directed to an abstract idea because it is directed towards a particular machine and claim covers a particular solution to a problem or a particular way to achieve a desired outcome e.g. see pgs. 12-13 of Remarks – Examiner disagrees.
An illness state correlation system configured to receive data feeds and correlate disparate data sets from the disparate data sources to provide verified reporting of illness states, happens to be mere data gathering and low level data processing that a general-purpose computer would be expected to do, see general purpose computing system, hardware and processor in paragraphs 32-33 of Applicant’s own specification. Furthermore, technology usage is neither described nor claimed to receive the data feeds and provide verified reporting of illness states. Therefore, is a part of the abstract idea and can’t be used to integrate the abstract idea into a practical application.
Applicant's arguments regarding the prior art rejection, filed 10/21/2025 have been fully considered but they are not persuasive. The revised amendments recited with "process the illness occurrence and the illness event to determine a correlation between the first taxonomy and the second taxonomy," and "assign a severity level to the illness occurrence based on the determination that the illness occurrence is related to the illness event and based on the geographic region” do not sufficiently overcome the art rejection. With the first taxonomy reported from healthcare provider data and the second taxonomy reported from social media content data, according to Applicant’s specification, see Li’s keyword searching in media sources in P0062 and P0225-P0228 where “disease symptoms” as a keyword in searching news documents and the demand for medical services P0165-P0166: Table 2, page 15, col. 2, lines 22-44 such as increased hospital visits and admissions in Asia (P0142). Also, see analysts reporting by entering staging value mentioned in P0286, P0291-P0292, shown in Fig. 3D in country’s report, and P0161-P0162: Table 1A, page 13, where disease events include groups Priority 1-4 as severity levels.
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.
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/T.S.W./Examiner, Art Unit 3687 01/30/2025
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687