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 .
DETAILED ACTION
Response to Amendment
In the preliminary amendment dated 08 May 202, the following has occurred: Claims 1-15 were cancelled; Claims 16-35 are new.
The present office action represents the first action on the merits.
Claims 16-35 are pending.
Priority
This application claims priority to PCT/IB2023/0007775 dated 03 November 2023, EP22208436.0 dated 18 November 2022, and U.S. Provisional Patent Application No. 63/424,250 dated 10 November 2022.
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 11 August 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
Notice to Applicant
The Examiner notes that the 25 page Specification dated 08 May 2025 labeled with the Attorney’s docket number does not contain page numbers; however, the 24 page Specification dated 08 May 2025 labeled “WO 2024/100457” does contain page numbers. The Examiner thus references the WO-labeled document when referring to the Specification.
Claim Interpretation
The Examiner understands and interprets claim terms in the following manner:
Impression = when an advertisement is served/provided/shown (see Spec. Pg. 6, Lns. 16-22). This is a term of art in the field of advertising. Its corollary is conversion and is when an advertisement is acted upon.
Clicks = selection of an element (see Spec. Pg. 6, Lns. 24-27).
Entity = a patient (see Spec. Pg. 5, Ln. 26), a customer account (Claim 28), a department or sub-department of a healthcare location (Claim 28), a healthcare professional (Claim 28), or a healthcare provider (Claim 28).
Claim Objections
Claim 25 recites “a potential disease diagnoses.” Because “a potential disease” is singular, the Examiner believes the claim should recite: a potential disease diagnosis. Appropriate correction is required.
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 16-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 16, 29, and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a Method, non-transitory computer storage media, and system for identifying potential disease diagnoses and/or healthcare providers, which are within a statutory category.
Step 2A1
The limitations of receiving geofencing data from one or more healthcare locations, the geofencing data relating to the disease comprising one or more impressions made at the healthcare location and/or one or more clicks made at the healthcare location; extracting, for each of a plurality of entities, a plurality of features from the geofencing data, wherein each entity is associated with a respective healthcare location in the one or more healthcare locations; processing, using a machine-learned model, the extracted features for each entity to determine an indication of whether a potential disease patient is present at the respective healthcare location associated with that entity and/or if a healthcare provider at the respective healthcare location associated with that entity is potentially seeking information relating to the disease; and in response to determining an indication that a potential disease patient is present at a healthcare location in the one or more healthcare locations and/or that a healthcare provider at the respective healthcare location associated with that entity is potentially seeking information relating to the disease, triggering an alert, the alert comprising the identity of said health care location and/or the entity associated with said healthcare location, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to identify potential disease diagnoses and/or healthcare providers in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving…extracting…processing…and triggereing” as indicated supra.
Other than reciting generic computer components (discussed infra), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The Examiner notes that Spec. Pg. 3, Ln. 33 (and Claim 27) describes the claimed “machine learning model” as logistic regression which is a model performable by a human and is thus interpreted to be part of Certain Methods of Organizing Human Activity. It is alternately a mathematical concept and the different types of abstract ideas are considered as one for the purposes of subject matter eligibility analysis.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of (Claim 16) a computer, (Claim 29) a non-transitory computer storage media, or (Claim 30) one or more computers having one or more storage devices that implements the identified abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., a generic computer or components thereof) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using (Claim 16) a computer, (Claim 29) a non-transitory computer storage media, or (Claim 30) one or more computers having one or more storage devices to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Claims 17-28 and 31-35 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 17, 19, 20, 31, 33, 34 merely describe(s) the feature(s), which further defines the abstract idea.
Claim(s) 18, 32 merely describe(s) receiving data and extracting features from the data, which further defines the abstract idea.
Claim(s) 21, 35 merely describe(s) how the features are extracted, which further defines the abstract idea.
Claim(s) 22 merely describe(s) the type of disease, which further defines the abstract idea.
Claim(s) 23 merely describe(s) how the extracted features are processed, which further defines the abstract idea.
Claim(s) 24, 25 merely describe(s) the content of the alert, which further defines the abstract idea.
Claim(s) 26 merely describe(s) receiving search data and extracting features from the search data, which further defines the abstract idea.
Claim(s) 27 merely describe(s) the machine learning model, which further defines the abstract idea.
Claim(s) 28 merely describe(s) the types of entities, which further defines the abstract idea.
Claim(s) 17 merely describe(s)…, which further defines the abstract idea.
Claim(s) 17 merely describe(s)…, which further defines the abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 16-35 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 16-28 each recite a “computer-implemented method” without any recitation in the body of each of the claims describing which step is implemented by a computer or how the computer may be involved. Each of the limitations purely pertain to data manipulation without describing whether a computer may be involved in any particular step or how it may be involved. See, e.g., Ex Parte Langemyr, Appeal No. 2008-1495 at Pg. 20, 2008 Pat App. LEXIS 13 (B.P.A.I. May 28, 2008) (finding that nominal recitation of computer-implementation in the preamble is insufficient to tie the particular steps of the method to the computer). Accordingly, it is unclear where and to what extent the computer-implementation described in the preamble may take place within the body of the claim. The Examiner suggests reciting “wherein each of the following steps are performed by the computer” or similar language.
Claims 16, 29, and 30 recite (Claim 16 being representative) “whether a potential disease patient is present at the respective healthcare location associated with that entity.” The claim is indefinite because it is unclear what entity “that entity” is referring to. The claim previously recited “a plurality of entities” and did not single out any particular entity. Alternately or in addition, the claim recites performing data processing for “each entity” and unclear whether “that entity” is referring to performing the determination of “an indication of whether a potential disease patient is present at the respective healthcare location” for each and every entity in the plurality of entities or just a particular entity.
By virtue of their dependence from Claim 16 or 30, this basis of rejection also applies to dependent Claims 17-28 and 31-35.
Claims 17 and 31 recite “wherein the plurality of features comprises: a number of impressions in a first time period; a number of impressions in a second time period; a number of clicks in the first time period; and a number of clicks in the second time period.” The claim is indefinite because it is unclear how these features can be extracted when they are not part of the geofencing data. Claim 16 recites “receiving geofencing data…comprising one or more impressions made at the healthcare location and/or one or more clicks made at the healthcare location; … extracting, for each of a plurality of entities, a plurality of features from the geofencing data….” Only one of “one or more impressions…and/or one or more clicks” is required by the claim. In the event (as is the case with the present art rejection) that one of these types of data is not present in the geofencing data (impressions for instance), it is unclear how these features can be extracted from the geofencing data. The Examiner suggests also reciting “and/ or” in Claim 17 between the two types of data.
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 of this title, 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) 16, 17, 21-23, 25-31, and 35 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Sadilek et al. (U.S. Pre-Grant Patent Publication No. 2019/0148023).
REGARDING CLAIM 16
Sadilek teaches the claimed computer implemented method of identifying potential disease diagnoses and/or healthcare providers, the method comprising: [Para. 0008 teaches the functionality is implemented via a computer.]
receiving geofencing data from one or more healthcare locations, [Para. 0086, 0090, 0091 teaches receiving data of users related to their location. Para. 0090 teaches that the users are within a particular geographic area such as a city and thus the data is interpreted as geofencing data. The Examiner interprets any location where the user is as a healthcare location and notes that users cannot all be in the same location. See also Para. 0036, 0113 describing locations as establishments.] the geofencing data relating to the disease comprising [The Examiner notes that the Applicant has self-defined geofencing data as the following.]
one or more impressions made at the healthcare location and/or
one or more clicks made at the healthcare location; [Para. 0022, 0087, 0088, 0105, 0107 teaches collecting search engine data that is connected to location data. Para. 0106 teaches that the search engine data includes “result selection actions” which, as would be understood by a person having skill in the art and anyone who has used a search engine, is interpreted as a “clicks.”]
extracting, for each of a plurality of entities, a plurality of features from the geofencing data, [Para. 0077, 0086, 0089 teaches that the search and location data is used to train a machine learned detection model, thus the data is extracted. Para. 0089 also teaches that additional features may be used to train the model.]
wherein each entity is associated with a respective healthcare location in the one or more healthcare locations; [Para. 0090 teaches that the users are within a city. The Examiner notes that the location where each user is located within the city is a respective healthcare location.]
processing, using a machine-learned model, the extracted features for each entity to determine an indication of [Para. 0089 teaches that the search and location data (features) is received by a machine-learned disease detection model.]
whether a potential disease patient is present at the respective healthcare location associated with that entity and/or [Para. 0035, 0112 teaches that the system identifies locations that are associated with both diseased (a potential disease patient) and non-diseased users. This indicates whether a diseased user and a non-diseased user (“that entity”) are in the same location.]
if a healthcare provider at the respective healthcare location associated with that entity is potentially seeking information relating to the disease; and
in response to determining
an indication that a potential disease patient is present at a healthcare location in the one or more healthcare locations and/or [Para. 0112, 0118 teaches providing an alert to a user based on the disease being determined to be at the location.]
that a healthcare provider at the respective healthcare location associated with that entity is potentially seeking information relating to the disease,
triggering an alert, […]. [Para. 0118 teaches providing the alert to a user.]
Sadilek may not explicitly teach the alert comprising the identity of said health care location and/or the entity associated with said healthcare location. However, the limitation claims information/labels that do not result in a manipulative difference between the information/labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the information/labels was substituted with nothing. Because Sadilek teaches that an alert containing information is provided, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the alert of the prior art with any other information/labels because the results would have been predictable. The Examiner notes that there is no functionality associated with the data that is applied in the alert and thus it is non-functional data.
Assuming arguendo that Sadilek does not teach determining whether a potential disease patient is present at every respective healthcare location associated with each and every user withing an area (geofenced area), the noted features would have been prima facie obvious to one of ordinary skill in the art at the time of filing in view of the teaching of Sadilek based on the duplication of parts rationale (see In re Harza, MPEP 2144.04(VI)(B)). Sadilek teaches determining that non-disease users are at locations with diseased users (see citations, supra). The application of the recited method to each and every user at each and every location withing a particular area (i.e., a city) produces no new and unexpected result which would result in patentable significance over the teaching of Sadilek; the application of the method to each and every user at each and every location withing a particular area does not change how the claim effects the user disease location determination.
REGARDING CLAIM 17
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein the plurality of features comprises:
[…];
a number of clicks in the first time period; and [Para. 0022, 0087, 0088, 0105, 0107 teaches collecting search engine data. Para. 0024 teaches that the search engine data includes a second search result is selected (a “result selection actions”), i.e., one click, at a first time.]
a number of clicks in the second time period. [Para. 0024 teaches that the search engine data includes a third search result that is selected (a “result selection actions”), i.e., also one click, after the second search result is selected, at a later time after the first time.]
Sadilek may not explicitly teach a number of impressions in a first time period and a number of impressions in a second time period. However, the limitation claims information/labels that do not result in a manipulative difference between the information/labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the information/labels was substituted with nothing. Because Sadilek teaches that collected data (features) is used (“extracted”) to train the machine-learned disease detection model, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the alert of the prior art with any other information/labels because the results would have been predictable. The Examiner notes that the “impressions” feature of Claim 1 is optional and that option was not taken. Thus, this data is never used in the claim and whether or not the data is present in the features does not affect the functionality of the claim.
REGARDING CLAIM 21
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek may not explicitly teach
wherein the method is repeated periodically,
wherein, at each repetition, extracting the plurality of features comprises extracting a plurality of features from geofencing data captured within a predefined period of time prior to the time of the repetition.
However, the noted features would have been prima facie obvious to one of ordinary skill in the art at the time of filing in view of the teaching of Sadilek based on the duplication of parts rationale (see In re Harza, MPEP 2144.04(VI)(B)). Sadilek teaches performing the noted method (see rejection of Claim 16 for citations) and teach that machine learned model training is performed on features extracted prior to completion of the method (a predefined period of time prior to the time of the repetition; see rejection of Claim 16 for citations). The application of the recited method periodically (i.e., doing the method again) produces no new and unexpected result which would result in patentable significance over the teaching of Sadilek; the application of the method periodically does not change how the claim effects the user disease location determination.
REGARDING CLAIM 22
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein the disease is a rare disease or ultra-rare disease. [Para. 0021 teaches that the disease is ebola, which is interpreted as a rare disease.]
REGARDING CLAIM 23
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein processing the extracted features for each entity to determine the indication of whether a potential individual with a disease is present at the respective healthcare location associated with that entity comprises, for each entity:
determining a probability
that a disease patient is present at the respective healthcare location associated with the entity using the machine-learned model and/or [Para. 0089 teaches that the search and location data (features) is received by a machine-learned disease detection model. Para. 0035, 0112 teaches that the system identifies locations that are associated with both diseased and non-diseased users. This indicates whether a diseased user and a non-diseased user (“that entity”) are in the same location. Para. 0032 teaches that the prediction includes a probability associated with it.]
that a healthcare provider at the respective healthcare location associated with that entity is potentially seeking information relating to the disease;
comparing the determined probability to a threshold probability level; and [Para. 0096 teaches that the probability is compared to a threshold.]
in response to determining that the determined probability is greater than the threshold probability level, indicating
that a potential disease patient is present at the respective healthcare location associated with the entity and/or [Para. 0096 teaches that when/if the probability is above the threshold, the user is indicated as having the disease at the location.]
that a healthcare provider at the respective healthcare location associated with that entity is potentially seeking information relating to the disease.
REGARDING CLAIM 25
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein triggering the alert comprises transmitting, […], an indication that a potential disease diagnoses or information about the disease is associated with a healthcare facility entity. [Para. 0045 teaches that the alert about the location of the diseased users (one of which is interpreted as a healthcare facility entity, there being no indication what this entails) is sent to a producer of medication that treats the disease about a current outbreak at a location.]
Sadilek may not explicitly teach a sales representative. However, the limitation claims information/labels that do not result in a manipulative difference between the information/labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the information/labels was substituted with nothing. Because Sadilek teaches that an alert is sent to an entity (a producer of medication), substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the alert of the prior art with any other information/labels because the results would have been predictable. The Examiner notes that the label applied to the entity to which the alert is transmitted does not affect the functionality of the claim.
REGARDING CLAIM 26
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
receiving search data relating to one or more search terms related to the disease; and [Para. 0022, 0087, 0088, 0105, 0107 teaches collecting search engine data that is connected to location data for users.]
extracting, for each of the plurality of entities, one or more features from the search data. [Para. 0077, 0086, 0089 teaches that the search and location data is used to train a machine learned detection model, thus the data is extracted.
REGARDING CLAIM 27
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein the machine-learned model comprises:
a logistic regression model;
a random forest model;
a neural network; [Para. 0066 teaches that the machine-learned model is a neural network.]
a generalized additive model; and/or a
XGBoost model.
REGARDING CLAIM 28
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein the plurality of entities comprises one or more of:
one or more customer accounts associated with a respective healthcare location;
one or more departments and/or sub-departments associated with a respective healthcare location;
one or more healthcare professionals associated with a respective healthcare location; and/or
one or more healthcare providers associated with a respective healthcare location. [Para. 0109 teaches that a user associated with a location is determined to have the disease. Per the interpretation in Claim 16, anywhere a user is located is interpreted as a healthcare location.]
Sadilek may not explicitly teach that the user is one or more healthcare providers. However, the limitation claims information/labels that do not result in a manipulative difference between the information/labels of the prior art and the functionally of the claimed method. The function taught by the prior art would be performed the same regardless of whether the information/labels was substituted with nothing. Because Sadilek teaches that a user is evaluated for a disease, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the alert of the prior art with any other information/labels because the results would have been predictable. The Examiner notes that the label applied to the user does not affect the functionality of the claim.
REGARDING CLAIM(S) 29
Claim(s) 29 is/are analogous to Claim(s) 16, thus Claim(s) 29 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 16.
REGARDING CLAIM(S) 30
Claim(s) 30 is/are analogous to Claim(s) 16, thus Claim(s) 30 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 16.
REGARDING CLAIM(S) 31
Claim(s) 31 is/are analogous to Claim(s) 17, thus Claim(s) 31 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 17.
REGARDING CLAIM(S) 35
Claim(s) 35 is/are analogous to Claim(s) 21, thus Claim(s) 35 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 21.
Claim(s) 18, 19, 32, and 33 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Sadilek et al. (U.S. Pre-Grant Patent Publication No. 2019/0148023) in view of Monaghan et al. (U.S. Pre-Grant Patent Publication No. 2021/0319905).
REGARDING CLAIM 18
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
receiving […] one or more sets of […] data relating to the disease from one or more of the healthcare locations, […]. [Para. 0022, 0089, 0108 teaches that location data can include additional feature data.]
Sadilek may not explicitly teach
one or more sets of lab test data…, each set of lab test data indicative of which lab tests have been performed by an entity at the healthcare location;
wherein the plurality of features is further extracted from the one or more sets of lab test data.
Monaghan at Para. 0036, 0047, 0049, 0080 teaches that it was known in the art of computerized healthcare, at the time of filing, to perform disease prediction by applying a machine learning model to lab data values
one or more sets of lab test data…, each set of lab test data indicative of which lab tests have been performed by an entity at the healthcare location; [Monaghan at Para. 0036, 0047 teaches a disease prediction system for predicting whether patients (the users of Sadilek) at a medical facility (a location; the location(s) of Sadilek) have an infectious disease using lab data (lab test data). Monaghan at Para. 0049 teaches that the lab data is provided to a machine learning disease prediction model (the machine-learned disease detection model of Sadilek).]
wherein the plurality of features is further extracted from the one or more sets of lab test data. [Monaghan at Para. 0049 teaches that the lab data is provided to a machine learning disease prediction model (the machine-learned disease detection model of Sadilek) meaning that the features were necessarily extracted, see Para. 0080.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the disease detection/prediction and reporting system of Sadilek to perform disease prediction by applying a machine learning model to lab data values as taught by Monaghan, with the motivation of improving the detection of infectious diseases at a particular location (see Monaghan at Para. 0006).
REGARDING CLAIM 19
Sadilek/Monaghan teaches the claimed computer implemented method of Claims 16 and 18. Sadilek/Monaghan further teaches
wherein the plurality of features comprises:
a number of lab orders and/or lab tests in a first time period; and [Monaghan at Para. 0047 periodic blood draws and laboratory analysis are performed once a month (the first instance being interpreted as a first time period) and this data is provided to the prediction system.]
a number of lab orders and/or lab tests in a second time period. [Monaghan at Para. 0047 periodic blood draws and laboratory analysis are performed once a month (the second instance being interpreted as a second time period) and this data is provided to the prediction system.]
REGARDING CLAIM(S) 32 AND 33
Claim(s) 32 and 33 is/are analogous to Claim(s) 18 and 19, thus Claim(s) 32 and 33 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 18 and 19.
Claim(s) 20 and 34 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Sadilek et al. (U.S. Pre-Grant Patent Publication No. 2019/0148023) in view of Eysenbach (Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance).
REGARDING CLAIM 20
Sadilek teaches the claimed computer implemented method of Claims 16 and 17.
Sadilek may not explicitly teach
wherein the first time period is between […X…] days and the second time period is between […Y…] days.
Eysenbach at Fig. 1 and associated text teaches that it was known in the art of infectious disease monitoring, at the time of filing, to monitor infectious disease web search clicks over a series of days
wherein the first time period is between […X…] days and the second time period is between […Y…] days. [Eysenbach at Fig. 1 and associated text teaches monitoring infectious disease web search clicks over a series of days]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the disease detection/prediction and reporting system of Sadilek to monitor infectious disease web search clicks over a series of days as taught by Eysenbach, with the motivation of improving the timeliness of detection of infectious diseases in a particular area (see Eysenbach at “Results” Para.).
Sadilek/Eysenbach may not explicitly teach that the ranges for collecting the first and second click data are 3 and 5 days and 5 and 14 days, respectively; however, the recited ranges would have been prima facie obvious based on the routine optimization rationale in view of . MPEP 214405(II). Eysenbach teaches collection of click-through data related to infectious disease outbakes occurs on a weekly timeframe, i.e., a first time period is a week, a second time period is a week, and so on. Eysenbach thus teaches that the first and second time periods (the time periods of Sadilek) are from zero to 14 days. It would have been obvious to optimize this range to the recited are 3 and 5 days for the first time period and 5 and 14 days for the second time period as part of a routine optimization of the data collected for the infectious disease prediction. A person having ordinary skill in the art would have been motivated to perform such optimization because there are a finite number of identified, predictable solutions, a person of ordinary skill would be motivated to explore such solutions in order to minimize costs and maximize effectiveness.
REGARDING CLAIM(S) 34
Claim(s) 34 is/are analogous to Claim(s) 20, thus Claim(s) 34 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 20.
Claim(s) 24 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Sadilek et al. (U.S. Pre-Grant Patent Publication No. 2019/0148023) in view of Lu et al. (U.S. Pre-Grant Patent Publication No. 2023/0160805).
REGARDING CLAIM 24
Sadilek teaches the claimed computer implemented method of Claim 16. Sadilek further teaches
wherein triggering the alert comprises transmitting, […] material relating to the disease. [Para. 0118 teaches that an alert containing data related to the disease and its location data is sent.]
Sadilek may not explicitly teach
to a healthcare professional at the identified healthcare location, educational material relating to the disease.
Lu at Para. 0075, 0089 teaches that it was known in the art of computerized healthcare, at the time of filing, to provide a care provider associated with a patient having a disease with an alert including care and treatment recommendations
to a healthcare professional at the identified healthcare location, educational material relating to the disease. [Lu at Para. 0075, 0089 teaches determining whether an in-hospital patient has an infectious disease (COVID-19) and, if so, providing an associated care provider with an alert including care and treatment recommendations (educational material relating to the disease; see Spec. Pg. 14, Lns 11-13 describing educational material as symptoms or treatments/therapies).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the disease detection/prediction and reporting system of Sadilek to provide a care provider associated with a patient having a disease with an alert including care and treatment recommendations as taught by Lu, with the motivation of improving patient care.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Apreleva et al. (U.S. Pre-Grant Patent Publication No. 2017/0308678) which discloses a prediction system that uses open-source data to predict a number of disease events based on search activity within Google for symptoms.
Morrow et al. (U.S. Pre-Grant Patent Publication No. 2019/0295725) which discloses a mobile application that collects geo-locations associated with patients that have been diagnosed with a disease.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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