DETAILED ACTION
Status
This Final Office Action is in response to the communication filed on 2 April 2026. No claims have been cancelled, claims 1, 16, and 19-20 are amended, and no new claims have been added. Therefore, claims 1-20 are pending and presented for examination.
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 .
Response to Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment overcomes the rejection(s) under 35 USC § 112; therefore, the Examiner withdraws the rejection(s).
Applicant’s response does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes the rejection(s) under 35 USC §§ 102 and/or 103; therefore, the Examiner places new grounds of rejection.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
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 6, 14, and 16-17 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation "the periodic search" in the first line. There is insufficient antecedent basis for this limitation in the claim – parent claim 1 indicates “re-executing the identifying … periodically using the disease keywords”, but that may or may not necessarily be a search or periodic search. For purposes of examination, and based on the interview conducted 22 September 2025, the Examiner interpreting the periodic identifying step as a/the periodic searching.
Claim 14 recites the limitation "re-executing a search" in the fourth line. There is insufficient antecedent basis for this limitation in the claim – parent claim 1 indicates “re-executing the identifying … periodically using the disease keywords”, but that may or may not necessarily be a search that would be re-executed. For purposes of examination, and based on the interview conducted 22 September 2025, the Examiner interpreting the periodic identifying step as a/the periodic searching that would be re-executed.
Independent claim 16 recites the limitation " the periodic search" in the last element. There is insufficient antecedent basis for this limitation in the claim – the only indication of “periodic” activity is the re-executing of the filtering; however, 1) filtering is not searching, and 2) as indicated earlier, .at the interview conducted 22 September 2025, Applicant indicated the identifying step was searching; however, now the filtering appears to be the only possible antecedent basis for a “periodic search”.
Claims 17-18 depend from claim 16, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 17-18 are also indefinite.
Claim 20 is amended to recite “A non-transitory machine readable storage medium as in claim 1”, so the claim alleges to still depend from claim 1, but claim 1 is a method claim. The claim may be interpreted as being a method and still depending from claim 1, or the claim may be interpreted as being intended to (i.e., supposed to) depend from the non-transitory machine readable storage medium of claim 19. For purposes of examination, claim 20 is being interpreted as depending from claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 1-15), system (claims 16-18), and non-transitory computer-readable storage medium (claims 19-20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a method of searching for output defined by bioinformatic markers of a person, comprising: receiving a biologic risk profile for the person which defines a plurality of disease risks expressed in the bioinformatic markers of the person, wherein the bioinformatic markers include at least one of genetic, genome, family genome, data about environmental factors interacting with the person's genes, or biological information about the person; receiving a disease interest profile representing diseases in which the person has interests; retrieving disease keywords from the biologic risk profile and the disease interest profile by referencing a medical disease keyword data store; weighting the disease keywords using the disease interest profile; mapping the disease keywords to medical publications, medical trials, or medical treatments to form a medical mapping; weighting the disease keywords using treatment progress rules according to the medical mapping; selecting disease keywords with a weighting above a defined weight threshold; and identifying at least one medical treatment that is associated with the disease keywords using a machine learning model to process the disease keywords and respective weightings as features for the machine learning model; providing output records associated with the medical treatment; and re-executing the identifying the at least one medical treatment periodically using the disease keywords as initiated using a watcher service or cron job.
Independent claims 16 and 19 are analyzed similarly to claim 1 since claim 16 is directed to a system for searching for assets related to bioinformatic markers of a person, comprising: at least one processor; a memory device including instructions that, when executed by the at least one processor, cause the system to: perform the same or similar activities as at claim 1 above, and claim 19 is directed to a non-transitory machine readable storage medium for searching for assets related to a person's genome including instructions embodied thereon, wherein the instructions, when executed by at least one processor: perform the same or similar activities as at claim 1 above.
The dependent claims (claims 2-15, 17-18, and 20) appear to be encompassed by the abstract idea of the independent claims since they merely indicate the weighting basis (claims 2, 5, 13, and 15), the type of model (claim 3), obtaining a financial planning profile defining a risk, a goal, and a time frame and identifying an asset that approximates the profile (claim 4), the periods at which the search is re-executed (every minute, hour, day, week, month, year) (claim 6), storing the biological risk and disease interest profiles, and what data would be included in each (claims 7-8, and 18), using the disease keywords as input for the model to identify assets (claim 9), trading to purchase stocks (claim 10), areas of investment (claim 11), identifying medical professionals that may be associated with the disease keywords (claim 12), re-executing based on a notification of changed inputs (claim 13), identifying an asset using a matrix to match keywords and weightings (claim 17), and/or using machine learning or mapping (as at claim 1) (claim 20).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of searching for and identifying assets defined by, or related to, bioinformatic markers of a person; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Certain methods of organizing human activity (e.g. fundamental economic principles or practices such as hedging, insurance, mitigating risk; commercial or legal interactions such as agreements, contracts, legal obligations, advertising, marketing or sales activities/behaviors, or business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) as based on the financial planning and investing in, or of, assets and the management of business and/or personal behavior or relationships between people, as well as following rules or instructions as indicated by the profiles; and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) as based on the observations, evaluations, judgments and/or opinions related to mapping, weighting, and identifying assets.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are receiving profile information and using a machine learning model to process the disease keywords and respective weightings as features for the machine learning model (at claim 1), and the indication of a system comprising: at least one processor; a memory device including instructions that, when executed by the at least one processor, cause the system to: perform the activities (at claim 16), and a non-transitory machine readable storage medium including instructions embodied thereon, wherein the instructions, when executed by at least one processor to perform the activities (at claim 19). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction 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 (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The receiving is merely receiving information, such as over a network, where the receiving is only defined with regard to what data is received. The using a machine learning model to process the disease keywords and respective weightings as features for the machine learning model is actually merely applying mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) – as based at least on the weighting and use of a machine learning model (including at several dependent claims), as well as matrix analysis. Combining multiple groupings of abstract ideas is still just conveying an abstract idea.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The only apparent description of the computers and/or network being used is Applicant Fig. 7 describing the computer(s), and Fig. 6 describing the network – all of which are described as merely servers communicating over a network, i.e., general purpose computer use and/or generic computer(s).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
Claims 1-3, 5-9, 11-15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde et al. (U.S. Patent Application Publication No. 2020/0151157, hereinafter Hyde) in view of Jain et al. (U.S. Patent No. 11,240,329, hereinafter Jain) and in further view of Lavin et al. (U.S. Patent Application Publication No. 2017/0103111, hereinafter Lavin) .
Claim 1: Hyde discloses a method of searching for output defined by bioinformatic markers of a person, comprising:
receiving a biologic risk profile for the person (see Hyde at least at, e.g., ¶¶ 0050, “Publication A includes various information, including Gene X 311, Investigator A 312, Disease Y 313, Institution Z 314, and Compound D 315. By performing the data transformation and association process discussed above, Ingest Engine 130 generates Publication A Associated Data 320, which includes Gene X Profile 321, Investigator A Profile 322, Disease Y Profile 323, Institution Z Profile 324, and Compound D Profile 325. In some embodiments, Ingest Engine 130 may be configured to query this data to assess the quality of association. Examples of such queries include “Has this gene been previously associated with this disease?” 351; “Has this investigator been previously associated with this disease?” 352; “Has this institution been previously associated with this disease?” 353; and “Has this compound been previously associated with this disease?” 354”, and 0273, “Ingestion of additional data sources such as patient medical records, insurer information, Medicare inpatient statistics, or patient genetic information would enable the existing system to be leveraged to analyze, predict, and measure trends in physician performance, diagnosis and therapeutic decision trees, cost benefit analysis of treatment, comparative effectiveness, reimbursement trends, and cause of adverse events. The system can enable software applications for use by physicians, hospitals, administrators, policy makers, insurers, government officials, and patients. The system and ontologies are also specifically designed to enable production of consumer/patient driven application for interaction with individual electronic medical records and background trends and data” – the ingestion indicated at 0050 also including the data ingested as at 0273, including patient genetic information; citation hereafter by number only);
receiving a disease interest profile representing diseases in which the person has interests (0050, 0273, as above);
retrieving disease keywords from the biologic risk profile and the disease interest profile by referencing a medical disease keyword data store (0212, “a data transformation step 251 involving drawing associations between data sources within the profiles (e.g. Molecule, Compound, Gene/Mechanism, Disease, Personnel, University/Commercial Entity, and Intellectual Property as discussed above)”, see 0041-0047 as introducing or discussing these categories, 0213, “publication title and abstract are searched for the presence of the gene/disease/molecule/investigator/institution or its synonym(s)”, see also 0215 as an example: “the Innovation Engine utilizes a list of keywords which draw further inference about the meaning of an imported piece of data. For example, if a publication contains gene X, molecule Y, and the word “inhibit”, “inhibition” and/or “inhibitor,” that publication is recorded as a publication that my describe inhibition of gene X by molecule Y. Similarly, it may be recorded that molecule Y may be an inhibitor of gene X”);
weighting the disease keywords using the disease interest profile (0214, “associations of higher confidence receive elevated weighting when considering profile ranking”);
mapping the disease keywords to medical publications, medical trials, or medical treatments to form a medical mapping (0214, “if an investigator recorded from a publications author list has been previously associated with the disease, gene, and/or molecule found in that same publication, the publication receives a higher ‘confidence’ or ‘quality’ score”);
weighting the disease keywords using treatment progress rules according to the medical mapping (0214, weighting related to confidence and/or quality as above, see also 0217-0222 as scoring relevance of categories);
selecting disease keywords with a weighting above a defined weight threshold (0257, “the top-ranked quartile”); and
identifying at least one medical treatment that is associated with the disease keywords using a machine learning model to process the disease keywords and respective weightings as features for the machine learning model (0210, “machine learning may be applied to determine weighting of score, e.g., when the attributes and subvariables of a given drug have been calculated and these attributes are then compared quantitatively to the attributes of drugs that historically have achieved success through value creation events … [and] the Innovation Engine assigns the variables and attributes with the highest influence with more weight in calculating new Scores”, see also 0234 and 0236);
providing output records associated with the medical treatment (0239, “FIG. 6 shows an example of a search and view provided by the Innovation Engine…. Based on these search criteria, the Innovation Engine displays a graphical illustration 604 of the associated data”, see Fig. 6).
Hyde, however, does not appear to explicitly disclose that the biologic risk profile defines a plurality of disease risks expressed in the bioinformatic markers of the person, wherein the bioinformatic markers include at least one of genetic, genome, family genome, data about environmental factors interacting with the person's genes, or biological information about the person, and re-executing the identifying the at least one medical treatment periodically using the disease keywords as initiated using a watcher service or cron job. Where Hyde does disclose ingestion of patient information, including patient genetic information (Hyde at 0273), Jain teaches that “The user profile 1108 includes various types of user information that can be adjusted over time to reflect changes in user's condition … [and] can include demographic information (e.g., age, ethnicity, race, gender), medical history information (e.g., pre-existing conditions, intake encounter information from EHR software), and family history (e.g., conditions, disease-related risks, family origins). Other fields include … genetic analysis (e.g., gene variants) and microbiome data” (Jain at column:lines 9:41-64), and “FIG. 2A …[illustrates] a technique for prioritizing program opportunities based on two program scoring criteria relating to user preference and research value is shown. In this example, user data 202B indicates that the user is interesting in improving his/her personal fitness and genomic data indicating that he/she is part of an at-risk population for an infection to be addressed by a vaccine being investigated by researchers. For example, the genomic data indicates that the user is a geriatric patient (e.g., older than 65-years old) with a respiratory disease, which makes him/her at-risk for contracting severe symptoms for COVID-19.” Jain further teaches “machine learning models that are trained using training datasets of other users of the software platform and research requirement data for known research studies that were previously completed. The training datasets can specify features indicating user interests in programs, which allow the learning models 110C to predict programs that are likely to be of interests for the user 104. For example, the training datasets may specify associations between a set of user attributes and the types of programs that users having one or more of the set of attributes previously enrolled. In this example, the learning models 110C may use pattern recognition based on the attributes of the user 104 and the set of attributes to predict the programs that a user 104 is likely to find beneficial based on his/her attributes” (Jain at 10:59-11:6). Therefore, the base system and/or methods of searching as in Hyde would be predictably improved or modified by the risk data techniques indicated in Jain so as to yield the predictable result of using disease risk data so as to better enable treatment addressing the potential disease. As such, the Examiner understands and finds that to include disease risk in the patient profile is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to better enable treatment addressing the potential disease.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the searching of Hyde with the risk data of Jain in order to include disease risk in the patient profile so as to better enable treatment addressing the potential disease.
The rationale for combining in this manner is that to include disease risk in the patient profile is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to better enable treatment addressing the potential disease as explained above.
Hyde in view of Jain, however, does not appear to explicitly disclose re-executing the identifying the at least one medical treatment periodically using the disease keywords as initiated using a watcher service or cron job. Lavin, though, teaches searching “when dealing with highly complex databases such as, those dealing with the human genome and its interplay with the environment and disease manifestation/expression” (Lavin at 0131), where “The search results may also automatically update at fixed time intervals (e.g., five or ten minutes, daily, weekly, etc.) to ensure that the user (401) is viewing the most current search results available from the search engine (401), as cached by the system. The system (400) may also or alternatively preserve a history of search results, allowing the user (401) to store the search and see how the results change over time, such as by having the search automatically re-run and update according to a defined schedule or frequency, or when manually instructed” (Lavin at 0111 – at least the automatic re-run and update indicating at least a watcher service). Therefore, the base system and/or methods of searching as in Hyde in view of Jain would be predictably improved or modified by the scheduled searching techniques indicated in Lavin so as to yield the predictable result of scheduling recurrent searches (so as to ensure the most current information is available. As such, the Examiner understands and finds that to schedule recurrent searches using a watcher service or cron job is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to ensure the most current information is available.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the searching of Hyde in view of Jain with the scheduled searching of Lavin in order to schedule recurrent searches using a watcher service or cron job so as to ensure the most current information is available.
The rationale for combining in this manner is that to schedule recurrent searches using a watcher service or cron job is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to ensure the most current information is available as explained above.
Claim 2: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, wherein weighting the disease keywords according to the medical mapping further comprises weighting the disease keywords based in part on at least one of: a number of medical publication occurrences, an impact factor of medical references (Hyde at 0227, scientific factors, such as journal impact factors and quantity/quality of medical citations), developmental stage of a study, (Hyde at 0076, variable scoring categories include drug development stage), commercial authors (Hyde at 0063, variable scoring categories include commercial entities), existence of a clinical trial (Hyde at 0096, variable scoring categories include clinical trials), stage of a clinical trial, commercial clinical trials, existence of drug treatments, or commerciality of drug treatments. (Hyde at 0209, variable scoring categories commercial entity scoring).
Claim 3: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, wherein the machine learning model is at least one of a classifier machine learning model, a regression model, a clustering model, a neural network model, or a random tree forest model (Hyde at 0210, the machine learning including regression being applied).
Claim 5: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising weighting medical treatments as greater than medical trials or medical publications (Hyde at 0031, mentions in a press release outweigh mentions in a PubMed document, and see 0242, stating that indications in the claims outweigh publication, see 0225 stating that indications are the therapeutic value of a molecule for a specific disease, such as prostate cancer).
Claim 6: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising wherein the periodic search is re- executed at least one of every: minute, hour, day, week, month, quarter, or year (Lavin at 0111, “automatically update at fixed time intervals (e.g., five or ten minutes, daily, weekly, etc.)”, as combined above and using the rationale as at the combination above).
Claim 7: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising storing the biologic risk profile in a data store (Hyde at 0022, “The foundation of the Innovation Engine is a model, which may include Ingest Engine 130 and Download Curate Engine 140”, 0023, “Interfacing with the model is a controller (e.g. implemented on a server), which may include Service Bus 105, Scoring Engine 110, and Search Index Data Store 120. Service Bus 105 is in communication with Score Engine 110 and Search Index Data Store 120, and may be configured to handle multiple data connectors (e.g. for ingest) and search work flows”; Jain at 37:30-34, “The collected data 706 also includes user profile data 706B. The user profile data 706B may be stored at the server 110 and used to indicate information associated with user 104”, as combined above and using the rationale as at the combination above) wherein the biologic risk profile includes genetic information (Hyde at 0273, “patient genetic information”; Jain at 9:41-64, “The user profile 1108 includes various types of user information that can be adjusted over time to reflect changes in user's condition … [and] can include demographic information (e.g., age, ethnicity, race, gender), medical history information (e.g., pre-existing conditions, intake encounter information from EHR software), and family history (e.g., conditions, disease-related risks, family origins). Other fields include … genetic analysis (e.g., gene variants) and microbiome data”, as combined above and using the rationale as at the combination above) from at least one of: transcriptomics, proteogenomic testing, functional medicine tests, or microbiome tests (Jain at 9:41-64, “The user profile 1108 includes various types of user information that can be adjusted over time to reflect changes in user's condition … [and] can include demographic information (e.g., age, ethnicity, race, gender), medical history information (e.g., pre-existing conditions, intake encounter information from EHR software), and family history (e.g., conditions, disease-related risks, family origins). Other fields include … genetic analysis (e.g., gene variants) and microbiome data”, as combined above and using the rationale as at the combination above).
Claim 8: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising storing the disease interest profile in a disease interest data store (Hyde at 0022, “The foundation of the Innovation Engine is a model, which may include Ingest Engine 130 and Download Curate Engine 140”, 0023, “Interfacing with the model is a controller (e.g. implemented on a server), which may include Service Bus 105, Scoring Engine 110, and Search Index Data Store 120. Service Bus 105 is in communication with Score Engine 110 and Search Index Data Store 120, and may be configured to handle multiple data connectors (e.g. for ingest) and search work flows”; Jain at 37:30-34, “The collected data 706 also includes user profile data 706B. The user profile data 706B may be stored at the server 110 and used to indicate information associated with user 104”, as combined above and using the rationale as at the combination above), wherein the disease interest data store includes a list of diseases from at least one of: diseases afflicting family or friends, diseases related to an investor's profession or business, or diseases related to a personal interest of an investor (Jain at 9:41-64, “The user profile 1108 includes various types of user information that can be adjusted over time to reflect changes in user's condition … [and] can include demographic information (e.g., age, ethnicity, race, gender), medical history information (e.g., pre-existing conditions, intake encounter information from EHR software), and family history (e.g., conditions, disease-related risks, family origins). Other fields include … genetic analysis (e.g., gene variants) and microbiome data”, as combined above and using the rationale as at the combination above).
Claim 9: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising identifying assets that are companies providing medical treatments for disease keywords by using the disease keywords as features submitted to a machine learning model in order to identify companies associated with the disease keywords (Hyde at 0013, “The provided systems and methods may be leveraged in a multitude of contexts factorially created by the array of entities being defined. For example, the present subject matter may be used to ask questions of people in a disease, drugs and genes, research topics and companies, etc. This creates value for users in all realms, including for example, life science, from basic to clinical science, as well as within the business context of biopharmaceuticals, life science tools, diagnostics, and patient care”, see also 0234 and 0236).
Claim 11: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising assets that are investments in non-profit organizations, investments in research, investments in charitable foundations or charitable gifts (Hyde at 0076, variable scoring categories include drug development stage, such as capital investment from venture capital and angel investors).
Claim 12: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising identifying medical professionals using the machine learning model which may be associated with the disease keywords and weightings for the person (Hyde at 0236, machine learning is used to determine the certain probabilities based on factors, such as the association between keywords is ranked, for example by giving a higher confidence to a disease when an investigator has been previously associated with the same disease, see 0214).
Claim 13: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising weighting disease keywords based on supplemental factors that are at least one of: regulatory burden, regulatory path, pre- market approval, predicate approval, minimal regulatory burden, physician adoption, insurance reimbursement, or investor interest (Hyde at 0076, variable scoring categories include drug development stage, such as capital investment from venture capital and angel investors).
Claim 14: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising:
receiving a notification that data inputs have changed for at least one of: disease keywords, medical publications, medical trials, or medical treatments (Hyde at 0235, “Innovation Engine includes an internal quality assurance alert system which automatically notifies a user when significant changes occur to Scores for, for example, molecules, key opinion leaders, genes/mechanisms, diseases, Universities, and Commercial Entities”, see also 0212, “once a publication or patent becomes associated with a profile, all associated data becomes a part of that profile. In some embodiments, one or more associative rules are run upon database updates daily”); and
re-executing a search to identify at least one asset (Hyde at 0236, calculating the probability of a transaction occurring based on the factors, i.e., changed score, which affect the probability, see also 0212, “once a publication or patent becomes associated with a profile, all associated data becomes a part of that profile. In some embodiments, one or more associative rules are run upon database updates daily”).
Claim 15: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, further comprising weighting the disease keywords using a disease risk to the person (Jain at 14:56-15:3, weighting patient characteristics, i.e., keywords, related to diseases such as respiratory disease and associated risk of COVID-19, as combined above and using the rationale as at the combination above).
Claim 19 is rejected on the same basis as claims 1 and 6 above since Hyde discloses a non-transitory machine readable storage medium for searching for assets related to a person's genome including instructions embodied thereon, wherein the instructions, when executed by at least one processor perform the same or similar activities as at claim 1 (see Hyde at 0012).
Claims 4, 10, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Jain and in further view of Lavin and in still further view of Williams et al. (U.S. Patent No. 5,999,918, hereinafter Williams).
Claim 4: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, but does not appear to explicitly disclose further comprising: obtaining a financial planning profile for the person which defines an amount of risk, a financial monetary goal and a time frame the person desires for investments; and identifying at least one asset with an amount of risk that approximates the financial planning profile. Williams, though, teaches “client profile information is gathered at step 135. As will be described in more detail below, this allows the present invention to store information about the client's age, income, financial goals, net worth, tax status, risk preferences and so forth. Once a client profile has been created, a preferred embodiment of the present invention creates an evaluation function for that profile. As will be apparent to those skilled in the art, an evaluation function could be as simple as finding maximum investment return, maximum risk-adjusted return, minimum loss, and so on. Alternatively, an evaluation function could be arbitrarily complex, taking into account such additional factors as after-tax returns, transaction cost models, client aversion to volatility or risk of loss over time, and so on” (Williams at 9:5-18) where “a profile objective of ‘value-style common stocks’ could be implemented by screening a securities database for stock value criteria such as low price/earnings (P/E) ratio and high dividend yield. Similarly, a time horizon (time until the purpose of the investment changes) that is short implies the need for low risk investments to preserve capital” (Williams at 18:45-50). Therefore, the base system and/or methods of searching as in Hyde in view of Jain and in further view of Lavin would be predictably improved or modified by the planning profile of Williams that indicates risk and time frame desires so as to yield the predictable result of using timing and financial risk data so as to emulate the goals and comfort levels of the customer. As such, the Examiner understands and finds that to include a planning profile indicating acceptable risk and time frame desires of the customer is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to emulate the goals and comfort levels of the customer by use timing and financial risk data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the searching of Hyde in view of Jain and in further view of Lavin with the planning profile of Williams in order to include a planning profile indicating acceptable risk and time frame desires of the customer so as to emulate the goals and comfort levels of the customer by use timing and financial risk data.
The rationale for combining in this manner is that to include a planning profile indicating acceptable risk and time frame desires of the customer is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to emulate the goals and comfort levels of the customer by use timing and financial risk data as explained above.
Claim 10: Hyde in view of Jain in further view of Lavin discloses the method as in claim 9, but does not appear to explicitly disclose further comprising executing a trading process to purchase at least one asset that is stock in the companies identified. Williams, though, teaches using a planning profile that indicates risk and time frame desires so as to emulate the goals and comfort levels of the customer (Williams at 9:5-18 and 18:45-50), where “the virtual investment advisor performs its security selection and trading processes at step 245. The investment support system may be informed at step 250 that the virtual investment advisor is recommending changes in the client's portfolio. In a preferred embodiment, the investment support system can obtain the client's approval for the changes” (Williams at 9:62-10:1) and “once the trade has been executed and confirmed to the investment support system, at step 270, the results of the trade are analyzed and reported to the user.” (Williams at 10:13-15). Therefore, the base system and/or methods of searching as in Hyde in view of Jain and in further view of Lavin would be predictably improved or modified by the trade approval and execution of Williams so as to yield the predictable result of recommending and executing investments to facilitate a user’s investment goals. As such, the Examiner understands and finds that to purchase stock(s) as recommended or identified is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to recommend and execute investments to facilitate a user’s investment goals.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the searching of Hyde in view of Jain and in further view of Lavin with the stock purchase of Williams in order to purchase stock(s) as recommended or identified so as to recommend and execute investments to facilitate a user’s investment goals.
The rationale for combining in this manner is that to purchase stock(s) as recommended or identified is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to recommend and execute investments to facilitate a user’s investment goals as explained above.
Claim 16 is rejected on the same basis as claims 1, 4, and 6 above since Hyde discloses a system for searching for assets related to bioinformatic markers of a person, comprising: at least one processor; a memory device including instructions that, when executed by the at least one processor, cause the system to perform the same or similar activities as at claims 1 and 4 above (see Hyde at least at 0012) and filtering out at least one asset with an amount of risk that does not match the financial planning profile (where following the profile including risk preferences and screening the database indicates filtering for risk not matching the profile).
Claim 17: Hyde in view of Jain in in further view of Lavin in still further view of Williams discloses the system as in claim 16, wherein at least one asset is identified using the disease keywords with weightings as entries in a matrix to match assets to disease keywords with weightings (Hyde, 0210: machine learning includes regression, which include matrices as a dataset, see 0013 and 0198).
Claim 18: Hyde in view of Jain in further view of Lavin in still further view of Williams discloses the system as in claim 16, further comprising storing the biologic risk profile in a data store (Hyde at 0022, “The foundation of the Innovation Engine is a model, which may include Ingest Engine 130 and Download Curate Engine 140”, 0023, “Interfacing with the model is a controller (e.g. implemented on a server), which may include Service Bus 105, Scoring Engine 110, and Search Index Data Store 120. Service Bus 105 is in communication with Score Engine 110 and Search Index Data Store 120, and may be configured to handle multiple data connectors (e.g. for ingest) and search work flows”; Jain at 37:30-34, “The collected data 706 also includes user profile data 706B. The user profile data 706B may be stored at the server 110 and used to indicate information associated with user 104”, as combined above and using the rationale as at the combination above), wherein the biologic risk profile includes genetic information (Hyde at 0273, “patient genetic information”; Jain at 9:41-64, “The user profile 1108 includes various types of user information that can be adjusted over time to reflect changes in user's condition … [and] can include demographic information (e.g., age, ethnicity, race, gender), medical history information (e.g., pre-existing conditions, intake encounter information from EHR software), and family history (e.g., conditions, disease-related risks, family origins). Other fields include … genetic analysis (e.g., gene variants) and microbiome data”, as combined above and using the rationale as at the combination above) from at least one of: transcriptomics, proteogenomic testing, functional medicine tests, or microbiome tests (Jain at 9:41-64, “The user profile 1108 includes various types of user information that can be adjusted over time to reflect changes in user's condition … [and] can include demographic information (e.g., age, ethnicity, race, gender), medical history information (e.g., pre-existing conditions, intake encounter information from EHR software), and family history (e.g., conditions, disease-related risks, family origins). Other fields include … genetic analysis (e.g., gene variants) and microbiome data”, as combined above and using the rationale as at the combination above).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hyde in view of Jain and in further view of Lavin and in still further view of Scott et al. (U.S. Patent Application Publication No. 2017/0116379, hereinafter Scott).
Claim 20: Hyde in view of Jain in further view of Lavin discloses the method as in claim 1, but does not appear to explicitly disclose further comprising using the disease keywords to create a vector in a K-means model which is in turn used to identify similar clusters to determine treatments or assets for an individual. Scott, though, teaches “a computing system for optimizing an individual's medical treatment” (Scott at 0009) such as “a method of optimizing an individual's treatment using the system provided in FIG. 1 begins with a similarity search that is performed to define a cohort of similar individuals on the basis of diseases and conditions and genome data availability. A grouper algorithm may be applied to automatically generate disease and clinical attribute groupings amongst the individuals in the comparison data set. A multi-dimensional vector is generated for each individual's clinical profile. In some embodiments, clustering and nearest-neighbor algorithms may then be applied to these high-dimensional data, such as k-means clustering with clusters with radii containing the individual in question, including greedy clustering, Lloyd's algorithm in the case of k-means clustering, and c-approximate r-Near Neighbor algorithms” (Scott at 0030), to help “enable determination of disease and condition risk and what sorts of medication an individual may take prophylactically for prevention” (Scott at 0068). Therefore, the base system and/or methods of searching as in Hyde in view of Jain in further view of Lavin would be predictably improved or modified by the vectoring techniques indicated in Scott so as to yield the predictable result of using K-means vector analysis for clustering in order to help enable disease risk determination and treatment. As such, the Examiner understands and finds that to generate a K-means vector to identify clusters for treatment determination is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to help enable determination of disease risk and treatment.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the searching of Hyde in view of Jain in further view of Lavin with the K-means vectoring to identify clusters of Scott in order to generate a K-means vector to identify clusters for treatment determination so as to help enable determination of disease risk and treatment.
The rationale for combining in this manner is that to generate a K-means vector to identify clusters for treatment determination is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to help enable determination of disease risk and treatment as explained above.
Response to Arguments
Applicant's arguments filed 2 April 2026 have been fully considered but they are not persuasive.
Applicant first argues the 101 rejection (Remarks at 1-7), repeating the same arguments almost verbatim – the only exception(s) being that the claim amendments are included in the current remarks, the pages are numbered as being before the claims (i.e., pages 1-7 instead of pages 8-15 at the 18 August 2025 Remarks), and the term “interests” (now at the second-to-last line of page 3, but at page 11, line 3 of the 18 August 2025 Remarks). Therefore, the Examiner relies on the answers provided as before, while updating the page references:
Applicant first argues the 101 rejection, first alleging that “The asserted abstract idea … is not included in the classification of ‘Certain methods of organizing human activity’. The present claims are not directed to social activities, teaching and people following rules or instructions.” (Id. at 2). However, the Examiner has included the basis for inclusion in the groupings as explained at the rejection above. Applicant then repeats much of claim 1 and again asserts that “claim 1 … is not an abstract idea that falls within any of the groupings defined by the 2019 Revised Guidance and MPEP 2106.04(a)”, but without any further explanation or reasoning.
Applicant then argues integration into a practical application (Remarks at 3-4), alleging “improvements to search technology which can identify treatments and products using weightings linked to a person's DNA, their disease interests, retrieving disease keywords from the biologic risk profile and the disease interest profile by referencing a medical disease keyword data store and treatment progress rules” (Id. at 3-4, emphasis at original). However, there is no improvement to the technology – if there is an improvement, that improvement is really just what data and/or sources is/are being considered for the search, and/or repeating the searching. The Examiner notes that SAP v. Investpic indicates that even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting (SAP v. Investpic, slip op at p. 2, line 22 – p. 3, line 13, 898 F.3d 1161, 1162 (Fed. Cir. 2018). And even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract (Id., slip op. at p. 10, lines 18-24, 898 F.3d 1161, 1167).
As a further explanation regarding the breadth of Applicant’s claims, the claims indicate receiving a person’s profiles – the profiles at the claims may be a “biological risk profile” and a “disease interest profile”, but that really just limits (if at all) the data that may be considered as in, or appropriate to, the profile. The claims then weight and map keywords in the received profile, so as to select keywords above a weight threshold and identify an asset associated with the keywords. The court in Electric Power Group indicates, as part of their analysis, that “[i]nformation as such is an intangible. Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas" (Electric Power Group, p. 7, internal citations omitted).
Applicant additionally alleges that “the search needs to be updated periodically (e.g., every day or week) and a human will not be mentally or physically able to search through a massive amount of material (e.g., every day or week) by physically reading medical publications. Further, humans cannot repetitively perform the searching needed (e.g., every few minutes, every day, every week).” (Remarks at 4). However, first, Applicant’s argument does not consider the full scope of the claim (Applicant’s argument omits consideration of some longer time periods included in the claims) and the argument is to performing activities that are not indicated as being re-executed, such as the retrieval and mapping of keywords – only the search itself is recited as re-executed. Second, people can schedule activities such as searches – for several decades the Examiner has been (and has been taught by others to) merely setting a calendar reminder to perform activities that can include renewing or repeating (i.e., re-executing) a search.
Applicant argues that “it is not practical or even possible to assemble multiple experts …” (Id. at 5); however, the argument is not commensurate with the claim – the claim does not gather or assemble experts, the claim merely is “referencing a medical disease keyword data store”.
Applicant then argues that “The treatment progress rule has not been shown in the cited art, and the treatment progress rule is a practical application of this technology” (Id.); however, this is prior art analysis – that anything not (allegedly) shown in the prior art is eligible. Besides the precedent that indicates this is not correct, the plethora of issued patents (that therefore have overcome the prior art) that have been found to merely be abstract ideas indicates the separation of prior art analysis and eligibility analysis.
Applicant then argues that “this technology improves search technology and provides concrete and practical technological benefits, such as enhanced precision in treatment or asset identification based on bioinformatics” (Id. at 6). However, the claims are not directed to, or providing, an improved search technology – the claims are using known technology (albeit in the medical and disease arena) to provide what may be an improved abstract idea, but as indicated by SAP above, an improved abstract idea is still an abstract idea.
Applicant then argues that the claimed invention “transforms bioinformatic data into a multidimensional insight generation tool, combining the health, investment, and financial planning domains. For example, it allows an individual with a known BRCA1 mutation to: 1) Discover top-ranked oncologists specializing in that mutation; 2) Identify active phase III trials for breast cancer treatments; 3) Evaluate biotech companies focusing on related gene therapies; and 4) Align those opportunities with a person's personal investment risk tolerance and time horizon. This is actionable intelligence rooted in real-time, computable biological data” (Id. at 7). However, the same principals can be applied to, or said for, almost any possible investment opportunity – a person can discover and gather all sorts of information regarding the latest or cutting-edge developments to identify possibilities, evaluate companies, and assess the associated risks with their risk tolerance levels. There apparently is a huge industry established for gathering and assessing all the data in relation to helping people and advising people for their investment and retirement accounts – the claims appear to merely apply the same principals to medical and disease concerns.
Applicant then argues analogy to Example 42 (Id. at 7-8); however, the Examiner notes that shortly after Example 42 was introduced (7 January 2019), the Federal Circuit in University of Florida Research Foundation, Inc. v. General Electric Co. 916 F.3d 1363, slip op. 2018-1284 (Fed. Cir. 2019) (on 26 February 2019) indicated that very highly similar claims were ineligible as directed to an abstract idea. Therefore, analogy to Example 42 to indicate eligibility is not persuasive.
Applicant then argues the prior art (Remarks at 8-12), apparently repeating verbatim (except the addition of the last words “and 19” at p. 12) the same arguments as at the 18 August 2025 Remarks (even asserting some claim terminology of the earlier claims that has now been removed). The Examiner notes that Applicant argues “The arguments for claim 1 also apply to claim 16 and its dependent claims 17, 18, and 19” (Remarks at 12); however, claim 9 is an independent claim – it does not depend from claim 16 as Applicant alleges.
Therefore, the Examiner relies on the answers provided as before (at least at the 18 August 2025 Remarks), while updating the page references and including the rejection as including Lavin in the combination:
Applicant first alleges that “Hyde does not teach or suggest ‘re-executing the searching periodically using the disease keywords’ as in claim 1 or ‘wherein the periodic search is re-executed at least one of every: minute, hour, day, week, month, or quarter’ as in dependent claim 6” (Id. at 8). However, Hyde is not relied on for the periodic re-execution of searching – Lavin is (and was) combined in at the rejection for this aspect of the claims.
Applicant then repeats previous arguments, i.e., that “Hyde actually teaches an investigator profile”, but does not indicate “Hyde as providing a biologic risk profile” (Id. at 9); however, Hyde at 0273 (as cited above and previously) further indicates a more complete indication of what information is ingested – including patient-specific information that includes genetic information and indicate therapy or treatment benefits and decisions, among other things, for that patient’s use. Applicant then again alleges that “The Office Action further cited Jain as teaching a profile that "defines a plurality of disease risks expressed in the bioinformatic markers of the person", see column 8, lines 41-53” (Remarks at 9); however, the rejection actually only cites to “Jain at column:lines 9:41-64” and “Jain at 10:59-11:6” (18 April 2025 Final Office Action at pp. 12-13, and again at the 2 October 2026 Non-Final Office Action at pp. 15-16). Applicant acknowledges that “Jain … teaches ‘genetic analysis (e.g., gene variants)’ in a profile for aligning participants and researchers for research studies, see column 9, lines 14 – 32”, and the Examiner notes that in order to perform genetic analysis, genetic information must necessarily be provided – which is all the claim requires.
Applicant then again argues that “Hyde also does not access a medical disease keyword data store. Hyde is mapping associations in the databases of publications. The claimed technology is linking biological risk and a disease interest profile (person) to the databases of medical publications, medical trials, or medical treatments” (Remarks at 10). However, there is no defined limit on what constitutes a ”medical disease keyword data store” – this is apparently any store of data that has keywords related to medical diseases, which is what databases of medical and/or disease related publications are.
Applicant then argues that “linking additional publications into a profile is not the same as using a medical disease keyword store to obtain disease keywords from a biologic risk profile and a disease interest profile” (Id.), but there is no definite indication of what is, or what is not, included in or constituting a “medical disease keyword store” – the biologic risk and disease interest profiles themselves are relating to medical issues, medicine, disease, etc., and therefore can be considered a form of “medical disease keyword store”.
Applicant then argues that “Hyde teaches weighting of an ‘author list that was previously associated with a disease’…. [which] is not the same as and does not suggest weighting keywords based on a disease interest profile” (Id.). However, an author’s name may be, and often or sometimes is, a keyword. In fact, claim 2 specifically indicates “commercial authors” as one of the weighting factors. Applicant argues with respect to 101 and eligibility that the claims are related to “assembl[ing] … experts” (i.e., that humans cannot do that), and accessing or using their “combined expertise” (Remarks at 5), but when the art actually says it will weight the insights provided by those that work with a particular disease (i.e., e.g., experts), Applicant then apparently contradicts themselves and/or reverses their position by indicating that weighting those persons is NOT what the claims would do. As such, the arguments appear contradictory, and reasonably negate each other.
Applicant then argues that the claimed mapping and progress rules is somehow different (Id. at 11), but again, even per Applicant’s arguments – weighting a person that has worked in the field of, or researched, a disease or treatment longer, or is more prominent in the field, whose research is mentioned more often, etc. is mapping keywords to treatment progress. There is apparently no actual treatment progress being measured, there is no limit on what the keywords are, there is no limit on what the weighting is (e.g., positive or negative, how strong, etc.) – it is just weighting the terms for searching and results.
Therefore, the Examiner is not persuaded by Applicant’s arguments.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Heavey et al., Discovery and delivery strategies for engineered live biotherapeutic products, Trends in Biotechnology, March 2022, Vol. 40, No. 3 https://doi.org/10.1016/j.tibtech.2021.08.002, downloaded 14 April 2025 from https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/transcriptomics, indicating “Transcriptomics is the study of the structure, function, and evolution of the transcriptome (i.e., the entirety of RNA transcripts produced by the genome) of a given organism or community of organisms under a variety of conditions” (at 356).
CronJob, from Kubernetes, undated, downloaded 2 June 2026 from https://kubernetes.io/docs/concepts/workloads/controllers/cron-jobs/, indicating a “CronJob is meant for performing regular scheduled actions such as backups, report generation, and so on” (at 1).
Automation with Cron job on Centos 8, screenshot of first page downloaded 2 June 2026 from Automation with Cron job on Centos 8 – Comtronic Blog, dated 6 April 2020, indicating “Cron is a time-based job scheduling daemon found in Unix-like operating systems, including Linux distributions. Cron runs in the background and tasks scheduled with cron, referred to as “cron jobs,” are executed automatically, making cron useful for automating maintenance-related tasks” (at 1).
Streat et al. (U.S. Patent Application Publication No. 2013/0080184, hereinafter Streat) indicates “Various embodiments relate generally to collecting, discovering, and managing healthcare information, care and treatment information for patients, friends, families, care-givers, health professionals, researchers, and social networks thereof” (Streat at 0002), where “the various embodiments, the HIS maybe configured to collect data from a plurality of sources, in at least one of the various embodiments, the collection process, may be initiated in a variety of ways, including, pre-planned periodic intervals (cron jobs), event driven triggers (e.g., internal processes, user behavior, and the like), aging of previously collected data, or the like” (Streat at 0120).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
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