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 .
Status of Claims
Claims 1-20 are pending in the present application with claims 1 and 13 being independent.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Each of independent claims 1 and 13 recites "generate a plurality of mappings between the at least one medical object associated with the proteomics data and corresponding state changes for each user." While [0072] of U.S. Patent App. No. 16/647,000 ("the '000 Application"), of which the present application is a continuation, broadly recites "Different medical object types can be mapped to standardized ontologies for ease of interpretation," neither this nor any other portion in the '000 Application discloses generating a plurality of mappings between the at least one medical object associated with the proteomics data and corresponding state changes for each user as required in independent claims 1 and 13.
Each of independent claims 1 and 13 also recites "utilize the plurality of mappings with at least one correlation analysis model to identify at least one correlation between proteomics data and state changes across the plurality of users based at least in part on, for the plurality of users: the proteomics data, and at least one of: medical history, patient-reported outcomes, sensor data, or treatment data." The Examiner cannot identify in the '647 Application any discussion of a "correlation analysis model" that uses the above-noted mappings to identify correlations between proteomics data and state changes across the plurality of users based at least in part on, for the plurality of users: the proteomics data, and at least one of: medical history, patient-reported outcomes, sensor data, or treatment data.
Each of independent claims 1 and 13 also recites "updating at least one predictive model for modelling disease progression, health state change, or both for the patient based at least in part on the at least one correlation." Again, the Examiner cannot identify any portion of the '647 Application disclosing such limitations.
Claims 2, 3, and 14 call for use of mass spectrometry analysis which is not disclosed in the '647 Application.
Claims 4 and 15 call for determining a compounded health trait score based on a combination of health data and the medical object and the state changes. While [0089]-[0097] of the specification of the '647 Application generally discusses generation of health scores, neither they nor any other portions of the '647 Application appear to disclose all the limitations of these claims.
-Claims 6 and 17 recite how using the mappings with the at least one correlation analysis model includes employing a PCA. While [0112] of the specification of the '647 Application generally discusses use of PCA to discover latent variables that group patients together, neither this nor any other portion of the '647 Application appears to disclose all the limitations of these claims. These claims also recite how using the mappings with the at least one correlation analysis model includes employing an ML classifier. While [0134] of the '647 Application generally discusses use of machine learning software to help extract unselected candidate proteins that seem to change for that individual in synchrony with worsening/improvement of their disease status, neither this nor any other portion of the '647 Application appears to disclose all the limitations of these claims.
-Claim 7 recites how the ML classifier is an RF algorithm trained using labeled health status data which is not disclosed in the '647 Application.
-Claims 8 and 18 call for validating the at least one predictive model by applying the model to a reserved test dataset and calculating a predictive accuracy metric which is not disclosed in the '647 Application.
-Claims 9 and 19 recite how the predictive model includes a personalized disease progression model trained for each individual user which is not disclosed in the '647 Application.
-Claims 10 and 20 call for generating a graphical representation of the identified correlation for display via a user interface. While the figures illustrate various graphical representations, they do not appear to disclose displaying the identified correlation (i.e., the correlation between proteomics data and state changes, etc. as recited in the claims).
The remaining claims are rejected based on their dependency from one of the above rejected claims.
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:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-12 are directed to a method (i.e., a process) and claims 13-20 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 13 includes limitations that recite at least one abstract idea. Specifically, independent claim 13 recites:
A computer-implemented system comprising:
at least one processor in communication with at least one non-transitory computer- readable medium having computer instructions stored thereon, wherein the at least one processor, upon execution of the computer instructions, is further configured to:
generate at least one medical object associated with proteomics data derived from biological samples collected from a plurality of users;
detect state changes in health status for each user based on at least one other medical object of each user, the at least one other medical object representing health data comprising at least one of:
user-reported data,
sensor data,
electronic health record data,
laboratory data,
imaging data,
at least one bioassay,
genetic data, or
third-party data;
generate a plurality of mappings between the at least one medical object associated with the proteomics data and corresponding state changes for each user;
utilize the plurality of mappings with at least one correlation analysis model to identify at least one correlation between proteomics data and state changes across the plurality of users based at least in part on, for the plurality of users:
the proteomics data, and at least one of:
medical history,
patient-reported outcomes,
sensor data, or
treatment data,
update at least one predictive model for modelling disease progression, health state change, or both for the patient based at least in part on the at least one correlation.
The Examiner submits that the foregoing underlined limitations recite “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a person could practically generate (e.g., with pen and paper) one or more objects (e.g., data structures, data tables, graphical representations, etc.) associated with proteomics data (e.g., types, quantities, etc.) derived from biological samples collected from a plurality of users (e.g., protein data such as hormones (e.g., insulin, etc.), enzymes (e.g., lipase, etc.), etc.); detect state changes in health status for each user based on at least one other medical object of each user (e.g., in relation to user-reported data, sensor data, electronic health record data, laboratory data, imaging data, at least one bioassay, genetic data, or third-party data) such as by detecting an elevated level past a threshold, etc.; generate a plurality of mappings between the at least one medical object associated with the proteomics data and corresponding state changes for each user such as by including the state changes in/near the data structure associated with the medical object; utilize the plurality of mappings with at least one correlation analysis model to identify at least one correlation between proteomics data and state changes across the plurality of users based at least in part on, for the plurality of users, the proteomics data, and at least one of: medical history, patient-reported outcomes, sensor data, or treatment data (e.g., via analyzing trends in the plurality of mappings, using any appropriate distance/similarity algorithm, etc. (correlation analysis model) to identify relevant/similar/correlated proteomics data); and update at least one predictive model (e.g. algorithm) for modelling disease progression, health state change, or both for the patient based at least in part on the at least one correlation (e.g., increasing a weight of particularly relevant proteomics data in the model).
These recitations, under their broadest reasonable interpretation, are similar to how the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in the claims were characterized to be "mental processes" in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Claims “directed to collection of information, comprehending the meaning of that collected information, and indication of the results, all on a generic computer network operating in its normal, expected manner,” fail step one of the Alice framework. In re Killian, 45 F.4th 1373, 1380 (Fed. Cir. 2022). Claims directed to “collecting, analyzing, manipulating, and displaying data’’ are abstract. Univ. of Fla. Research Found., Inc. v. General Elec. Co., 916 F.3d 1363, 1368 (Fed. Cir. 2019). Claims directed to organizing, storing, and transmitting information determined to be directed to an abstract idea. Cyberfone Sys., L.L.C. v. CNN Interactive Grp., Inc., 558 F. App’x 988, 992 (Fed. Cir. 2014).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 4-6, 8, 11, 12, and 15-18 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 4 and 15 call for determining at least one combination of health data of the at least one medical object and generating at least one compounded health trait score based at least in part on the at least one combination and the state changes which is practically performable in the human mind with pen and paper ("mental processes") at such high level of generality (e.g., via mentally analyzing the combined health data and the state changes to determine a score).
-Claims 5 and 16 call for scheduling a collection of an additional biological sample in response to detecting a state change which a person can practically perform in the human mind with pen and paper ("mental processes") at such high level of generality.
-Claims 6 and 17 recite how utilizing the plurality of mappings with at least one correlation analysis model includes employing principal component analysis which constitutes "mathematical concepts" because it relates to mathematical calculations.
-Claim 8 and 18 call for calculating a predictive accuracy metric which is practically performable in the human mind with pen and paper ("mental processes") at such high level of generality.
Claim 11 recites how the sensor data includes data obtained from wearable devices which just further defines the "mental process" abstract idea discussed above.
-Claim 12 recites how the third-party data includes at least one of: third-party electronic health record data accessed via an application programming interface, third-party imaging data, third-party bioassay data, or third-party genetic data which just further defines the "mental process" abstract idea discussed above.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computer-implemented system comprising:
at least one processor in communication with at least one non-transitory computer- readable medium having computer instructions stored thereon, wherein the at least one processor, upon execution of the computer instructions, is further configured to:
generate at least one medical object associated with proteomics data derived from biological samples collected from a plurality of users;
detect state changes in health status for each user based on at least one other medical object of each user, the at least one other medical object representing health data comprising at least one of:
user-reported data,
sensor data,
electronic health record data,
laboratory data,
imaging data,
at least one bioassay,
genetic data, or
third-party data;
generate a plurality of mappings between the at least one medical object associated with the proteomics data and corresponding state changes for each user;
utilize the plurality of mappings with at least one correlation analysis model to identify at least one correlation between proteomics data and state changes across the plurality of users based at least in part on, for the plurality of users:
the proteomics data, and at least one of:
medical history,
patient-reported outcomes,
sensor data, or
treatment data,
update at least one predictive model for modelling disease progression, health state change, or both for the patient based at least in part on the at least one correlation.
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the computer-implemented system including the processor in communication with the non-transitory computer-readable medium having computer instructions stored thereon, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 13 and analogous independent claim 1 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 13 and analogous independent claim 1 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 2, 3, and 14 recite how generating the at least one medical object associated with the proteomics data includes applying mass spectrometry analysis (e.g., tandem mass spectrometry) to the biological samples which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea or altering how the abstract idea is carried out (see MPEP § 2106.05(h)).
-Claims 6, 7, and 17 recite how utilizing the plurality of mappings with at least one correlation analysis model includes employing an ML classifier (e.g., an RF algorithm using trained labeled health status data) which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
-Claim 8 and 18 call for validating the at least one predictive model by applying the model to a reserved test dataset which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
-Claims 9 and 19 recite how the predictive model includes a personalized disease progression model trained for each individual user which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Claims 10 and 20 call for generating a graphical representation of the identified correlation for display via a user interface which merely amounts to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 13 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the computer-implemented system including the processor in communication with the non-transitory computer-readable medium having computer instructions stored thereon, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 2, 3, and 14 recite how generating the at least one medical object associated with the proteomics data includes applying mass spectrometry analysis (e.g., tandem mass spectrometry) to the biological samples which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea or altering how the abstract idea is carried out (see MPEP § 2106.05(h)).
-Claims 6, 7, and 17 recite how utilizing the plurality of mappings with at least one correlation analysis model includes employing an ML classifier (e.g., an RF algorithm using trained labeled health status data) which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
-Claim 8 and 18 call for validating the at least one predictive model by applying the model to a reserved test dataset which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
-Claims 9 and 19 recite how the predictive model includes a personalized disease progression model trained for each individual user which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Claims 10 and 20 call for generating a graphical representation of the identified correlation for display via a user interface which merely amounts to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Therefore, claims 1-20 are ineligible under 35 USC §101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Int’l Pub. No. WO 2015/191562 to Francois (“Francois”) discloses collecting and structuring of health data including omics data of a group of individuals, where omics data can include proteomics data obtained by analysis of cells in a sample obtained from the subject/patient, and where the collected health/omics/proteomics data is configured into a structured data format (generating a medical object). Francois also discloses analyzing user symptom data (user-reported data) to detect a deterioration/change in a patient’s health status, (where such symptom data is in the form of another “medical object) and storing values/ranges of data collected by monitoring module (which can include proteomics data (and thus the above-noted structured medical object of the proteomics data) in connection with deterioration in patient health states (generating mappings/connections between the proteomic object and corresponding state changes).
U.S. Patent App. Pub. No. 2019/0073448 to Kochura et al. (“Kochura”) discloses method for identifying a change associated with a state of health of a user. In one embodiment, the method includes at least one computer processors receiving monitoring data associated with monitoring a user, where the monitoring data is generated by one or more sensors. The method further includes determining a state of health of the monitored user by analyzing the monitoring data utilizing one or more models. The method further includes determining a level of urgency based, at least in part, upon the determined state of health of the monitored user. The method further includes transmitting one or more respective notifications to one or more devices based, at least in part, on the determined state of health of the user and the corresponding level of level of urgency, and where a notification includes a determined state of health and the corresponding determined state of urgency associated with the monitored user.
NPL “Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare” to Tresp et al. (“Tresp”) discloses an overview of the recent trends toward digitalization and large-scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. The recent political initiatives designed to shift care delivery processes from paper to electronic are discussed, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. Newly developed networks of healthcare providers, research organizations, and commercial vendors to jointly analyze data for the development of decision support systems are discussed and the trend toward continuous healthcare where health is monitored by wearable and stationary devices is addressed; a related development is that patients increasingly assume responsibility for their own health data. Finally, recent initiatives toward a personalized medicine, based on advances in molecular medicine, data management, and data analytics are discussed.
NPL “A Pathway Proteomic Profile of Ischemic Stroke Survivors Reveals Innate Immune Dysfunction in Association with Mild Symptoms of Depression – A Pilot Study” to Nguyen et al. (“Nguyen”) discloses an examination of the serum proteome of stroke patients (n = 44, mean age = 63.62 years) and correlation of these with the Montgomery–Åsberg Depression Rating Scale (MADRS) scores at 3 months post-stroke. Overall, the patients presented with mild depression symptoms on the MADRS, M = 6.40 (SD = 7.42). A discovery approach utilizing label-free relative quantification was employed utilizing an LC-ESI–MS/MS coupled to a LTQ-Orbitrap Elite (Thermo-Scientific). Identified peptides were analyzed using the gene set enrichment approach on several different genomic databases that all indicated significant downregulation of the complement and coagulation systems with increasing MADRS scores. Complement and coagulation systems are traditionally thought to play a key role in the innate immune system and are established precursors to the adaptive immune system through pro-inflammatory cytokine signaling. Both systems are known to be globally affected after ischemic or hemorrhagic stroke. Thus, the results suggest that lowered complement expression in the periphery in conjunction with depressive symptoms post-stroke may be a biomarker for incomplete recovery of brain metabolic needs, homeostasis, and inflammation following ischemic stroke damage. Further proteomic investigations are now required to construct the temporal profile, leading from acute lesion damage to manifestation of depressive symptoms. Overall, the findings provide support for the involvement of inflammatory and immune mechanisms in PSD symptoms and further demonstrate the value and feasibility of the proteomic approach in stroke research.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHON A. SZUMNY whose telephone number is (303) 297-4376. The examiner can normally be reached Monday-Friday 7-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham, can be reached at 571-272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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