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
This action is in reply to the application filed on January 16, 2025.
2. Claim(s) 1-20 are currently pending and have been examined.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1, 8 and 15 are directed to an abstract idea, explicitly analyzing physiological data to predict a medical condition and initiate a response.
Independent Claim 1 recites “receiving, a first set of patient data corresponding to a first plurality of physiological sensor measurements associated with one or more urinary parameters; determining a time series of measurements for the one or more urinary parameters, wherein determining the time series of measurements comprises: detecting a set of constant values in measurement data; in response to detecting the set of constant values in the measurement data, abstaining from processing the measurement data for a preset interval of time; and collecting, after passage of the interval of time a second set of patient data corresponding at least partially to a second plurality of urinary parameters; based on the first set of patient data, the measurement data, the second set of patient data, the second plurality of physiological measurements, and the time series of measurements, generating a forecast of a likelihood of urolithiasis over a future time interval; and based on the generated forecast, initiating one or both of (a) an intervening action and (b) generation, instructions to modify a treatment program.”
Independent Claim 8 recites “receiving, a first set of patient data corresponding to a first plurality of physiological sensor measurements associated with one or more urinary parameters; determining a time series of measurements, from the plurality of physiological measurements, for the one or more urinary parameters, wherein determining the time series of measurements comprises: detecting a set of constant values in measurement data corresponding to the plurality of physiological measurements; in response to detecting the set of constant values in the measurement data, abstaining from processing the measurement data for a preset interval of time; and collecting, after passage of the interval of time a second set of patient data corresponding at least partially to a second plurality of physiological measurements associated with the one or more urinary parameters; based on the first set of patient data, the measurement data, the second set of patient data, the second plurality of physiological measurements, and the time series of measurements, generating a forecast of a likelihood of urolithiasis over a future time interval; and based on the generated forecast, initiating one or both of (a) an intervening action and (b) generation, instructions to modify a treatment program.”
Independent Claim 15 recites “receiving, a first set of patient data corresponding to a first plurality of physiological measurements associated with one or more urinary parameters; determining a time series of measurements, from the plurality of physiological measurements, for the one or more urinary parameters, wherein determining the time series of measurements comprises: detecting a set of constant values in measurement data corresponding to the plurality of physiological measurements; in response to detecting the set of constant values in the measurement data, abstaining from processing the measurement data for a preset interval of time; and collecting, after passage of the interval of time a second set of patient data corresponding at least partially to a second plurality of physiological measurements associated with the one or more urinary parameters; based on the first set of patient data, the measurement data, the second set of patient data, the second plurality of physiological measurements, and the time series of measurements, generating a forecast of a likelihood of urolithiasis over a future time interval; and based on the generated forecast, initiating one or both of (a) an intervening action and (b) generation, instructions to modify a treatment program.”
The limitations of Claims 1, 8 and 15, as drafted, under its broadest reasonable interpretation, covers the performance of a “Mental Process” which are concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and “Mathematical Concepts” which are concepts performed that encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components. That is, other than reciting, “non-transitory computer-readable medium, processor(s), electronic digital memory, medical records computer system, physiological sensor measurements, collecting… second set of patient data, initiating an intervening action, generation of instructions to modify a treatment program” nothing in the claim element precludes the step from practically being performed in the mind and generating a forecast of a likelihood using predictive modeling and statistical analysis as abstract mathematical concepts. For example, but for the “processor” language, “receiving” receiving, a first set of patient data corresponding to a first plurality of physiological sensor measurements associated with one or more urinary parameters. Similarly, the determining a time series of measurements for the one or more urinary parameters, covers being performed in the mind and generating a forecast of a likelihood using predictive modeling and statistical analysis as abstract mathematical concepts, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers being performed in the mind and generating a forecast of a likelihood using predictive modeling and statistical analysis as abstract mathematical concepts, but for the recitation of generic computer components, then it falls within the “Mental Process and Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “non-transitory computer-readable medium, processor(s), electronic digital memory, medical records computer system, physiological sensor measurements, collecting… second set of patient data, initiating an intervening action, generation of instructions to modify a treatment program” to perform all of the “receiving, determining, detecting, collecting, generating and initiating” steps. The “non-transitory computer-readable medium, processor(s), electronic digital memory, medical records computer system, physiological sensor measurements, collecting… second set of patient data, initiating an intervening action, generation of instructions to modify a treatment program” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Claim 1 has the following additional elements (i.e., at least one processor / set of hardware processors, electronic digital memory, medical records computer system, physiological sensor measurements (data sourced from sensors), collecting… second set of patient data (data acquisition step), initiating an intervening action, generation of instructions to modify a treatment program, treatment program stored at a memory). Claim 8 has the following additional elements (i.e., non-transitory computer-readable medium, processor(s), electronic digital memory, medical records computer system, physiological sensor measurements, collecting… second set of patient data, initiating an intervening action, generation of instructions to modify a treatment program). Claim 15 has the following additional elements (i.e., one or more hardware processors, electronic digital memory, medical records computer system, physiological sensor measurements, data collection components (implicit via receiving/collecting steps), intervening action capability, treatment program stored in memory). Looking to the specification, these components are described at a high level of generality (¶ 61 and 63; One embodiment of interface 142 takes the form of a graphical user interface and application, which may be embodied as a software application (e.g., decision support application 140) operating on one or more mobile computing devices, tablets, smartphones, front-end terminals in communication with back-end computing systems, laptops, or other computing devices. In an embodiment, the application includes the PowerChart® software manufactured by Cerner Corporation. In an embodiment, interface 142 includes a Web-based application (which may take the form of an applet or app) or set of applications usable to manage user services provided by an embodiment of the technologies described herein.). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, although the claims add “[storage]” steps, it is only considered as insignificant extrasolution activity. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-7, 9-14 and 16-20). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mental Process and Mathematical Concepts,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No.: US 20150339442 A1 to OLEYNIK in view of Pub. No.: US 20150031992 A1 to Monga et al.
As per Claim 1, OLEYNIK teaches a computer-implemented method comprising:
-- receiving, via at least one processor of a set of hardware processors that is associated with an electronic digital memory at a medical records computer system, a first set of patient data corresponding to a first plurality of physiological sensor measurements associated with one or more urinary parameters (see OLEYNIK paragraphs 121, 139 and 143 (e.g. urinary system); FIG. 6 is an illustration of an exemplary process flow of 24/7 (168) monitoring patients relative to the objective medical data with the steps with respect to FIG. 11. The patient is monitored by a portable medical monitoring device 150 (e.g. smartphones, tablets, glasses/goggles, watches, wearable devices, medical stickers), or electronic underwear 170 attached with electronic device (e.g. sensor), or textile electrodes fabric garment 172, where the portable medical monitoring device 150 operates in conjunction with an implantable device as shown and described in FIGS. 7A-C as a method to read a patient's blood pressure, heart rate, and other vital medical data.
At step 352, the smartphone 150 in turn transmits the real time medical data to the medical main server 12. At step 354, the intelligent medical engine 14 is configured to analyze the real time objective medical data of the patient 148 relative to the patient's 148 previously stored objective medical data in the central database 16 to determine if the comparison would invoke a medical alert to the patient's medical doctor and to the patient. If one of the parameters in the patient's 148 real time objective medical data exceeds a threshold of the patient's 148 previously stored objective medical data, then the intelligent medical engine 14 is configured to send a medical alert to a medical professional associated with patient 148 and to the patient's 148 portable medical monitoring device 150 to inform the patient 148 at step 360. At the same time in step 356, the intelligent medical engine 14 is configured to store the resulting real time objective medical data from the patient 148 in the central database 16 by adding the resulting objective medical data to the existing patient's 148 EMR System.);
determining a time series of measurements, from the plurality of physiological sensor measurements, for the one or more urinary parameters, wherein determining the time series of measurements comprises (see OLEYNIK paragraphs 110, 121, 139 and 143; timeline junctures):
-- detecting a set of constant values in measurement data corresponding to the plurality of physiological sensor measurements (see OLEYNIK paragraphs 110, 121, 139 and 143; After a period of time, the same patient in third scenario 157a in which the post-treatment significant parameters measurement indicates that the disease amount has remained unchanged);
-- in response to detecting the set of constant values in the measurement data, abstaining from processing the measurement data for a preset interval of time (see OLEYNIK paragraphs 110, 121, 139 and 143; After a period of time, the same patient in third scenario 157a in which the post-treatment significant parameters measurement indicates that the disease amount has remained unchanged, producing a new corresponding treatment protocol C (2nd-line therapy/change to a different drug) 157b.); and
-- collecting, after passage of the interval of time and via the at least one processor or the set of hardware processors associated with the electronic digital memory, a second set of patient data corresponding at least partially to a second plurality of physiological sensor measurements associated with the one or more urinary parameters (see OLEYNIK paragraphs 121, 139 and 143 (e.g. urinary system); FIG. 6 is an illustration of an exemplary process flow of 24/7 (168) monitoring patients relative to the objective medical data with the steps with respect to FIG. 11. The patient is monitored by a portable medical monitoring device 150 (e.g. smartphones, tablets, glasses/goggles, watches, wearable devices, medical stickers), or electronic underwear 170 attached with electronic device (e.g. sensor), or textile electrodes fabric garment 172, where the portable medical monitoring device 150 operates in conjunction with an implantable device as shown and described in FIGS. 7A-C as a method to read a patient's blood pressure, heart rate, and other vital medical data.
At step 352, the smartphone 150 in turn transmits the real time medical data to the medical main server 12. At step 354, the intelligent medical engine 14 is configured to analyze the real time objective medical data of the patient 148 relative to the patient's 148 previously stored objective medical data in the central database 16 to determine if the comparison would invoke a medical alert to the patient's medical doctor and to the patient. If one of the parameters in the patient's 148 real time objective medical data exceeds a threshold of the patient's 148 previously stored objective medical data, then the intelligent medical engine 14 is configured to send a medical alert to a medical professional associated with patient 148 and to the patient's 148 portable medical monitoring device 150 to inform the patient 148 at step 360. At the same time in step 356, the intelligent medical engine 14 is configured to store the resulting real time objective medical data from the patient 148 in the central database 16 by adding the resulting objective medical data to the existing patient's 148 EMR System.);
-- based on the first set of patient data, the measurement data, the second set of patient data, the second plurality of physiological sensor measurements, and the time series of measurements, generating a forecast of a likelihood of urolithiasis over a future time interval (see OLEYNIK paragraphs 64; The present disclosure provides methods of assessing the risk of a subject/patient in developing a disease or condition in the future or in having a disease or condition recur during or after treatment (with time). Information, such as family medical history and subject's medical history can be inputted into the computer system for estimating the subject's risk for developing a disease or condition in the future. Based on the assessment, the health care provider can recommend a specific therapeutic agent, a change in diet, weight loss, and exercise for preventing the development of the disease or condition.); and
-- based on the generated forecast, initiating one or both of (a) an intervening action and (b) generation, via the at least one processor or the one or more hardware processors, instructions to modify a treatment program stored at a memory associated with the electronic digital memory at the medical records computer system (see OLEYNIK paragraphs 64; The present disclosure provides methods of assessing the risk of a subject/patient in developing a disease or condition in the future or in having a disease or condition recur during or after treatment (with time). Information, such as family medical history and subject's medical history can be inputted into the computer system for estimating the subject's risk for developing a disease or condition in the future. Based on the assessment, the health care provider can recommend a specific therapeutic agent, a change in diet, weight loss, and exercise for preventing the development of the disease or condition.).
OLEYNIK fails to explicitly teach urolithiasis.
Monga et al. teaches medical image data is provided to a feature extractor 14. In accordance with an aspect of the present invention, a plurality of features used in predicting the composition of a kidney stone (e.g. urolithiasis) includes a pair of a color values drawn from preselected, spatially distinct locations within the kidney stone, or a function of the pair of attenuation values (see Monga et al. paragraph 16).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference Monga et al. within the systems/methods as taught by reference OLEYNIK with the motivation of providing a system for determining the composition of a kidney stone from a medical CT image implementing, a feature set containing the first and second color values, thereby imaging can be used for identifying the material of the kidney stone (see Monga et al. paragraph 18).
As per Claim 2, OLEYNIK and Monga et al. teach the computer-implemented method of claim 1, wherein the intervening action is initiated with respect to a target patient to prevent a future occurrence corresponding to the generated forecast (see OLEYNIK paragraphs 64; Based on the assessment, the health care provider can recommend a specific therapeutic agent, a change in diet, weight loss, and exercise for preventing the development of the disease or condition.).
The obviousness of combining the teachings of OLEYNIK and Monga et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 3, OLEYNIK and Monga et al. teach the computer-implemented method of claim 1, wherein the first set of patient data corresponds to measurements that were entered or automatically determined using one or both of the medical records computer system and a set of sensors in proximity to a target patient (see OLEYNIK paragraphs 59, 121, 139 and 143 (e.g. urinary system); FIG. 6 is an illustration of an exemplary process flow of 24/7 (168) monitoring patients relative to the objective medical data with the steps with respect to FIG. 11. The patient is monitored by a portable medical monitoring device 150 (e.g. smartphones, tablets, glasses/goggles, watches, wearable devices, medical stickers), or electronic underwear 170 attached with electronic device (e.g. sensor), or textile electrodes fabric garment 172, where the portable medical monitoring device 150 operates in conjunction with an implantable device as shown and described in FIGS. 7A-C as a method to read a patient's blood pressure, heart rate, and other vital medical data.
At step 352, the smartphone 150 in turn transmits the real time medical data to the medical main server 12. At step 354, the intelligent medical engine 14 is configured to analyze the real time objective medical data of the patient 148 relative to the patient's 148 previously stored objective medical data in the central database 16 to determine if the comparison would invoke a medical alert to the patient's medical doctor and to the patient. If one of the parameters in the patient's 148 real time objective medical data exceeds a threshold of the patient's 148 previously stored objective medical data, then the intelligent medical engine 14 is configured to send a medical alert to a medical professional associated with patient 148 and to the patient's 148 portable medical monitoring device 150 to inform the patient 148 at step 360. At the same time in step 356, the intelligent medical engine 14 is configured to store the resulting real time objective medical data from the patient 148 in the central database 16 by adding the resulting objective medical data to the existing patient's 148 EMR System.).
The obviousness of combining the teachings of OLEYNIK and Monga et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 4, OLEYNIK and Monga et al. teach the computer-implemented method of claim 3, wherein the automatic determining of the measurements corresponds to collecting, via one or more physiologic sensors, biometric data from the target patient or fluid derived from the patient (see OLEYNIK paragraphs 121, 139 and 143 (e.g. urinary system); FIG. 6 is an illustration of an exemplary process flow of 24/7 (168) monitoring patients relative to the objective medical data with the steps with respect to FIG. 11. The patient is monitored by a portable medical monitoring device 150 (e.g. smartphones, tablets, glasses/goggles, watches, wearable devices, medical stickers), or electronic underwear 170 attached with electronic device (e.g. sensor), or textile electrodes fabric garment 172, where the portable medical monitoring device 150 operates in conjunction with an implantable device as shown and described in FIGS. 7A-C as a method to read a patient's blood pressure, heart rate, and other vital medical data.
At step 352, the smartphone 150 in turn transmits the real time medical data to the medical main server 12. At step 354, the intelligent medical engine 14 is configured to analyze the real time objective medical data of the patient 148 relative to the patient's 148 previously stored objective medical data in the central database 16 to determine if the comparison would invoke a medical alert to the patient's medical doctor and to the patient. If one of the parameters in the patient's 148 real time objective medical data exceeds a threshold of the patient's 148 previously stored objective medical data, then the intelligent medical engine 14 is configured to send a medical alert to a medical professional associated with patient 148 and to the patient's 148 portable medical monitoring device 150 to inform the patient 148 at step 360. At the same time in step 356, the intelligent medical engine 14 is configured to store the resulting real time objective medical data from the patient 148 in the central database 16 by adding the resulting objective medical data to the existing patient's 148 EMR System.).
The obviousness of combining the teachings of OLEYNIK and Monga et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 5, OLEYNIK and Monga et al. teach the computer-implemented method of claim 1, wherein the intervening action comprises one or more of modifying treatment, scheduling treatment, and issuing a notification to a caregiver (see OLEYNIK paragraphs 64; The present disclosure provides methods of assessing the risk of a subject/patient in developing a disease or condition in the future or in having a disease or condition recur during or after treatment (with time). Information, such as family medical history and subject's medical history can be inputted into the computer system for estimating the subject's risk for developing a disease or condition in the future. Based on the assessment, the health care provider can recommend a specific therapeutic agent, a change in diet, weight loss, and exercise for preventing the development of the disease or condition.).
The obviousness of combining the teachings of OLEYNIK and Monga et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 6, OLEYNIK and Monga et al. teach the computer-implemented method of claim 1, wherein the intervening action comprises at least one of ordering an additional diagnostic, scheduling a diagnostic, or any combination of ordering additional diagnostic and scheduling diagnostics (see OLEYNIK paragraphs 94; In one embodiment, the disease and course of treatment for a patient is obtained based on data in the system which is obtained from other patients with similar medical history, symptoms, and conditions and their success and/or failure with a specific course of treatment. Through the iterative process of comparison, classification, and degrouping of parameters inputted for the patient, the system provides a disease and course of treatment for the patient. As an example, patients diagnosed with cancer have several options for treatment, such as hormonal therapy, radiation therapy, biologically targeted therapy, chemotherapy, and surgery. However, depending on the patient's medical history, previous diagnostic test results, and the particular type of cancer, one or more of the options may not be appropriate. The methods disclosed herein enable a physician to access the information on other patients. Based on the medical information and the success rate of the course of treatments for other patients with similar medical history, symptoms, and conditions compiled in the system, a health care provider can recommend one or more suitable options for treatment to the cancer patient seeking treatment.).
The obviousness of combining the teachings of OLEYNIK and Monga et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claim 7, OLEYNIK and Monga et al. teach the computer-implemented method of claim 1, wherein the first plurality of physiological sensor measurements comprises a sequence of measurements, each subsequent measurement obtained after at least one minimum preset time interval has elapsed from a previous measurement (see OLEYNIK paragraphs 110, 121, 139 and 143; After a period of time, the same patient in third scenario 157a in which the post-treatment significant parameters measurement indicates that the disease amount has remained unchanged).
The obviousness of combining the teachings of OLEYNIK and Monga et al. is discussed in the rejection of claim 1, and incorporated herein.
As per Claims 8-14, Claims 8-14 are directed to a one or more non-transitory media having computer-readable instructions that, when execute by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations. Claims 8-14 recite the same or substantially similar limitations as those addressed above for Claims 1-7 as taught by OLEYNIK and Monga et al. Claim 17 is therefore rejected for the same reasons as set forth above for Claims 1-7 respectively.
As per Claims 15-20, Claims 15-20 are directed to a system having one or more hardware processors configured to facilitate a plurality of operations. Claims 15-20 recite the same or substantially similar limitations as those addressed above for Claims 1-7 as taught by OLEYNIK and Monga et al. Claim 17 is therefore rejected for the same reasons as set forth above for Claims 1-7 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub. No.: US 20140358585 A1; The present invention relates to an apparatus and method for implementing a medical critical results communication, including: receiving an identification and a classification of a finding of criticality on a patient; creating and transmitting a critical results communication to a recipient by electronic methods; receiving and storing an acknowledgement from the recipient that the critical results communication was received; receiving and storing feedback and/or the initiation of clinical intervention and a follow-up action from the recipient, and transmitting same to the health care provider sender; receiving and storing diagnostic confirmation from the recipient and/or the healthcare provider sender; performing an analysis of critical results data, and performing a compliance analysis with stored established medical standards; and providing the critical results data analysis and the compliance analysis to at least the health care provider sender.
Pub. No.: US 20080125666 A1; Biological data, such as human heart rate data, is acquired and processed in a non-linear manner to facilitate an assessment of the physiological state of the subject, and/or to assist in predicting incipient disorders or instability. Determinism, laminarity and recurrence measures are derived for a rolling sample of a time series of said data. The recurrence measure can be the Euclidean threshold (.epsilon..sub.thresh) at a given recurrence rate. A representation, such a colour coded matrix or multi-dimensional vector, is formed from a combination of the derived determinism, laminarity and recurrence measures. The representation can then be analysed to detect indicators of physiological instability, such as arrhythmia, or to discriminate between arrhythmias. The analysis may be performed visually, or in an automated manner in real time, such as in an ambulatory or implanted device, or post hoc by a bedside monitor.
Pub. No.: US 20090098553 A1; This invention relates to methods for determining the presence of cancer in a subject based on the analysis of the expression levels of an under-expressed tumour marker (TM) and at least one other TM. Specifically, this invention relates to the determination of a cancer, particularly bladder cancer, by performing ratio, regression or classification analysis of the expression levels of at least one under-expressed TM, particularly an under-expressed bladder TM (BTM), and at least one over-expressed TM, particularly an over-expressed BTM. In various aspects, the invention relates to kits and devices for carrying out these methods.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683