DETAILED ACTION
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 .
Priority
This application filed 05/25/2022 is a National Stage entry of PCT/KR2021/010486, with an International Filing Date of 08/09/2021, and claims foreign priority to Korean Application 10-2021-0055135, filed 04/28/2021. The claims are therefore examined as filed on 04/28/2021, the effective filing date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further review of the priority application(s).
Claim Status
Claims 1-10 are pending.
Claims 1-10 are examined.
Claims 1-10 are rejected.
Information Disclosure Statement
The Information Disclosure Statements are in compliance with the provisions of 37 CFR 1.97. Accordingly, all references have been considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the “database unit”, and “neural network modeling unit”, in claim 5.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts, without significantly more.
The MPEP at MPEP 2106 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of matter?
(2A)(1) Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea?
(2A)(2) Do the claims recite additional elements that integrate the judicial exception into a practical application?
(2B) If the claims recite a judicial exception and do not integrate the judicial exception, do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?
With regard to step (1) (Are the claims directed to a process, machine, manufacture or composition of matter?): Yes. The claims are directed to one of the statutory classes. Claims 1-4 are directed to a process (a method), claim 5 is directed to a machine product (a device), and claims 6-10 are also directed to a process (a method performed by a device).
With regard to step (2A)(1) (Do the claims recite a judicially recognized exception?): Yes. The claims recite the abstract ideas of processing data using mental steps and mathematical concepts, and observing the processed data. Claims that recite nothing more than abstract ideas, natural phenomena, or laws of nature are not eligible for patent protection (see MPEP 2106.04).
Abstract ideas include mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (See MPEP 2106.04(a)(2)). In particular, these abstract ideas include but are not limited to:
Converting collected information into a single standardized data structure format (mental process; the human mind is capable of formatting data so that it is standardized, by applying defined rules to data types/values; claim 1)
Generating a model for estimating HbA1c level using patient information (mental process/mathematical concept; the human mind is capable of creating a model to estimate a value from data, and doing so is a mathematical concept; claims 1, 5)
Calculating a degree of compliance based on a response to a message or to therapeutic intervention (mental process/mathematical concept; the human mind is capable of calculating a value from data, and performing a calculating is a mathematical concept; claim 2-4, 8-9)
Generating information on the basis of acquired log data (mental process; the human mind is capable of using data to generate other data; claim 3, 7)
Applying information to a model for estimating HbA1c level to estimate an HbA1c level (mental process/mathematical concept; the human mind is capable of applying data to a model to obtain a result, and doing so is equivalent to performing a mathematical calculation; claim 6, 8)
Determining exercise index as a value according to exercise time corresponding to type of exercise (mental process/mathematical concept; the human mind is capable of determining a value from time data, doing so is equivalent to performing a calculation; claim 7)
Establishing a patient management plan on the basis of collected patient information and a management goal (mental process; the human mind is capable of establishing a plan based on collected data; claim 9)
Providing a therapeutic message (mental process; the human mind is capable of providing a message; claim 9)
Providing content including a text message and image for increasing the degree of compliance with the therapeutic intervention (mental process; the human mind is capable of providing a text message and image; claim 10)
Therefore, the claims recite elements that constitute one or more judicial exceptions.
With regard to step (2A)(2) (Do the claims recite additional elements that integrate the judicial exception into a practical application?): No. Claim 1 and its dependents recite the additional element of collecting patient information/HbA1c level and training an artificial intelligence model. Claim 5 recites the additional element of a device comprising units/software for collecting information and applying it to a machine learning model. Claim 6 and its dependents also recite the additional element of a device for collecting information, applying the information to a trained artificial intelligence model, and providing an estimated HbA1C level through a user interface of a patient terminal.
While the claims recite the additional element of collecting and outputting data, such steps that only amount to necessary data gathering and outputting, without any technical details of how the data is obtained/output that integrate the judicial exception, are insignificant extrasolution activities that do not add a meaningful limitation to the claims (see MPEP 2106.05(g)). As a result, the judicial exception is not integrated into a practical application. Similarly, while the claims recite additional elements related to the use of a [computing] device, they do not provide any specific details by which the device or units/software of the device performs or carries out the judicial exception listed in step (2A)(1), nor do they provide any details of how specific structures of the device or computer are used to implement these functions. The judicial exception is therefore not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea, as they amount to simply implementing the abstract idea on a computer (see MPEP 2106.05(f)). This also applies to the use of artificial intelligence/machine learning to process and output data, as an artificial intelligence or machine learning model, without recited structure outside of general computer components, is also analogous to implementing an abstract idea of data analysis on a computer. Because the claims do not recite any additional elements that integrate the judicial exception into a practical application, the claims as a whole are directed to an abstract idea.
With regard to step (2B) (Do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?): No.
The claims recite an abstract idea with additional elements; however, these additional elements are general computer elements added to abstract ideas, and non-particular instructions to apply the abstract idea by linking it to a field of use or extrasolution activity (see MPEP 2106.05(f-h)). General computer elements used to perform an abstract idea do not provide an inventive concept, and similarly, non-particular instructions to gather or output data do not provide an inventive concept. Non-particular instructions to gather or output data, including biological data, are also considered well-understood, routine and conventional activities (see MPEP 2106.05(d), which indicates that limitations such as “Receiving or transmitting data over a network” from Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, “Storing and retrieving information in memory” from Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, and “Determining the level of a biomarker in blood by any means” from Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017) are recognized as conventional activities). The claims therefore do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As a result, the claims as a whole do not provide an inventive concept.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim Rejection
Claims 1-3, 5-6, and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over ZAITCEV 2020 “A Deep Neural Network Application for Improved Prediction of HbA1c in Type 1 Diabetes” in view of TESCHLER 2020 “Systems And Methods For Creating And Utilizing Adaptive Care Systems” (US 20200258603 A1).
Claim Interpretation and Scope and Contents of Prior Art
Claim 1 recites a method of training an artificial intelligence model for estimating a hemoglobin A1c (HbA1c) level, comprising collecting patient information including exercise information and bioinformation of a patient; collecting an actual HbA1c level of the patient; converting the collected patient information and actual HbA1c level into a single standardized data structure format; and training an artificial intelligence model using the converted patient information and actual HbA1c level to generate an artificial intelligence model for estimating an HbA1c level, wherein the bioinformation includes at least one of a blood sugar level, a blood pressure, a heart rate, and a menstrual cycle, and the exercise information is generated on the basis of patient life log data acquired by a patient terminal. With respect to these limitations, ZAITCEV teaches a method of training an artificial intelligence model for estimating HbA1c (Abstract), involving collecting patient information that includes HbA1c level, demographic data and 84 days of self-monitored blood glucose (blood sugar) measurements (pg 7 par 2), which includes information on behaviors associated with the measurements (pg 2 col 2, p4 col 2 par 4). ZAITCEV also teaches converting the data into a standardized format through time series preprocessing (pg 2 col 2, pg 3 col 1), and training an artificial neural network with the patient data and HbA1c level to create a model for estimating HbA1c level (Abstract, Fig 1,pg 7 par 2).
ZAITCEV also teaches quantifying behaviors using rates of blood glucose events in the model (pg 4 col 2), and that exercise in particular can impact blood glucose such that it is recommended that blood glucose be tested before, during and after exercise (pg 2 col 2), but does not specifically teach generating patient exercise information on the basis of patient life log data acquired from a terminal.
However, TESCHLER teaches systems and methods for monitoring patients for health care management, which includes a patient care app connected to patient sensors for gathering biometric and logged data such as compliance with an exercise regimen [0002, 6, 61-62, 68, 103]. One of ordinary skill in the art would be motivated to apply this data using the AI modeling methods of ZAITCEV to more accurately estimate HbA1c level for a patient.
The above art also applies to claim 5, which recites a device for performing some of the methods of claim 1, and claim 6, which recites a diabetic management method performed by a device that also recites some of the methods of claim 1.
Claim 2 recites the limitations wherein the patient information includes a degree of compliance with therapeutic intervention, the therapeutic intervention includes provision of a medication notification message for a prescribed medicine through a user interface of the patient terminal, and the degree of compliance is calculated on the basis of a response to the medication notification message. With respect to this limitation, ZAITCEV teaches gathering and including relevant demographic information for training its model (pg 2 par 3, pg 7 par 2), but does not teach specifically teach that this includes a degree of compliance. However TESCHLER teaches the logged patient information includes determining a level of compliance with a personal care plan and medication requirement though the patient terminal device, which includes providing the patient messages reminding the patient of medication to be taken and patient response [0090-0104].
Claim 3 recites the limitation wherein the patient information further includes at least one of prescription information, physical information, life information, and a degree of compliance with therapeutic intervention, the prescription information includes prescribed medicine information and medication guidance information, the physical information includes at least one of a height, a weight, and a waist size of the patient, the life information includes at least one of a sleep index and an activity index, the life information is generated on the basis of the life log data acquired by the patient terminal possessed by the patient, the therapeutic intervention includes provision of an exercise recommendation message, and the degree of compliance is calculated on the basis of a response of the user to the therapeutic intervention. With respect to this limitation, ZAITCEV teaches gathering and including relevant demographic information for training its model, including age, gender and amount of time the patient has had diabetes (pg 2 par 3, pg 7 par 2), but does not teach the other claimed information. However, TESCHLER teaches the logged patient information can include a level of compliance based on response to a medication requirement, prescribed medicine information and guidance [0090-0104], height and weight [0128], and physical activity [0057-59]; and also teaches providing exercise recommendation messages based on patient data [0006, 0076].
Claim 8 recites the limitation of providing therapeutic intervention on the basis of the patient information; acquiring a response to the therapeutic intervention; calculating a degree of compliance on the basis of the response; and applying the patient information including the degree of compliance to the artificial intelligence model to estimate an HbA1c level. With respect to these limitations, ZAITCEV teaches applying the patient information to an artificial intelligence model to estimate HbA1c (Abstract), while TESCHLER teaches determining a level of response compliance with a personal care plan and medication requirement though the patient terminal device, which includes providing the patient messages reminding the patient of medication to be taken [0090-0104]. One of ordinary skill would be motivated to add level of compliance as additional demographic information in the model, as adherence to therapeutic intervention would impact the resulting HbA1c level.
Claim 9 recites the limitation of establishing a patient management plan on the basis of the collected patient information and a management goal; providing, to the patient terminal, a therapeutic intervention message for executing the therapeutic intervention according to the established patient management plan; acquiring a response to the therapeutic intervention message; and calculating the degree of compliance with the therapeutic intervention on the basis of the response, wherein the degree of compliance is determined on the basis of a response time for the therapeutic intervention message output to the user interface of the patient terminal. ZAITCEV does not teach these limitations, however TESCHLER teaches establishing a management plan based on collected patient information and management goals [Abstract, 006-8, 138-150], and also teaches providing the patient intervention messages and acquiring patient response, and determining a level of response compliance based on response time [0090-0104].
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in patient health care systems and analysis would have been motivated to combine the teachings of ZAITCEV with the teachings of TESCHLER, in order to achieve the claimed invention, because exercise and compliance with medical instructions both have a known impact on HbA1c level, and when combined into a model would improve the accuracy of an HbA1c level estimation. A person of ordinary skill would reasonably expect success from combining these teachings, as both ZAITCEV and TESCHLER teach methods of using patient data to determine a health outcome, and the data and intervention strategies of TESCHLER can be applied using the model of ZAITCEV to produce an accurate estimate of HbA1c level that can be acted upon. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over ZAITCEV 2020 “A Deep Neural Network Application for Improved Prediction of HbA1c in Type 1 Diabetes” in view of TESCHLER 2020 “Systems And Methods For Creating And Utilizing Adaptive Care Systems” (US 20200258603 A1) as applied to claims 1-3, 5-6, and 8-9 above, and further in view of PEREIRA 2019 “Using Health Chatbots for Behavior Change: A Mapping Study.”
Claim Interpretation and Scope and Contents of Prior Art
ZAITCEV in view of TESCHLER teaches the limitations of claims 1-3, 5-6, and 8-9 above.
Claim 4 recites the limitation wherein the patient information includes a degree of compliance with therapeutic intervention, a chatbot converts therapeutic intervention information generated on the basis of the patient information and a measured or estimated HbA1c level into a therapeutic intervention message, and the degree of compliance is calculated on the basis of a time at which a response to the therapeutic intervention message output to the patient terminal is input to the patient terminal. With respect to this limitation, TESCHLER teaches the logged patient information includes determining a level of compliance with a personal care plan and medication requirement though the patient terminal device, which includes providing the patient messages reminding the patient of medication to be taken, and patient response to messages [0090-0104], and also teaches inputting patient HbA1C level to determine the care plan [0107-108], but does not teach that a chatbot converts the information into an intervention message. However, PEREIRA reviews health chatbot technology, and teaches that chatbots in healthcare are well known in the art, and can be used to gather data and provide intervention messages and reminders (Abstract, pg 135-136).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in patient health care systems and analysis would have been motivated to combine the teachings of ZAITCEV in view of TESCHLER with the teachings of PEREIRA, in order to achieve the claimed invention, because health chat bots are a well-known technology in healthcare that are able to assist in various tasks by messaging patients, including counseling, health-monitoring and medication adherence (pg 134). A person of ordinary skill would reasonably expect success from combining these teachings, as both ZAITCEV in view of TESCHLER and PEREIRA teach methods of patient monitoring and medical compliance, and because the chatbot technology of PEREIRA could be combined with the patient communication methods of TESCHLER to increase effective communication and compliance with medical advice. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claims 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over ZAITCEV 2020 “A Deep Neural Network Application for Improved Prediction of HbA1c in Type 1 Diabetes” in view of TESCHLER 2020 “Systems And Methods For Creating And Utilizing Adaptive Care Systems” (US 20200258603 A1) as applied to claims 1-3, 5-6, and 8-9 above, and further in view of SALVI 2018 “An m-Health system for education and motivation in cardiac rehabilitation: the experience of HeartCycle guided exercise.”
Claim Interpretation and Scope and Contents of Prior Art
ZAITCEV in view of TESCHLER teaches the limitations of claims 1-3, 5-6, and 8-9 above.
Claim 7 recites the limitation wherein the patient information includes exercise information and bioinformation, the bioinformation includes at least one of an actual HbA1c level, a blood sugar level, a blood pressure, a heart rate, and a menstrual cycle, the exercise information is an exercise index generated on the basis of patient life log data acquired by the patient terminal, and the exercise index is a value determined according to an exercise time corresponding to each type of exercise.
With respect to these limitations, ZAITCEV teaches that the patient information includes HbA1c level, demographic data and 84 days of self-monitored blood glucose (blood sugar) measurements (pg 7 par 2), which includes information on behaviors associated with the measurements (pg 2 col 2, p4 col 2 par 4). ZAITCEV also teaches quantifying behaviors using rates of blood glucose events in the model (pg 4 col 2), and that exercise in particular can impact blood glucose such that it is recommended that blood glucose be tested before, during and after exercise (pg 2 col 2), but does not teach generating patient exercise information on the basis of patient life log data acquired from a terminal and generating an exercise index from an exercise time corresponding to each type of exercise.
However, TESCHLER teaches systems and methods for monitoring patients for health care management, which includes a patient care app connected to patient sensors for gathering biometric and logged data such as compliance with an exercise regimen [0002, 6, 61-62, 68, 103], and SALVI further teaches systems and methods for monitoring the health of patients that generates an exercise index based on exercise time and type (pg 305 col 2, Fig 2, Fig 4). One of ordinary skill in the art would be motivated to apply this data using the AI modeling methods of ZAITCEV to more accurately estimate HbA1c level for a patient.
Claim 10 recites the limitation of providing content including a text message and an image for increasing the degree of compliance with the therapeutic intervention to the patient terminal together with the therapeutic intervention message or after the providing of the therapeutic intervention message. With respect to these limitations, TESCHLER teaches providing the patient messages reminding the patient of medication to be taken and increasing compliance [0090-0104], but does not specify that messages are text and image. However SALVI teaches a system that provides messages to a patient terminal that includes both text and image messages (Fig 1-2).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in patient health care systems and analysis would have been motivated to combine the teachings of ZAITCEV in view of TESCHLER with the teachings of SALVI, in order to achieve the claimed invention, because the level/type of exercise and duration have a known impact on HbA1c level, and when added into a model would improve the accuracy of an HbA1c level estimation, and because a combination of text and image messages can improve patient behavior and compliance with medical recommendations (Abstract). A person of ordinary skill would reasonably expect success from combining these teachings, as both ZAITCEV in view of TESCHLER and SALVI teach methods of using patient data to determine a health outcome and provide intervention strategies. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY C LEVERETT whose telephone number is (571)272-5494. The examiner can normally be reached 8:00am - 5:00pm M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz R. Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.L./
Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687