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 .
Formal Matters
Applicant's response, filed 02 April 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-20 are currently pending and have been examined.
Claims 1, 6, 10, 15, and 19 have been amended.
Claims 1-20 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 09/17/2020 claiming benefit to Parent Application 17/024,557.
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 19-20 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.
Step 1 – Statutory Categories of Invention:
Claims 19-20 are drawn to a method, which is a statutory category of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 19 recites a method for generating a predictive association model in part performing the steps of obtaining historical data associated with a patient, wherein the historical data comprises real-valued data and occurrences of meal consumption by the patient; transforming the real-valued data to categorical values for a plurality of fields of patient data; identifying a plurality of associations between the categorical values for the plurality of fields; determining for associations of the plurality of associations, co-occurrence information, the co-occurrence information indicating a co-occurrence of an association of the plurality of associations with consumption of a meal; and generating a predictive association model by selecting a subset of the plurality of associations associated with probably meal consumption based on the co-occurrence information.
These steps of collecting and processing patient data to determine a medication adjustment amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)).
Dependent claim 20 recites, in part, wherein the real-valued data comprises data indicative of a geographic location of the patient.
Each of these steps of the preceding dependent claim only serve to further limit or specify the features of independent claim 19 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 19 recites one or more processors of a computing device. The specification defines the computer and corresponding interface and input device/controls as a general purpose processor and hardware components (see the instant Detailed Description in ¶ 0047 and ¶ 0043). The use of one or more processors, in this case to generate a predictive model, only recites the computer and corresponding hardware as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 19 recites one or more processors of a computing device. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 19-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Roy et al. (US Patent App No 20180174675)[hereinafter Roy].
Claim 19 is rejected because Roy teaches on all elements of the claim:
a method performed by one or more processors of a computing device for generating a predictive association model, comprising is taught in the Detailed Description ¶ 0031, ¶ 0038, ¶ 0090-91, ¶ 0115, and ¶ 0127 (teaching on training a meal detection event algorithm for processing different categorical input data to generate an insulin adjustment)
obtaining, using the one or more processors of the computing device, historical data associated with a patient, wherein the historical data comprises real-valued data and occurrences of meal consumption by the patient is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving historical raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type)
transforming, using the one or more processors of the computing device, the real-valued data to categorical values for a plurality of fields of patient data is taught in the Detailed Description in ¶ 0098-100 (teaching on determining a category for at least one of the historical raw data input types - here the time of day is associated with breakfast period, lunch period, etc. and filtering out (treated as synonymous to transforming) other contextual patient meal data)
identifying, using the one or more processors of the computing device, a plurality of associations between the categorical values for the plurality of fields; determining, using the one or more processors of the computing device, for associations of the plurality of associations, co-occurrence information, the co-occurrence information indicating a co-occurrence of an association of the plurality of associations with consumption of a meal; and is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0115 (teaching on training a model to map the contextual data including the categorized data to an expected value (treated as synonymous to identifying and determining a co-occurrence association) to determine if the meal event occurred)
generating a predictive association model based by selecting a subset of the plurality of associations associated with probably meal consumption based on the co-occurrence information is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0115 (teaching on training the predictive model to map new contextual data including the categorized data to an expected historical value to determine if the meal event occurred)
As per claim 20, Roy discloses all of the limitations of claim I9. Roy also discloses the following:
the method of claim 19, wherein the real-valued data comprises data indicative of a geographic location of the patient is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Roy et al. (US Patent App No 20180174675)[hereinafter Roy] in view of Vehi et al., Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning, 26(1) Health Informatics Journal 703-718 (March 2020) [hereinafter Vehi].
As per claim 1, Roy teaches on the following limitations of the claim:
a method performed by one or more processors of a computing device for monitoring a physiological condition of a patient, the method comprising is taught in the Detailed Description ¶ 0031, ¶ 0038, ¶ 0090-91, and ¶ 0127 (teaching on a meal detection event algorithm for processing different categorical input data to generate an insulin adjustment)
obtaining, using the one or more processors of the computing device, a predictive association model associated with the patient, wherein the predictive association model comprises an association of two or more categorical state values that are predictive of the patient consuming a meal is taught in the Detailed Description in ¶ 0095-96 (teaching on a meal detection event predictive algorithm wherein meal event categorical data (treated as categorical state values) are predictive of a patient consuming a meal)
obtaining, using the one or more processors of the computing device, real-time data associated with the patient is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type)
determining, using the one or more processors of the computing device, a current state of the patient based at least in part on the real-time data is taught in the Detailed Description in ¶ 0098-100 (teaching on determining a category for at least one of the raw data input types - here the time of day is associated with breakfast period, lunch period, etc. and filtering out other contextual patient meal data wherein the categories is indicative of the eating state of the patient)
determining, using the one or more processors of the computing device, whether the two or more current state categorical values match the two or more categorical state values of the association of the predictive association model; predicting consumption of a meal in response to determining the two or more current state categorical values match the two or more categorical state values of the association; and is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0150 (teaching on mapping (treated as synonymous to matching) the contextual data including the categorized data to an expected value to determine if the meal event occurred - Examiner notes in the "activity" prediction model, the term matching is explicitly utilized to compare historical data to certain event characteristics)
in response to predicting the consumption of the meal, automatically delivering insulin to compensate for a glycemic response to consumption of the predicted meal is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values)
Roy fails to teach the following limitation of claim 1. Vehi, however, does teach the following:
transforming the real-time data into two or more current state categorical values, each of the two or more current state categorical values indicative of a current state of the patient is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification wherein the classifications are indicative of a blood glucose state of the patient)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
As per claim 2, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, wherein the two or more categorical state values comprise a location of the patient is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
As per claim 3, the combination of Roy and Vehi discloses all of the limitations of claim 2. Roy also discloses the following:
the method of claim 2, wherein the two or more categorical state values comprise a duration of time the patient has been in the location is taught in the Detailed Description in ¶ 0098-100 and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including concurrent timestamps and locations associated with a previous event)
As per claim 4, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, wherein obtaining the real-time data comprises obtaining location data based on global positioning system (GPS) data is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
As per claim 5, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy fails to teach the following; Vehi, however, does disclose:
the method of claim 1, wherein transforming the real-time data into two or more current state categorical values comprises determining a Boolean value for each of the two or more current state categorical values by clustering the real-time data and assigning the Boolean value to the clustered data is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
As per claim 6, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, further comprising determining a bolus dosage of insulin based on a predicted nutritional content of the predicted meal, wherein delivering the insulin comprises delivering the determined bolus dosage is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values including nutritional components of the meal)
As per claim 7, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, further comprising causing an indication of a relationship of the two or more categorical state values and prediction of the patient consuming the meal to be presented in a user interface for verification is taught in the Detailed Description in ¶ 0118 (teaching on presenting the identified meal event to the user on a user interface for verification)
As per claim 8, the combination of Roy and Vehi discloses all of the limitations of claim 7. Roy also discloses the following:
the method of claim 7, wherein the user interface comprises controls that allow modification of the relationship of the two or more categorical state values and prediction of consumption of the meal is taught in the Detailed Description in ¶ 0118-119 (teaching on presenting the identified meal event to the user on a user interface for verification wherein the patient may modify the event thus necessarily altering the relationship between the variables and the prediction outcome)
As per claim 9, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, further comprising combining real-time data associated with a plurality of data sources based on timing information prior to transforming the real-time data into two or more current state categorical values is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type and combining as historical event data for the user before preprocessing the training data via filtering or categorization)
As per claim 10, Roy teaches on the following limitations of the claim:
a system comprising is taught in the Detailed Description ¶ 0031, ¶ 0038, ¶ 0090-91, and ¶ 0127 (teaching on a meal detection event algorithm for processing different categorical input data to generate an insulin adjustment)
one or more processors; and one or more processor-readable media storing instructions which, when executed by one or more processors, cause performance of is taught in the Detailed Description in ¶ 0080-81, ¶ 0124, and in the Figures at fig. 8 (teaching on a client device implementing the meal detection event algorithm on a computer with a processor and memory)
obtaining a predictive association model associated with the patient, wherein the predictive association model comprises an association of two or more categorical state values that are predictive of the patient consuming a meal is taught in the Detailed Description in ¶ 0095-96 (teaching on a meal detection event predictive algorithm wherein meal event categorical data (treated as categorical state values) are predictive of a patient consuming a meal)
obtaining real-time data associated with the patient is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type)
determining a current state of the patient based at least in part on the real-time data is taught in the Detailed Description in ¶ 0098-100 (teaching on determining a category for at least one of the raw data input types - here the time of day is associated with breakfast period, lunch period, etc. and filtering out other contextual patient meal data wherein the categories is indicative of the eating state of the patient)
predicting consumption of a meal in response to determining the two or more current state categorical values match the two or more categorical state values of the association; and is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0150 (teaching on mapping (treated as synonymous to matching) the contextual data including the categorized data to an expected value to determine if the meal event occurred - Examiner notes in the "activity" prediction model, the term matching is explicitly utilized to compare historical data to certain event characteristics)
in response to predicting the consumption of the meal, automatically delivering insulin to compensate for a glycemic response to consumption of the predicted meal is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values)
While Roy teaches generally on transforming input data into categorical values, fails to teach the following limitation of claim 10. Vehi, however, does teach the following:
determining whether the two or more current state categorical values match the two or more categorical state values of the association of the predictive association model, each of the two or more current state categorical values indicative of a current state of the patient is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification wherein the classifications are indicative of a blood glucose state of the patient)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
As per claim 11, the combination of Roy and Vehi discloses all of the limitations of claim 10. Roy also discloses the following:
the system of claim 10, wherein the two or more categorical state values comprise a location of the patient is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
As per claim 12, the combination of Roy and Vehi discloses all of the limitations of claim 11. Roy also discloses the following:
the system of claim 11, wherein the two or more categorical state values comprise a duration of time the patient has been in the location is taught in the Detailed Description in ¶ 0098-100 and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including concurrent timestamps and locations associated with a previous event)
As per claim 13, the combination of Roy and Vehi discloses all of the limitations of claim 10. Roy also discloses the following:
the system of claim 10, wherein obtaining the real-time data comprises obtaining location data based on global positioning system (GPS) data is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
As per claim 14, the combination of Roy and Vehi discloses all of the limitations of claim 10. Roy fails to teach the following; Vehi, however, does disclose:
the system of claim 10, wherein transforming the real-time data into two or more current state categorical values comprises determining a Boolean value for each of the two or more current state categorical values by clustering the real-time data and assigning the Boolean value to the clustered data is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
As per claim 15, the combination of Roy and Vehi discloses all of the limitations of claim 10. Roy also discloses the following:
the system of claim 10, wherein the instructions further cause performance of determining a bolus dosage of insulin based on a predicted nutritional content of the predicted meal, wherein delivering the insulin comprises delivering the determined bolus dosage is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values including nutritional components of the meal)
As per claim 16, the combination of Roy and Vehi discloses all of the limitations of claim 10. Roy also discloses the following:
the system of claim 10, wherein the instructions further cause performance of causing an indication of a relationship of the two or more categorical state values and prediction of the patient consuming the meal to be presented in a user interface for verification is taught in the Detailed Description in ¶ 0118 (teaching on presenting the identified meal event to the user on a user interface for verification)
As per claim 17, the combination of Roy and Vehi discloses all of the limitations of claim 16. Roy also discloses the following:
the system of claim 16, wherein the user interface comprises controls that allow modification of the relationship of the two or more categorical state values and prediction of consumption of the meal is taught in the Detailed Description in ¶ 0118-119 (teaching on presenting the identified meal event to the user on a user interface for verification wherein the patient may modify the event thus necessarily altering the relationship between the variables and the prediction outcome)
As per claim 18, the combination of Roy and Vehi discloses all of the limitations of claim 10. Roy also discloses the following:
the system of claim 10, wherein the instructions further cause performance of combining real-time data associated with a plurality of data sources based on timing information prior to transforming the real-time data into two or more current state categorical values is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type and combining as historical event data for the user before preprocessing the training data via filtering or categorization)
Response to Arguments
Applicant's arguments filed 02 April 2026 for claims 1-18 with respect to 35 USC § 101 have been fully considered and are persuasive. The § 101 rejection of claims 1-18 has been withdrawn.
Applicant's arguments filed 02 April 2026 for claims 19-20 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the generation of a prediction model for identifying a meal event from patient behavior data by comparing said data to historical data is not a method of organizing human activity. Examiner disagrees. First Examiner sustains that the claims are similar to example iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). Examiner submits that the abstract idea does not read on the entirety of the inventive concept – specifically on the term “using the one or more processors of the computing device”. However, the use of electronic means for performing the abstract idea is not enough to overcome Step 2A Prong 1 (2019 Revised Patent Subject Matter Eligibility Guidance, 84 FED. REG. 4 (January 7, 2019) at p. 8 footnote 54 further citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316-18 (Fed. Cir. 2016) where the electronic implementation of human activity was not adequate to overcome Step 2A Prong 1).
Applicant's arguments filed 02 April 2026 for claims 19-20 with respect to 35 USC § 102 have been fully considered but they are not persuasive. First, Applicant asserts that the newly amended limitations are not taught by Roy. Examiner disagrees. The newly recited limitations only serve to elaborate on the clustering process already established by Examiner as taught by Roy. Second, Applicant asserts that as the limitation stating “transforming the real-value data into categorical values” is rejected in a separate and distinct independent claim under Vehi, Roy cannot teach on the limitation in claim 19. The scope of claims 19 and 1 are distinguishable and the transforming limitation is claimed with distinct language. Examiner carries no burden to reject distinct independent claims under identical rationales.
Applicant's arguments filed 02 April 2026 for claims 1-18 with respect to 35 USC § 103 have been fully considered but they are not persuasive. Applicant first asserts that Examiner’s mapping is “unclear” as the rejection of independent claim 10 and 1 are not identical, citing Examiner’s statement in an interview. Examiner first notes that the substance of the interview was directed towards the subject matter eligibility rejection and noted that the proposed amendments would not overcome the current prior art rejection explicitly. Examiner is not held to the content of an interview and an interview never serves to supersede the rejection made of record. Again, Examiner carries no burden to reject distinct independent claims under identical rationales.
Furthermore, Applicant’s characterization of the rejections is incorrect. Both independent claims 1 and 10 have Roy teaching the transforming step to categorical values. Examiner included the teachings of Vehi to read on the transforming step in view of Applicant’s own disclosure and dependent claims 5 and 14. While the claims are currently recited at such a high level of generality that “transforming” could be any possible process, Applicant’s own disclosure teaches on a hierarchical clustering for binary classification normalization process that Applicant has renamed “transforming”. In view of the amendments to both independent claims 1 and 10 limiting the each of the two or more current state categorical values to be indicative of a current state of the patient, Examiner has clarified the rejection above.
Applicant’s asserts that Vehi fails to teach on determining a state of a patient based on categorical values indicative because Vehi teaches on identifying a glucose profile from historical data for a patient. Examiner is not persuaded these are distinguishable – that is Vehi teaches on selecting a glucose pattern (i.e. a state of the patient) from a plurality of possible states (i.e. the categorical values) which Applicant noted is 20 different possible profiles.
Applicant then asserts that real-time data and simulated data are not analogous. Examiner is not persuaded. Replacing simulated data for real time data as taught by Roy does not change how the clustering algorithm performs and Applicant has provided no evidence of such. That is, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Finally, Applicant asserts that Vehi teaches that “the different clusters are generally directed to clustering data across different subjects”. There is no evidence in the claim or instant specification that the “transformation” process occurs solely from a single individual’s data set. The clusters however do represent the possible outcomes for which the single patient’s current data is compared.
Regarding dependent claims 7 and 16, Applicant asserts that fails to teach on displaying the categorical state values with the prediction of the patient consuming the meal on the display. Examiner notes that the claim only requires an indication of the state categorical state values. Examiner notes that the predicted meal event is an indication of the state values. If Applicant wishes for the display to show the categorical state values, the claim should recite displaying said data directly instead of merely an “indication of” said values.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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