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 .
Acknowledgements
This communication is in response to Remarks filed on 2/26/2026.
Claims 1-47 were previously canceled.
Claim 48 is amended.
Claim 64-67 are new.
Claims 48-67 are currently pending and have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/02/2023, 10/21/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 48-67 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.
Claim 48, 64, 67 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim system for determining glucose patterns.
The limitations of […] receive medical monitoring data […]; define separate pre-intervention and post-intervention datasets of the received medical monitoring data; determine a glucose pattern for each of a plurality of corresponding time-of-day periods in the separate pre-intervention and post-intervention datasets, wherein each glucose pattern is generated using […] statistical aggregation of stored values assigned to the same time-of-day period, across a plurality of monitoring days, to produce a categorical glucose pattern indicator the glucose pattern comprising at least one of a high and a low pattern; determine text […] based at least in part on the glucose pattern for each of corresponding time-of-day periods; […] and output the text […], as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a wireless communication circuit, at least one processor coupled with the wireless communication circuitry and a memory, (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the wireless communication circuit, at least one processor coupled with the wireless communication circuitry and a memory, this claim encompasses a person looking at data of a patient, defining pre and post intervention datasets, determining a glucose pattern, and determining text based on the glucose pattern for each of the corresponding time of day periods in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of (claim 1) a wireless communication circuit, at least one processor coupled with the wireless communication circuitry and a memory, that implements the identified abstract idea. The wireless communication circuit, processor coupled with the wireless communication circuitry and a memory, is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions of computing, determining, and selecting) 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.
The claim further recites the additional element of a sensor control device worn by a patient and patient-facing computing device that implements the identified abstract idea. The sensor control device worn by a patient and patient-facing computing device is not described by the applicant and is recited at a high-level of generality (i.e., a generic sensor control device and genetic patient-facing computing device performing a generic computer functions of collecting and processing data) 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.
The claim further recites the additional element of an interactive user interface and display screen. The interactive user interface and display screen merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Utilization of the interactive user equates to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a wireless communication circuit, at least one processor coupled with the wireless communication circuitry and a memory to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of a sensor control device and patient-facing computing device amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of an interactive user interface and a display screen was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
Claims 49-63, 65-66 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 53 merely describes defining datasets. Claim 55 merely describes analyzing data. Claim 56 merely describes glucose pattern indicators. Claim 57 merely describes ranking texts. Claim 59 merely describes glucose patterns. Claim 60 merely describes processing of datasets. Claim 61 merely describes defining of datasets. Claim 62 merely describes analyzing datasets. Claim 63, 66 merely describes comparing patterns and describing data.
Claims 49-52, 54, 58 also includes the additional element of “an interactive user interface” which is analyzed the same as in the independent claim and does not provide a practical application or significantly more for the same reasons. Claim 49-51, 65 merely describes determining text. Claim 52 merely describes providing a signal to display text. Claim 54 merely describes defining datasets. Claim 58 merely describes display of text strings.
Claim Rejections - 35 USC § 102
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 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 48-63 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hayter (US 20180226150)
CLAIM 48
Hayter teaches A system for providing human-readable observations and recommendations in treatment assessment for an interactive user interface, the system comprising: a wireless communication circuitry configured to receive medical monitoring data from a sensor control device worn by a patient; (Hayter para 43 teaches components can be in a wireless communication with each other. Para 40-41 further teaches a computing system and device that may be a wearable smart device. Para 95 further teaches a patient may be wearing a sensor control device to collect data.)
at least one processor coupled with the wireless communication circuitry and a memory, the memory storing program instructions that, when executed by the at least one processor, cause the at least one processor to: (Hayter para 273 teaches processing circuitry, a memory and instruction to carry out process. See also para 50-53 for a reader device processor, communication processor, and applications processor.)
define separate pre-intervention and post-intervention datasets of the received medical monitoring data; (Hayter para 292 teaches calculating metrics for before and after each intervention and performing analysis and comparison. See also, para 293-295, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets. )
determine a glucose pattern for each of a plurality of corresponding time-of-day periods in the separate pre-intervention and post-intervention datasets; (Hayter para 295 teaches displaying a glucose pattern before and after an intervention.)
wherein each glucose pattern is generated using processor-executed statistical aggregation of stored measurement values assigned to the same time-of-day period across a plurality of monitoring days, to produce a categorical glucose pattern indicator (Hayter para 295 teaches an average of glucose values for the post breakfast time period across a plurality of days. See also, para 293-294, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets for the same day and time periods throughout the day. Para 69 teaches multi-day periods such as 3 days, one week, two weeks and others. Para 180 teaches a mean or median line being traced across a typical day made from data across a plurality of days. See also Fig 19A. See Fig. 58 for low, moderate, high glucose categorical indicators. Examiner notes “to produce a categorical glucose pattern indicator” is intended use and holds no patentable weight)
the glucose pattern comprising at least one of a high pattern and a low pattern (Hayter para 295 teaches an average of glucose values for the post breakfast time period. See also, para 293-294, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets for the same day and time periods throughout the day and periods of high glucose and low glucose (i.e., high and low patterns).)
determine text for display in the interactive user interface based at least in part on the glucose pattern for each of a plurality of corresponding time-of-day periods; and (Hayter para 295 taches displaying text in display indicating the intervention was successful. See also Fig. 58 element 5806 for text display of “Success! Action plan seems to have reduced breakfast glucose highs and variability””. Examiner notes additional textual elements of Fig. 58 are also determined based at least in part on the glucose pattern.)
output the text to the interactive user interface on a display screen associated with a patient-facing computing device. (Hayter para 295 taches displaying text in display indicating the intervention was successful. See also Fig. 58 element 5806 for text display of “Success! Action plan seems to have reduced breakfast glucose highs and variability””. Examiner notes additional textual elements of Fig. 58 are also output on the interactive user interface. Para 119 teaches the display on the reader device notifying a patient regarding an episode relating to glucose readings. )
CLAIM 49
Hayter teaches to determine the text for display in the user interface using a data structure to look up a predetermined value indexed by at least two separate indicators of the glucose pattern for each of the corresponding time-of-day periods. (Hayter Fig. 58 shows displaying text using a table (i.e., data structure with predetermined values corresponding to time of day periods and episode type (i.e., two separate indicators)). Para 177 further teaches information may be displayed via a data structure such as a table or graph and teaches filters may be exercise indicators, meal indicators, sleep indicators, medication dose indicators, textual notes/comments. Para 285 teaches suggestions for glucose patterns for two metrics such as “high” and “lunch”)
CLAIM 50
Hayter teaches to determine the text for display in the user interface based on a time-of-day indicator for each of the corresponding time-of-day periods. (Hayter Fig. 58 element 1900-1 teaches displaying text based on a time of day indicator such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 47-49, 53A, 58, 60 )
CLAIM 51
Hayter teaches to determine the text for display in the user interface based on at least one additional glucose pattern indicator for at least one period adjacent to the corresponding time-of-day periods. (Hayter Fig. 58 element 1900-1 teaches displaying text based low, median and variability of mean, and rapid rise in glucose (i.e., glucose pattern indicator) for period adjacent to time of day periods such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 47-49, 53A, 58, 60 )
CLAIM 52
Hayter teaches to provide a signal for displaying the text to the interactive user interface. (Hayter Fig. 58 element 1900-1 teaches displaying text based on a time of day indicator such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 48-49, 53A, 58, 60. Examiner notes the system is computer implemented on a display and a signal is necessary for computer devices to display information. See also Para 48-50 teaching displaying data on a computing device )
CLAIM 53
Hayter teaches wherein defining the pre-intervention and post- intervention datasets is performed implicitly based on memory state after the most-recent monitoring data is accessed. (Hayter Fig. 58 element 1900-1 displaying text based on a time of day indicator such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 48-49, 53A, 58, 60. Para 299 teaches the most recent day is displayed by default (i.e., implicitly based on memory state after the most recent monitoring data is assessed). Para 297 further teaches metrics may be taken over a time period of such as an hour, day, week.)
CLAIM 54
Hayter teaches wherein defining the pre-intervention and post-intervention datasets is based on user input via the interactive user interface. (Hayter para 299 teaches the user may navigate between days meeting criteria. Examiner notes that navigation between days would change the pre-intervention and post-intervention datasets for each day since each day may be different. Para 63 and 67 also teaches user input via the user interface to change data displayed which examiner notes would change the pre and post intervention dataset displayed for that time period shown)
CLAIM 55
Hayter teaches analyze medical monitoring data collected by a sensor control device over a predetermined period, (Para 95 teaches a patient may be wearing a sensor control device to collect data. Para 47 teaches a sensor control device may collect data according to a predetermined schedule)
and define the time-of-day periods based on data characteristics indicating one or more meal events. (Hayter Fig. 58 element 1900-1 teaches displaying text based on a time of day indicator such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 48-49, 53A, 58, 60 )
CLAIM 56
Hayter teaches wherein the glucose pattern indicators comprise a high indicator and a low indicator. (Hayter Fig. 58 element 3512-1teaches a high and low indicator for each time period.)
CLAIM 57
Hayter teaches to rank texts for different time-of- day periods in a ranked order for the output. (Hayter Fig. 58 element 5802 teaches a ranked order of text for different time periods where 1 is overnight lows, and 2 is highs after breakfast. See also para 167 which teaches report features discussed by Hayter such as ordering of information can be modified. Examiner notes modifying the order of elements is analogous to a ranked order as well. See also para 194-195 which teaches text responses may be order by descending number of responses to facilitate quick identification of the response or problem that occurs most frequently or identify the particular days where problems occurred. )
CLAIM 58
Hayter teaches to display text strings with associated glucose patterns and times-of-day on a user interface device. (Hayter Fig. 47 element 4703 teaches displaying text based on a time of day indicator such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 48-49, 53A, 58, 60 )
CLAIM 59
Hayter teaches to process the pre-intervention dataset to determine a first glucose pattern and separately process the post-intervention dataset to determine a second glucose pattern. (Hayter Fig. 58 teaches processing of pre and post intervention datasets with low, high, and rapid rise in glucose patterns which are determined by processing of the dataset as described in para 60 which teaches processing measurements by determining if measurements violate a threshold or violation of an area of the integral of a sequence of measurements threshold.)
CLAIM 60
Hayter teaches to process the pre-intervention dataset according to a first processing method and process the post-intervention dataset according to a second processing method. (Hayter para 60 teaches processing measurements by determining if measurements violate a threshold or violation of an area of the integral of a sequence of measurements threshold. Examiner notes Fig. 58 teaches pre and post intervention datasets with low, high, and rapid rise in glucose which are determined by processing of the dataset as described in para 60. )
CLAIM 61
Hayter teaches to automatically define the pre- intervention and post-intervention datasets based on identifying predetermined patterns in the medical monitoring data. (Hayter Fig. 47 element 4703 teaches displaying text based on a time of day indicator such as “Before Breakfast, After Breakfast, After Lunch, After Dinner, After Bedtime”. See also Fig. 32, 35B, 48-49, 53A, 58, 60. Para 297 teaches metrics may be taken over a time period of such as an hour, day, week. Examiner notes data metrics in this time period is the most recent monitoring period and would implicitly define the pre-intervention and post-intervention dataset based on the time period, (hour, day week) in order to show the change. Para 299 teaches the most recent day is displayed by default. )
CLAIM 62
Hayter teaches analyze the pre-intervention dataset to identify a first glucose pattern or a first glucose event based on a first set of predetermined patterns, and (Hayter para 293 teaches analyzing glucose data before an intervention for a pattern of overnight lows)
analyze the post-intervention dataset to identify a second glucose pattern or a second glucose event based on a second set of predetermined patterns. (Hayter para 293 teaches analyzing glucose data after an intervention for a pattern of overnight lows to see how this has changed indications of how the identified pattern has changed (e.g., improved, worsened, generally unchanged, etc.), and/or whether the intervention was successful and/or by how much (e.g., very successful, moderately successful, moderately failed, severely failed, etc.). Examiner notes “a second glucose pattern” is interpreted using the broadest reasonable interpretation to include any pattern in the post-intervention dataset. Examiner further notes “a second glucose event based on the second set of predetermined patterns” is interpreted using the broadest reasonable interpretation to include any glucose event that makes up a pattern based on the second set of predetermined patterns which would be analogous to the overnight lows event data being observed for changes after the intervention)
CLAIM 63
Hayter teaches wherein the text includes a user-facing interpretation comparing the pre-intervention and post-intervention glucose patterns and (Hayter para 292 teaches calculating metrics for before and after each intervention and performing analysis and comparison. See also at least para 293-295, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets.)
indicating a direction or magnitude of change in glycemic control for each time-of-day period. (Hayter para 60 teaches rapid rise, rapid fall in glucose level, magnitude, and rate of change. See also at least para 187, 205, 230, 234 for rapid rise. See also Fig. 58 teaches time of day periods with text output on a display for pre-intervention and post-intervention glucose patterns and indicates “rapid rise” as direction and magnitude of change)
CLAIM 64
A computer-implemented method for providing human-readable observations and recommendations in treatment assessment for an interactive user interface, the method comprising: receiving, via wireless communication circuitry, medical monitoring data from a sensor control device worn by a patient; (Hayter para 43 teaches components can be in a wireless communication with each other. Para 40-41 further teaches a computing system and device that may be a wearable smart device. Para 95 further teaches a patient may be wearing a sensor control device to collect data.)
defining, by at least one processor, separate pre-intervention and post-intervention datasets of the received medical monitoring data; (Hayter para 292 teaches calculating metrics for before and after each intervention and performing analysis and comparison. See also, para 293-295, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets. )
determining, by the at least one processor, a glucose pattern for each of a plurality of corresponding time-of-day periods in the separate pre-intervention and post-intervention datasets, (Hayter para 295 teaches displaying a glucose pattern before and after an intervention.)
wherein each glucose pattern is generated using processor-executed statistical aggregation of stored measurement values assigned to the same time-of-day period across a plurality of monitoring days to produce a categorical glucose pattern indicator, the glucose pattern comprising at least one of a high pattern and a low pattern; (Hayter para 295 teaches an average of glucose values for the post breakfast time period across a plurality of days. See also, para 293-294, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets for the same day and time periods throughout the day. Para 69 teaches multi-day periods such as 3 days, one week, two weeks and others. Para 180 teaches a mean or median line being traced across a typical day made from data across a plurality of days. See also Fig 19A. See Fig. 58 for low, moderate, high glucose categorical indicators. Examiner notes “to produce a categorical glucose pattern indicator” is intended use and holds no patentable weight)
determining, by the at least one processor, text for display in the interactive user interface based at least in part on the glucose pattern for each of the plurality of corresponding time-of-day periods; (Hayter para 295 taches displaying text in display indicating the intervention was successful. See also Fig. 58 element 5806 for text display of “Success! Action plan seems to have reduced breakfast glucose highs and variability””. Examiner notes additional textual elements of Fig. 58 are also determined based at least in part on the glucose pattern. )
and outputting the text to the interactive user interface on a display screen associated with a patient- facing computing device. (Hayter para 295 taches displaying text in display indicating the intervention was successful. See also Fig. 58 element 5806 for text display of “Success! Action plan seems to have reduced breakfast glucose highs and variability””. Examiner notes additional textual elements of Fig. 58 are also output on the interactive user interface. Para 119 teaches the display on the reader device notifying a patient regarding an episode relating to glucose readings.)
CLAIM 65
wherein determining the text for display comprises accessing a data structure storing predetermined text values indexed by at least two separate indicators of the glucose pattern for each of the corresponding time-of-day periods. (Hayter Fig. 58 shows displaying text using a table (i.e., data structure with predetermined values corresponding to time of day periods and episode type (i.e., two separate indicators)). Para 177 further teaches information may be displayed via a data structure such as a table or graph and teaches filters may be exercise indicators, meal indicators, sleep indicators, medication dose indicators, textual notes/comments. Para 285 teaches suggestions for glucose patterns for two metrics such as “high” and “lunch”)
CLAIM 66
wherein the text includes a user-facing interpretation comparing the pre-intervention and post-intervention glucose patterns and indicating a direction or magnitude of change in glycemic control for each time-of-day period. (Hayter para 60 teaches rapid rise, rapid fall in glucose level, magnitude, and rate of change. See also at least para 187, 205, 230, 234 for rapid rise. See also Fig. 58 teaches time of day periods with text output on a display for pre-intervention and post-intervention glucose patterns and indicates “rapid rise” as direction and magnitude of change)
CLAIM 67
A non-transitory computer-readable medium storing program instructions that, when executed by at least one processor, cause the at least one processor to: receive medical monitoring data from a sensor control device worn by a patient; (Hayter para 43 teaches components can be in a wireless communication with each other. Para 40-41 further teaches a computing system and device that may be a wearable smart device. Para 95 further teaches a patient may be wearing a sensor control device to collect data.)
define separate pre-intervention and post-intervention datasets of the received medical monitoring data; (Hayter para 292 teaches calculating metrics for before and after each intervention and performing analysis and comparison. See also, para 293-295, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets. )
determine a glucose pattern for each of a plurality of corresponding time-of-day periods in the separate pre-intervention and post-intervention datasets using statistical aggregation of stored measurement values assigned to the same time-of-day period across a plurality of monitoring days to produce a categorical glucose pattern indicator; (Hayter para 295 teaches an average of glucose values for the post breakfast time period across a plurality of days. See also, para 293-294, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets for the same day and time periods throughout the day. Para 69 teaches multi-day periods such as 3 days, one week, two weeks and others. Para 180 teaches a mean or median line being traced across a typical day made from data across a plurality of days. See also Fig 19A. See Fig. 58 for low, moderate, high glucose categorical indicators. Examiner notes “to produce a categorical glucose pattern indicator” is intended use and holds no patentable weight )
determine text for display in an interactive user interface based at least in part on the glucose pattern for each of the plurality of corresponding time-of-day periods; and (Hayter para 295 taches displaying text in display indicating the intervention was successful. See also Fig. 58 element 5806 for text display of “Success! Action plan seems to have reduced breakfast glucose highs and variability””. Examiner notes additional textual elements of Fig. 58 are also determined based at least in part on the glucose pattern.)
output the text to the interactive user interface on a display screen associated with a patient- facing computing device. (Hayter para 295 taches displaying text in display indicating the intervention was successful. See also Fig. 58 element 5806 for text display of “Success! Action plan seems to have reduced breakfast glucose highs and variability””. Examiner notes additional textual elements of Fig. 58 are also output on the interactive user interface. Para 119 teaches the display on the reader device notifying a patient regarding an episode relating to glucose readings. )
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140350369 Budiman
Para 87 teaches a timeframe of days and present percentiles of glucose readings. See also Fig. 1
US 11222724 Davis
Claim 4, “detected pattern of the one or more real-time glucose concentration values comprising at least one of nighttime lows or post-prandial highs.”
Vienica, Continuous Glucose Monitoring: A Review of Available Systems, September 2019
Section Dexcom G4 Platinum with Share teaches high or low glucose readings
Response to Arguments Regarding U.S.C. 102 Rejection
Applicant argues:
Independent claim 48 requires generation of a glucose pattern using processor-executed statistical aggregation of stored measurement values assigned to the same time-of-day period across a plurality of monitoring days to produce a categorical glucose pattern indicator, wherein the glucose pattern comprises at least one of a high pattern and a low pattern. As amended, claim 48 thus requires that the statistical aggregation yields a discrete, categorical glucose pattern indicator generated by the system as a computational output, rather than averaged values, graphical displays, or clinician interpretation of glucose behavior as described in the cited references.
The Office Action cites portions of the reference describing analysis, review, or display of glucose information collected over time, including discussion of averages or multi-day glucose profiles. However, even assuming the cited passages involve multi-day data or averaging, the Office Action does not identify disclosure of the claimed statistical aggregation workflow that produces categorical glucose pattern indicators as outputs of the system. Averaging or plotting glucose values over time does not inherently result in the generation of categorical pattern indicators, such as "high" or "low" pattern classifications, as required by amended claim 48.
Anticipation requires disclosure of the claimed elements in the arrangement recited by the claim, not merely disclosure of related concepts in isolation. Here, the rejection relies on disclosures directed to reporting, visualization, or comparison of glucose information, but does not demonstrate that the cited reference performs processor-executed aggregation of time-of-day-aligned glucose data across multiple monitoring days to generate categorical glucose pattern indicators as system-produced analytical results. The cited reference's discussion of averaged curves, graphical regions, or displayed summaries does not satisfy this requirement, as such visual or descriptive outputs are not equivalent to producing discrete categorical indicators generated by computational logic.
In particular, the Office Action does not identify where the cited reference assigns stored measurement values to corresponding time-of-day periods across multiple monitoring days and statistically aggregates those aligned datasets to produce categorical glucose pattern indicators comprising at least one of a high pattern and a low pattern. Nor does the rejection identify disclosure of generating such categorical indicators as defined analytical outputs of the aggregation step. Instead, the cited disclosures describe presentation or evaluation of glucose information for human interpretation, which is not equivalent to the claimed processor-driven operation that generates categorical glucose pattern determinations as analytical outputs of the system.
Because the Office Action relies on isolated disclosures relating to multi-day glucose analysis without demonstrating the claimed aggregation-to-categorization workflow as a unified arrangement, the rejection does not meet the requirements for anticipation under 35 U.S.C. § 102. Accordingly, independent claim 48, as amended, is not anticipated by the cited reference.
Examiner responds:
In light of amendment, Examiner has referenced additional portions of Hayter to address new limitations. Hayter para 295 teaches an average of glucose values for the post breakfast time period across a plurality of days. See also, para 289, 293-294, 297, 373 and Fig. 58 which shows an image of comparing pre and post intervention datasets for the same day and time periods throughout the day. Para 69 teaches multi-day periods such as 3 days, one week, two weeks and others. Para 180 teaches a mean or median line being traced across a typical day made from data across a plurality of days. See also Fig 19A. See Fig. 58 for low, moderate, high glucose categorical indicators.
However, Examiner notes “to produce a categorical glucose pattern indicator” is the intended effect or use of the “wherein each glucose pattern is generated using processor-executed statistical aggregation of stored measurement values assigned to the same time-of-day period across a plurality of monitoring day” step and holds no patentable weight.
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.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687