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 .
Remarks
This action is in response to the amendments received on 1/2/26. Claims 1-23 are pending in the application. Applicants arguments are carefully and respectfully considered.
Claims 19-23 are rejected under 35 U.S.C. 101.
Claims 1-8 and 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Darby et al. (US 2017/0098042), and further in view of Smart et al. (US 2021/0334275).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Darby in view of Smart, and further in view of Monroe et al., Temporal Event Sequence Simplification, published December 2013.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Darby et al. (US 2017/0098042), and further in view of Woods (US 2021/0374142).
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Johns (US 12,033,747), and further in view of Smart et al. (US 2021/0334275).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Johns in view of Smart, and further in view of Kobayashi (US 2023/0012637).
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 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
With respect to claim 19, Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
The limitation of “determine whether the single date in the second event data is equal to the start date of the first date range” and “determine whether the single date in the second event data is equal to the end date of the first date range”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “determining” in the context of this claim encompasses the user mentally analyzing data. Similarly, the limitation of “generate a binarized value” and “generate a second binarized value for the second event data value”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “generate” in the context of this claim encompasses the user thinking of an appropriate value. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. Claims 19 and 20 recite a processor to execute the operations, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “access first event data”, “access second event data”, “store the binarized value”, and “store the second binarized value.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do 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 elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “access first event data” and “access second event data”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to “store the binarized value” and “store the second binarized value”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts") and “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claim 20, the limitations are directed towards the above addressed limitations and do not provide significantly more than the abstract idea.
With respect to claim 21, Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
The limitation of “determining that the subject had or is having an adverse reaction to the pharmaceutical based on the data obtained from the scalable data structure”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “determining” in the context of this claim encompasses the user mentally analyzing data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. Claims 19 and 20 recite a processor to execute the operations, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “obtaining data from a scalable data structure with first event data and second event data”, “administering a pharmaceutical to a subject” and “discontinuing administration of the pharmaceutical.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do 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 elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “obtaining data from a scalable data structure with first event data and second event data”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to “administering a pharmaceutical to a subject” and “discontinuing administration of the pharmaceutical”, these steps are commonly used in the prior art. See Hougaard (US 6,639,515) Col. 1 Li. 15-49 and Liu et al. (US 2022/0172805) pa 0001-0003.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claim 22, the claim limitations are directed towards generating a visualization and transmitting the visualization. The courts have found limitations directed towards such visualization of data to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
With respect to claim 23, the claim limitations do not further integrate the above identified judicial exception into a practical application and do not provide significantly more than the abstract idea.
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 1-8 and 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Darby et al. (US 2017/0098042), and further in view of Smart et al. (US 2021/0334275).
With respect to claim 1, Darby teaches a system, comprising: a processor programmed to:
access first event data comprising a first event data value and a corresponding first date range for which the first event data value pertains (Darby, pa 0087, the first device 102 and the second device 108 may send the first data set 118 and the second data set 120 to the intermediary device 114 without communication between the first device 102 and the second device 108. The intermediary device 114 can act as a conduit to transfer data sets between the first and/or second devices 102, 108 and/or can process the data sets to merge it or facilitate the merging. & pa 0091, The second data set 120 may comprise a second data 116 obtained from the second device 108 over the period of time Tp. The second data 116 may be in the form of a block of data entries. The data entries may each be associated with a time. In the illustrated configuration, the block of data comprising one or more data the second data set 120 may be contiguous between a third time T3 and a fourth time T4);
access second event data comprising a second event data value and a corresponding single date for which the second event data value pertains (Darby, pa 0087, the first device 102 and the second device 108 may send the first data set 118 and the second data set 120 to the intermediary device 114 without communication between the first device 102 and the second device 108. The intermediary device 114 can act as a conduit to transfer data sets between the first and/or second devices 102, 108 and/or can process the data sets to merge it or facilitate the merging. & pa 0091, the first data 113 may be noncontiguous between a first time Tl and a second time T2 over the period of time Tp, e.g., the first data 113 may comprise one or more gaps or missing entries in the first data 113 over the period of time Tp.);
…
translate the first event data and the second event data into a time series of events in which: (a) the first event data value in the first event data is associated with the single date in second event data and (b) the second event data value in the second event data is associated with the first date range in the first event data (Fig. 3 & pa 0091, If the period of time Tp related to the first data set 118 is the same as the period of time Tp related to the second data set 120, and if the first and second data sets 118, 120 together do not comprise multiple data entries associated with the same time, then the intermediary device 114 may integrate the first and second data sets 118, 120 to create a third data set 122. The integration may be such that the data entries of the first data set 118 and the data entries of the second data set 120 are arranged in order such that the times associated with the data entries are set in a temporally sequential order with respect to one another. For example, in the illustrated configuration, the first and second data sets 118, 120 may be arranged such that the first data block 113A of the first data 113 comes first the data block of the second data 116 comes second, and the second data block 113B of the first data 113 comes third such that the third data set 122 comprises data entries of the first data 118 and the second data set 120 arranged in temporal succession and such that the third data set 122 represents a complete set of data spanning over the period of time Tp. );
populate the scalable data structure based on the structured schema and the translated time series of events, wherein a number of a plurality of rows is based on a start date in the first date range, an end date in the first date range, and the second single date in the second event data and wherein each row from among the plurality of rows has a plurality of columns each corresponding to the distinct values derived from the first event data and the second event data (Darby, pa 0091, The integration may be such that the data entries of the first data set 118 and the data entries of the second data set 120 are arranged in order such that the times associated with the data entries are set in a temporally sequential order with respect to one another. For example, in the illustrated configuration, the first and second data sets 118, 120 may be arranged such that the first data block 113A of the first data 113 comes first the data block of the second data 116 comes second, and the second data block 113B of the first data 113 comes third such that the third data set 122 comprises data entries of the first data 118 and the second data set 120 arranged in temporal succession and such that the third data set 122 represents a complete set of data spanning over the period of time Tp.).
generate, for display, a visualization based on the populated scalable data structure (Darby, pa 0092, The third data set 122 may be used to generate a report comprising the entries of the third data set 122 and/or one or more functions of one or more entries of the third data & pa 0083, The report may be sent to one or more of the medical devices, to a physician, to an insurer, or to another person, apparatus, or system.).
Darby doesn't expressly discuss generate a structured schema for a scalable data structure in which a number of columns is based on a number of distinct values derived from the first event data and the second event data.
Smart teaches access first event data comprising a first event data value and a corresponding first date range for which the first event data value pertains (Smart, Fig 3A, data sets 302, 312, and 324 & pa 0048, Iterative join operation 300 includes merged data sets 302, 312, 324, and 336, each of which is a different data set type. In particular, data set 302 is a ticker-brand data set, data set 312 is another data set type ("data set type A"), data set 324 is another data set type ("data set type B"), and data set 336 is another data set type ("data set type C").);
access second event data comprising a second event data value and a corresponding single date for which the second event data value pertains (Smart, Fig 3A, data set 336 & pa 0048, Iterative join operation 300 includes merged data sets 302, 312, 324, and 336, each of which is a different data set type. In particular, data set 302 is a ticker-brand data set, data set 312 is another data set type ("data set type A"), data set 324 is another data set type ("data set type B"), and data set 336 is another data set type ("data set type C").);
generate a structured schema for a scalable data structure in which a number of columns is based on a number of distinct values derived from the first event data and the second event data (Smart, pa 0026, Standardization module 130 may initially transform a data set into a data frame, which is a two-dimensional tabular arrangement of data values. The data frame may include conceptual tuples of an input data set as columns, and rows that contain values corresponding to the values for the values of each record in the input data set.);
translate the first event data and the second event data into a time series of events in which: (a) the first event data value in the first event data is associated with the single date in second event data and (b) the second event data value in the second event data is associated with the first date range in the first event data (Smart, Fig. 3A & pa 0034, Data of each data set may be grouped according to distinct combinations of the values in the ID and timestamp columns, and a single value can be computed for each remaining column that represents that column. Examiner note: merged dataset 346 shows sequential dates and appropriate attribute values for that date according to the datasets 302, 312, 324, and 336);
populate the scalable data structure based on the structured schema and the translated time series of events, wherein a number of a plurality of rows is based on a start date in the first date range, an end date in the first date range, and the second single date in the second event data and wherein each row from among the plurality of rows has a plurality of columns each corresponding to the distinct values derived from the first event data and the second event data (Smart, Fig. 3A & pa 0059, Merging module 135 then produces a resulting merged data set in the standardized schema by combining the rows that are associated with each other in the schemas of the merged and new data sets into the schema of the new data set, thus creating a resulting merged data set that includes rows populated with values that are correctly associated with each other rather than references of matches between other data sets.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Darby with the teachings of Smart because it juxtaposes disparate data in a manner that supports the discovery of new relationships between entities (Smart, pa 0014).
With respect to claim 2, Darby in view of Smart teaches the system of claim 1, wherein to generate the structured schema, the processor is further programmed to: parse the first event data value from the first event data; and generate a first column in the structured schema, the first column having a first column name based on the first event data value (Smart, pa 0047, Merging module 135 may combine data sets 205 and 225 by performing separate union operations to concatenate the data sets in a row-wise manner. In particular, a row of data set 205 may be joined via a union operation with a row of data set 225 based on a matching date value of column 210 and/or location value of column 215. Thus, each row of data set 230 will include the values of column 220 ("sensor measure 1") from data set 205 and values of column 230 ("sensor measure 2") from data set 225.);
With respect to claim 3, Darby in view of Smart teaches the system of claim 2, wherein to generate the structured schema, the processor is further programmed to: parse the second event data value from the second event data; and generate a second column in the structured schema, the second column having a second column name based on the second event data value (Smart, pa 0047, Merging module 135 may combine data sets 205 and 225 by performing separate union operations to concatenate the data sets in a row-wise manner. In particular, a row of data set 205 may be joined via a union operation with a row of data set 225 based on a matching date value of column 210 and/or location value of column 215. Thus, each row of data set 230 will include the values of column 220 ("sensor measure 1") from data set 205 and values of column 230 ("sensor measure 2") from data set 225.).
With respect to claim 4, Darby in view of Smart teaches the system of claim 1, wherein to translate the first event data, the processor is further programmed to: determine whether the single date in the second event data is equal to the start date of the first date range; generate a binarized value based on the second event data value and the determination of whether the single date in the second event data is equal to the start date of the first date range (Darby, pa 0092, The report may comprise a binary indicator ( e.g. 'yes,' 'no,' ' true,' 'false,' 'pass,' 'fail,' etc) derived from the data entries indicating whether the patient succeeded or did not succeed in achieving a level of adherence to a therapy regime relative to a threshold level of adherence.); store the binarized value as a column value of a column for a row corresponding to the start date (Darby, pa 0108, the function of the data entries of the third data set may comprise an indicator related to the adherence of a patient to a therapy regime. In some such configurations, the indicator may comprise a binary indicator).
With respect to claim 5, Darby in view of Smart teaches the system of claim 4, wherein to translate the second event data, the processor is further programmed to: determine whether the single date in the second event data is equal to the end date of the first date range; generate a second binarized value based on the second event data value and the determination of whether the single date in the second event data is equal to the end date of the first date range (Darby, pa 0092, The report may comprise a binary indicator ( e.g. 'yes,' 'no,' ' true,' 'false,' 'pass,' 'fail,' etc) derived from the data entries indicating whether the patient succeeded or did not succeed in achieving a level of adherence to a therapy regime relative to a threshold level of adherence.); store the second binarized value as a second column value of a second column for a second row corresponding to the end date (Darby, pa 0108, the function of the data entries of the third data set may comprise an indicator related to the adherence of a patient to a therapy regime. In some such configurations, the indicator may comprise a binary indicator).
With respect to claim 6, Darby in view of Smart teaches the system of claim 1, wherein to translate the second event data, the processor is further programmed to: determine whether the single date in the second event data is within the first date range; generate a binarized value based on the first event data value and the determination of whether the single date in the second event data is within the first date range (Darby, pa 0092, The report may comprise a binary indicator ( e.g. 'yes,' 'no,' ' true,' 'false,' 'pass,' 'fail,' etc) derived from the data entries indicating whether the patient succeeded or did not succeed in achieving a level of adherence to a therapy regime relative to a threshold level of adherence.); store the binarized value as a column value of a column for a row corresponding to the single date (Darby, pa 0108, the function of the data entries of the third data set may comprise an indicator related to the adherence of a patient to a therapy regime. In some such configurations, the indicator may comprise a binary indicator).
With respect to claim 7, Darby in view of Smart teaches the system of claim 1, wherein the first event data value pertains to a symptom that was reported during the first date range (Darby, pa 0088, The data may comprise, for example, compliance data, AHI (apnea-hypopnea index) data, sleep quality data, data related to the number of hours the medical devices were used, or other types of data).
With respect to claim 8, Darby in view of Smart teaches the system of claim 1, wherein the second event data value pertains to a test result that was obtained at the single date (Darby, pa 0088, The data may comprise, for example, compliance data, AHI (apnea-hypopnea index) data, sleep quality data, data related to the number of hours the medical devices were used, or other types of data).
With respect to claims 10-17, the limitations are essentially the same as claims 1-8, and are rejected for the same reasons.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Darby in view of Smart, and further in view of Monroe et al., Temporal Event Sequence Simplification, published December 2013.
With respect to claim 9, Darby in view of Smart teaches the system of claim 1, as discussed above. Darby in view of Smart doesn't expressly discuss wherein to generate the visualization, the processor is further programmed to: generate a timeline based on rows in the scalable data structure; for each row in the scalable data structure: for each column in the scalable data structure, determine whether a column value for the column represents an event of interest and generate an event marker along the timeline corresponding to the row depending on whether the column value for the column represents an event of interest.
Monroe teaches wherein to generate the visualization, the processor is further programmed to: generate a timeline based on rows in the scalable data structure (Monroe, pg. 2229, 2nd pa, aggregated view of a dataset by grouping records with the same event sequence); for each row in the scalable data structure: for each column in the scalable data structure, determine whether a column value for the column represents an event of interest and generate an event marker along the timeline corresponding to the row depending on whether the column value for the column represents an event of interest (Monroe, pg. 2229, Fig. 2, align visualization timeline with stroke event of sample dataset).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Darby in view of Smart to have included the teachings of Monroe because it provides a visualization of the dataset that indicates events that occurred around that point (Monroe, pg. 2229, Fig. 2).
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Darby et al. (US 2017/0098042), and further in view of Woods (US 2021/0374142).
With respect to claim 19, Darby teaches a non-transitory computer readable medium storing instructions that, when executed by a processor, programs the processor to:
access first event data comprising a first event data value and a corresponding first date range for which the first event data value pertains, the first date range having a start date and an end date (Darby, pa 0087, the first device 102 and the second device 108 may send the first data set 118 and the second data set 120 to the intermediary device 114 without communication between the first device 102 and the second device 108. The intermediary device 114 can act as a conduit to transfer data sets between the first and/or second devices 102, 108 and/or can process the data sets to merge it or facilitate the merging. & pa 0091, The second data set 120 may comprise a second data 116 obtained from the second device 108 over the period of time Tp. The second data 116 may be in the form of a block of data entries. The data entries may each be associated with a time. In the illustrated configuration, the block of data comprising one or more data the second data set 120 may be contiguous between a third time T3 and a fourth time T4);
access second event data comprising a second event data value and a corresponding single date for which the second event data value pertains (Darby, pa 0087, the first device 102 and the second device 108 may send the first data set 118 and the second data set 120 to the intermediary device 114 without communication between the first device 102 and the second device 108. The intermediary device 114 can act as a conduit to transfer data sets between the first and/or second devices 102, 108 and/or can process the data sets to merge it or facilitate the merging. & pa 0091, The second data set 120 may comprise a second data 116 obtained from the second device 108 over the period of time Tp. The second data 116 may be in the form of a block of data entries. The data entries may each be associated with a time. In the illustrated configuration, the block of data comprising one or more data the second data set 120 may be contiguous between a third time T3 and a fourth time T4);
determine whether the single date in the second event data is equal to the start date of the first date range (Darby, pa 0091, If the period of time Tp related to the first data set 118 is the same as the period of time Tp related to the second data set 120, and if the first and second data sets 118, 120 together do not comprise multiple data entries associated with the same time, then the intermediary device 114 may integrate the first and second data sets 118, 120 to create a third data set 122);
generate a binarized value based on the second event data value and the determination of whether the single date in the second event data is equal to the start date of the first date range; store the binarized value in association with the start date (Darby, pa 0092, The report may comprise a binary indicator ( e.g. 'yes,' 'no,' ' true,' 'false,' 'pass,' 'fail,' etc) derived from the data entries indicating whether the patient succeeded or did not succeed in achieving a level of adherence to a therapy regime relative to a threshold level of adherence.);
determine whether the single date in the second event data is equal to the end date of the first date range (Darby, pa 0091, If the period of time Tp related to the first data set 118 is the same as the period of time Tp related to the second data set 120, and if the first and second data sets 118, 120 together do not comprise multiple data entries associated with the same time, then the intermediary device 114 may integrate the first and second data sets 118, 120 to create a third data set 122);
generate a second binarized value for the second event data value based on the determination of whether the single date in the second event data is equal to the end date of the first date range; and store the second binarized value in association with the end date (Darby, pa 0092, The report may comprise a binary indicator ( e.g. 'yes,' 'no,' ' true,' 'false,' 'pass,' 'fail,' etc) derived from the data entries indicating whether the patient succeeded or did not succeed in achieving a level of adherence to a therapy regime relative to a threshold level of adherence.).
Darby doesn't expressly discuss generate a second binarized value.
Woods teaches generate a second binarized value for the second event data value based on the determination of whether the single date in the second event data is equal to the end date of the first date range; and store the second binarized value in association with the end date (Woods, pa 0091, Prescription information was translated into a series of ‘0’s and ‘1’s by their release dates and days' supply and allow ‘stashing’. For example, 2 30-day prescriptions released on Jul. 30, 2010 and Aug. 25, 2010 were represented by 60 ‘1’s from Jul. 30, 2010 through Sep. 27, 2010. The binary digit indicated whether the patient was on the medication that day (1=YES, 0=NO). To assess degrees of medication overlap, composite binary strings were created from the 2 individual medication strings by comparing characters at the same position. If characters were both ‘1’, then the corresponding position of the composite string is ‘1’ (overlap); otherwise it's ‘0’ (no overlap). An example of a medication string meeting satisfying certain criteria is shown in FIG. 7. Criteria may be associated with temporal information, prescription information, or other relevant information.)
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Darby with the teachings of Woods because it reduces storage needed (Woods, pa 0119).
With respect to claim 20, Darby in view of Woods teaches non-transitory computer readable medium storing instructions of claim 19, wherein the instructions, when executed, further programs the processor to: determine whether the single date in the second event data is within the first date range (Darby, pa 0091, If the period of time Tp related to the first data set 118 is the same as the period of time Tp related to the second data set 120, and if the first and second data sets 118, 120 together do not comprise multiple data entries associated with the same time, then the intermediary device 114 may integrate the first and second data sets 118, 120 to create a third data set 122); generate a third binarized value based on the first event data value and the determination of whether the single date in the second event data is within the first date range; store the third binarized value in association with the single date (Woods, pa 0091, Prescription information was translated into a series of ‘0’s and ‘1’s by their release dates and days' supply and allow ‘stashing’. For example, 2 30-day prescriptions released on Jul. 30, 2010 and Aug. 25, 2010 were represented by 60 ‘1’s from Jul. 30, 2010 through Sep. 27, 2010. The binary digit indicated whether the patient was on the medication that day (1=YES, 0=NO). To assess degrees of medication overlap, composite binary strings were created from the 2 individual medication strings by comparing characters at the same position. If characters were both ‘1’, then the corresponding position of the composite string is ‘1’ (overlap); otherwise it's ‘0’ (no overlap). An example of a medication string meeting satisfying certain criteria is shown in FIG. 7. Criteria may be associated with temporal information, prescription information, or other relevant information. & Darby, pa 0092, The report may comprise a binary indicator ( e.g. 'yes,' 'no,' ' true,' 'false,' 'pass,' 'fail,' etc) derived from the data entries indicating whether the patient succeeded or did not succeed in achieving a level of adherence to a therapy regime relative to a threshold level of adherence.).
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Johns (US 12,033,747), and further in view of Smart et al. (US 2021/0334275).
With respect to claim 21, Johns teaches a method of administering a treatment, comprising:
administering a pharmaceutical to a subject (Johns, Col. 4 Li. 33-35, the event device 1 is configured to receive information via input devices 70 regarding activities occurring during a code event & Col. 4 Li. 40-47, The content of such information might include occurrence of medical events (such as application of treatment and medication), media recordings of medical events (such as audio, image, or video recordings), patient data or values (such as heart rate, blood pressure, etc.), and or media recordings of patient data or values (such as audio, image, or video recordings of patient appearance or behavior).;
obtaining data from a scalable data structure with first event data and second event data (Johns, Col. 4 Li. 53-57, the event device 1 may use patient data or values to customize the desired medication or treatment or otherwise generate and output additional medical information based upon the received medical event information), the first event data indicating a first event associated with the subject during a first date range after the administering and the second event data indicating a second event experienced by the subject at a single date after the administering (Johns, Col. 4 Li. 48-52, The event device 1 preferably stores the information received in the secure memory 50. Where applicable, the event device 1 preferably applies timestamps to the stored 50 information, such as to document the occurrence of certain events or activities and their respective time of occurrence. & Col. 4 Li. 40-47, The content of such information might include occurrence of medical events (such as application of treatment and medication), media recordings of medical events (such as audio, image, or video recordings), patient data or values (such as heart rate, blood pressure, etc.), and or media recordings of patient data or values (such as audio, image, or video recordings of patient appearance or behavior).),
determining that the subject had or is having an adverse reaction to the pharmaceutical based on the data obtained from the scalable data structure (Johns, Col. 5 Li. 40-42, The event device 1 might identify an interaction problem such as overlapping or conflicting medications or treatments.); and
discontinuing administration of the pharmaceutical based on the determination (Johns, Col. 4 Li. 53-57, the event device 1 may use patient data or values to customize the desired medication or treatment or otherwise generate and output additional medical information based upon the received medical event information).
Johns doesn't expressly discuss wherein a number of columns of the scalable data structure is based on a number of distinct values derived from the first event data and the second event data and a number of a plurality of rows is based on a start date in the first date range, an end date in the first date range, and the second single date in the second event data and wherein each row from among the plurality of rows has a plurality of columns each corresponding to the distinct values derived from the first event data and the second event data;
Smart teaches wherein a number of columns of the scalable data structure is based on a number of distinct values derived from the first event data and the second event data and a number of a plurality of rows is based on a start date in the first date range, an end date in the first date range, and the second single date in the second event data and wherein each row from among the plurality of rows has a plurality of columns each corresponding to the distinct values derived from the first event data and the second event data (Smart, Fig. 3A & pa 0034, Data of each data set may be grouped according to distinct combinations of the values in the ID and timestamp columns, and a single value can be computed for each remaining column that represents that column. Examiner note: merged dataset 346 shows sequential dates and appropriate attribute values for that date according to the datasets 302, 312, 324, and 336).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Johns with the teachings of Smart because it juxtaposes disparate data in a manner that supports the discovery of new relationships between entities (Smart, pa 0014).
With respect to claim 22, Johns in view of Smart teaches the method of claim 21, further comprising: generating a visualization based on the first event data and the second event data; and transmitting the visualization to support clinical diagnostics in a medical decision support system (Johns, Col. 5 Li. 37-46, during a particular code event, the event device 1 might receive information from an external server 94 which identifies medications or other treatments to be administered. The event device 1 might identify an interaction problem such as overlapping or conflicting medications or treatments. The event device 1 might then communicate such problem to the external server 94. In this manner, the event device 1 may serve as a "smart" device relative to the activities which are occurring during the particular code event.).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Johns in view of Smart, and further in view of Kobayashi (US 2023/0012637).
With respect to claim 23, Johns in view of Smart teaches the method of claim 21, as discussed above. Johns in view of Smart doesn't expressly discuss wherein the adverse reaction comprises Drug Reaction with Eosinophilia and Systemic Symptoms.
Kobayashi teaches wherein the adverse reaction Drug Reaction with Eosinophilia and Systemic Symptoms (Kobayashi, pa 0107, (1) A simulation system that performs a simulation related to a specific disease or symptom for which a major index for diagnosis is assigned, the simulation system including: a database created on the basis of inspection results collected from a large number of subjects; an entry section through which a freely selectable value is entered for each of a plurality of inspection items contained in the inspection results; and a calculation unit that collates the value of each inspection item having been entered through the entry section, with the database, and derives a score regarding the specific disease or symptom … The simulation system according to (1), wherein the specific disease or symptom is eosinophilia).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Johns in view of Smart with the teachings of Kobayashi because the analysis can give a new awareness to doctors (Kobayashi, pa 0077).
Response to Amendment
35 U.S.C. 112
The amendments to claim 23 have overcome the 35 U.S.C. 112 rejection. The 35 U.S.C. 112 rejection of claim 23 is withdrawn.
Response to Arguments
35 U.S.C. 101
Applicant argues that the claims are eligible under 35 U.S.C. 101. The Examiner respectfully disagrees. Applicant has not explained how the claim limitations go beyond a mental process or how they recite significantly more than the abstract idea.
35 U.S.C. 103
With respect to claims 1 and 10, Applicant argues that Smart fails to teach “generate a structured schema for a scalable data structure in which a number of columns is based on a number of distinct values derived from the first event data and the second event data” because the tabular structure is generated regardless of whether the underlying data values are distinct, and the same table would be produced even if all event values were identical. The Examiner respectfully disagrees. Smart discloses that “conceptual tuples of an input dataset” are included as columns by standardization module. Input datasets are standardized into a tabular arrangement to produce a merged data set. When creating the standardized, merged dataset, data of each data set may be grouped according to distinct combinations of the values in the ID and timestamp columns. Therefore, the columns corresponding to the ID and timestamp columns are based on distinct values from the event data.
With respect to claims 19 and 21, Applicant argues that Darby fails to teach “determine whether the single date in the second event data is equal to the start date of the first date range; generate a binarized value based on the second event data value and the determination of whether the single date in the second event data is equal to the start date of the first date range; store the binarized value in association with the start date; determine whether the single date in the second event data is equal to the end date of the first date range; generate a second binarized value for the second event data value based on the determination of whether the single date in the second event data is equal to the end date of the first date range; and store the second binarized value in association with the end date” because Darby’s table would not show derived columns indicating whether the event coincided with the start or end of a treatment period and cannot distinguish, at the table level, whether an adverse event occurred at the beginning of treatment, at the end of treatment, or somewhere in between. The Examiner respectfully disagrees. Applicant has not pointed out how the limitations of the claim are different from Darby’s teachings. The claims to not require “derived columns indicating whether the event coincided with the start or end of a treatment period” or to “distinguish, at the table level, whether an adverse event occurred at the beginning of treatment, at the end of treatment, or somewhere in between.” Additionally, Darby teaches checking the first and second data sets for data entries associated with the same time before integrating the first and second data sets to create a third data set (pa 0091). The data entries of the first and second data sets are associated with a time (pa 0091). This provides “determine whether the single date in the second event data is equal to the end date of the first date range.”
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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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169