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 7/17/26. Claims 1-18 and 21-23 are pending in the application. Claims 19 and 20 have been cancelled. Applicants arguments are carefully and respectfully considered.
Claims 21-23 are rejected under 35 U.S.C. 101.
Claims 1-3 and 10-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Smart et al. (US 2021/0334275).
Claims 4-8 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Smart, and further in view of Darby et al. (US 2017/0098042).
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.
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 21-23 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 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”, this fails to meaningfully limit the claim because it does not require any particular application of the pharmaceutical, and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
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 § 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 10-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Smart et al. (US 2021/0334275).
With respect to claim 1, Smart 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 (Smart, Fig 3A, dataset type A 312, date range 2010.04.01-2020.03.31, event data value place address 123 main street & pa 0024, Input data sets may be obtained from database 145 and/or from one or more of databases 155 of database servers 150A-150N);
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 single date 2020.03.01, event data value lat & lon & pa 0024, Input data sets may be obtained from database 145 and/or from one or more of databases 155 of database servers 150A-150N);
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 date range in the first event data (Smart, Fig. 3A, dataset type A 312, date range 2010.04.01-2020.03.31) and the single date in the second event data (Smart, Fig. 3A, dataset type C 336, date 2020.03.01) into a common temporal representation (Smart, Fig. 3A, merged dataset 346, date 2020.03.01) and represent the first event data value and the second event data value relative to common temporal positions based on temporal relationships between the first date range and the single date, in which: (a) the first event data value in the first event data is associated with the single date in second event data (Smart, Fig. 3A in merged dataset 346, place address of column 354 is associated with single date 2020.03.01 in column 348) 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 in merged data set 346, lat & lon values are associated with the date with the first date range of first event data & 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.);
populate the scalable data structure based on the structured schema and the common temporal representation, wherein each row represents one of the common temporal positions and includes inherited event data values corresponding to multiple event data records relative to that common temporal position, and 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.).
With respect to claim 2, 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, 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 claims 10-12, the limitations are essentially the same as claims 1-3, and are rejected for the same reasons.
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 4-8 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Smart, and further in view of Darby et al. (US 2017/0098042).
With respect to claim 4, Smart teaches the system of claim 1, as discussed above.
Darby teaches 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).
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 Smart with the teachings of Darby because it shows whether an object has or doesn’t have a particular feature (Darby, pa 0092).
With respect to claim 5, Smart in view of Darby 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, Smart teaches the system of claim 1, as discussed above.
Darby teaches 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).
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 Smart with the teachings of Darby because it provides data of the particular usage over the time period so that analysis and actions can be appropriately taken (Darby, pa 0006).
With respect to claim 7, Smart teaches the system of claim 1, as discussed above.
Darby teaches 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).
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 Smart with the teachings of Darby because it provides data of the particular usage over the time period so that analysis and actions can be appropriately taken (Darby, pa 0006).
With respect to claim 8, Smart teaches the system of claim 1, as discussed above.
Darby teaches 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).
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 Smart with the teachings of Darby because it provides data of the particular usage over the time period so that analysis and actions can be appropriately taken (Darby, pa 0006).
With respect to claims 13-17, the limitations are essentially the same as claims 4-8, and are rejected for the same reasons.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Smart, and further in view of Monroe et al., Temporal Event Sequence Simplification, published December 2013.
With respect to claim 9, Smart teaches the system of claim 1, as discussed above. 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 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).
With respect to claim 18, the limitations are essentially the same as claim 9, and are rejected for the same reasons.
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 the scalable data structure represents the first data range and the single date as common temporal positions and includes inherited event data values corresponding to multiple event data records relative to the common temporal positions (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.), and 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 in merged data set 346, lat & lon values are associated with the date with the first date range of first event data & 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.).
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 Arguments
35 U.S.C. 101
Applicant argues that claims 21-23 are eligible under 35 U.S.C. 101 because they are similar to Vanda by controlling a specific therapeutic intervention. The Examiner respectfully disagrees. In the decision of Vanda Pharm. Inc. v. West-Ward Pharmaceuticals Int’l Ltd., there is contrast to Mayo Collaborative Servs. v. Prometheus Labs., Inc. Claim 21 recites “determining that the subject had or is having an adverse reaction to the pharmaceutical based on the data obtained from the scalable data structure” which is similar to the “indication” provided by Mayo discussed in the decision of Vanda. See page 31 of Vanda Pharm. Inc. v. West-Ward Pharmaceuticals Int’l Ltd. Similarly, the claims herein do not prescribe a specific regimen to take as a result of that determination. Vanda provided a specific dosage regimen based on the results of genetic testing, which is not analogous to the present claim limitations. Further, Although the representative claim in Mayo recited administering a thiopurine drug to a patient, the claim as a whole was not directed to the application of a drug to treat a particular disease. See id. at 74, 87. Importantly, the Supreme Court explained that the administering step was akin to a limitation that tells engineers to apply a known natural relationship or to apply an abstract idea with computers. See pg. 29 of Vanda Pharm. Inc. v. West-Ward Pharmaceuticals Int’l Ltd. The same logic applies to “discontinuing administration.”
Applicant argues that the claimed determination is not performed on generic medical records, but rather the scalable data structure that is a specialized, computer-generated representation that translates heterogeneous event data having different temporal representations into a unified time series to facilitate clinical analysis and prediction, providing a treatment decision that is based upon a particular data structure. The Examiner respectfully disagrees. Enfish, LLC v. Microsoft Corp. held that the claimed database software designed as a "self-referential" table is patent eligible under 35 U.S.C. § 101 because it is not directed to an abstract idea. In Enfish, the courts determined that the specification must distinguish between the instant invention and conventional solutions (e.g. by disclosing one or more problems with the conventional solutions). There must also be limitations in the claim that disclose how the data structure assists in providing a technological solution to the identified problem. See 2106.04(d)(1). However, the claim herein merely uses the computer as a tool to process the information from the scalable data structure (“… the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools” see MPEP 2106.05(a)(I)).
Applicant argues that claim 21 is directed to patent eligible subject matter under Step 2A Prong 2 because it is similar to USPTO Examples 29 and 43. The Examiner respectfully disagrees. Example 29 discusses several possible claim structures and Applicant has not pointed to which issue provides similar issues. Claim 21 fails to provide additional elements that integrate the abstract idea into a practical application. In Example 43, the “administering” limitation is similar to claim 21 and is analyzed under Step 2A Prong 2 as not integrate the recited judicial exception into a practical application because it does not provide any information as to how the patient is to be treated, or what the treatment is, but instead covers any possible treatment that a doctor decides to administer to the patient.
35 U.S.C. 103
With respect to claims 1 and 10, Applicant argues that Smart fails to teach translating heterogeneous temporal representations into a common temporal representation because Smart is not concerned with underlying temporal representations. The Examiner respectfully disagrees. Smart teaches automating the merging of multiple location-based and other data sets by algorithmically collating the separate data sets into a single unified data set. Data sets may first be merged by type, and each data set type may then be merged with one or more location-based data sets. (Smart, pa 0014). Applicant has not pointed to claim limitations that requires “underlying temporal representations” that are different from the data sets merged in Smart. The rows shown in the merged dataset in Smart Fig. 3A provide a common temporal representation.
Applicant argues that Smart does not teach populating the claimed scalable data structure that generates rows from temporal positions because the claimed rows are generated from temporal anchor positions. The Examiner respectfully disagrees. The rows of the merged data set taught by Smart are generated from the dates of the datasets themselves, providing the claimed “temporal position” by representing the date corresponding to that data value.
Applicant argues that Smart does not teach populating the claimed scalable data structure that generates rows from temporal positions because the claimed rows include inherited event data values corresponding to multiple event data records relative to that common temporal position. The Examiner respectfully disagrees. As in Fig. 3A, merged data set 346 includes values and references to values from other data sets associated with the same date. This provides inherited event data values.
Applicant argues that Smart does not teach populating the claimed scalable data structure that generates rows from temporal positions because the claimed rows are populated by propagating event values according to temporal relationships among the event records. The Examiner respectfully disagrees. The claims do not require “propagating event values”, however, FIG. 3B depicts an combine operation 375 to produce a resulting merged data set 366. As depicted, data set 366 is populated with values from the referenced data sets instead of indicating the values (Smart, pa 0053).
Applicant argues that Smart does not disclose expanding a date-range event into per-date event instances. The Examiner respectfully disagrees. The amended claims now require populating the data structure such that each row represents one of the common temporal positions. Applicant has not pointed to specific claim language that represents the “expanding” idea, but this new language seems to suggest this feature. Smart provides a representation such that a row includes data representing the common data for a particular date, as seen in the merged dataset of Fig. 3A.
Conclusion
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169
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