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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 8-13 are directed to a method (i.e., a process), claims 1-7 are directed to a system (i.e., a machine), and claims 14-20 are directed to one or more non-transitory computer-readable media (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 8 includes limitations that recite at least one abstract idea. Specifically, independent claim 8 recites:
8. A computer-implemented method, comprising: accessing, via one or more hardware processors, a set of timeseries data elements corresponding to target information received via a set of records stored at an electronic digital memory of a medical records computer system; generating one or more timeseries trajectory clusters based at least in part on the set of timeseries data elements; identifying at least a distance between a first data point representing a first health record and a second data point associated with a first timeseries trajectory cluster of the one or more timeseries trajectory clusters; determining, via the one or more hardware processors and based at least partially on the distance, a similarity between the first timeseries trajectory cluster and the first health record; estimating a predicted utilization level of health care resources for a patient associated with the first health record based on the first timeseries trajectory cluster; and electronically writing, via the one or more hardware processors and based on one or both of the first timeseries trajectory cluster and the similarity, encoded data to the electronic digital memory at the medical records computer system, the encoded data representing the predicted utilization level of health care resources for the patient associated with the first health record.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because accessing a set of timeseries data elements corresponding to target information; generating one or more timeseries trajectory clusters based at least in part on the set of timeseries data elements; identifying at least a distance between a first data point representing a first health record and a second data point associated with a first timeseries trajectory cluster of the one or more timeseries trajectory clusters; determining based at least partially on the distance, a similarity between the first timeseries trajectory cluster and the first health record; estimating a predicted utilization level of health care resources for a patient associated with the first health record based on the first timeseries trajectory cluster; and data representing the predicted utilization level of health care resources for the patient associated with the first health record amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.
Accordingly, the claim recites at least one abstract idea.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 8, and 14, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting one or more hardware processors, an electronic digital memory, a records computer system, and one or more non-transitory computer-readable media to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. 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 claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the one or more hardware processors, electronic digital memory, records computer system, and one or more non-transitory computer-readable media are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of accessing data, generating data, identifying data, determining data, estimating data, and writing data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2-7, 9-13, and 15-20 are ultimately dependent from Claim(s) 1, 8, and 14 and include all the limitations of Claim(s) 1, 8, and 14. Therefore, claim(s) 2-7, 9-13, and 15-20 recite the same abstract idea. Claims 2-7, 9-13, and 15-20 describe further limitations regarding wherein a timeseries data element of the set of timeseries data elements is determined based on reference data that is associated with a population of patients; estimate a future healthcare resources metric for the patient associated with the first health record; wherein each timeseries data element of the set of timeseries data elements comprises complexity information associated with one or both of a medication complexity and a care complexity; determining that the first health record is a match with one or both of the first timeseries trajectory cluster and a health record associated with the first timeseries trajectory cluster; identifying frequent itemsets associated with the first timeseries trajectory cluster; and based on the frequent itemsets, scheduling resources for treating the patient; wherein the frequent itemsets include order recommendation information, and determining an order recommendation for the patient based on the frequent itemsets. These are all just further describing the abstract idea recited in claims 1, 8, and 14, without adding significantly more.
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 the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1, 8, and 14 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to one or more hardware processors accessing data elements, records stored at an electronic digital memory, and electronically writing encoded data to the electronic digital memory, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of electronic recordkeeping and storing and retrieving information in memory. See MPEP 2106.05(d)(II).
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Objections
Claim 3 is objected to because of the following informalities: delete “(New)” at the end of line 2. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The newly added recitation of "identifying at least a distance between a first data point representing a first health record and a second data point associated with a first timeseries trajectory cluster of the one or more timeseries trajectory clusters; determining, via the one or more hardware processors and based at least partially on the distance, a similarity between the first timeseries trajectory cluster and the first health record…electronically writing, via the one or more hardware processors and based on one or both of the first timeseries trajectory cluster and the similarity, encoded data to the electronic digital memory at the medical records computer system, the encoded data representing the predicted utilization level of health care resources for the patient associated with the first health record" within claims 1, 8, and 14 appear to constitute new matter. The newly added recitation of “based on the frequent itemsets, scheduling resources for treating the patient” within claims 6, 13, and 19 also appear to constitute new matter.
In particular, Applicant does not point to, nor was the Examiner able to find support for this newly added language within the specification as originally filed. As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed specification and claims.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6 and 8-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lynn et al. (US 2007/0191697 A1) in view of Friedlander et al. (US 2009/0299766 A1), and further in view of Keen (US 2007/0203761 A1).
(A) Referring to claim 1, Lynn discloses A system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising (abstract and para. 86 of Lynn):
accessing, via the one or more hardware processors, a set of timeseries data elements corresponding to target information received via a set of records stored at an electronic digital memory of a medical records computer system (para. 251 & 252 of Lynn; a signal pattern viewer is provided. This viewer is preferably accessible directly from the patient's medical record in the hospital information system or from, for example, the display of a mechanical ventilator. When a healthcare worker requests access, the viewer component loads and accesses the physiologic time series data from a local database or through services to a centralized data repository into which the time series from the patient monitors are continuously or intermittently updated and stored. The view component processes and analyzes the physiologic time series data and the image of the processed signals is then displayed. The interrogator can include memory for storage of the ventilation datasets and software for analyzing those data sets for sleep disordered breathing as by detecting clusters.);
generating one or more timeseries trajectory clusters based at least in part on the set of timeseries data elements (para. 251, 254, 145, 238, 403, 404, and 322 of Lynn; a time series is provided comprised of the difference in slope of pulse waveform at the second site and the first site (for example, of the difference in slope of the upstroke at each site). These relational time series can then be analyzed to monitor sympathetic tone, which rises for example with drug infusion or hemorrhage and for detection of clusters to indicate the presence of clusters of variations in sympathetic tone or the presence of sleep apnea or other disease processes.).
Lynn does not expressly disclose identifying at least a distance between a first data point representing a first health record and a second data point associated with a first timeseries trajectory cluster of the one or more timeseries trajectory clusters; determining, via the one or more hardware processors and based at least partially on the distance, a similarity between the first timeseries trajectory cluster and the first health record; estimating a predicted utilization level of health care resources for a patient associated with the first health record based on the first timeseries trajectory cluster; and electronically writing, via the one or more hardware processors and based on one or both of the first timeseries trajectory cluster and the similarity, encoded data to the electronic digital memory at the medical records computer system, the encoded data representing the predicted utilization level of health care resources for the patient associated with the first health record.
Friedlander discloses identifying at least a distance between a first data point representing a first health record and a second data point associated with a first cluster of the one or more clusters (para. 65, 66, 70, 78, 85, & 97 of Friedlander; patient A 414, patient B 416, and patient C 418 are assigned into cluster 1 406, all of FIGS. 4A-4B. Clustered records from treatment cohort 610 are the records for patients in the treatment cohort. Every patient is assigned to a primary cluster, and a Euclidean distance to all other clusters is determined. The distance is a distance, such as distance 426, separating patient B 416 and the center or cluster prototype of cluster 1 406 of FIG. 4B. In FIG. 4B, patient B 416 is grouped into the primary cluster of cluster 1 406 because of proximity. Distances to cluster 2 408, cluster 3 410, and cluster 4 412 are also determined.); determining, via the one or more hardware processors and based at least partially on the distance, a similarity between the first cluster and the first health record (para. 65 of Friedlander; Scores are generated based on the distance between each patient record and each of the cluster prototypes. Scores closer to zero have a higher degree of similarity to the cluster prototype. The higher the score, the more dissimilar the record is from the cluster prototype.);
Keen discloses estimating a predicted utilization level of health care resources for a patient associated with the first health record based on the first timeseries trajectory cluster (para. 19 & 20 of Keen; Utilizing historical patient-procedure data collected over time, an optimal set of procedure parameters can be predicted for a new patient procedure to take place within a healthcare institution. The historical data can be used, for example, to predict where the procedure should be performed for the new patient and with what piece of equipment, when the procedure should be performed for the new patient, who should perform the procedure for the new patient, and other procedure details such as body positions for best scan results, contrast agents, and procedure timing factors (e.g., 12 hour fast, then contrast, then wait one-half hour, then scan, then wait two hours, then scan again). The predictive scheduling (e.g., so as to identify optimal resource utilization and/or patient experience) can be accomplished by using data mining techniques. Data mining is sorting through data to identify patterns and establish relationships. Example data mining parameters include: association (looking for patterns where one event is connected to another event), sequence or path analysis (looking for patterns where one event leads to another later event), classification (looking for new patterns, which may result in a change in the way the data is organized but that is ok), clustering (finding and visually documenting groups of facts not previously known), and forecasting (discovering patterns in data that can lead to reasonable predictions about the future).); and electronically writing, via the one or more hardware processors and based on one or both of the first timeseries trajectory cluster and the similarity, encoded data to the electronic digital memory at the medical records computer system, the encoded data representing the predicted utilization level of health care resources for the patient associated with the first health record (para. 8, 9, 17-22, 50, and 51 of Keen; The predictive scheduling (e.g., so as to identify optimal resource utilization and/or patient experience) can be accomplished by using data mining techniques. The system includes a learning module for transforming received historical patient-procedure data into a schema, and building one or more prediction models using the transformed patient-procedure data stored in the schema. The system further includes a classifier module for predictively scheduling a medical procedure for the patient, based on target patient-procedure data and the one or more prediction models. In response to receiving new historical patient-procedure, the learning module may be further configured for updating the one or more prediction models. The classifier module can be further configured for determining if procedure predictions by the models satisfy a given threshold, and determining if a quorum of predictions agree. The system functionality can be implemented, for example, in software (e.g., executable instructions encoded on one or more computer-readable mediums), hardware (e.g., gate level logic or one or more ASICs), firmware (e.g., one or more microcontrollers with I/O capability and embedded routines for carrying out the functionality described herein), or some combination thereof. The patient records can be merged. Similarly, two different patients' records can be de-merged. Also, a sub-set of a single patient's records can be removed from consideration, if so desired (e.g., where the predictive scheduling system is configured to train only on diagnostic and imaging records).).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Friedlander and Keen within Lynn. The motivation for doing so would have been to determine treatments having a highest probability of success (para. 12 of Friedlander) and to maximize resource and people utilization (para. 3 of Keen).
(B) Referring to claims 2, 9, and 15, Lynn discloses wherein a timeseries data element of the set of timeseries data elements is determined, via the one or more hardware processors, based on reference data that is associated with a population of patients and that is stored at the medical records computer system (para. 232, 253, 254, and 335 of Lynn).
(C) Referring to claims 3, 10, and 16, Lynn discloses comprising an estimation component configured to estimate a future healthcare resources metric for the patient associated with the first health record (para. 240-242 of Lynn).
(D) Referring to claims 4, 11, and 17, Lynn discloses wherein each timeseries data element of the set of timeseries data elements comprises complexity information associated with one or both of a medication complexity and a care complexity (para. 55, 58, and 87 of Lynn).
(E) Referring to claims 5, 12, and 18, Lynn does not expressly disclose wherein the operations further comprise determining that the first health record is a match with one or both of the first timeseries trajectory cluster and a health record associated with the first timeseries trajectory cluster.
Friedlander discloses wherein the operations further comprise determining that the first health record is a match with one or both of the first cluster and a health record associated with the first cluster (para. 75 and 92 of Friedlander).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Friedlander within Lynn. The motivation for doing so would have been for optimal selection (para. 92 of Friedlander).
(F) Referring to claims 6, 13, and 19, Lynn does not disclose wherein the operations further comprise: identifying frequent itemsets associated with the first timeseries trajectory cluster; and based on the frequent itemsets, scheduling resources for treating the patient.
Friedlander discloses wherein the operations further comprise: identifying frequent itemsets associated with the first cluster and based on the frequent itemsets, scheduling resources for treating the patient (para. 63, 64, 179-182, and 266-269 of Friedlander).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Friedlander within Lynn. The motivation for doing so would have been to improve patient health and reduce costs (para. 183 of Friedlander).
(G) Claims 8 and 14 differ from claim 1 by reciting “A computer-implemented method, comprising:” (abstract of Lynn) and “One or more non-transitory computer-readable media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations, the operations comprising:” (para. 68 and 103 of Lynn).
The remainder of claims 8 and 14 repeat the same limitations as claim 1, and are therefore rejected for the same reasons given above.
Claim(s) 7 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lynn et al. (US 2007/0191697 A1) in view of Friedlander et al. (US 2009/0299766 A1), in view of Keen (US 2007/0203761 A1), and further in view of Schneider (US 2012/0173261 A1).
(A) Referring to claims 7 and 20, Lynn, Friedlander, and Keen do not disclose wherein the frequent itemsets include order recommendation information, and wherein the operations further comprise determining an order recommendation for the patient based on the frequent itemsets.
Schneider discloses wherein the frequent itemsets include order recommendation information, and wherein the operations further comprise determining an order recommendation for the patient based on the frequent itemsets (para. 26, 29, and 35-39 of Schneider).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Schneider within Lynn, Friedlander, and Keen. The motivation for doing so would have been to increase efficiency (para. 3 of Schneider).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited but not applied prior art teaches an intelligent health benefit design system ( US 2008/0147441 A1); adaptive prediction of changes of physiological/pathological states using processing of biomedical signals (US 2004/0230105 A1); anomaly detection method and anomaly detection system (US 2012/0041575 A1); detecting and quantifying patient motion during tomosynthesis scans (US 2012/0033868 A1); and methods and systems for assessing clinical outcomes (US 2009/0318775 A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LENA NAJARIAN whose telephone number is (571)272-7072. The examiner can normally be reached Monday - Friday 9:30 am-6 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687
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