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 .
Response to Amendment
In the amendment filed 11/28/2025, the following has occurred: claims 1, 5, and 9 have been amended. Now, claims 1-12 remain pending.
The previous rejections under 35 U.S.C. 112(b) are withdrawn based on the amendments to the claims.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claims 1, 5, and 9 recite (claim 1 representative) the classifier comprising chaned classifier structures wherein a first structure predicts a base cost for specified diagnostic information and a second structure of specified demographic information, the second structure responding to a receipt of the base cost from the first structure in order to provide a modified form of the base cost; producing an index of field-value pairs representative of form based fields and corresponding values for the fields and then, in reference to a list of known fields, selecting one or more different fields in the index associated with diagnostic information including both demographic data for the patient and also diagnostic data; generating a healthcare profile based upon a presence of a selection of words in the index previously associated with a particular course of treatment; computing a cost of the particular course of treatment by initially submitting the diagnostic data to a first classifier structure adapted to correlate a base cost of the course of treatment with the diagnostic data, the first classifier structure responding to the submission of the diagnostic data with the base cost, and subsequently submitting the base cost and the demographic data to a second classifier structure adapted to correlate a modification in cost to the demographic data, the second classifier structure responding to the submission of the base cost and the demographic data with the modified cost; and storing the modified cost in a database in association with the patient.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, a user could manually acquire information representing the classifier structures, create on paper or on a form an index of data representing pairs of values and selecting fields associated with diagnostic and demographic data; manually generate a profile from words associated with a course of treatment, calculate a cost of the treatment by correlating a base cost with the diagnostic data and correlating a modification in cost with demographic data, and store the data. But for the recitation of generic computer components, such manual steps encompass Certain Methods of Organizing Human Activity. Additionally, calculating a base cost and a modified cost is a fundamental economic practice, which is also a subgrouping of Certain Methods of Organizing Human Activity.
Claims 2-4, 6-8, and 10-12 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, these claims further encompass computing profitability, transmitting a report, and further expand on the cost of treatment. As explained above, these manual steps encompass Certain Methods of Organizing Human Activity.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with adding elements similar to adding the words “apply it” to the abstract idea, and generally linking the abstract idea to a particular technological environment, along with insignificant, extra-solution data gathering activity.
Claims 1-12 directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “loading a classifier into memory,” “a host computing platform comprising one or more computers, each with memory and one or processing units including one or more processing cores; and, a health care delivery economics prediction module comprising computer program instructions enabled while executing in the memory of at least one of the processing units of the host computing platform,” “a non-transitory computer readable storage medium having program instructions stored therein, the instructions being executable by at least one processing core of a processing unit to cause the processing unit to perform a method.” The written description discloses that the recited computer components encompass generic components including “a host computing platform that has one or more computers, each with memory and one or processing units including one or more processing cores” (see paragraph 0011). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-12, directly or indirectly, recite the following additional elements that amount to no more than extra-solution, data gathering activity: “receiving a raster image of a document.” Additionally, the following additional elements are similar to adding the words “apply it” to the abstract idea and generally linking the abstract idea to a particular technological environment: “performing OCR upon the document to produce parseable text.” As set forth in MPEP 2106.05(g) insignificant, extra-solution activity, such as insignificant application and data transmission, is an example of when an abstract idea has not been integrated into a practical application. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-12, directly or indirectly also recite a first and second “classifier structure.” The broadest reasonable interpretation, in view of the specification, of “classifier structure” is a data structure. Therefore, the recitations of these data structures are part of the abstract idea, which do not integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Insignificant, extra solution, data gathering activity (e.g. receiving a raster image) has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
Storing and retrieving information in memory (e.g. loading a classifier into memory) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Generally linking the abstract idea to a particular technological environment (e.g. performing OCR upon the document) does not amount to significantly more than the abstract idea (see MPEP 2016.05(h) and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016)).
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
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) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fidone, US Patent Application Publication No. 2018/0366213 in view of Caton, US Patent Application Publication No. 2014/0355069 and further in view of Kido, US Patent No. 2017/0024784.
As per claim 1, Fidone teaches a health care delivery economics prediction method comprising: loading a classifier into memory, the classifier, wherein a first structure predicts a base cost for specified diagnostic information (see paragraph 0051; baseline cost to treat is determined directly from ICD9 or ICD10 codes (diagnostic data); paragraph 0088 describes a model that may perform the cost calculation from diagnosis codes, serving as a first classifier structure; paragraph 0053; describes that all this information may be stored (loaded into memory)); receiving healthcare data associated with a patient (see paragraph 0005; enters patient into EMR system); producing data associated with diagnostic information including both demographic data for the patient and also diagnostic data (see paragraph 0049; enters patient diagnosis, paragraph 0051; additional inputs such as age and gender); generating a healthcare profile based upon data previously associated with a particular course of treatment (see paragraph 0005; healthcare profile is the described value baseline for approved medical treatments, which is based on historical data associated with patient visits); computing a cost of the particular course of treatment by initially submitting the diagnostic data to a first classifier structure adapted to correlate a base cost of the course of treatment with the diagnostic data, the first classifier structure responding to the submission of the diagnostic data with the base cost (see paragraph 0051; baseline cost to treat is determined directly from ICD9 or ICD10 codes (diagnostic data). Cost to treat determined from Diagnostic code correlates to the two items; paragraph 0088 describes a model that may perform the cost calculation from diagnosis codes, serving as a first classifier structure), and subsequently submitting the base cost to correlate a modification in cost (see paragraph 0136; baseline cost to treat may be modified at one or more points during a patient visit); and, storing the modified cost in a database in association with the patient (see paragraphs 0053 and 0073; examples of storing cost data in a database; All input information may be associated with the patient’s electronic medical record).
Fidone does not explicitly teach receiving a raster image of a document and performing OCR upon the document to produce parseable text; and a presence of a selection of words in the parseable text.
Caton teaches receiving a raster image of a document and performing OCR upon the document to produce parseable text (see paragraphs 0106-0106; receives raster image of document and performs OCR to perform pattern recognition); and a presence of a selection of words in the text (see paragraph 0108; output of pattern recognition includes identified text strings). Canton further teaches the pattern recognition can be applied to recognize patient data (see paragraph 0107). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to apply the document processing of Canton to acquire and extract data, including demographic and diagnostic data, processed by Fidone with the motivation of acquiring data from forms containing secure content (see paragraph 0107 of Canton).
Findone and Caton does not explicitly teach the chained classifier structures including a first structure and a second structure of specified demographic information, the second structure responding to a receipt of the base cost from the first structure in order to provide a modified form of the base cost; producing an index of field-value pairs representative of form based fields and corresponding values for the fields and then, in reference to a list of known fields, selecting one or more different fields in the index associated with diagnostic information including both demographic data for the patient and also diagnostic data; the cost modification is accomplished by submitting the base cost and the demographic data to a second classifier structure adapted to correlate a modification in cost to the demographic data, the second classifier structure responding to the submission of the base cost and the demographic data with the modified cost.
Kido teaches a chained classifier structures including a first structure and a second structure of specified demographic information (see Figures 4-11, 13-14, 17; shows a series of related data structures, some of which included specified demographic information), the second structure responding to a receipt of the base cost from the first structure in order to provide a modified form of the base cost (see paragraph 0008; calculates a first cost and adjusts coefficients to calculate adjusted costs; paragraphs 0179, 181, and 0186-0187 discuss implementing the chained data structures to retrieve an associated data related to cost and adjusted cost of events and treatments); producing an index of field-value pairs representative of form based fields and corresponding values for the fields (see Figures 4-11, 13-14, and 17; data structures are shown in field-values, where an index is considered a linking field, such as medical institution ID or Patient ID); and then, in reference to a list of known fields, selecting one or more different fields in the index associated with diagnostic information including both demographic data for the patient and also diagnostic data (see paragraph 0131; a target episode (field) can be selected for cost calculating; paragraph 0138; selected episode is consistent with medical-knowledge management database; paragraph 0155; selected fields include diagnostic-treatment knowledge information; paragraph 0201; patient ID also linked to demographic factor); the cost modification is accomplished by submitting the base cost and the demographic data to a second classifier structure adapted to correlate a modification in cost to the demographic data, the second classifier structure responding to the submission of the base cost and the demographic data with the modified cost (see paragraph 0200; calculating cost adjustment based on demographic data of the patient). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to identify a modified cost based on demographic data in the system of Finone with the motivation of expanding the cost-based recommendations for treatments to include demographic data in the system of Finone as a known cost adjustment coefficient for episodes of care (paragraph 0002 of Kido).
As per claim 2, Fidone, Canton, and Kido teaches the method of claim 1 as described above. Fidone further teaches computing a margin of profitability for the course of treatment and storing the margin with the modified cost in the database (see paragraph 0073; baseline cost is adjusted for profit margin, which is then stored in cost database).
As per claim 3, Fidone, Canton, and Kido teaches the method of claim 1 as described above. Fidone further teaches transmitting a report of the course of treatment and modified cost to the patient (see paragraph 0006; cost (paragraph 136 for modified cost) from patient-related events can be displayed). Fidone does not explicitly teach that the patient is from the parseable text. Canton further teaches generating a patient listed in the parseable text (see paragraph 0107; pattern recognition can recognize patient identifying information). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to apply the document processing of Canton to acquire data processed by Fidone with the motivation of acquiring data from forms containing secure content (see paragraph 0107 of Canton).
As per claim 4, Fidone, Canton, and Kido teaches the method of claim 1 as described above. Fidone further teaches aggregating computed costs for multiple different received costs of like healthcare profile, receiving an actual cost of delivery of the course of treatment for corresponding patients and storing statistics determined from the actual cost of delivery for the corresponding patients in a data store for use in computing the cost of the particular cost of treatment for a newly received treatment (see paragraph 0032; associates a plurality of costs with performing a plurality of treatment services and aggregates the results; paragraph 0052 baseline costs are compared to aggregate costs; paragraph 0057 each time a new treatment cost is added, the aggregated cost is updated; paragraph 0127; actual charges are compared against aggregated cost statistics).
As noted above, Fidone does not explicitly teach acquiring the data from raster documents. Canton further teaches receiving multiple different raster documents including newly received raster images (see paragraph 0018; one or more documents may be received). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to apply the document processing of Canton to acquire data processed by Fidone with the motivation of acquiring data from forms containing secure content (see paragraph 0107 of Canton).
Claims 5-12 recite substantially similar system and device limitations to method claims 1-4 and, as such, are rejected for similar reasons as given above.
Response to Arguments
In the remarks filed 11/28/2025, Applicant argues (1) the 103 rejections do not address “a first structure predicts a base cost for specified diagnostic information and a second structure of specified demographic information, the second structure responding to a receipt of the base cost from the first structure in order to provide a modified form of the base cost”, only relying on Kido to teach “a first structure”; (2) the recited features of producing an index of data fields is not insignificant, extra-solution activity, and this argument has not been addressed in the previous office action; (3) in applying Kido, to teach a chained classifier including a first structure and a second structure, the rejection omits the first structure including “which predicts a base cost of specified diagnostic information.
In response to arguments (1) and (3), one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In the rejections, the teachings of Kido have been combined with those of Fidone to teach these limitations. Specifically, Findone is relied on to teach a first structure predicts a base cost for specified diagnostic information (see paragraph 0051; baseline cost to treat is determined directly from ICD9 or ICD10 codes (diagnostic data); paragraph 0088 describes a model that may perform the cost calculation from diagnosis codes, serving as a first classifier structure; paragraph 0053; describes that all this information may be stored (loaded into memory)). Kido is then relied on to teach a first structure and a second structure of specified demographic information (see Figures 4-11, 13-14, 17; shows a series of related data structures, some of which included specified demographic information), the second structure responding to a receipt of the base cost from the first structure in order to provide a modified form of the base cost (see paragraph 0008; calculates a first cost and adjusts coefficients to calculate adjusted costs; paragraphs 0179, 181, and 0186-0187 discuss implementing the chained data structures to retrieve an associated data related to cost and adjusted cost of events and treatments). The rejection does not rely on the teachings of Kido, alone, to teach all of these limitations. Rather, it is the combination of Fidone and Kido that teach these limitations. Therefore, the examiner respectfully maintains that the combination of Fidone and Kido teach these limitations and that all of the recited limitations have been addressed by the combination of Fidone, Caton, and Kido.
In response to argument (2), the examiner respectfully disagrees that this argument was not previously addressed. The argument focused on whether certain features were insignificant, extra-solution activity. As previously pointed out, the only recited element that has been found to recite insignificant, extra-solution activity is the step of receiving a raster image of a healthcare document. This step continues to be treated as an additional element and continues to be found to be insignificant data gathering. As previously explained, the remaining argued limitations of producing an index of filed-value pairs and selecting different fields in the index are part of the abstract idea. As set forth in the rejection, a user could create, on paper or on a form, an index of data representing pairs of values and selecting fields associated with diagnostic and demographic data. This is part of the identified Certain Methods of Organizing Human Activity. In the arguments, Applicant notes paragraph 0017 of the specification, presumably to highlight technical improvements resulting from the claimed invention. However, the technical element of using OCR to gather data from a fax image amounts the additional element of insignificant, extra-solution data gathering activity identified above. There is nothing described in this portion of the specification, or any other portion of the specification, that there is a technical improvement to OCR of facsimile images. It is only the source of data that is subsequently analyzed. Therefore, the examiner respectfully maintains that the claims recite an abstract idea without significantly more, as set forth above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 5:00.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683