DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This application is in response to the amendment filed on 12/17/25. Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/17/25 has been considered by the examiner.
Drawings
Replacement drawings were received on 12/17/25. These drawings are acceptable.
Double Patenting
Applicant is advised that should claim 15 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 a judicial exception (i.e, a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
35 USC 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these “four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of Section 101 even if the subject matter is otherwise new and useful.” In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter? Applicant’s claims fall within at least one of the four categories of patent eligible subject matter because claims 1-12, and 16-20 are drawn to a method, claim 13 is drawn to a system; and claims 14-15 are drawn to computer program products (See 112(b) rejection for claim 14).
Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not complete the eligibility analysis. Claims drawn only to an abstract idea, a natural phenomenon, and laws of nature are not eligible for patent protection. As described in MPEP 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l,134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
In 2019, the United States Patent and Trademark Office (USPTO) prepared revised guidance (2019 Revised Patent Subject Matter Eligibility Guidance) for use by USPTO personnel in evaluating subject matter eligibility. The framework for this revised guidance, which sets forth the procedures for determining whether a patent claim or patent application claim is directed to a judicial exception (laws of nature, natural phenomena, and abstract ideas), is described in MPEP sections 2106.03 and 2106.04.
As explained in MPEP 2106.04(a)(2), the 2019 Revised Patent Subject Matter Eligibility Guidance explains that abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Moreover, this guidance explains that a patent claim or patent application claim that recites a judicial exception is not ‘‘directed to’’ the judicial exception if the judicial exception is integrated into a practical application of the judicial exception. A claim that recites a judicial exception, but is not integrated into a practical application, is directed to the judicial exception under Step 2A and must then be evaluated under Step 2B (inventive concept) to determine the subject matter eligibility of the claim.
Step 2A asks: Does the claim recite a law of nature, a natural phenomenon (product of nature) or an abstract idea? (Prong One) If so, is the judicial exception integrated into a practical application of the judicial exception? (Prong Two) A claim recites a judicial exception when a law of nature, a natural phenomenon, or an abstract idea is set forth or described in the claim. While the terms “set forth” and “describe” are thus both equated with “recite”, their different language is intended to indicate that there are different ways in which an exception can be recited in a claim. For instance, the claims in Diehr set forth a mathematical equation in the repetitively calculating step, while the claims in Mayo set forth laws of nature in the wherein clause, meaning that the claims in those cases contained discrete claim language that was identifiable as a judicial exception. The claims in Alice Corp., however, described the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.” A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
In the instant case, claims 1-20 recite(s) a method and system for certain methods of organizing human activities, which is subject matter that falls within the enumerated groupings of abstract ideas described in MPEP 2106.04 (2019 Revised Patent Subject Matter Eligibility Guidance) Certain methods of organizing human activities includes fundamental economic practices, like insurance; commercial interactions (i.e. legal obligations, marketing or sales activities or behaviors, and business relations). Organizing human activity also encompasses managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions.) The recited method and system are drawn to identifying/selecting an optimal format to produce a medical findings report.
In particular, the claims 1, 13, and 14-15 recite a method and system for:
providing an analysis function configured to ascertain, for a medical dataset, at least one reference dataset from the plurality of comparison datasets;
ascertaining…the at least one reference dataset from the plurality of comparison datasets by applying the analysis function to the medical dataset and the plurality of comparison datasets;
(wherein ascertaining of the at least one reference dataset includes) generating a data descriptor for identifying at least one key feature of the medical dataset.
identifying… the computing system, at least one document model structure for the patient based on the at least one reference medical findings report associated with the at least one reference dataset;
providing… the at least one document model structure for producing a medical findings report for the patient to be assessed
This judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
While abstract ideas, natural phenomena, and laws of nature are not eligible for patenting by themselves, claims that integrate these exceptions into an inventive concept are thereby transformed into patent-eligible inventions. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2354, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)). Thus, the second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Id. An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute Sections 102, 103, and 112 inquiries for the better established inquiry under Section 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the Section 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp.,838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9).
As described in MPEP 2106.05, Step 2B of the Office’s eligibility analysis is the second part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. _, 134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961 (2012)). Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional steps amount to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Examples of insignificant extra-solution activity include mere data gathering, selecting a particular data source or type of data to be manipulated, and insignificant application. In the instant case, claims 1, 13, and 14-15 additionally recite: receiving…a medical dataset of the patient to be assessed; providing…a plurality of comparison datasets, the plurality of comparison datasets differing from the medical dataset. These additional steps amount to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)
Claims 1 and 13-15 recites additional limitation(s), including the steps of “a computing system.” Claim 13 recites “an interface configured to perform…” Claims 14-15 recite a computer program product, memory of a (programmable) computer system and computer readable storage medium. The additional component is/are is a generic components that perform functions well-understood, routine and conventional activities that amount to no more than implementing the abstract idea with a computerized system.
The generic nature of the computer system used to carryout steps of the recited method is underscored by the system description in the instant application, which discloses: “The front-end computing facility 10 can have a user interface for this purpose, for instance comprising a display and/or an input facility. The front-end computing facility 10 can have a processor. The processor can have a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processing processor, an integrated circuit (digital or analog), or combinations of the aforementioned components, and further facilities for providing a medical findings report MBB according to embodiments. The front-end computing facility 10 can comprise, for example, a desktop PC, laptop or a tablet.” (par. 180)
The specification also describes: “The back-end computing facility 20 can have a processor. The processor can have a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processing processor, an integrated circuit (digital or analog), or combinations of the aforementioned components, and further facilities for providing a document model structure DVS according to embodiments. The back-end computing facility 20 can be implemented as an individual component or have a group of computers, for instance a cluster. Such a system can be called a server system. Depending on the embodiment, the back-end computing facility 20 can be a local server. In addition, the back-end computing facility 20 can have a main memory such as a RAM, for instance in order to store temporarily the patient data PD, data filters DF, individual information EI, or report templates BT. The back-end computing facility 20 is designed, for instance via computer-readable instructions, by design and/or hardware.” (par. 193)
The application explains: “the medical information system 40 can have one or more databases (not shown). In particular, the databases can be realized in the form of one or more Cloud storage modules. Alternatively, the databases can be realized as a local or distributed storage medium, for instance as a PACS (Picture Archiving and Communication System), a hospital information system (HIS) a laboratory information system (LIS), an electronic medical record (EMR) information system, and/or further medical information systems.” (see par. 181) Additionally, the specification describes: “The storage facility RD can be in the form of a central or local database. The storage facility RD in particular can be part of a server system. The storage facility RD in particular can be part of the medical information system 40. The storage facility RD is designed to store a number of comparison datasets VDS. The storage facility RD can also be referred to as a data source or database.”
Such language describing the computer system underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of generic system components.
Furthermore, the courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05 (d) (II)). Among these are the following features, which are recited in claims 1 and 13-15:
- 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
- Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
- 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); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
- Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and
Claims 2-12 and 16-20 are dependent from Claim 1 and include(s) all the limitations of claim(s) 1. However, the additional limitations of the claims 2-12 and 16-20 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or additional steps which amount to insignificant extra solution activities. Therefore, claim(s) 2-12 and 16-20 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Because Applicant’s claimed invention recites a judicial exception that is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself, the claimed invention is not patent eligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a computing system configured to” and “an interface configured to …” (claim 13)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buckler et al (US 20190074082 A1) in view of Colley et a (US 20230267553 A1).
Claims 1, and 14-15:
Buckler discloses a computer-implemented method for providing a document model structure for producing a medical findings report in an assessment of a patient to be assessed, the computer-implemented method comprising:
receiving, at a computing system, a medical dataset of the patient to be assessed; (par. 41-report generation module configured to receive a data set including quantitative and objective measurements of one or more biological properties of a patient (e.g., from imaging data)…. analyze the data set and generate and display a report for the patient based on ontology from the ontological data model; Fig. 4-5; getting patient data including encounter data-pars. 50-51; par. 54-: Reporting is comprised of processing to compose a given patient's current encounter report 410 and processing of previously processed encounters 420 for longitudinal analysis. All encounters may then used to create the trend analysis 430 if multiple encounters are found. The current encounter may then taken and used to compose a narrative 440 as another way to view the current encounter's data; par. 85-88)
providing, at the computing system, a plurality of comparison datasets, the plurality of comparison datasets differing from the medical dataset, wherein each of the plurality of comparison datasets has at least one completed reference medical findings report; (par. 41: report generation module configured to receive a data set including quantitative and objective measurements of one or more biological properties of a patient (e.g., from imaging data)…. analyze the data set and generate and display a report for the patient based on ontology from the ontological data model…, the report may utilize pre-computed data points and information from older data sets…the report may include longitudinal trend analysis based on ontology from the ontological data model- Report includes data sets from older data sets for longitudinal analysis (i.e. comparison datasets); See also: par. 54-58- Reporting is comprised of processing to compose a given patient's current encounter report 410 and processing of previously processed encounters 420 for longitudinal analysis (i.e. medical and comparison datasets); Fig. 4-5; See also par. 75-77, par. 82: regarding previous/prior reference medical findings report, the system uses previous reports with older data/measurements: [0075] “Patient MRN000 was seen 3 years prior to the present encounter. Overall plaque burden increased by 120%, with lesion xyz growing 2 mm in length. In 2011, the most clinically vulnerable presentation 6 mm distal to the bifurcation was most likely type IV, but this location is now more likely a V… [0077] The report generator is accessed either when an analysis is first performed in the client, or if an existing report is updated in the browser. Using Django, for example, a context of all previous steps is put into HTML templates to create the page. The Chart view report is organized into hierarchical observation contexts. The following elements comprise the report for each available encounter; [0082] the report module may generate as output an HTML page that can be edited and/or an export file)
providing an analysis function configured to ascertain, for a medical dataset, at least one reference dataset from the plurality of comparison datasets; (par. 51- trend analysis provided by previous encounters may impact the narrative by adding context and/or by establishing a basis of comparison for current results; par. 84- Computational models (415) may be utilized for initializing reference ranges. For example, reference ranges for biological properties may be initialized at least in part from histology (however, may be overridden by the user, and similar section analysis of phenotypes).; par. 88; See also par. 92: Ground truth or reference data from defined patient cohorts is analyzed to determine distributions of values, annotated by age and sex as well as compilation of measurements vectors that may be used for comparison purposes).
ascertaining, by the computing system, the at least one reference dataset from the plurality of comparison datasets by applying the analysis function to the medical dataset and the plurality of comparison datasets; (par. 51; par. 88- The cross-section level has all the of the same data elements taken out as with the target or lesion, but also presents measurements using reference ranges and computes a similarity metric relative to a collection of potentially similar sections that have been annotated with a known phenotype.)
identifying, by the computing system, at least one document model structure for the patient; and (par. 92- 93: from the perspective of the reporting applications, a data structure for each type of measurement is produced that enables given values to be represented on user request ; par. 105-110)
providing, by the computing system, the at least one document model structure for producing a medical findings report for the patient to be assessed. (par. 105-110; [0109] That causes the generate_context method to be called, which creates a context data structure, which is passed to the django templates, which are then processed by the Django template processor. [0110]The output of that is HTML, CSS, and javascript, which is all returned back to the client in a HTTP response; par. 118-119- generated report: [0119] a tab representing the encounter used to generate this report, sequenced chronologically with any other encounters found to have been previously exported for this patient.)
Claim 1 has been amended to further recite:
Identifying…at least one document model structure for the patient based on the at least one completed reference medical findings report.
wherein the at least one document model structure includes an at least partially empty document template;
wherein ascertaining of the at least one reference dataset includes generating a data descriptor for identifying at least one key feature of the medical dataset.
Buckler discloses the method/system of claim 1 subtantially as claimed. Buckler further discloses the use of document templates for report generation (par. 53- generate_context may create (on the server side) a context data structure, which is passed to the web framework (Django, in an example) templates; par. 77 : Using Django, for example, a context of all previous steps is put into HTML templates to create the page. The Chart view report is organized into hierarchical observation contexts; par. 82: templates may be used to define content constraints for specific types of documents/reports; par. 105; par. 128; par. 130: When a user requests a report on the client, same as above, but also causes the generate_context method to be called, which creates a context data structure, which is passed to the templates, which are then processed by the template processor. The output of that is HTML, CSS, and javascript, which is all returned back to the client in a HTTP response.)
Buckler does not expressly disclose, but Colley teaches a system method further comprising:
Identifying…at least one document model structure for the patient based on the at least one completed reference medical findings report (par. 56: The MLA may identify distinguishing characteristics, pixels, or colors from the logo and the thickness of the border of the header a seemingly random placement as a unique document identifier which is consistent between reports. Furthermore, even if a document is identified as “Form CA217b”, the MLA may not use the text for identification purposes but instead identify, for example, that the pixels of the “F” are in a slanted line; par. 65-67- [0066] The system may be configured to recognize various types of NGS medical information from the captured report; par. 74-78: [0078]- The MLA then may use that information, alone or in combination with other extracted data such as the Collection Date value, a Version No., etc., to access a stored library of templates. For example, with regard to FIG. 4, the system may include one or more stored document templates for documents created by “ABC Labs,” and the Oct. 20, 2018, collection date may inform the MLA as to which document to use when there are multiple documents.)
wherein the at least one document model structure includes an at least partially empty document template, and (par. 75- Templates for each document may be one component of the model, along with identifiers for each template, regions or masks, features or fields and tools or instructions for how to extract those features or fields; par. 87- The predefined model that is associated with each of the templates may contain reference fields to identify each of the fields that may be extracted to generate the extracted patient information; par. 183-185, par.188; par. 231, par. 241-project specific template for generation predictions )
wherein ascertaining of the at least one reference dataset includes generating a data descriptor for identifying at least one key feature of the medical dataset.(par.255; 257-259: generating cohort data based a feature of interest—[0258]: Fields of the query may be extracted and processed by the database to identify a group, or “cohort”, of patients in the database who are similar to the physician's patient… Both the text and the linked entity may be stored to ensure the medical concept/relevance of each entry is accurately recorded. Similar patients having features matching one or more of the features included in the query may be identified and added into a cohort of similar patients…[0259]- The medical information of the cohort of similar patients, or summary analysis thereof, may then be provided to a report generator for processing to identify trends of the patients' case histories during diagnosis and treatment and generate a cohort report)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the system and method of Buckler with the teachings of Colley, with the motivation of providing means for identifying and structuring patient records and reports, while minimizing reliance on human/manual analysis and review. (Colley: par. 7)
Claim 2. Buckler teaches the computer-implemented method as claimed in claim 1, further comprising: receiving, at the computing system, input directed to the medical dataset, wherein the analysis function is further configured to ascertain the at least one reference dataset additionally based on the input directed to the medical dataset, and the ascertaining ascertains the at least one reference dataset by applying the analysis function to the input. (par. 54-58; Fig. 5; par. 84-88-reference ranges and top level measurements obtained; par. 88- The cross-section level has all the of the same data elements taken out as with the target or lesion, but also presents measurements using reference ranges and computes a similarity metric relative to a collection of potentially similar sections that have been annotated with a known phenotype.)
Claim 3. Buckler discloses the computer-implemented method as claimed in claim 2, wherein the input is directed to one or more of the following inputs:
defining a region of interest in the medical dataset; (par. 87-areas of interest may be determined using ranges initialized based on cohorts of patients for quartiles, however they can be changed by the user) detecting a medical abnormality in the medical dataset; producing a measured value of an abnormality exhibited in the medical dataset; selecting an analysis tool for producing a measured value of an abnormality exhibited in the medical dataset; or setting one or more reproduction parameters for presenting the medical dataset in a user interface. (par. 46, par. 62: determining and presenting abnormal findings)
Claim 4. Buckler teaches The computer-implemented method as claimed in claim 1, further comprising: providing a detection function configured to detect medical abnormalities in the medical dataset in an automated manner, the detection function being different from the analysis function; and applying, by the computing system, the detection function to the medical dataset to detect at least one medical abnormality and provide information about the at least one medical abnormality, wherein the analysis function is further configured to ascertain the at least one reference dataset additionally based on information about at least one medical abnormality detected in the medical dataset, and the ascertaining ascertains the at least one reference dataset additionally by applying the analysis function to the information about the at least one medical abnormality. (par. 43-CA detection of pathology/abnormalities; par. 54- automatic lesion detection; Fig. 5; par. 89-90-At step 560, pathology may be detected. Using a rule-based system on the lesion level measurements, pathologies are then determined. The same ranges as with detecting lesions are used)
Claim 5. Buckler teaches the computer-implemented method as claimed in claim 1, further comprising: obtaining, by the computing system, a medical abnormality in the medical dataset; determining, by the computing system, a development over time of the medical abnormality based on at least one of the medical dataset or further medical information on the patient to be assessed, the further medical information being different from the medical dataset, wherein the analysis function is further configured to ascertain the at least one reference dataset additionally based on the development over time of the medical abnormality, and the ascertaining ascertains the at least one reference dataset additionally by applying the analysis function to the development over time. (par. 41-system utilizes longitudinal trend analysis of datapoints ; par. 56- the reporting module may use the knowledge graph to retrieve information across multiple encounters, thereby enabling a longitudinal trend analysis, e.g., of given identified lesions or targets, as well as enabling summary measures for the patient as a whole; par. 89-90; par. 123- longitudinal trends charts for patients with multiple encounters may be accessed by pressing the Trends tab at the top of the Chart view)
Claim 6. Buckler discloses the computer-implemented method as claimed in claim 1, wherein the reference medical findings reports associated with the plurality of comparison datasets are each based on at least one standardized template component for producing a medical findings report, the identifying includes identifying at least one template component based on the at least one reference medical findings report associated with the at least one reference dataset, and the providing the at least one document model structure includes providing the at least one template component as a document model structure. (par. 82-standardization: , templates may be used to define content constraints for specific types of documents/reports. Relationships between a given report, the subject of the report, the observer making the report, related or supportive data, and the observations themselves may be represented in the RDBMS and may be output using rationalized concepts of DICOM SR with those of HL7 Clinical Document Architecture (CDA) (DICOM PS3.20 2016c—Imaging Reports using HL7 CDA). The HL7 CDA is an XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange; par. 124)
Claim 7. Buckler discloses the computer-implemented method as claimed in claim 1, further comprising: receiving, by the computing system, input directed to editing of the at least one document model structure; producing, by the computing system, a medical findings report based on the at least one document model structure and the input directed to the editing; and providing, by the computing system, the medical findings report. (par. 124- These fields are initialized by the processing described above, but presented in edit boxes prior to export. Drill downs are presented with show/hide buttons, which are initially hidden but capable of being shown on selection.)
Claim 8. Buckler disclsoses the computer-implemented method as claimed in claim 1, wherein the at least one document model structure is populated with an assessment text containing natural language; and the computer-implemented method further includes providing a language analysis algorithm, which is configured to adapt the assessment text to the medical dataset by evaluating the assessment text and the medical dataset, adapting, by the computing system, the assessment text to the medical dataset by applying the language analysis algorithm to the assessment text and the medical dataset, and pre-populating, by the computing system, the at least one document model structure with the adapted assessment text, wherein the providing provides the pre-populated at least one document model structure. (par. 35)
Claim 9. Buckler discloses the computer-implemented method as claimed in claim 1, wherein the analysis function is configured to calculate a similarity measure between the medical dataset and a comparison dataset, the similarity measure indicating a similarity between the medical dataset and the comparison dataset, and the ascertaining the at least one reference dataset includes calculating a similarity measure for each of the plurality of comparison datasets by applying the analysis function to the medical dataset and the plurality of comparison datasets, and the at least one reference dataset is ascertained from the plurality of comparison datasets based on the similarity measures. (par. 88-the cross-section level has all the of the same data elements taken out as with the target or lesion, but also presents measurements using reference ranges and computes a similarity metric relative to a collection of potentially similar sections that have been annotated with a known phenotype)
Claim 10. Buckler discloses the computer-implemented method as claimed in claim 9, wherein the analysis function is configured to extract at least one of a data descriptor from the medical dataset or a corresponding data descriptor from the comparison dataset, the data descriptor stating attributes of an underlying medical dataset that are relevant to ascertaining reference datasets, and the corresponding data descriptor stating attributes of an underlying medical dataset that is relevant to ascertaining the reference datasets, and calculate a similarity measure between the medical dataset and the comparison dataset based on associated data descriptors by inputting the associated data descriptors into a similarity metric, wherein the similarity measure for the comparison dataset is calculated by obtaining a data descriptor from the medical dataset by applying the analysis function to the medical dataset, obtaining a corresponding data descriptor for the comparison dataset by applying the analysis function to the comparison dataset, and calculating the similarity measure for the comparison dataset based on the data descriptor and the corresponding data descriptor by applying the analysis function to the data descriptor and the corresponding data descriptor. (par. 88-the cross-section level has all the of the same data elements taken out as with the target or lesion, but also presents measurements using reference ranges and computes a similarity metric relative to a collection of potentially similar sections that have been annotated with a known phenotype; par. 122-similarity criteria)
Claim 11. Buckler teaches the computer-implemented method as claimed in claim 1, wherein the analysis function comprises a trained function. (par. 39-40)
Claim 12. Buckler discloses the computer-implemented method as claimed in claim 1, wherein the identifying of the at least one document model structure includes identifying a plurality of document model structures for selection by a user, the providing provides the plurality of document model structures to the user via a user interface, the computer-implemented method further includes receiving, via the user interface, input by the user selecting the at least one document model structure, and the providing provides the at least one document model structure selected by the user in the user interface for further revision by the user. (fig. 6; par. 54)
Claim 13 Buckler discloses a system for providing a document model structure for producing a medical findings report for a patient to be assessed, the system comprising:
an interface (par. 45-46: The reporting system logic and user interfaces may be architected with a client-server infrastructure capable of efficient operation and implemented in the best of contemporary frameworks) configured to receive a medical dataset of the patient to be assessed, (par. 41-report generation module configured to receive a data set including quantitative and objective measurements of one or more biological properties of a patient (e.g., from imaging data)…. analyze the data set and generate and display a report for the patient based on ontology from the ontological data model; Fig. 4-5; getting patient data including encounter data-pars. 50-51; par. 54-: Reporting is comprised of processing to compose a given patient's current encounter report 410 and processing of previously processed encounters 420 for longitudinal analysis. All encounters may then used to create the trend analysis 430 if multiple encounters are found. The current encounter may then taken and used to compose a narrative 440 as another way to view the current encounter's data; par. 85-88) and to provide a plurality of comparison datasets, each of the plurality of comparison datasets having at least one completed reference medical findings report, and the plurality of comparison datasets being different from the medical dataset; and (par. 41: report generation module configured to receive a data set including quantitative and objective measurements of one or more biological properties of a patient (e.g., from imaging data)…. analyze the data set and generate and display a report for the patient based on ontology from the ontological data model…, the report may utilize pre-computed data points and information from older data sets…the report may include longitudinal trend analysis based on ontology from the ontological data model- Report includes data sets from older data sets for longitudinal analysis (i.e. comparison datasets); See also: par. 54-58- Reporting is comprised of processing to compose a given patient's current encounter report 410 and processing of previously processed encounters 420 for longitudinal analysis (i.e. medical and comparison datasets); Fig. 4-5; See also par. 75-77, par. 82: regarding previous/prior reference medical findings report, the system uses previous reports with older data/measurements: [0075] “Patient MRN000 was seen 3 years prior to the present encounter. Overall plaque burden increased by 120%, with lesion xyz growing 2 mm in length. In 2011, the most clinically vulnerable presentation 6 mm distal to the bifurcation was most likely type IV, but this location is now more likely a V… [0077] The report generator is accessed either when an analysis is first performed in the client, or if an existing report is updated in the browser. Using Django, for example, a context of all previous steps is put into HTML templates to create the page. The Chart view report is organized into hierarchical observation contexts. The following elements comprise the report for each available encounter; [0082] the report module may generate as output an HTML page that can be edited and/or an export file)
a computing system configured to:
host an analysis function configured to ascertain, for the medical dataset, at least one reference dataset from the plurality of comparison datasets, (par. 51- trend analysis provided by previous encounters may impact the narrative by adding context and/or by establishing a basis of comparison for current results; par. 84- Computational models (415) may be utilized for initializing reference ranges. For example, reference ranges for biological properties may be initialized at least in part from histology (however, may be overridden by the user, and similar section analysis of phenotypes).; par. 88; See also par. 92: Ground truth or reference data from defined patient cohorts is analyzed to determine distributions of values, annotated by age and sex as well as compilation of measurements vectors that may be used for comparison purposes).
ascertain the at least one reference dataset from the plurality of comparison datasets by applying the analysis function to the medical dataset and the plurality of comparison datasets, (par. 51; par. 88- The cross-section level has all the of the same data elements taken out as with the target or lesion, but also presents measurements using reference ranges and computes a similarity metric relative to a collection of potentially similar sections that have been annotated with a known phenotype.)
identify at least one document model structure for the patient based on the at least one reference medical findings report associated with the at least one reference dataset, and ((par. 92- 93: from the perspective of the reporting applications, a data structure for each type of measurement is produced that enables given values to be represented on user request ; par. 105-110)
provide the at least one document model structure via the interface. ((par. 105-110; [0109] That causes the generate_context method to be called, which creates a context data structure, which is passed to the django templates, which are then processed by the Django template processor. [0110]The output of that is HTML, CSS, and javascript, which is all returned back to the client in a HTTP response; par. 118-119- generated report: [0119] a tab representing the encounter used to generate this report, sequenced chronologically with any other encounters found to have been previously exported for this patient.)
Claim 13 has been amended to further recite:
Identify…at least one document model structure for the patient based on the at least one completed reference medical findings report.
wherein the at least one document model structure includes an at least partially empty document template;
wherein ascertaining of the at least one reference dataset includes generating a data descriptor for identifying at least one key feature of the medical dataset.
Buckler discloses the method/system of claim 1 subtantially as claimed. Buckler further discloses the use of document templates for report generation (par. 53- generate_context may create (on the server side) a context data structure, which is passed to the web framework (Django, in an example) templates; par. 77 : Using Django, for example, a context of all previous steps is put into HTML templates to create the page. The Chart view report is organized into hierarchical observation contexts; par. 82: templates may be used to define content constraints for specific types of documents/reports; par. 105; par. 128; par. 130: When a user requests a report on the client, same as above, but also causes the generate_context method to be called, which creates a context data structure, which is passed to the templates, which are then processed by the template processor. The output of that is HTML, CSS, and javascript, which is all returned back to the client in a HTTP response.)
Buckler does not expressly disclose, but Colley teaches a system method further comprising:
Identify…at least one document model structure for the patient based on the at least one completed reference medical findings report (par. 56: The MLA may identify distinguishing characteristics, pixels, or colors from the logo and the thickness of the border of the header a seemingly random placement as a unique document identifier which is consistent between reports. Furthermore, even if a document is identified as “Form CA217b”, the MLA may not use the text for identification purposes but instead identify, for example, that the pixels of the “F” are in a slanted line; par. 65-67- [0066] The system may be configured to recognize various types of NGS medical information from the captured report; par. 74-78: [0078]- The MLA then may use that information, alone or in combination with other extracted data such as the Collection Date value, a Version No., etc., to access a stored library of templates. For example, with regard to FIG. 4, the system may include one or more stored document templates for documents created by “ABC Labs,” and the Oct. 20, 2018, collection date may inform the MLA as to which document to use when there are multiple documents.)
wherein the at least one document model structure includes an at least partially empty document template, and (par. 75- Templates for each document may be one component of the model, along with identifiers for each template, regions or masks, features or fields and tools or instructions for how to extract those features or fields; par. 87- The predefined model that is associated with each of the templates may contain reference fields to identify each of the fields that may be extracted to generate the extracted patient information; par. 183-185, par.188; par. 231, par. 241-project specific template for generation predictions )
wherein ascertaining of the at least one reference dataset includes generating a data descriptor for identifying at least one key feature of the medical dataset.(par.255; 257-259: generating cohort data based a feature of interest—[0258]: Fields of the query may be extracted and processed by the database to identify a group, or “cohort”, of patients in the database who are similar to the physician's patient… Both the text and the linked entity may be stored to ensure the medical concept/relevance of each entry is accurately recorded. Similar patients having features matching one or more of the features included in the query may be identified and added into a cohort of similar patients…[0259]- The medical information of the cohort of similar patients, or summary analysis thereof, may then be provided to a report generator for processing to identify trends of the patients' case histories during diagnosis and treatment and generate a cohort report)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the system and method of Buckler with the teachings of Colley, with the motivation of providing means for identifying and structuring patient records and reports, while minimizing reliance on human/manual analysis and review. (Colley: par. 7)
Claim 16. Buckler discloses The computer-implemented method as claimed in claim 2, wherein the reference medical findings reports associated with the plurality of comparison datasets are each based on at least one standardized template component for producing a medical findings report, the identifying includes identifying at least one template component based on the at least one reference medical findings report associated with the at least one reference dataset, and the providing the at least one document model structure includes providing the at least one template component as a document model structure. (par. 82-standardization: , templates may be used to define content constraints for specific types of documents/reports. Relationships between a given report, the subject of the report, the observer making the report, related or supportive data, and the observations themselves may be represented in the RDBMS and may be output using rationalized concepts of DICOM SR with those of HL7 Clinical Document Architecture (CDA) (DICOM PS3.20 2016c—Imaging Reports using HL7 CDA). The HL7 CDA is an XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange; par. 124)
Claim 17. Buckler teaches the computer-implemented method as claimed in claim 2, further comprising: providing a detection function configured to detect medical abnormalities in the medical dataset in an automated manner, the detection function being different from the analysis function; and applying, by the computing system, the detection function to the medical dataset to detect at least one medical abnormality and provide information about the at least one medical abnormality, wherein the analysis function is further configured to ascertain the at least one reference dataset additionally based on information about at least one medical abnormality detected in the medical dataset, and the ascertaining ascertains the at least one reference dataset additionally by applying the analysis function to the information about the at least one medical abnormality. (par. 41-system utilizes longitudinal trend analysis of datapoints ; par. 56- the reporting module may use the knowledge graph to retrieve information across multiple encounters, thereby enabling a longitudinal trend analysis, e.g., of given identified lesions or targets, as well as enabling summary measures for the patient as a whole; par. 89-90; par. 123- longitudinal trends charts for patients with multiple encounters may be accessed by pressing the Trends tab at the top of the Chart view)
Claim 18: Buckler teaches the computer-implemented method as claimed in claim 2, further comprising: receiving, by the computing system, input directed to editing of the at least one document model structure; producing, by the computing system, a medical findings report based on the at least one document model structure and the input directed to the editing; and providing, by the computing system, the medical findings report. (par. 82-standardization: templates may be used to define content constraints for specific types of documents/reports; par. 124)
Claim 19. Buckler teaches the computer-implemented method as claimed in claim 4, further comprising: obtaining, by the computing system, the at least one medical abnormality in the medical dataset; determining, by the computing system, a development over time of the at least one medical abnormality based on at least one of the medical dataset or further medical information on the patient to be assessed, the further medical information being different from the medical dataset, wherein the analysis function is further configured to ascertain the at least one reference dataset additionally based on the development over time of the at least one medical abnormality, and the ascertaining ascertains the at least one reference dataset additionally by applying the analysis function to the development over time. (par. 41-system utilizes longitudinal trend analysis of datapoints ; par. 56- the reporting module may use the knowledge graph to retrieve information across multiple encounters, thereby enabling a longitudinal trend analysis, e.g., of given identified lesions or targets, as well as enabling summary measures for the patient as a whole; par. 89-90; par. 123- longitudinal trends charts for patients with multiple encounters may be accessed by pressing the Trends tab at the top of the Chart view)
Claim 20. Buckler teaches the computer-implemented method as claimed in claim 5, wherein the reference medical findings reports associated with the plurality of comparison datasets are each based on at least one standardized template component for producing a medical findings report, the identifying includes identifying at least one template component based on the at least one reference medical findings report associated with the at least one reference dataset, and the providing the at least one document model structure includes providing the at least one template component as a document model structure. (Par. 142)
Response to Arguments
Applicant's arguments filed 12/17/25 have been fully considered but they are not persuasive.
(A) Applicant argues that the claims are not drawn to an abstract idea, especially as amended. More specifically, applicant argues that the claims are not drawn to “certain methods of organizing human activity.”
In response, the Examiner disagrees. The claimed invention, particularly as recited in claims 1 and 13, is drawn to identifying/selecting an optimal format to produce a medical findings report. The amendments to claims 1 and 13 are noted. More specifically, the phrase “ascertaining of the at least one reference dataset includes generating a data descriptor for identifying at least one key feature of the medical dataset” recites an additional step of the process, and “wherein the at least one document model structure includes an at least partially empty document template” is an additional description of the data used in the process. However, the additional limitations further define the recited abstract idea, and do not render the claimed invention patent eligible.
Furthermore, the recited abstract idea is a judicial exception, and is is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e).
(B) Applicant argues that the claim rejections under 35 USC 112(b) are moot, in light claim amendments submitted on 12/17/25.
In response, the claim rejections under 35 USC 112(b) have been withdrawn in light of the claim amendments.
(C) Applicant argues the Buckler reference does not disclose “a document template being identified or selected based on "at least one completed reference medical findings report associated with the at least one reference dataset," as is the case with the "at least one document model structure," which "includes an at least partially empty document template," as recited in amended claims 1 and 13.
In response, new grounds of rejection have been applied to address the claims as amended.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ozeran (US 20220319652 A1) – discloses a system and method for systems and methods for interrogating raw clinical documents for characteristic data. A user can utilize a template created in the “Templates” module in order to allow a data abstractor to ingest clinical records from various sources (such as a clinical record) and convert that data to normalized and system optimized structured data sets specified in part by the template, according to some embodiments. Templates may be associated with a project, so that any source documents which are abstracted for the project automatically are associated with the template. (par. 205)
Messina et al (Messina, Pablo & Pino, Pablo & Parra, Denis & Soto, Alvaro & Besa, Cecilia & Uribe, Sergio & Andia, Marcelo & Tejos, Cristian & Prieto, Claudia & Capurro, Daniel. (2020). “A Survey on Deep Learning and Explainability for Automatic Image-based Medical Report Generation.” 10.48550/arXiv.2010.10563.)
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 Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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/Rachel L. Porter/Primary Examiner, Art Unit 3684