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 .
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 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.
Restriction Election
Applicant’s election without traverse of alpha-keratin in the reply filed on 08/14/2025 is acknowledged.
Claims 19-20 were withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 08/14/2025.
After further consideration, it was determined the species are obvious variants, so the restriction requirement is withdrawn. Claims 19 and 20 have been rejoined for examination.
Claim Status
Claims 1-30 are pending.
Claims 1 and 4 are objected to.
Claims 1-30 are rejected.
Priority
The instant Application was filed 10/13/2021 and does not claim the benefit of an earlier filed application.
Accordingly, each of claims 1-30 are afforded the effective filing date of 10/13/2021.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 1/13/2022 and 05/16/2024 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Signed copies of the IDS documents are included with this Office Action.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 341-1, 341-2, 341-M, 344-1, 344-2, 344-M, 346-1, 346-2, 346-M, 416, and 452.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
The claims are objected to for the following informalities:
Claim 1 recites “the computer system implementing an analysis module producing an objective- diagnostic indicator by analyzing of the subject-sample data and data derived a database containing subject-sample data for samples from a plurality of prior subjects”. Please amend to “the computer system implementing an analysis module producing an objective- diagnostic indicator by analyzing [[of]] the subject-sample data and data derived from a database containing subject-sample data from samples from a plurality of prior subjects” for readability.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to system, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 1:
produces subject sample data including measurements of the sample
producing an objective diagnostic indicator by analyzing of the subject-sample data and data derived a database containing subject-sample data for samples from a plurality of prior subjects
Dependent claim 8:
a data encryption device encrypting the subject-sample data before transmission to the server system.
Dependent claim 15:
subject-sample data is depersonalized prior receipt by the server system.
Dependent claim 16:
the machine learning process is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
Dependent claim 26:
training dataset is updated as new sample data are uploaded to the computer database.
Dependent claims 3, 14, 18-22, 24-25 and 28-29 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, , claim 3 further limits the sample data of claim 1, claim 4 further limits the data acquisition system of claim 1, claim 14 further limits the database of claim 1, claims 18- 21 further limit the statistical analysis of claim 17, claim 22 further limits the analysis module of claim 1, claim 23-25 further limit the machine learning process of claim 1, claim 28 further limits the objective-diagnostic indicator of claim 1, and claim 29 further limits the cancer of claim 28.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually produce, depersonalize, train and update. Without further detail as to the methodology involved in “produces subject sample data”, “producing an objective diagnostic indicator”, “depersonalizing”, and “updating as new sample data under the BRI, one may simply, for example, use pen and paper to diagnosis malignancies. Some of these steps require mathematical techniques such as “trained using a training dataset “as is disclosed in the specification [0043].
Therefore, claim 1 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1:
receive the subject-sample data from the data acquisition system,
Dependent claim 4:
A diffractometer,
Dependent claim 5:
receive the measurements of the sample from the diffractometer,
transmit the subject-sample data to the server database, the server system processing the subject-sample data using a data analytics process that provides the objective diagnostic indicator based on the sample.
Dependent claim 6:
to transmit the subject-sample data to the server system
Dependent claim 9:
a global positioning system (GPS) device
Dependent claim 12:
to measure samples and
transmit subject-sample data to the server system, the additional data acquisition systems being at different geographic locations from the data acquisition system.
Dependent claims 2-3, 13, 27, and 30 recite steps that further limit the recited additional elements in the claims. For example, claims 2-3, 13, 27, and 30 further limit the sample of claim 1, claims 10-11 further limit the diffractometer of claim 4
The claims also include non-abstract computing elements. For example, independent claim 1 includes a server system and claim 5 includes computer system.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “receiving” and “measuring”, and to data outputting, such as “transmit” and “store”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a computing system and server system” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Further steps directed to additional non-abstract elements of a diffractometer and GPS, are insignificant extra-solution activity as they are mere data gathering steps.
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), 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), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1 and 5 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0061-0062]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
With respect to claim 4, the limitations of “a diffractometer” is well-understood, routine, and conventional in the art. The process of measurements of the sample including diffraction pattern data is well known and may be performed by using a diffractometer Lazerev [WO 2021/257457 A1, published 12/23/2021, newly cited). Lazarev teaches wherein the human-tissue-analyzer subsystem includes at least one tissue diffractometer operatively coupled to a computer database PC over a network and is configured for acquisition of in vitro samples of human-tissue data, and transfer of the human- tissue data to the computer database over the network [Fig. 1].
With respect to claim 9, the limitations of “a GPS” is well-understood, routine, and conventional in the art. The process of using a data encryption device that includes a global positioning system (GPS) device, the subject-sample data including GPS data identifying a location where the sample was measured is well known and may be performed as geomapping function is employed during encryption process to combine the recipient's geographic location, time and an encryption key to produce geo-secured key for transmission with message [p. 242, col. 1, par 2] as disclosed by Karimi (Karimi, Rohollah, and Mohammad Kalantari. "Enhancing security and confidentiality on mobile devices by location-based data encryption." 2011 17th IEEE international conference on networks. IEEE, 2011, newly cited).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 13, and 27-30 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chennubhotla (WO 2020/131658 A1, published 06/25/2020, newly cited).
Claim 1 is directed to a diagnostic system comprising: a data acquisition system that measures a sample from a subject and produces subject- sample data including measurements of the sample; and a server system connected to receive the subject-sample data from the data acquisition system, the computer system implementing an analysis module producing an objective- diagnostic indicator by analyzing of the subject-sample data and data derived a database containing subject-sample data for samples from a plurality of prior subjects.
Chennubhotla teaches in Fig. 1 an in vitro human-tissue analysis and communication system that produces a quantitative diagnostic indicator for in vitro human-tissue as disclosed in paragraph [p. 3, par. 2] analyzed by the system, comprising:
a human-tissue-analyzer subsystem 30 that includes at least one human-tissue analyzer constructed to analyze in vitro samples of human tissue and to produce a quantitative-diagnostic indicator of each sample; and a two-way communication subsystem 45 constructed to allow the human-tissue-analyzer subsystem to send and receive information relevant to the quantitative-diagnostic indicators [Fig. 1]. Chennubhotla further teaches a processing apparatus that in tum includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data [p. 3, par. 2] which reads on a analysis module.
Claim 3 is directed to the system of claim 1, wherein the subject-sample data comprises one or more of diffraction pattern data, in vitro image data, subject data, genetic data, and pathology lab image data.
Chennubhotla teaches a comprehensive computational systems pathology spatial analysis (CSPSA) computer platform capable of integrating, visualizing and modeling high dimensional in situ or in vitro cellular and subcellular resolved imaging data [p. 1, par. 1].
Claim 13 is directed to the system of claim 1, wherein the sample comprises one or more of a surgical sample, a resection sample, a pathology sample, and a biopsy sample.
Chennubhotla teaches digital pathology refers to the acquisition, storage and display of histologically stained tissue samples where a large volume of patient data, consisting of 3-50 slides, is generated from biopsy samples and is visually evaluated by a pathologist, under a microscope [p. 1, par. 2].
Claim 27 is directed to the system of claim 1, wherein the subject-sample data further comprises subject data comprising one or more of a species, an age, a weight, a body condition score, a sex, ancestry data, genetic data, behavioral data of the subject.
Chennubhotla discloses multicellular in vitro models permit the study of spatio-temporal cellular heterogeneity and heterocellular communication that recapitulates human tissue that can be applied to investigate the mechanisms of disease progression in vitro, to test drugs and to characterize the structural organization and content of these models for potential use in transplantation [p. 19, par. 4] which reads on a species.
Claim 28 is directed to the system of claim 1, wherein the objective-diagnostic indicator comprises an indicator of a likelihood that the sample indicates positive or negative probability for any of disease, cancer, and pathological abnormalities including cases caused by environmental exposure or heavy metal poisoning of the subject.
Chennubhotla discloses wherein a number of the identified microdomains are associated with an outcome specific variable that is in the form of time to recurrence or that is indicative of disease progression [claim 8].
Claim 29 is directed to the system of claim 28, wherein the cancer is one of breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, and vaginal cancer.
Chennubhotla discloses colon cancer progression as an example, wherein the basin with label 1 represents the pre-cancerous status, the basin with label 2 represents the small polyp status, the basin with label 3 represents the large polyp status, and the basin with label 4 represents the invasive colon cancer status [p. 19, par. 4].
Claim 30 is directed to the system of claim 1, wherein the subject-sample data includes pathology lab image data that includes micrographs of stained in vitro tissue specimens.
Chennubhotla discloses digital pathology refers to the acquisition, storage and display of histologically stained tissue samples [p.1, par. 2].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla, as applied to claim 1 as above in view of James et al. (James, Veronica J. "Fiber diffraction of skin and nails provides an accurate diagnosis of malignancies." International journal of cancer 125.1 (2009): 133-138, cited on IDS 01/13/2022).
Claim 2 is directed to the system of claim 1, wherein the sample contains at least one of a- keratin and collagen from hair, nails, claws, hooves, skin, tissue, and biological samples of internal organs of the subject.
Chennubhotla discloses a method of analyzing disease progression from multi-parameter cellular and subcellular imaging data obtained from a plurality of tissue samples from a plurality of patients or a number of multicellular in vitro models is provided [p. 2, par. 4]. Chennubhotla is silent on the sample contains at least one of a- keratin and collagen from hair, nails, claws, hooves, skin, tissue, and biological samples of internal organs of the subject.
However, James discloses fiber diffraction of skin and nails provides an accurate diagnosis of malignancies [title]. James further discloses the use of fiber diffraction patterns of skin or fingernails, using X-ray sources, as a biometric diagnostic method for detecting neoplastic disorders including but not limited to melanoma, breast, colon and prostate cancers [abstract]. James also discloses fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4]. James further discloses nails consist of rigid and durable dense keratinized plates where some human and animal samples were examined on the BioCAT Facility, Advanced Photon Source, and were found to provide excellent diffraction pictures [p. 134, col. 1, par. 3].
In regards to claim(s) 2, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta with James as they both disclose fiber diffraction for diagnosing malignancies. The motivation would have been to replace or combine the lipids and collagen of Chennubholta with the a-keratin of James as fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4] for early diagnosis of malignancies correlates directly with a better prognosis as disclosed by James [abstract].
B. Claims 4-7, 10, and 17-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla, as applied to claim 1 as above in view of Lazarev et al. (Lazarev, Pavel, et al. "Human tissue X-ray diffraction: breast, brain, and prostate." Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No. 00CH37143). Vol. 4. IEEE, 2000, cited on IDS 01/13/2022).
Claim 4 is directed to the system of claim 1, wherein the data acquisition system comprises a diffractometer, the measurements of the sample including diffraction pattern data measured using the sample.
Chennubholta is silent on wherein the data acquisition system comprises a diffractometer, the measurements of the sample including diffraction pattern data measured using the sample.
However, Lazarev teaches wherein the human-tissue-analyzer subsystem includes at least one tissue diffractometer operatively coupled to a computer database PC over a network as shown by double arrow in Fig. 1 [p. 3231, Fig. 1].
Claim 5 is directed to the system of claim 4, wherein the data acquisition system further comprises a computer system configured to receive the measurements of the sample from the diffractometer, transmit the subject-sample data to the server database, the server system processing the subject-sample data using a data analytics process that provides the objective- diagnostic indicator based on the sample.
Chennubholta is silent on a computer system configured to receive the measurements of the sample from the diffractometer, transmit the subject-sample data to the server database, the server system processing the subject-sample data using a data analytics process that provides the objective- diagnostic indicator based on the sample.
However, Lazarev discloses in wherein at least one computer processor is operatively coupled to the at least one tissue diffractometer, and the at least one computer processor is configured to: i. receive data from the data group; ii. transmit data from the data group; and iii. process the data from the data group for a human subject using a data analytics algorithm that provides a quantitative diagnostic indicator for the in vitro sample or for a subject from which the in vitro sample was derived [p.3231, Fig. 1 and p.3232, Fig. 3].
Claim 6 is directed to the system of claim 5, wherein the computer system provides a user interface that allows a user to transmit the subject-sample data to the server system.
Chennubholta is silent on user interface that allows a user to transmit the subject-sample data to the server system.
However, Lazarev discloses an PC [p. 3231, Fig. 1]. Lazarev further discloses a user interface that allows an individual subject or a healthcare provider to upload the individual subject's sample data for an in vitro sample to the computer database in exchange for processing of the sample data to receive the quantitative-diagnostic indicator for the in vitro sample or for the individual subject [p. 3231, Fig. 1].
Claim 7 is directed to the system of claim 6, wherein the user interface is further configured to allow the user to upload a signed consent form or make payments to the server system.
Chennubholta is silent on user interface is further configured to allow the user to upload a signed consent form or make payments to the server system.
However, Lazarev discloses an PC [p. 3231, Fig. 1]. Lazarev further discloses a user interface that allows an individual subject or a healthcare provider to upload the individual subject's sample data for an in vitro sample to the computer database in exchange for processing of the sample data to receive the quantitative-diagnostic indicator for the in vitro sample or for the individual subject [p. 3231, Fig. 1]. The limitation of “wherein the user interface is further configured to allow an individual subject or their healthcare provider to make payments or upload an individual subject's signed consent form” is determined to be within the ordinary skilled art in order to have an automated system.
Claim 10 is directed to the system of claim 4, wherein the diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements.
Chennubhotla is silent on SAXS.
However, Lazarev discloses small angle X-ray diffraction data from samples of fresh human tissue of prostate, breast and brain [abstract].
Claim 17 is directed to the system of claim 1, wherein the analysis module performs a statistical analysis of diffraction pattern data or a function of diffraction pattern data.
Chennubhotla is silent on statistical analysis.
However, Lazarev discloses the coefficients a, b, c, IT and peak characteristics form the space of ULAX parameters, and statistical analysis of these parameters was performed using the STATISTICA software. The correlation between the ULAX parameters and pathohistology/histostereometry data were analyzed [p. 3231 col. 2, par. 3].
Claim 18 is directed to the system of claim 17, wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a factor analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
Chennubhotla is silent on statistical analysis.
However, Lazarev discloses wherein the statistical analysis comprises a determination of a structural periodicity of collagen [p. 3232, col. 2, par. 1-2].
Claim 20 is directed to the system of claim 17, wherein the statistical analysis comprises a determination of a structural periodicity of one or more lipids in the sample.
Chennubhotla is silent on statistical analysis.
However, Lazarev discloses wherein the statistical analysis comprises a determination of a structural periodicity of lipids [p. 3232, col. 2, par. 1-2].
In regards to claim(s) 4-7, 10, 17-18, and 20, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta with Lazarev as they both disclose fiber diffraction for diagnosing malignancies. In view of this, it would have been obvious at the time of the claimed invention was filed to modify the teaching of Chennubholta in order to perform X-ray diffraction measurements of human tissues for pathological purposes (see Abstract of Lazarev). This rejection is in consistent with the Supreme Court Decision of the KSR International Co. v. Teleflex, Inc. case: applying a known technique to a known device (method or product) ready for improvement to yield predictable results.
C. Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla and Lazarev, as applied to claim 4 above, and in further view of Balwani (EP 3547181 A1, published on 02/19/2019, newly cited).
Claim 8 is directed to the system of claim 4, wherein the data acquisition system further comprises a data encryption device encrypting the subject-sample data before transmission to the server system.
Chennubhotla and Lazarev are silent on data encryption.
However, Balwani discloses systems and methods for a distributed clinical laboratory [title]. Balwani further discloses he method comprises transmitting electronic sample processing data from at least one of a plurality of distributed, sample processing units to a laboratory information system having an interface for communicating over a computer network; wherein a data pathway for sample processing data from the one of the sample processing units to the laboratory information system comprises traversing at least one wide area network, wherein the data along the data pathway is handled by at least: a central database collecting said sample processing data from a plurality of sample processing units, and a listener application configured to processing data from the central database for paired sample processing unit data [abstract]. Balwani also discloses some embodiments may have a decryption key for data received from SPU 100 and an encryption key for sending data to the listener application 50. This set of keys allows for separate encryption and decryption for a) communication from the SPI 100 to at least one server 120 in the cloud and for b) communication from the at least one server120 in the cloud to the listener application 50 [0067].
Claim 9 is directed to the system of claim 4, wherein the data acquisition system further comprises a data encryption device that includes a global positioning system (GPS) device, the subject-sample data including GPS data identifying a location where the sample was measured.
Chennubhotla and Lazarev are silent on GPS.
However, Balwani discloses by way of nonlimiting example, some embodiments may be configured to run in only one geolocation and/or in a set of locations set by an authorized entity such as an authorized user or the laboratory [0068]. Balwani further discloses some embodiments can also improve test integrity by securing identity through geolocation of the device 100 and/or LIS 30 and then optionally, querying whether the location of device 100 is acceptable for performing sample processing [0068]. Balwani also discloses geolocation may occur by way of GPS, internet IP address, connection to wireless access point, cellular data tower(s), or other techniques known or developed in the future and may include hardware onboard device for geolocation sensing purposes [0068].
In regards to claim(s) 8-9, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta and Lazarev with Balwani as they disclose systems and methods for a distributed clinical laboratory. The motivation would have been to replace or combine the system of Chennubholta and Lazarev with the method of transferring data between health information systems of Balwani to improve test integrity by securing identity through geolocation of the device [0068], to improve efficiency where laboratory test results managed by an LIS are faxed to the physician and thus the traditional laboratory test result handling and reporting [0003], and increase security for data [0095].
D. Claims 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla, as applied to claim 1 as above and in view of Mistry et al. (Mistry, Dharmica AH, Joseph Haklani, and Peter W. French. "Identification of breast cancer-associated lipids in scalp hair." Breast cancer: basic and clinical research 6 (2012): BCBCR-S9607, newly cited).
Claim 11 is directed to the system of claim 4, wherein the diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
Chennubhotla is silent on WAXS.
However, Mistry discloses identification of breast cancer-associated lipids in scalp hair [title]. Mistry further discloses x-ray diffraction assessment required the analysis of individual hair fibers from each subject, which were loaded onto specially, designed sample holders [p. 115, col. 1, par. 3]. Mistry also discloses Synchrotron X-ray diffraction experiments were carried out as described in previous publications on the Small Angle X-ray Scattering—Wide Angle X-ray Scattering (SAXS-WAXS) beam line at the Australian Synchrotron, Melbourne [p. 115, col. 1 par. 3].
Claim 22 is directed to the system of claim 1, wherein the analysis module performs one or more machine learning processes selected from a supervised learning process, an unsupervised learning process, a semi-supervised learning process, a reinforcement learning process, and a deep learning process.
Chennubhotla is silent on machine learning.
However, Mistry discloses data processing for all LCMS/GCMS data using multivariate statistical analysis: principal component analysis (unsupervised analysis) and principle least squares—discriminant analysis (supervised analysis) [p. 116, col. 2, par. 2].
In regards to claim(s) 11 and 22, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta and Mistry as they both disclose discloses x-ray diffraction assessment required the analysis of individual hair fibers. The motivation would be to filed to modify the teaching of Chennubholta in order to perform X-ray diffraction measurements of human tissues for pathological purposes . This rejection is in consistent with the Supreme Court Decision of the KSR International Co. v. Teleflex, Inc. case: applying a known technique to a known device (method or product) ready for improvement to yield predictable results.
E. Claims 12, 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla, as applied to claim 1 as above and in view of further view of Balwani (EP 3547181 A1, published on 02/19/2019, previously cited).
Claim 12 is directed to the system of claim 1, further comprising one or more additional data acquisition systems configured to measure samples and transmit subject-sample data to the server system, the additional data acquisition systems being at different geographic locations from the data acquisition system.
Chennubhotla is silent on one or more additional data acquisition systems configured to measure samples and transmit subject-sample data to the server system, the additional data acquisition systems being at different geographic locations from the data acquisition system.
However, Balwani discloses the method comprises transmitting electronic sample processing data from at least one of a plurality of distributed, sample processing units to a laboratory information system having an interface for communicating over a computer network [abstract]. Balwani further discloses by way of nonlimiting example, some embodiments may be configured to run in only one geolocation and/or in a set of locations set by an authorized entity such as an authorized user or the laboratory [0068].
Claim 14 is directed to the system of claim 1, wherein the database resides in the cloud.
Chennubhotla is silent on the cloud.
However, Balwani discloses a network connected to the cloud [fig. 1B, item 110].
Claim 15 is directed to the system of claim 1, wherein the subject-sample data is depersonalized prior receipt by the server system.
Chennubhotla is silent on subject-sample data is depersonalized.
However, Balwani discloses for increase security purposes, in some embodiments, there is no patient data stored on the sample processing units. Balwani further discloses there may be bar code data associated with samples, but in at least one embodiment, no patient data is on the sample, in the sample identifier, or on the SPU [0098].
Claim 16 is directed to the system of claim 15, wherein a key for mapping the depersonalized subject sample data in the database to the subject is stored in one of a local institutional database or in personal files of a person responsible for the subject.
Chennubhotla is silent on subject-sample data is depersonalized.
However, Balwani discloses processing the sample data to provided processed sample data that is stored in the database, wherein processed sample data is sent to the LIS [claim 3].
In regards to claim(s) 12 and 14-16, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta and Lazarev with Balwani as they disclose systems and methods for a distributed clinical laboratory. The motivation would have been to replace or combine the system of Chennubholta and Lazarev with the method of transferring data between health information systems of Balwani to improve test integrity by securing identity through geolocation of the device [0068], to improve efficiency where laboratory test results managed by an LIS are faxed to the physician and thus the traditional laboratory test result handling and reporting [0003], and increase security for data [0095].
F. Claims 19, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla in view of Lazarev, as applied to claim 17 above, and in further view of James et al. (James, Veronica J. "Fiber diffraction of skin and nails provides an accurate diagnosis of malignancies." International journal of cancer 125.1 (2009): 133-138, cited on IDS 01/13/2022).
Claim 19 is directed to the system of claim 17, wherein the statistical analysis comprises a determination of a structural periodicity of a-keratin and collagen in the sample.
Chennubhotla is silent on structural periodicity of a-keratin and collagen. Lazarev discloses wherein the statistical analysis comprises a determination of a structural periodicity of collagen [p. 3232, col. 2, par. 1-2], but is silent on a-keratin.
However, James discloses fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4].
Claim 21 is directed to the system of claim 17, wherein the statistical analysis comprises a determination of a structural periodicity of a-keratin in the sample.
Chennubhotla is silent on structural periodicity of a-keratin and collagen. Lazarev discloses wherein the statistical analysis comprises a determination of a structural periodicity of collagen [p. 3232, col. 2, par. 1-2], but is silent on a-keratin.
However, James discloses fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4].
In regards to claim(s) 19 and 21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta and Lazarev with James as they disclose fiber diffraction for diagnosing malignancies. The motivation would have been to replace or combine the lipids and collagen of Chennubholta with the a-keratin of James as fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4] for early diagnosis of malignancies correlates directly with a better prognosis as disclosed by James [abstract].
G. Claims 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over Chennubhotla in view of Mistry, as applied to claim 22 above, and in further view of Rehman et al (Rehman, Aasia, Muheet Ahmed Butt, and Majid Zaman. "A survey of medical image analysis using deep learning approaches." 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021, newly cited).
Claim 23 is directed to the system of claim 22, wherein the machine learning process comprises a deep learning process.
Chennubhotla is silent on a deep learning process. Mistry discloses data processing for all LCMS/GCMS data using multivariate statistical analysis: principal component analysis (unsupervised analysis) and principle least squares—discriminant analysis (supervised analysis) [p. 116, col. 2, par. 2], but is silent on deep learning process.
However, Rehman discloses a survey of medical image analysis using deep learning approaches [title]. Rehman further discloses x-Rays are one of the common methods utilized to produce images of the internal part of the body [p. 1335, col. 2, par. 4].
Claim 24 is directed to the system of claim 23, wherein the deep learning process comprises one of a convolutional neural network, a recurrent neural network, and a recurrent convolutional neural network.
Chennubhotla is silent on a deep learning process.
However, Rehman discloses multiple CNN based models for cancer classification [p. 1336, table.1 and fig. 1].
Claim 25 is directed to the system of claim 22, wherein the machine learning process is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
Chennubhotla is silent on machine learning process is trained.
However, Rehman discloses a trained CNN model [p. 1336, fig. 1]. Rehman further discloses the model was trained utilizing 385 MRI’s containing 161tumor’s for training and testing [p. 1337, col. 1, par1].
Claim 26 is directed to the system of claim 25, wherein the training dataset is updated as new sample data are uploaded to the computer database.
Chennubhotla discloses the invention pertains to digital pathology, and, in particular, to a comprehensive computational systems pathology spatial analysis (CSPSA) computer platform capable of integrating, visualizing and modeling high dimensional in situ or in vitro cellular and subcellular resolved imaging data [p. 1, par. 1], but is silent on the training dataset.
However, However, Rehman discloses a trained CNN model that is adds the new input into the training the model [p. 1336, fig. 1].
In regards to claim(s) 23-26, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chennubholta and Mistry with Rehman as they disclose data processing for medical image analysis. The motivation would be to modify the teaching of Chennubhotla modified by Mistry and Rehman in order to improve the machine performance by using numerous hierarchical layers with non-linear activations the term Deep Learning was coined in which the main aim of the machine is to learn the parameters of a complex model utilizing the training samples in such a way that the machine constantly improves in performance as disclosed by Rehman [p. 1334, col. 1,par. 1].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
I. Claims 1-6, 8-13, 15-18, and 22-30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 62-80 of U.S. Patent No. 12237083. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are similar in scope and are therefore anticipated by the claims of the 12237083 patent, as set forth in the following table:
Instant Application # 17/500766
Patent No. 112237083
Claim(s)
Limitation(s)
Claim(s)
Limitation(s)
1
A diagnostic system comprising:a data acquisition system that measures a sample from a subject and produces subject- sample data including measurements of the sample; and a server system connected to receive the subject-sample data from the data acquisition system, the computer system implementing an analysis module producing an objective- diagnostic indicator by analyzing of the subject-sample data and data derived a database containing subject-sample data for samples from a plurality of prior subjects.
62
A system comprising:(a) one or more diffraction apparatuses operatively coupled to a computer database over a network, wherein the one or more diffraction apparatuses are configured to collect sample data comprising diffraction pattern data for in vitro samples and transfer the sample data, or data derived therefrom, to the computer database over the network; and (b) one or more computer processors operatively coupled to the one or more diffraction apparatuses, wherein the one or more computer processors are individually or collectively configured to:(i) receive the sample data, or the data derived therefrom, from at least one of the one or more diffraction apparatuses;(ii) transmit the sample data, or the data derived therefrom, from at least one of the one or more diffraction apparatuses to the computer database; and(iii) process the sample data, or the data derived therefrom, for an individual in vitro sample using a data analytics algorithm that provides a computer-aided diagnostic indicator for the in vitro sample or for a subject from which the in vitro sample was derived, wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof.
2
The system of claim 1, wherein the sample contains at least one of a- keratin and collagen from hair, nails, claws, hooves, skin, tissue, and biological samples of internal organs of the subject.
72
The system of claim 71, wherein the statistical analysis comprises a determination of a structural periodicity of collagen, a structural periodicity of one or more lipids, or a structural periodicity of a tissue.
3
The system of claim 1, wherein the subject-sample data comprises one or more of diffraction pattern data, in vitro image data, subject data, genetic data, and pathology lab image data.
68
The system of claim 62, wherein the sample data further comprises pathology lab image data, subject data, or any combination thereof.
4
The system of claim 1, wherein the data acquisition system comprises a diffractometer, the measurements of the sample including diffraction pattern data measured using the sample.
62
one or more diffraction apparatuses operatively coupled to a computer database over a network, wherein the one or more diffraction apparatuses are configured to collect sample data comprising diffraction pattern data for in vitro samples and transfer the sample data
5
The system of claim 4, wherein the data acquisition system further comprises a computer system configured to receive the measurements of the sample from the diffractometer, transmit the subject-sample data to the server database, the server system processing the subject-sample data using a data analytics process that provides the objective- diagnostic indicator based on the sample.
62
receive the sample data, or the data derived therefrom, from at least one of the one or more diffraction apparatuses;(ii) transmit the sample data, or the data derived therefrom, from at least one of the one or more diffraction apparatuses to the computer database; and(iii) process the sample data, or the data derived therefrom, for an individual in vitro sample using a data analytics algorithm that provides a computer-aided diagnostic indicator for the in vitro sample or for a subject from which the in vitro sample was derived, wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof
6
The system of claim 5, wherein the computer system provides a user interface that allows a user to transmit the subject-sample data to the server system.
63
The system of claim 62, further comprising a user interface that allows an individual subject or a healthcare provider to upload the individual subject's sample data for an in vitro sample to the computer database in exchange for processing of the sample data to receive the computer-aided diagnostic indicator for the in vitro sample or for the individual subject.
8
The system of claim 4, wherein the data acquisition system further comprises a data encryption device encrypting the subject-sample data before transmission to the server system.
65
The system of claim 62, wherein the one or more diffraction apparatuses comprise a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted sample data, and wherein when transferred to the computer database the encrypted sample data is used to track changes in location of the one or more diffraction apparatuses.
9
The system of claim 4, wherein the data acquisition system further comprises a data encryption device that includes a global positioning system (GPS) device, the subject-sample data including GPS data identifying a location where the sample was measured.
65
The system of claim 62, wherein the one or more diffraction apparatuses comprise a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted sample data, and wherein when transferred to the computer database the encrypted sample data is used to track changes in location of the one or more diffraction apparatuses.
10
The system of claim 4, wherein the diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements.
66
The system of claim 62, wherein the one or more diffraction apparatuses are configured to perform small angle X-ray scattering (SAXS) measurements or wide angle X-ray scattering (WAXS) measurements.
11
The system of claim 4 wherein the diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
66
The system of claim 62, wherein the one or more diffraction apparatuses are configured to perform small angle X-ray scattering (SAXS) measurements or wide angle X-ray scattering (WAXS) measurements.
12
The system of claim 1, further comprising one or more additional data acquisition systems configured to measure samples and transmit subject-sample data to the server system, the additional data acquisition systems being at different geographic locations from the data acquisition system.
64
The system of claim 62, comprising two or more diffraction apparatuses located in two or more different geographic locations.
13
The system of claim 1, wherein the sample comprises one or more of a surgical sample, a resection sample, a pathology sample, and a biopsy sample.
67
The system of claim 62, wherein the in vitro samples comprise a surgical sample, a resection sample, a pathology sample, a biopsy sample, or any combination thereof.
15
The system of claim 1, wherein the subject-sample data is depersonalized prior receipt by the server system.
69
The system of claim 62, wherein the sample data transferred to the computer database are depersonalized prior to the transfer.
16
The system of claim 15, wherein a key for mapping the depersonalized subject sample data in the database to the subject is stored in one of a local institutional database or in personal files of a person responsible for the subject.
70
The system of claim 69, wherein a key for mapping the depersonalized sample data stored in the computer database to an individual subject is stored in a local institutional database or in the individual subject's personal files.
17
The system of claim 1, wherein the analysis module performs a statistical analysis of diffraction pattern data or a function of diffraction pattern data.
62
wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof
18
The system of claim 17, wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a factor analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
71
wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
22
The system of claim 1, wherein the analysis module performs one or more machine learning processes selected from a supervised learning process, an unsupervised learning process, a semi-supervised learning process, a reinforcement learning process, and a deep learning process
73
wherein the data analytics algorithm comprises a machine learning algorithm, wherein the machine learning algorithm comprises a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof.
23
The system of claim 22, wherein the machine learning process comprises a deep learning process.
74
The system of claim 73, wherein the machine learning algorithm is a deep learning algorithm, and wherein the deep learning algorithm is a convolutional neural network, a recurrent neural network, or a recurrent convolutional neural network.
24
The system of claim 23, wherein the deep learning process comprises one of a convolutional neural network, a recurrent neural network, and a recurrent convolutional neural network.
74
The system of claim 73, wherein the machine learning algorithm is a deep learning algorithm, and wherein the deep learning algorithm is a convolutional neural network, a recurrent neural network, or a recurrent convolutional neural network.
25
The system of claim 22, wherein the machine learning process is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
75
wherein the machine learning algorithm is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
26
The system of claim 25, wherein the training dataset is updated as new sample data are uploaded to the computer database
76
The system of claim 75, wherein the training dataset is updated as new sample data are uploaded to the computer database.
27
The system of claim 1, wherein the subject-sample data further comprises subject data comprising one or more of a species, an age, a weight, a body condition score, a sex, ancestry data, genetic data, behavioral data of the subject
77
wherein the sample data further comprises subject data comprising an individual subject's age, sex, ancestry data, genetic data, behavioral data, or any combination thereof, wherein the sample is from the individual subject.
28
The system of claim 1, wherein the objective-diagnostic indicator comprises an indicator of a likelihood that the sample indicates positive or negative probability for any of disease, cancer, and pathological abnormalities including cases caused by environmental exposure or heavy metal poisoning of the subject.
78
wherein the computer-aided diagnostic indicator for the in vitro sample comprises an indicator of a likelihood that the sample is positive or negative for a cancer.
29
The system of claim 28, wherein the cancer is one of breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, and vaginal cancer.
79
he system of claim 78, wherein the cancer comprises breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, vaginal cancer, or any combination thereof.
30
wherein the subject-sample data includes pathology lab image data that includes micrographs of stained in vitro tissue specimens.
80
wherein the sample data comprises pathology lab image data, and wherein the pathology lab image data comprises micrographs of stained in vitro tissue specimens.
A. Claims 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 62-80 of U.S. Patent No. 112237083, as applied to claims 1-7, 8-13, 15-18, and 22-30 above, in view of Madine et al. (M. M. Madine et al., "Fully Decentralized Multi-Party Consent Management for Secure Sharing of Patient Health Records," in IEEE Access, vol. 8, pp. 225777-225791, 2020, newly cited).
Claim 7 is directed to the system of claim 6, wherein the user interface is further configured to allow the user to upload a signed consent form or make payments to the server system.
U.S. Patent No. 12237083 is silent on interface is further configured to allow the user to upload a signed consent form or make payments to the server system.
However, Madine discloses fully decentralized multi-party consent management for secure sharing of patient health records [title]. Madine further discloses patients are able to upload medical document bundles [p. 225783, fig. 2]. Madine also discloses implemented multi-party authorization and threshold cryptographic using blockchain technology to allow the patients to securely share and grant access to their medical documents along with sharing their secret keys [p. 225789, col. 1, par. 6].
In regards to claim(s) 7, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine U.S. Patent No. 12237083 with Madine as they both disclose healthcare management systems. In view of this, it would have been obvious at the time of the claimed invention was filed to modify the teaching of U.S. Patent No. 12237083 to include uploading a signed consent form to the server system.
B. Claims 14 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 62-80 of U.S. Patent No. 12237083, as applied to claims 1-7, 8-13, 15-18, and 22-30 above, in view of Balwani (EP 3547181 A1, published on 02/19/2019, previously cited).
Claim 14 is directed to the system of claim 1, wherein the database resides in the cloud.
U.S. Patent No. 112237083 is silent on the cloud.
However, Balwani discloses a network connected to the cloud [fig. 1B, item 110].
In regards to claim(s) 14, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine U.S. Patent No. 12237083 with Balwani as they disclose systems and methods for a distributed clinical laboratory. The motivation would have been to replace or combine the system of U.S. Patent No. 12237083 with the method of transferring data between health information systems of Balwani to improve test integrity by securing identity through geolocation of the device [0068], to improve efficiency where laboratory test results managed by an LIS are faxed to the physician and thus the traditional laboratory test result handling and reporting [0003], and increase security for data [0095].
C. Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 62-80 of U.S. Patent No. 12237083, as applied to claims 1-7, 8-13, 15-18, and 22-30 above, in view of James et al. (James, Veronica J. "Fiber diffraction of skin and nails provides an accurate diagnosis of malignancies." International journal of cancer 125.1 (2009): 133-138, cited on IDS 01/13/2022).
Claim 21 is directed to the system of claim 17, wherein the statistical analysis comprises a determination of a structural periodicity of a-keratin in the sample.
U.S. Patent No. 12237083 is silent on structural periodicity of a-keratin and collagen.
However, James discloses fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4].
In regards to claim(s) 21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine U.S. Patent No. 12237083 and Lazarev with James as they disclose fiber diffraction for diagnosing malignancies. The motivation would have been to replace or combine the lipids and collagen of U.S. Patent No. 12237083 with the a-keratin of James as fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4] for early diagnosis of malignancies correlates directly with a better prognosis as disclosed by James [abstract].
II. Claims 1-18, and 22-30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5-6, 8-31 of U.S. Patent No. 12094610. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are anticipated by the claims of the 12094610 patent, as set forth in the following table:
Instant Application # 17/500766
Patent No. 12094610
Claim(s)
Limitation(s)
Claim(s)
Limitation(s)
1
A diagnostic system comprising:a data acquisition system that measures a sample from a subject and produces subject- sample data including measurements of the sample; and a server system connected to receive the subject-sample data from the data acquisition system, the computer system implementing an analysis module producing an objective- diagnostic indicator by analyzing of the subject-sample data and data derived a database containing subject-sample data for samples from a plurality of prior subjects.
1
An in vitro human-tissue analysis and communication system that produces a quantitative diagnostic indicator for in vitro human-tissue analyzed by the system, comprising: a human-tissue-analyzer subsystem that includes at least one human-tissue analyzer constructed to analyze in vitro samples of human tissue and to produce a quantitative-diagnostic indicator of each sample; and a two-way communication subsystem constructed to allow the human-tissue-analyzer subsystem to send and receive information relevant to the quantitative- diagnostic
2
The system of claim 1, wherein the sample contains at least one of a- keratin and collagen from hair, nails, claws, hooves, skin, tissue, and biological samples of internal organs of the subject.
19-20
wherein the statistical analysis comprises a determination of a structural periodicity of collagen.
wherein the statistical analysis comprises a determination of a structural periodicity of a tissue.
3
The system of claim 1, wherein the subject-sample data comprises one or more of diffraction pattern data, in vitro image data, subject data, genetic data, and pathology lab image data.
1
subsystem includes a plurality of tissue diffractometers located in plural and different geographic locations operatively coupled to a computer database over a network
4
The system of claim 1, wherein the data acquisition system comprises a diffractometer, the measurements of the sample including diffraction pattern data measured using the sample.
1
process the human-tissue data using a data analytics algorithm that provides a quantitative- diagnostic indicator of the in vitro sample of human tissue,
5
The system of claim 4, wherein the data acquisition system further comprises a computer system configured to receive the measurements of the sample from the diffractometer, transmit the subject-sample data to the server database, the server system processing the subject-sample data using a data analytics process that provides the objective- diagnostic indicator based on the sample.
1
receive the sample data, or the data derived therefrom, from at least one of the one or more diffraction apparatuses;(ii) transmit the sample data, or the data derived therefrom, from at least one of the one or more diffraction apparatuses to the computer database; and(iii) process the sample data, or the data derived therefrom, for an individual in vitro sample using a data analytics algorithm that provides a computer-aided diagnostic indicator for the in vitro sample or for a subject from which the in vitro sample was derived, wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof
6
The system of claim 5, wherein the computer system provides a user interface that allows a user to transmit the subject-sample data to the server system.
5
further comprising a user interface that allows an individual subject or a healthcare provider to upload the individual subject's sample data for an in vitro sample to the computer database in exchange for processing of the sample data to receive the quantitative-diagnostic indicator for the in vitro sample or for the individual subject.
7
The system of claim 6, wherein the user interface is further configured to allow the user to upload a signed consent form or make payments to the server system.
6
wherein the user interface is further configured to allow an individual subject or their healthcare provider to make payments or upload an individual subject's signed consent form.
8
The system of claim 4, wherein the data acquisition system further comprises a data encryption device encrypting the subject-sample data before transmission to the server system.
8
The system of claim [[4]]1, wherein the one or more diffraction apparatus at least one tissue diffractometer comprises a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted sample data, and wherein when transferred to the computer database the encrypted sample data is used to track changes in location of the one or more diffraction apparatus at least one tissue diffractometer.
9
The system of claim 4, wherein the data acquisition system further comprises a data encryption device that includes a global positioning system (GPS) device, the subject-sample data including GPS data identifying a location where the sample was measured.
8
The system of claim 1, wherein the one or more diffraction apparatus at least one tissue diffractometer comprises a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted sample data, and wherein when transferred to the computer database the encrypted sample data is used to track changes in location of the one or more diffraction apparatus at least one tissue diffractometer.
10
The system of claim 4, wherein the diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements.
9
at least one tissue diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements
11
The system of claim 4 wherein the diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
10
at least one tissue diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
12
The system of claim 1, further comprising one or more additional data acquisition systems configured to measure samples and transmit subject-sample data to the server system, the additional data acquisition systems being at different geographic locations from the data acquisition system.
1
at least one computer processor coupled to at least one tissue diffractometer of the plurality of tissue diffractometers is configured to receive the human-tissue data from the at least one tissue diffractometer and transmit the human-tissue data to the computer database; andat least one additional computer processor coupled to the computer database configured to (i) process the human-tissue data using a data analytics algorithm that provides a quantitative- diagnostic indicator of the in vitro sample of human tissue, (ii) receive data from a data group;(iii) transmit data from the data group
13
The system of claim 1, wherein the sample comprises one or more of a surgical sample, a resection sample, a pathology sample, and a biopsy sample.
11
wherein the in vitro samples comprise a surgical sample, a resection sample, a pathology sample, a biopsy sample, or any combination thereof.
14
The system of claim 1, wherein the database resides in the cloud.
14
The system of claim 1, wherein the database resides in the cloud.
15
The system of claim 1, wherein the subject-sample data is depersonalized prior receipt by the server system.
15
The system of claim 14, wherein the sample data transferred to the computer database are depersonalized prior to the transfer.
16
The system of claim 15, wherein a key for mapping the depersonalized subject sample data in the database to the subject is stored in one of a local institutional database or in personal files of a person responsible for the subject.
16
wherein a key for mapping the depersonalized sample data stored in the computer database to an individual subject is stored in a local institutional database or in the individual subject's personal files.
17
The system of claim 1, wherein the analysis module performs a statistical analysis of diffraction pattern data or a function of diffraction pattern data.
17
wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof.
18
The system of claim 17, wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a factor analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
18
wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
22
The system of claim 1, wherein the analysis module performs one or more machine learning processes selected from a supervised learning process, an unsupervised learning process, a semi-supervised learning process, a reinforcement learning process, and a deep learning process
23
wherein the machine learning algorithm comprises a supervised learning algorithm, an unsupervised learning algorithm, a semi- supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof.
23
The system of claim 22, wherein the machine learning process comprises a deep learning process.
24
wherein the machine learning algorithm is a deep learning algorithm.
24
The system of claim 23, wherein the deep learning process comprises one of a convolutional neural network, a recurrent neural network, and a recurrent convolutional neural network.
25
wherein the deep learning algorithm is a convolutional neural network, a recurrent neural network, or a recurrent convolutional neural network.
25
The system of claim 22, wherein the machine learning process is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
26
wherein the machine learning algorithm is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
26
The system of claim 25, wherein the training dataset is updated as new sample data are uploaded to the computer database
27
wherein the training dataset is updated as new sample data are uploaded to the computer database.
27
The system of claim 1, wherein the subject-sample data further comprises subject data comprising one or more of a species, an age, a weight, a body condition score, a sex, ancestry data, genetic data, behavioral data of the subject
28
wherein the sample data further comprises subject data comprising an individual subject's age, sex, ancestry data, genetic data, behavioral data, or any combination thereof, wherein the sample is from the individual subject.
28
The system of claim 1, wherein the objective-diagnostic indicator comprises an indicator of a likelihood that the sample indicates positive or negative probability for any of disease, cancer, and pathological abnormalities including cases caused by environmental exposure or heavy metal poisoning of the subject.
29
wherein the quantitative-diagnostic indicator for the in vitro sample comprises an indicator of a likelihood that the sample is positive or negative for a cancer.
29
The system of claim 28, wherein the cancer is one of breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, and vaginal cancer.
30
wherein the cancer comprises breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, vaginal cancer, or any combination thereof.
30
wherein the subject-sample data includes pathology lab image data that includes micrographs of stained in vitro tissue specimens.
31
wherein the pathology lab image data comprises micrographs of stained in vitro tissue specimens.
A. Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-31 of U.S. U.S. Patent No. 12094610, as applied to claims 1-18, and 22-30 above, in view of James et al. (James, Veronica J. "Fiber diffraction of skin and nails provides an accurate diagnosis of malignancies." International journal of cancer 125.1 (2009): 133-138, cited on IDS 01/13/2022).
Claim 21 is directed to the system of claim 17, wherein the statistical analysis comprises a determination of a structural periodicity of a-keratin in the sample.
U.S. Patent No. 12094610 is silent on structural periodicity of a-keratin and collagen.
However, James discloses fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4].
In regards to claim(s) 21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine U.S. Patent No. 12094610 and Lazarev with James as they disclose fiber diffraction for diagnosing malignancies. The motivation would have been to replace or combine the lipids and collagen of U.S. Patent No. 12094610 with the a-keratin of James as fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4] for early diagnosis of malignancies correlates directly with a better prognosis as disclosed by James [abstract].
III. Claims 1-18, and 22-30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-32 of U.S. Patent No. 11751828. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are similar in scope and are therefore anticipated by the claims of the 11751828 patent, as set forth in the following table:
Instant Application # 17/500766
Patent No. 11751828
Claim(s)
Limitation(s)
Claim(s)
Limitation(s)
1
A diagnostic system comprising: a data acquisition system that measures a sample from a subject and produces subject- sample data including measurements of the sample; and a server system connected to receive the subject-sample data from the data acquisition system, the computer system implementing an analysis module producing an objective- diagnostic indicator by analyzing of the subject-sample data and data derived a database containing subject-sample data for samples from a plurality of prior subjects.
1
An in vivo human-tissue analysis and communication system that produces a diagnostic indicator for human tissue analyzed by the system, comprising: at least one computer processor operatively coupled to a tissue diffractometer and to a computer database over a network, wherein the at least one computer processor is configured for acquisition of human tissue data, wherein the human tissue data is chosen from a data group consisting of in situ image data, in situ diffraction pattern data, and subject data, wherein the in situ diffraction pattern data is acquired from the tissue diffractometer, wherein the at least one computer processor is configured to analyze the human tissue data and to produce the diagnostic indicator; and a communication interface configured to allow the at least one computer processor to send and receive information relevant to the diagnostic indicator,wherein the at least one computer processor uses the communication interface to receive the human tissue data from the tissue diffractometer and transmit the human tissue data to the computer database over the network.
2
The system of claim 1, wherein the sample contains at least one of a- keratin and collagen from hair, nails, claws, hooves, skin, tissue, and biological samples of internal organs of the subject.
20
wherein the statistical analysis comprises a determination of a structural periodicity of collagen.
3
The system of claim 1, wherein the subject-sample data comprises one or more of diffraction pattern data, in vitro image data, subject data, genetic data, and pathology lab image data.
1
wherein the at least one computer processor is configured for acquisition of human tissue data, wherein the human tissue data is chosen from a data group consisting of in situ image data, in situ diffraction pattern data, and subject data,
4
The system of claim 1, wherein the data acquisition system comprises a diffractometer, the measurements of the sample including diffraction pattern data measured using the sample.
1
coupled to a tissue diffractometer and to a computer database over a network, wherein the at least one computer processor is configured for acquisition of human tissue data, wherein the human tissue data is chosen from a data group consisting of in situ image data, in situ diffraction pattern data, and subject data,
5
The system of claim 4, wherein the data acquisition system further comprises a computer system configured to receive the measurements of the sample from the diffractometer, transmit the subject-sample data to the server database, the server system processing the subject-sample data using a data analytics process that provides the objective- diagnostic indicator based on the sample.
3
wherein the at least one computer processor uses the communication interface and process the human tissue data using a data analytics algorithm that provides the diagnostic indicator of the human tissue.
6
The system of claim 5, wherein the computer system provides a user interface that allows a user to transmit the subject-sample data to the server system.
6
comprising a user interface that allows a human subject or their healthcare provider to upload the subject data from the data group to the computer database in exchange for processing of the subject data to receive the diagnostic indicator for the human tissue.
7
The system of claim 6, wherein the user interface is further configured to allow the user to upload a signed consent form or make payments to the server system.
7
wherein the user interface is further configured to allow the human subject or their healthcare provider to make payments or upload a human subject's signed consent form.
8
The system of claim 4, wherein the data acquisition system further comprises a data encryption device encrypting the subject-sample data before transmission to the server system.
9
wherein the tissue diffractometer includes a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted in situ image data, encrypted in situ diffraction pattern data, encrypted subject data, or any combination thereof, wherein the encrypted in situ image data, encrypted in situ diffraction pattern data, encrypted subject data, or any combination thereof that is transferred to the computer database tracks changes in location of the tissue diffractometer.
9
The system of claim 4, wherein the data acquisition system further comprises a data encryption device that includes a global positioning system (GPS) device, the subject-sample data including GPS data identifying a location where the sample was measured.
8
wherein the tissue diffractometer includes a data encryption device that includes a global positioning system (GPS) positioning sensor and generates encrypted in situ image data, encrypted in situ diffraction pattern data, encrypted subject data, or any combination thereof, wherein the encrypted in situ image data, encrypted in situ diffraction pattern data, encrypted subject data, or any combination thereof that is transferred to the computer database tracks changes in location of the tissue diffractometer.
10
The system of claim 4, wherein the diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements.
10
at least one tissue diffractometer is configured to perform small angle X-ray scattering (SAXS) measurements
11
The system of claim 4 wherein the diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
11
at least one tissue diffractometer is configured to perform wide angle X-ray scattering (WAXS) measurements.
12
The system of claim 1, further comprising one or more additional data acquisition systems configured to measure samples and transmit subject-sample data to the server system, the additional data acquisition systems being at different geographic locations from the data acquisition system.
8
comprising at least two of the tissue diffractometers, each being located in different geographic locations
13
The system of claim 1, wherein the sample comprises one or more of a surgical sample, a resection sample, a pathology sample, and a biopsy sample.
11
wherein the tissue diffractometer is further configured to perform mammography.
14
The system of claim 1, wherein the database resides in the cloud.
15
wherein the database resides in the cloud.
15
The system of claim 1, wherein the subject-sample data is depersonalized prior receipt by the server system.
16
wherein the in situ image data, the in situ diffraction pattern data, the subject data, or any combination thereof transferred to the computer database is depersonalized prior to transfer.
16
The system of claim 15, wherein a key for mapping the depersonalized subject sample data in the database to the subject is stored in one of a local institutional database or in personal files of a person responsible for the subject.
17
wherein a key for mapping depersonalized in situ image data, depersonalized in situ diffraction pattern data, depersonalized subject data, or any combination thereof stored in the computer database to a human subject is stored in a local institutional database or in personal files of the human subject.
17
The system of claim 1, wherein the analysis module performs a statistical analysis of diffraction pattern data or a function of diffraction pattern data.
18
wherein the data analytics algorithm comprises a statistical analysis of diffraction pattern data or a function thereof.
18
The system of claim 17, wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a factor analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
19
wherein the statistical analysis comprises determination of a pair-wise distance distribution function, determination of a Patterson function, a calculation of a Porod invariant, a cluster analysis, a dispersion analysis, determination of one or more molecular structural periodicities, or any combination thereof.
22
The system of claim 1, wherein the analysis module performs one or more machine learning processes selected from a supervised learning process, an unsupervised learning process, a semi-supervised learning process, a reinforcement learning process, and a deep learning process
24
wherein the machine learning algorithm comprises a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof.
23
The system of claim 22, wherein the machine learning process comprises a deep learning process.
24
wherein the machine learning algorithm comprises a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof.
24
The system of claim 23, wherein the deep learning process comprises one of a convolutional neural network, a recurrent neural network, and a recurrent convolutional neural network.
26
wherein the deep learning algorithm is a convolutional neural network, a recurrent neural network, or a recurrent convolutional neural network.
25
The system of claim 22, wherein the machine learning process is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
27
wherein the machine learning algorithm is trained using a training dataset comprising pathology lab image data, diffraction pattern data, subject data, or any combination thereof from one or more control samples.
26
The system of claim 25, wherein the training dataset is updated as new sample data are uploaded to the computer database
27
wherein the training dataset is updated as new sample data are uploaded to the computer database.
27
The system of claim 1, wherein the subject-sample data further comprises subject data comprising one or more of a species, an age, a weight, a body condition score, a sex, ancestry data, genetic data, behavioral data of the subject
29
wherein the subject data comprises a human subject's age, sex, ancestry data, genetic data, behavioral data, or any combination thereof.
28
The system of claim 1, wherein the objective-diagnostic indicator comprises an indicator of a likelihood that the sample indicates positive or negative probability for any of disease, cancer, and pathological abnormalities including cases caused by environmental exposure or heavy metal poisoning of the subject.
30
wherein the diagnostic indicator for the human tissue comprises an indicator of a likelihood that the human subject has cancer.
29
The system of claim 28, wherein the cancer is one of breast cancer, brain cancer, bone cancer, lung cancer, cervical cancer, bladder cancer, head and neck cancer, kidney cancer, intestinal cancer, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, throat cancer, oral cancer, and vaginal cancer.
31
wherein the indicator of the likelihood that the human subject has cancer is an indicator of the likelihood that the human subject has breast cancer.
30
wherein the subject-sample data includes pathology lab image data that includes micrographs of stained in vitro tissue specimens.
31
wherein the pathology lab image data comprises micrographs of stained in vitro tissue specimens.
A. Claims 21 and 30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 62-80 of U.S. Patent No. 11751828, as applied to claims 1-7, 8-13, 15-18, and 22-30 above, in view of James et al. (James, Veronica J. "Fiber diffraction of skin and nails provides an accurate diagnosis of malignancies." International journal of cancer 125.1 (2009): 133-138, cited on IDS 01/13/2022).
Claim 21 is directed to the system of claim 17, wherein the statistical analysis comprises a determination of a structural periodicity of a-keratin in the sample.
U.S. Patent No. 11751828 is silent on structural periodicity of a-keratin and collagen.
However, James discloses fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4].
Claim 30 is directed to the system of claim 1, wherein the subject-sample data includes pathology lab image data that includes micrographs of stained in vitro tissue specimens.
U.S. Patent No. 11751828 is silent on micrographs of stained in vitro tissue specimens.
However, James discloses composite diffraction pattern is from fingernails [p. 136. Fig. 2].
In regards to claim(s) 21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine U.S. Patent No. 11751828 and Lazarev with James as they disclose fiber diffraction for diagnosing malignancies. The motivation would have been to replace or combine the lipids and collagen of U.S. Patent No. 11751828 with the a-keratin of James as fiber diffraction techniques have now been used extensively in the study of muscle, collagen and keratin [p. 133, col. 1, par. 4] for early diagnosis of malignancies correlates directly with a better prognosis as disclosed by James [abstract].
Conclusion
No claims are allowed.
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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600
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