DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 14 is objected to because of the following informalities: There appears to be extra words that make the second limitation confusing. Appropriate correction is required.
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.
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Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of copending Application No. 17/368,251 in view of NPL document: “Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren’s syndrome”. As explained below, Lednev discloses the same method and systems as the present application, the only difference being the specific disease to be detected. However, Chen discloses a similar method and system that is used to detect Sjogren’s Syndrome disease. As explained below, it would have been obvious to one of ordinary skill in the art before the effective filing date to specifically detect Sjogren’s Syndrome disease as disclosed by Chen using the device of Lednev as doing so simply requires training the model with the appropriate data for the specific disease to be detected.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2022/0000414 ("Lednev") in view of NPL document: “Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren’s syndrome” Chen, Xiaomei ; Wu, Xue ; Chen, Chen ; Luo, Cainan ; Shi, Yamei ; Li, Zhengfang ; Lv, Xiaoyi ; Chen, Cheng ; Su, Jinmei ; Wu, Lijun, Scientific reports, 2023-03, Vol.13 (1), p.5137-6, Article 5137, https://doi.org/10.1038/s41598-023-29943-9, herein referred to as “Chen”.
Regarding claim 1, Lednev discloses a method for detecting a [disease], the method comprising:
providing a biological sample (104, Fig. 1) from a human subject (102, Fig. 1);
subjecting at least a portion of the biological sample (paragraph [0023]) to a Raman hyperspectroscopic analysis (paragraph [0024]) to produce a sample spectroscopic signature (108, Fig. 1, paragraph [0034]) for the biological sample (paragraph [0023]);
analyzing the produced sample spectroscopic signature using a predetermined statistical model (118, Fig. 1, paragraph [0038]), the predetermined statistical model based on spectroscopic signatures for a plurality of modeling samples (paragraph [0038]), wherein the spectroscopic signatures for each of the plurality of modeling samples are associated with [the disease] (paragraph [0038], for example, cognitive diseases); and
correlating the produced sample spectroscopic signature with a presence of [the disease] (paragraph [0039]) based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (paragraph [0039]).
Lednev does not disclose that the disease is Sjogren’s Syndrome disease.
However, Chen discloses a method for detecting Sjogren’s Syndrome disease, comprising: providing a biological sample (blood, see section “Sample preparation”) from a human subject (see “Patient selection”); subjecting at least a portion of the biological sample to a Raman spectroscopic analysis to produce a sample spectroscopic signature for the biological sample (see section “Raman spectral data acquisition”); analyzing the produced sample spectroscopic signature (see Data analysis and Spectral comparison) using a predetermined statistical model (see section “Algorithm description”), and correlating the produced sample spectroscopic signature with a presence of Sjogren’s Syndrome (see last paragraph in Discussion section) based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (see “Model evaluation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to specifically detect Sjogren’s Syndrome disease as disclosed by Chen using the device of Lednev as doing so simply requires training the model with the appropriate data for the specific disease to be detected.
Regarding claim 2, Lednev in view of Chen discloses the method of claim 1, and Lednev further discloses determining a likelihood of the presence of [SjD] (see Lednev paragraph [0042], potential detection of disease, and Chen teaches SjD).
Regarding claim 3, Lednev in view of Chen discloses the method of claim 2, and Lednev further discloses that correlating of the produced sample spectroscopic signature further includes:
identifying the human subject (102, Fig. 1) as being associated with a predetermined likelihood of the presence [SjD] (see Lednev paragraph [0039], and Chen teaches SjD); and detecting the presence of [SjD] based upon the predetermined likelihood of [SjD] (Lednev, paragraph [0039]).
Regarding claim 4, Lednev in view of Chen discloses the method of claim 1, and Lednev further discloses:
the biological sample is a saliva sample (paragraph [0022]); and subjecting of at least the portion of the saliva sample to the spectroscopic analysis (paragraph [0024]) further includes:
performing Raman hyperspectroscopy (paragraphs [0024], [0052]) on at least the portion of the saliva sample, the Raman hyperspectroscopy including one of the group consisting of: near-infrared (NIR) Raman spectroscopy, Raman microspectroscopy, Surface Enhanced Raman spectroscopy (SERS), surface enhanced resonance Raman spectroscopy (SERRS), Fourier transform Raman spectroscopy, and coherent anti- Stokes Raman Spectroscopy (paragraphs [0024], [0052]).
Regarding claim 5, Lednev in view of Chen discloses the method of claim 1, and Lednev further discloses that the subjecting of at least the portion of the biological sample to the spectroscopic analysis further includes:
exposing biomolecules of the biological sample to a spectroscopic analysis, the biomolecules including at least one of structural properties, conformational properties, or compositional variations that define the produced sample spectroscopic signature for the biological sample (paragraph [0034]).
Regarding claim 6, Lednev in view of Chen discloses the method of claim 5, and Lednev further discloses that the biomolecules include at least one of: proteins, lipids, peptides, amino acids, electrolytes, mucus, enzymes, or antibacterial species (paragraph [0034]).
Regarding claim 7, Lednev in view of Chen discloses the method of claim 1, and Lednev further discloses that the subjecting at least the portion of the biological sample to the spectroscopic analysis further includes:
subjecting a plurality of portions of the biological sample to the spectroscopic analysis to produce a plurality of distinct sample spectroscopic signatures for the biological sample, each of the plurality of portions positionally distinct from others in the biological sample (paragraphs [0023], [0053]).
Regarding claim 8, Lednev in view of Chen discloses the method of claim 7, and Lednev further discloses that the analyzing of the produced sample spectroscopic signature using the predetermined statistical model further includes:
analyzing each of the plurality of the produced sample spectroscopic signatures using the predetermined statistical model (paragraph [0056]); and correlating of the produced sample spectroscopic signature with a presence of [SjD] based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (paragraph [0058]).
Regarding claim 9, Lednev in view of Chen discloses the method of claim 8, and Lednev further discloses that: determining a diagnosis of SjD based upon on each of the plurality of correlated, produced sample spectroscopic signatures (paragraph [0058]).
Regarding claim 10, Lednev in view of Chen discloses the method of claim 1, and Lednev further discloses that:
discarding predetermined portions of the produced sample spectroscopic signature prior to the analyzing of the produced sample spectroscopic signature using the predetermined statistical model (paragraph [0054]), wherein the discarded predetermined portions of the produced sample spectroscopic signature are inconclusive for correlating the produced sample spectroscopic signature with spectroscopic signatures for the presence of [SjD] (paragraph [0054]).
Regarding claim 11, Lednev in view of Chen discloses the method of claim 1, and Lednev further disclose the produced sample spectroscopic signature for the biological sample includes a vibrational signature of the provided biological sample (paragraph [0030]).
Regarding claim 12, Lednev discloses a system for detecting [a disease], comprising:
a spectroscopy device (106, Fig. 1) subjecting at least a portion of a biological sample (104, Fig. 1) from a human (102, Fig. 1) to a spectroscopic analysis (paragraph [0024]) to produce a sample spectroscopic signature (108, Fig. 1, paragraph [0034]) for the biological sample (paragraph [0023]); and
at least one computing device (110, Fig. 1) in operable communication with the spectroscopy device (106, Fig. 1), the at least one computing device (110, Fig. 1) configured to detect [the disease] in the human subject (102, Fig. 1) by:
analyzing the produced sample spectroscopic signature using a predetermined statistical model (118, Fig. 1, paragraph [0038]), the predetermined statistical model based on spectroscopic signatures for a plurality of modeling samples (paragraph [0038]), wherein the spectroscopic signatures for each of the plurality of modeling samples are associated with [the disease] (paragraph [0038], for example, cognitive diseases); and
correlating the produced sample spectroscopic signature with a presence of [the disease] (paragraph [0039]) based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (paragraph [0039]).
Lednev does not disclose that the disease is Sjogren’s Syndrome disease.
However, Chen discloses a method for detecting Sjogren’s Syndrome disease, comprising: providing a biological sample (blood, see section “Sample preparation”) from a human subject (see “Patient selection”); subjecting at least a portion of the biological sample to a Raman spectroscopic analysis to produce a sample spectroscopic signature for the biological sample (see section “Raman spectral data acquisition”); analyzing the produced sample spectroscopic signature (see Data analysis and Spectral comparison) using a predetermined statistical model (see section “Algorithm description”), and correlating the produced sample spectroscopic signature with a presence of Sjogren’s Syndrome (see last paragraph in Discussion section) based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (see “Model evaluation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to specifically detect Sjogren’s Syndrome disease as disclosed by Chen using the device of Lednev as doing so simply requires training the model with the appropriate data for the specific disease to be detected.
Regarding claim 13, Lednev in view of Chen discloses the system of claim 12, and Lednev further discloses that the at least one computing device (110, Fig. 1) is further configured to determine a likelihood of [SjD] (see Lednev paragraph [0042], potential detection of disease, and Chen teaches SjD) in the human subject (102, Fig. 1).
Regarding claim 14, Lednev in view of Chen discloses the system of claim 13, and Lednev further discloses that the at least one computing device (110, Fig. 1) correlates the produced sample spectroscopic signature further by: identifying the human subject (102, Fig. 1) as being associated with the likelihood of [SjD] (see Lednev paragraph [0039], and Chen teaches SjD); and detecting [SjD] in the human subject (102, Fig. 1) with the association with the likelihood of [SjD] (Lednev, paragraph [0039]).
Regarding claim 15, Lednev in view of Chen discloses the system of claim 12, and Lednev further discloses that the spectroscopy device subjects at least the portion of the biological sample to the spectroscopic analysis by performing spectroscopy on at least the portion of the biological sample, the spectroscopy selected from the group consisting of:
near-infrared (NIR) Raman spectroscopy, Raman microspectroscopy, Surface Enhanced Raman spectroscopy (SERS), surface enhanced resonance Raman spectroscopy (SERRS), Raman hyper spectroscopy, Fourier transform Raman spectroscopy, IR absorption spectroscopy, Fourier Transform Infrared absorption (FTIR), Attenuated Total Reflection (ATR) FTIR, IR reflection spectroscopy, vibrational spectroscopy, and coherent anti-Stokes Raman Spectroscopy (paragraphs [0024], [0052]).
Regarding claim 16, Lednev in view of Chen discloses the system of claim 12, and Lednev further discloses that the spectroscopy device subjects at least the portion of the biological sample to the spectroscopic analysis by exposing biomolecules of the biological sample to a spectroscopic analysis, the biomolecules including at least one of:
structural properties, conformational properties, or compositional variations that define the produced sample spectroscopic signature for the biological sample (paragraph [0034]); and
wherein the biomolecules include at least one of: proteins, lipids, peptides, amino acids, electrolytes, mucus, enzymes, or antibacterial species (paragraph [0034]).
Regarding claim 17, Lednev in view of Chen discloses the system of claim 12, and Lednev further discloses that:
the biological sample is saliva (paragraph [0022]); and
the spectroscopy device subjects at least the portion of the saliva sample to the spectroscopic analysis by subjecting a plurality of portions of the saliva sample to the spectroscopic analysis to produce a plurality of distinct sample spectroscopic signatures for the saliva sample, each of the plurality of portions positionally distinct from others in the saliva sample (paragraphs [0023], [0053]).
Regarding claim 18, Lednev in view of Chen discloses the system of claim 17, and Lednev further discloses that the at least one computing device (110, Fig. 1) analyzes the produced sample spectroscopic signature using the predetermined statistical model by:
analyzing each of the plurality of the produced sample spectroscopic signatures using the predetermined statistical model (paragraph [0056]); and correlates the produced sample spectroscopic signature with a presence of [SjD] by correlating each of the plurality of produced sample spectroscopic signatures based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (paragraph [0058]).
Regarding claim 19, Lednev in view of Chen discloses the system of claim 18, wherein the at least one computing device (110, Fig. 1) configured to detect [SjD] in the human subject further by determining a final, predetermined diagnosis of [SjD] based upon produced sample spectroscopic signatures (paragraph [0058]).
Regarding claim 20, Lednev discloses a system for detecting [a disease], comprising:
a spectroscopic means (106, Fig. 1) for subjecting at least a portion of a biological (104, Fig. 1) from a human (102, Fig. 1) to a spectroscopic analysis to produce a sample spectroscopic signature (108, Fig. 1, paragraph [0034]) for the biological sample (paragraph [0023]); and
a computing means (110, Fig. 1) in operable communication with the spectroscopy means (106, Fig. 1), the computing means (110, Fig. 1) for detecting [the disease] in the human subject (102, Fig. 1) by:
analyzing the produced sample spectroscopic signature using a predetermined statistical model (118, Fig. 1, paragraph [0038]), the predetermined statistical model based on spectroscopic signatures for a plurality of modeling samples (paragraph [0038]), wherein the spectroscopic signatures for each of the plurality of modeling samples are associated with [the disease] (paragraph [0038], for example, cognitive diseases); and
correlating the produced sample spectroscopic signature with a presence of [the disease] (paragraph [0039]) based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (paragraph [0039]).
Lednev does not disclose that the disease is Sjogren’s Syndrome disease.
However, Chen discloses a method for detecting Sjogren’s Syndrome disease, comprising: providing a biological sample (blood, see section “Sample preparation”) from a human subject (see “Patient selection”); subjecting at least a portion of the biological sample to a Raman spectroscopic analysis to produce a sample spectroscopic signature for the biological sample (see section “Raman spectral data acquisition”); analyzing the produced sample spectroscopic signature (see Data analysis and Spectral comparison) using a predetermined statistical model (see section “Algorithm description”), and correlating the produced sample spectroscopic signature with a presence of Sjogren’s Syndrome (see last paragraph in Discussion section) based on the spectroscopic signatures for each of the plurality of modeling samples of the predetermined statistical model (see “Model evaluation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to specifically detect Sjogren’s Syndrome disease as disclosed by Chen using the device of Lednev as doing so simply requires training the model with the appropriate data for the specific disease to be detected.
Conclusion
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/MONICA T TABA/Examiner, Art Unit 2878
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