DETAILED ACTION
Claim Status
Claims 1-36 are rejected.
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 .
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 04/13/2023, 05/16/2023, 05/23/2023, 02/05/2024, 08/25/2025, are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of list of references cited from each IDS is included with this Office Action.
Drawings
The drawings filed on 10/18/2022 are accepted.
Priority
This application was filed on 10/18/2022. A PCT for this application was filed on 12/28/2022. The following nine U.S. provisional application numbers this application claims priority to are listed below with their filing dates:
63294830
12/29/2021
63294827
12/29/2021
63294820
12/29/2021
63294828
12/29/2021
63294813
12/29/2021
63294816
12/29/2021
63351283
06/10/2022
63351299
06/10/2022
63351317
06/10/2022
Domestic benefit is acknowledged. The effective filing date of claims 1-36 is 12/29/2021.
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 1-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea
•
1, 35, and 36. recite identifying a particular group of rare pathogenic variants in the particular gene;
1, 35, and 36. recite identifying carriers in a cohort of individuals that carry at least one rare pathogenic variant from the particular group of rare pathogenic variants;
1, 35, and 36. recite identifying non-carriers in the cohort of individuals that do not carry any rare pathogenic variant from the particular group of rare pathogenic variants;
1, 35, and 36. recite using a burden test to determine an effect size of the particular group of rare pathogenic variants on the particular phenotype in dependence upon a carrier status that separates the carriers from the non-carriers.
2. The computer-implemented method of claim 1, wherein the particular phenotype is a quantitative biomarker phenotype.
3. The computer-implemented method of claim 2, wherein the effect size of the particular group of rare pathogenic variants is determined for the quantitative biomarker phenotype using a two-tailed t-test on a linear regression component of the burden test.
4. The computer-implemented method of claim 3, wherein the two-tailed t-test determines a difference between average phenotype measurements of the carriers and the non-carriers.
5. The computer-implemented method of claim 4, wherein the two-tailed t-test produces two p-values.
6. The computer-implemented method of claim 4, wherein the phenotype measurements are drug usage-corrected.
7. The computer-implemented method of claim 4, wherein the phenotype measurements are covariate-corrected and normalized.
8. The computer-implemented method of claim 4, wherein the phenotype measurements are common variant-corrected.
9. The computer-implemented method of claim 1, wherein the particular phenotype is a categorical clinical diagnosis phenotype.
10. The computer-implemented method of claim 9, wherein the effect size of the particular group of rare pathogenic variants is determined for the categorical clinical diagnosis phenotype as a beta coefficient for the carrier status.
11. The computer-implemented method of claim 10, wherein the beta coefficient is determined using a logistic regression component of the burden test.
12. The computer-implemented method of claim 11, wherein the logistic regression component is fitted to predict a clinical diagnosis label from the carrier status and a plurality of covariates.
13. The computer-implemented method of claim 12, wherein the logistic regression component encodes the carrier status as a binary indicator variable.
14. The computer-implemented method of claim 12, wherein the logistic regression component regresses out the plurality of covariates.
15. The computer-implemented method of claim 12, wherein the plurality of covariates includes age, sex, genetic principal components, ethnicity, common variants, and a bias term.
16. The computer-implemented method of claim 12, wherein the logistic regression component produces a p-value.
17. The computer-implemented method of claim 1, wherein the particular group of rare pathogenic variants is identified for different burden tests using different rarity thresholds that apply to allele frequencies.
18. The computer-implemented method of claim 17, wherein the particular group of rare pathogenic variants has an allele frequency that is less than 0.001.
19. The computer-implemented method of claim 1, wherein the particular group of rare pathogenic variants is identified for different burden tests using different pathogenicity thresholds that apply to pathogenicity scores.
21. The computer-implemented method of claim 19, wherein the different pathogenicity thresholds are different percentile thresholds.
22. The computer-implemented method of claim 1, further including using respective burden tests to determine respective effect sizes of respective groups of rare pathogenic variants in respective genes associated with the particular phenotype.
23. The computer-implemented method of claim 22, further including generating a rare variant polygenic risk score for the particular phenotype and for a particular individual in the cohort of individuals based on a weighted sum of the respective effect sizes.
24. The computer-implemented method of claim 23, wherein the weighted sum of the respective effect sizes is weighted by the carrier status of the particular individual across the respective genes.
25. The computer-implemented method of claim 24, wherein the particular individual has the carrier status of a carrier for the particular gene when the particular individual carries at least one rare pathogenic variant from the particular group of rare pathogenic variants.
26. The computer-implemented method of claim 25, wherein a weight of one is used when the particular individual has the carrier status of the carrier.
27. The computer-implemented method of claim 24, wherein the particular individual has the carrier status of a non-carrier for the particular gene when the particular individual does not carry any rare pathogenic variant from the particular group of rare pathogenic variants.
28. The computer-implemented method of claim 27, wherein a weight of zero is used when the particular individual has the carrier status of the non-carrier.
29. The computer-implemented method of claim 1, wherein rare pathogenic variants in the particular group of rare pathogenic variants are loss-of-function variants.
30. The computer-implemented method of claim 29, wherein the rare pathogenic variants are missense variants.
31. The computer-implemented method of claim 30, further including separately determining the effects of the loss-of-function variants and the missense variants on the phenotypes using separate burden tests.
32. The computer-implemented method of claim 1, further including using trained coefficients of a linear regression component and a logistic regression component to generate a rare variant polygenic risk score for test data.
33. The computer-implemented method of claim 32, wherein the test data includes individuals with outlier phenotype measurements.
34. generating a rare variant polygenic risk score for the particular phenotype and for a particular individual in a cohort of individuals based on a weighted sum of the respective effect sizes, wherein the weighted sum of the respective effect sizes is weighted by a carrier status of the particular individual across the respective genes
Using a burden test, as in claims 1, and 34-36, is a mathematical relationship and a mental process because a human being can compute a t-test or equivalent using a pen and paper. Identifying a group of rare pathogenic variants, and identifying carriers and non-carriers in a cohort, are mental processes because a human being with access to a dataset could identify these with a pen and paper. Dependent claims 2-19, 21-22, 24-31, and 33 modify how the burden test is calculated, and are as such part of the judicial exception.
Claims 2-5 further limit the implementation of the burden test and the data used.
Claims 6-8 further limit the format of the input data.
Claims 9-16 specify that the effect size is determined by logistic regression, which is a mental process and a mathematical relationship because a human is capable of performing logistic regression on a pen and paper.
Claims 17-19, 21-22, 31 set limitations on the rare pathogenic variants, and specify that the burden test is calculated in using different tests, but this does not preclude them from being performed with a pen and paper.
Generating a polygenic risk score, as in claim 23, is a mental process and a mathematical relationship because a human being could compute PRS with a pen and paper.
Claims 24-28 limit how carrier status is incorporated into the calculation – weighting a value with a 0 or 1 based on carrier status is a mental process because it is capable of being performed in the human mind.
Claims 29-30 further limit the data used for the pathogenic variants.
Linear regression and logistic regression, as in claim 32, is a mental process and mathematical relationship because they are verbal equivalents for algorithms that can be carried out with a pen and paper.
Claim 33 specifies that the test data includes outlier phenotype measurements, which is a mental process because a human can identify outliers in a dataset.
While claims 1, 20, and 34-36 recite performing some aspects of the analysis with a “computer-implemented method”, “neural network”, “system”, or “non-transitory computer-readable medium”, there are no additional limitations that indicate that the “computer-implemented method”, “neural network”, “system”, or “non-transitory computer-readable medium” requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-36 recite an abstract idea ( Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
• Claims 1 and 34 recite A computer-implemented method
Claim 20 recites that the pathogenicity scores are generated by a convolutional neural network
Claim 35 recites A system including one or more processors coupled to memory, the memory loaded with computer instructions to determine effects of rare pathogenic variants on phenotypes, the instructions, when executed on the processors, implement actions
Claim 36 recites A non-transitory computer readable storage medium impressed with computer program instructions to determine effects of rare pathogenic variants on phenotypes, the instructions, when executed on a processor, implement a method
There are no limitations that indicate that the claimed “computer-implemented method”, “neural network”, “system”, or “non-transitory computer-readable medium” or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-36 are directed to an abstract idea ( Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
Claims 1 and 34 recite A computer-implemented method
Claim 20 recites that the pathogenicity scores are generated by a convolutional neural network
Claim 35 recites A system including one or more processors coupled to memory, the memory loaded with computer instructions to determine effects of rare pathogenic variants on phenotypes, the instructions, when executed on the processors, implement actions
Claim 36 recites A non-transitory computer readable storage medium impressed with computer program instructions to determine effects of rare pathogenic variants on phenotypes, the instructions, when executed on a processor, implement a method
As discussed above, there are no additional limitations to indicate that the claimed “computer-implemented method”, “neural network”, “system”, or “non-transitory computer-readable medium” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No). As such, claims 1-36 are not patent eligible.
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.
Claims 1-2, 9-14, 16-22, 29-31, and 35-36 are rejected under 35 U.S.C. 103 as being unpatentable over Povysil et al. (Nature Reviews Genetics volume 20, pages747–759 (2019), 05/16/2023 IDS reference, hereafter “Povysil”).
Regarding claim 1, Povysil teaches a method of determining rare variant contributions to disease (pg 748 left col ¶ 1). Povysil is silent as to the computer implementation of the method, but the courts have determined that automating a manual activity such as implementing an algorithm on a computer is prima facie obvious, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).Povysil describes a method for identifying a group of rare pathogenic variants in a gene, and identifying carriers and non-carriers that have or do not have the variants from that group (see pg 748 left col ¶ 1, pg 748 right col ¶ 1 for identification, pg 749 left col ¶ 3 “sample selection” for finding carriers and non-carriers. Method is outlined in figure 1 and box 1). Povysil uses a burden test to determine an aggregated effect size of the group of variants (pg 752 left col ¶ 4-5, figure 2), and specifically determines in dependence on carrier status (pg 757 left col ¶ 3).
Moving on to claim 2, the phenotypes of interest are quantitative biomarker phenotypes (Povysil pg 748 left col last ¶, “qualifying variant”).
Regarding claim 9, the phenotype of interest in Povysil may be a categorical clinical diagnosis phenotype such as epilepsy (pg 755 figure 2 description).
With respect to claims 10 and 11, a beta coefficient is determined using a logistic regression (Povysil pg 752 left col ¶ 4, “test for association”), and it is used for the carrier status (Povysil box 1).
For claim 12, the logistic regression is fitted to predict diagnosis (Povysil figure 1).
Towards claim 13, carrier status is modeled as a binary indicator (Povysil figure 1).
With claim 14, the logistic regression regresses out covariates (Povysil figure 1, box 1).
Considering claim 16, the logistic regression produces a p-value (Povysil box 1).
For claim 17, there is a suggestion to use different types of aggregate statistics or “burden tests”, such as linear mixed models or Fisher’s exact test (Povysil pg 752 left col ¶ 4, “test for association”).
Looking at claim 18, MAF thresholds below .001 are shown in box 1 of Povysil.
As for claim 19, there is a suggestion to use different types of aggregate statistics or “burden tests”, such as linear mixed models or Fisher’s exact test (Povysil pg 752 left col ¶ 4, “test for association”).
In claims 20 and 21, convolutional neural networks are discussed as one solution for percentile threshold pathogenicity detection on pg 751 right col ¶ 4, with a citation note stating the PrimateAI solution that is described further in the instant specification.
Next, with claim 22, there is a suggestion to use different types of aggregate statistics or “burden tests”, such as linear mixed models or Fisher’s exact test (Povysil pg 752 left col ¶ 4, “test for association”).
Moving forward to claim 29, loss-of-function variants are included in the group of variants in Povysil (pg 748 left col ¶ 2 – “introduction to rare variant collapsing”).
In thinking about claim 30, missense variants are part of the collection of variants of Povysil (pg 751 right col ¶ 3).
Separate determination of the effects of different variant types, as in claim 31, is suggested in Povysil in the same paragraph.
Claims 35 and 36 restate claim 1 of Povysil on a “system” and on a “non-transitory computer-readable medium). The courts have determined that automating a manual activity such as implementing an algorithm on a computer is prima facie obvious, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claims 1-2, 9-14, 16-22, 29-31, and 35-36, an invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to determine the effects of rare pathogenic variants on phenotypes using rare-variant collapsing analysis in the text of Povysil. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as the introduction of Povysil explains how the rare-variant collapsing analysis was developed to meet the challenges of polygenic analysis on rare variants. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the rare-variant collapsing analysis by automating It on a computer in order to gain the advantages of automatic implementation.
Claims 3-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Povysil as applied to claims 1-2, 9-14, 16-22, 29-31, and 35-36 above, and further in view of Curtis (European Journal of Human Genetics volume 27, pages114–124 (2019)).
The limitations of claims 1-2 have been taught by Povysil above.
For claims 3-4, a t-test for determining differences between phenotypes of carriers and non-carriers is taught by Curtis (abstract). Curtis uses a t-test to find differences in a disease “risk score” between cases and controls, which is a kind of average phenotype measurement (pg 117 right col ¶ 2).
The t-test is a type of burden test, as explained by Curtis (abstract). Burden tests produce p-values, as shown by box 1 of Povysil, so claim 5 is taught by Curtis.
Curtis teaches correcting for common variants, as in claim 8 (pg 119 left col ¶ 1).
Regarding claims 3-5 and 8, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use a t-test and to correct for common variants in the text of Curtis. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there is nothing blocking the addition of t-test and correction to the algorithm of Povysil. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the algorithm of Povysil by adding correction and a t-test, in order to find differences between the phenotypes of carriers and non-carriers.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Povysil and Curtis as applied to claims 1-2, 9-14, 16-22, 29-31, and 35-36above, and further in view of Xing et al. (Genet Epidemiol. 2010 November ; 34(7): 769–772, hereafter “Xing”).
Povysil and Curtis teach the limitations of claims 1-5 and 8.
For claims 6 and 7, Povysil and Curtis are silent. Xing explains (pg 3 ¶ 2): “A conventional wisdom in classic linear regression is that adjusting for covariates associated with the response variable can improve the precision of estimates by reducing the residual variance [Fisher, 1932].” This is a suggestion to correct for covariates in analysis.
Regarding claims 6-7, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a suggestion to use covariate correction in the text of Xing. There would be a
reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there is nothing blocking the algorithm of Xing from having covariate correction. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Povysil by adding covariate correction, in order to increase accuracy of the predictions.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Povysil, Curtis, and Xing as applied to claims 1-2, 9-14, 16-22, 29-31, and 35-36 above, and further in view of Altman et al. (Pacific Symposium on Biocomputing, 2019, hereafter “Altman”), Cirulli et al. (Nature Communications, 2020, 11:542), and gbernstein’s discussion on the pymc forums (https://discourse.pymc.io/t/linear-regression-bias-intercept-term-baked-into-covariate-data/2979, 2019, hereafter “Bernstein”).
Povysil, Curtis and Xing teach the limitations these claims are dependent upon.
Concerning claim 15, Age and sex are discussed as covariates in the “sample selection” section (Povysil pg 749 left col ¶ 3). There is a suggestion for the possibility to add other covariates in the “gene set collapsing” section (Povysil pg 755 left col ¶ 2). Cirulli teaches using common variants as covariates (pg 6 right col ¶ 3). Race is used as a covariate in Altman, as are principal components (pg 33 ¶ 2, pg 299 ¶ 2). Using a bias term as a covariate is discussed in Bernstein (¶ 1).
Regarding claim 15, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use common variants, race, principal components, and a bias term as covariates in the text of Cirulli, Altman, and Bernstein. There would be a
reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there is nothing blocking these variables from being added as covariates into the algorithm of Povysil. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the algorithm of Povysil by adding the covariates, in order to increase the accuracy of the prediction.
Claims 23-34 are rejected under 35 U.S.C. 103 as being unpatentable over Povysil, as applied to claims 1-2, 9-14, 16-22, 29-31, and 35-36 above, and further in view of Yosuke (WO2020242976A1, 05/16/2023 IDS Reference).
Povysil teaches the limitations this claim is dependent upon.
Regarding claim 23, Yosuke teaches generating a polygenic risk score based on effect sizes (claim 1, fig 10A).
For claims 24-28, weighting the carrier status of the individual across genes with a weight of 0 or 1 and using rare pathogenic variant status as a marker of carrier status is taught by Povysil (fig. 1, box 1).
For claim 32, Linear (spec ¶ 178) and logistic regression (spec ¶ 186) are used in combination to generate PRS in Yosuke.
Claim 33 mentions “outlier phenotype measurements” described as such in ¶ 54 of the instant specification: “In some cases, a human genetic code may include an outlier, called a genetic variant, that may be common among individuals of a relatively small group of the human population.” The method of Povysil focuses on testing rare variants (abstract), so this is read on by Povysil.
Claim 34 restates the burden test of claim 1 and the carrier status weighting of claim 24, which are taught by Povysil, but modifies it by specifically outputting a polygenic risk score, which is taught by claim 1 of Yosuke.
Regarding claims 23-34, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use generation of polygenic risk scores in the text of Yosuke, in order to determine disease risk when a large number of variants are involved (Yosuke specification ¶ 2-3). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both methods are directed toward creating a score for a group of genes. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Povysil by having it output a polygenic risk score, in order to predict polygenic risk for rare variants.
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.
Claims 1-3, 9-13, 20, 35-36 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 5-6, 9, 17 and 20 of copending Application No. 17/968,285 (reference application, hereafter ‘285). Claim 1 of 17/968,285 implements claim 1 of the instant application but with the addition of grid search, so claim 1 is anticipated.
Claim 2 is taught by claim 5 of ‘285. Claim 3 is taught by claim 6 of ‘285. Claim 9 is taught by claim 9 of ‘285. Claim 20 is taught by claim 4 of ‘285. Claims 10-13 are taught by claim 10 of ‘285. Claims 35 and 36 are taught by claims 17 and 20 of ‘285 respectively. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at 571-272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights