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
Claims 3, 7 and 18 are objected to because of the following informalities:
Tables from the specifications should not be referenced in claims unless otherwise impossible to do so. All glycopeptides, proteins, and linear regression models from the tables should be listed in the claims. See MPEP 2173.5(s): Where possible, claims are to be complete in themselves. Incorporation by reference to a specific figure or table “is permitted only in exceptional circumstances where there is no practical way to define the invention in words and where it is more concise to incorporate by reference than duplicating a drawing or table into the claim. Incorporation by reference is a necessity doctrine, not for applicant’s convenience.” Ex parteFressola, 27 USPQ2d 1608, 1609 (Bd. Pat. App. & Inter. 1993) (citations omitted).
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because they constitute abstract ideas and laws of nature.
Regarding independent claim 1, a two-step analysis is performed:
Step 1: Does the claim fall within a statutory category?
Yes, it is a method/process.
Step 2A, Prong 1: Identify the law of nature/natural phenomenon/abstract ideas.
A method for determining the age of a biological sample from a subject, the method comprising measuring a relative abundance of at least one glycopeptide in the biological sample and comparing the relative abundance of the at least one glycopeptide to an age prediction model, wherein the age prediction model comprises the relative abundance of the at least one glycopeptide in at least one control biological sample, wherein each control biological sample is from a control individual of a known age, thereby determining the age of the biological sample.
Evaluating the age of a biological sample by comparing the relative abundance of glycopeptides to a control sample in an age prediction model is considered a mental process, where it is evaluated based on one’s observation, evaluation, judgment, and opinion using data as comparison, making this an abstract idea. Additionally, associating glycopeptides abundance as a correlation for sample age is a law of nature.
Step 2A Prong 2: Has the abstract idea been integrated into a particular practical application?
The claim as a whole does not integrate the abstract idea into a practical application. Other than the abstract idea, claim 1 recites the additional elements: Measuring a relative abundance of glycopeptide. With respect to the additional element mentioned, they represent insignificant extra solution activity (e.g., mere data gathering). Thus, there is no application of the abstract idea, much less a particular practical application.
Step 2B: Does the claim recite any elements which are significantly more than the abstract idea?
Claim 1 does not provide an inventive concept (significantly more than the abstract idea). Measuring a relative abundance of glycopeptide is considered insignificant extra solution activity (e.g., mere data gathering). Furthermore, the additional elements above are well understood, routine, and conventional activities within the prior art (see 35 U.S.C. 102 and 103 rejections).
Dependent claims 2-20 do not resolve any of the issues discussed above because they involve limitations with more insignificant extra-solution activity, no particular practical application, or is an abstract idea. Claim 2 recites that the age of the subject is based on the age of the biological sample (i.e., glycan age), which does not provide an inventive concept more than the abstract idea of claim 1. Claims 2-5 and 7-10 recite what glycopeptides can be used in measurement, claim 11 reciting the age prediction model comprising the relative abundance of at least one glycopeptide in a plurality of control groups, claims 12-13 being biological liquid samples like blood, claims 14-15 performing measuring by mass spectrometry, claim 16 performing a calculation of a relative response of glycopeptide in a mass spec curve, claim 18 discussing what linear model is used in the age prediction model, and claim 19-20 being the source of the biological samples are all insignificant extra solution activities (e.g., mere data gathering). Claims 6 and 17 performs a comparison/correlation between the sample and a control, which is an abstract idea.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Merleev et al. ("A site‑specific map of the human plasma glycome and its age and gender‑associated alterations", as cited in the IDS).
Regarding claim 1, Merleev et al. teaches a method for determining the age of a biological sample from a subject (see pg. 1/Abstract, disclosing 13 glycopeptides were found to be associated with gender and 41 to be associated with age. Using just five age‑associated glycopeptides, a highly accurate age prediction model was constructed and validated.), the method comprising measuring a relative abundance of at least one glycopeptide in the biological sample (see pg. 1/Abstract, pg. 3/Introduction, disclosing that multiple reaction monitoring mass spectrometry is used in constructing a detailed site-specific structural map of the relative abundance of human plasma glycome in healthy individuals and to characterize the glycans’ inter- and intra-molecular correlations). and comparing the relative abundance of the at least one glycopeptide to an age prediction model, wherein the age prediction model comprises the relative abundance of the at least one glycopeptide in at least one control biological sample, wherein each control biological sample is from a control individual of a known age, thereby determining the age of the biological sample (see pg. 1/Abstract, pg. 3/Introduction, disclosing glycan alterations associated with age and gender (common covariates in biomarker research and discovery) were also identified and multi-analyte classifiers capable of predicting age as a age prediction model utilizing five age-associated glycopeptides were constructed and validated.).
Regarding claim 2, Merleev et al. teaches the method of claim 1, wherein the age of the subject is determined based on the age of the biological sample (see pg. 8/Discussion, Fig. 4, disclosing that an age prediction model revealed that five sites of glycosylation were sufficient to accurately predict the age of 97 individuals. The exceptional performance of this model to predict age is a testament of how the human plasma glycome is a reflection of human biological processes, in this case, aging.).
Regarding claim 3, Merleev et al. teaches the method of claim 1, wherein the at least one glycopeptide comprises any of the glycopeptides in Table 2 (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age.).
Regarding claim 4, Merleev et al. teaches the method of claim 1, wherein the at least one glycopeptide comprises IgGI-3510, IgGI-5410, IgM-209-5411, IgM-J-5412, Haptoglobin (Hp)-241-7602, or a combination thereof (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age.).
Regarding claim 5, Merleev et al. teaches the method of claim 1, wherein the at least one glycopeptide comprises IgGI-3510, IgGI-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602 (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age.).
Regarding claim 6, Merleev et al. teaches the method of claim 1, wherein the method further comprises measuring a concentration of at least one protein in the biological sample and comparing the concentration of the at least one protein to the age prediction model, and wherein the age prediction model further comprises the concentration of the at least one protein in the at least one control biological sample (see pg. 5/Intra- and inter-protein glycan association pg. 7/Prediction models for age, Fig. 4, disclosing that the relative abundance of a particular glycan at a defined site correlated with the protein’s serum concentration. A second combined age-prediction model, which included serum protein concentrations as additional variables, was also constructed. The resulting model contained six glycopeptides (IgG1-3510, IgG1-5410, IgG2-3410, IgM-209-5411, IgM-J-5412, Hp-241-7602) and 1 serum protein (IgG3).).
Regarding claim 7, Merleev et al. teaches the method of claim 6, wherein the at least one protein comprises any of the proteins in Table 2 (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age.).
Regarding claim 8, Merleev et al. teaches the method of claim 6, wherein the at least one protein comprises IgG3 (see pg. 7/Prediction models for age, disclosing a second combined age-prediction model, which included serum protein concentrations as additional variables, was also constructed. The resulting model contained six glycopeptides (IgG1-3510, IgG1-5410, IgG2-3410, IgM-209-5411, IgM-J-5412, Hp-241-7602) and 1 serum protein (IgG3).).
Regarding claim 9, Merleev et al. teaches the method of claim 8, wherein the at least one glycopeptide comprises IgGl-3510, IgGl-5410, IgG2-3410, IgM-209-5411, IgM-J-5412, Hp-241-7602, or a combination thereof (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age.).
Regarding claim 10, Merleev et al. teaches the method of claim 8, wherein the at least one glycopeptide comprises IgGI-3510, IgGI-5410, IgG2-3410, IgM-209-5411, IgM-J-5412, and Hp- 241-7602 (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age.).
Regarding claim 11, Merleev et al. teaches the method of claim 1, wherein the age prediction model comprises the relative abundance of the at least one glycopeptide in a plurality of control biological samples (see pg. 1, Abstract, disclosing that the age prediction model was developed by mapping out the relative abundances of the most common 159 glycopeptides in the plasma of 97 healthy volunteers. Using age and gender as relevant covariables, five age-associated glycopeptides were chosen to be used in constructing and validating the age prediction model.).
Regarding claim 12, Merleev et al. teaches the method of claim 1, wherein the biological sample and the control biological sample are liquid samples (see pg. 9/Sample preparation, disclosing that whole blood samples were extracted from enrolled individuals, where plasma (i.e., a liquid sample) was further separated and prepared.).
Regarding claim 13, Merleev et al. teaches the method of claim 1, wherein the biological sample and the control biological sample are blood samples, serum samples, plasma samples, or a combination thereof (see pg. 9/Sample preparation, disclosing that whole blood samples were extracted from enrolled individuals, where plasma was further separated and prepared.).
Regarding claim 14, Merleev et al. teaches the method of claim 1, wherein measuring the relative abundance of the at least one glycopeptide comprises mass spectrometry (see pg. 3/Introduction, disclosing using Multiple Reaction Monitoring Mass Spectrometry for performing site-specifically characterization of the human glycome in a rapid and reproducible fashion, allowing to construct a detailed structural map of the human plasma glycome of healthy individuals and to characterize the glycans' inter- and intra- molecular correlations.).
Regarding claim 15, Merleev et al. teaches the method of claim 1, wherein measuring the relative abundance of the at least one glycopeptide comprises multiple reaction monitoring mass spectrometry (see pg. 3/Introduction, disclosing using Multiple Reaction Monitoring Mass Spectrometry for performing site-specifically characterization of the human glycome in a rapid and reproducible fashion, allowing to construct a detailed structural map of the human plasma glycome of healthy individuals and to characterize the glycans' inter- and intra- molecular correlations.).
Regarding claim 16, Merleev et al. teaches the method of claim 15, wherein measuring the relative abundance of the at least one glycopeptide comprises calculating a relative response of the at least one glycopeptide as an area under a mass spectrometry curve of the at least one glycopeptide divided by an area under a corresponding mass spectrometry curve of a non-glycosylated reference peptide from the same protein as the at least one glycopeptide (see pg. 9/UPLC-ESI-QqQ-MS analysis, disclosing that enzymatically-prepared samples containing both peptides and glycopeptides were directly analyzed using an Agilent 1290 infinity liquid chromatography (LC) system coupled to an Agilent 6490 triple quadrupole (QqQ) mass spectrometer. The Multiple Reaction Monitoring Mass Spectrometer method used requires predetermined knowledge of the peptide or glycopeptide's LC retention time and its collision induced dissociation behavior. Glycopeptide relative responses were calculated under the curves of the glycopeptide and a non-glycosylated reference peptide from the same protein.).
Regarding claim 17, Merleev et al. teaches the method of claim 1 wherein the age prediction model comprises a linear regression model or a multiple linear regression model based on a correlation between the relative abundance of the at least one glycopeptide in the at least one control biological sample and the age of the control individual (see pg. 1/Abstract, pg. 6/Prediction models for age, disclosing linear regression models comprised of either glycopeptides only or a mixture of glycopeptides and proteins were thus constructed utilizing a forward stepwise selection method. Since age and gender are relevant covariates in biomarker research, these variables were also characterized. 13 glycopeptides were found to be associated with gender and 41 to be associated with age. Using just five age‑associated glycopeptides, a highly accurate age prediction model was constructed and validated.).
Regarding claim 18, Merleev et al. teaches the method of claim 17, wherein the age prediction model comprises one of the multiple linear regression models of Table 5 (see pg. 6/Prediction models for age, disclosing a “glycan only” model revealed that five sites of glycosylation (IgG1-3510, IgG1-5410, IgM-209-5411, IgM-J-5412, and Haptoglobin (Hp)-241-7602) were sufficient to accurately predict age. See also pg. 7/Prediction models for age, disclosing a second combined age-prediction model, which included serum protein concentrations as additional variables, was also constructed. The resulting model contained six glycopeptides (IgG1-3510, IgG1-5410, IgG2-3410, IgM-209-5411, IgM-J-5412, Hp-241-7602) and 1 serum protein (IgG3).).
Regarding claim 19, Merleev et al. teaches the method of claim 1, wherein the subject is male or female (see pg. 5/Analysis of covariates, disclosing that studies mainly on either released glycans or tryptic peptides of purified IgG have demonstrated that age and gender can alter the glycosylation of serum proteins, where the levels of IgM was affected by gender, with males showing lower plasma levels of IgM than females (0.49 mg/mL vs 0.87 mg/mL [SD 0.6], respectively).).
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.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Merleev et al. as applied to claim 1 above, and further in view of Gudelj et al. ("Estimation of human age using N-glycan profiles from bloodstains", as cited in the IDS).
Regarding claim 20, Merleev et al. fails to teach wherein the biological sample is from a criminal forensics investigation.
However, in the analogous art of "Estimation of human age using N-glycan profiles from bloodstains", Gudelj et al. teaches strong associations between human plasma N-glycans and age, and further studies application for the biological phenomenon in the field of forensics. Blood from 526 blood donors from different parts of Croatia was collected on bloodstain cards during the period 2004–2007and stored at 4°C for 6–9 years. Glycosylation profiles of the bloodstains were analyzed using hydrophilic interaction ultra performance liquid chromatography and divided into 38 glycan groups (GP1-GP38). A statistically significant correlation between N-glycan profiles of bloodstains and chronological age was found and a statistical model that can be used for the age prediction was designed (see Gudelj et al., pg. 955/Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the age prediction model for glycopeptides of Merleev et al. to incorporate the usage of the model for the field of forensics (as taught by Gudelj et al.), for the benefit of being able to estimate the human age of a blood stain from forensic samples by using protein glycosylation from the blood plasma (see Gudelj et al., pg. 955/Introduction).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tracy C Colena whose telephone number is (571)272-1625. The examiner can normally be reached Mon-Thus 8:00am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lyle Alexander can be reached at (571) 272-1254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TRACY CHING-TIAN COLENA/ Examiner, Art Unit 1797
/JENNIFER WECKER/ Primary Examiner, Art Unit 1797
Prosecution Insights