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 .
Information Disclosure Statement
The IDS filed 3/8/2023 have been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 9/21/2020 filed 9/21/2020 is acknowledged.
Status of Claims
Claims 1-47 are under examination
Claim Objections
Claims 17, 26 and 33 are objected to because of the following informalities:
Claim 17, step (a) and claim 26 recite a letter “m” which appears to be a typographical error.
Claim 33, step (b) recites “microbial nucleic” which is a typographical error and should recite “microbial nucleic acids.”
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 1-47 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-32 are drawn to a method, so a process.
Claims 33-47 are drawn to a computer system comprising a processor, so a machine.
Step 2A Prong One: Identification of an Abstract Idea
Claims 1 and 17 recite:
1. detecting a microbial presence in a biological sample of a subject with cancer, as in claims 1 and 17.
This step reads on data analysis which can be performed by the human mind and is therefore an abstract idea.
2. removing contaminated microbial features from the microbial presence, thereby producing a decontaminated microbial presence, as in claims 1 and 17.
Under Broadest Reasonable Interpretation, this step reads on filtering or removing data of unwanted microbial features. Such can be performed by the human mind or with math and is therefore an abstract idea.
3. comparing decontaminated microbial presence to a microbial presence, thereby generating a microbial-cancer comparison dataset, as in claim 1.
This step reads on a comparison that can be performed by the human mind and is therefore an abstract idea.
4. generating an association between decontaminated microbial presence and the metastatic cancer, as in claim 17.
This step reads on a comparison or analysis that can be performed by the human mind and is therefore an abstract idea.
4. determining the presence or lack thereof metastatic cancer from the microbial cancer comparison data set, as in claim 1.
This step reads on an analysis that can be performed by the human mind and is therefore an abstract idea.
Claim 33 recites:
1. separate microbial nucleic acids from non-microbial nucleic acids of the one or more nucleic acids of the biological sample.
This step reads on filtering nucleic acid data which can be performed by the human mind or with math and is therefore an abstract idea.
2. identify microbial presence of the microbial nucleic acids.
This step reads on an analysis that can be performed by the human mind and is therefore an abstract idea.
3. removing contaminated microbial features from the microbial presence, thereby producing a table of decontaminated microbial presence.
Under Broadest Reasonable Interpretation, this step reads on filtering data from unwanted microbial features and then organizing the data into a table. Such can be performed by the human mind or with math and is therefore an abstract idea.
4. input the table of decontaminated microbial presence into a machine learning model.
This step is drawn to math because the generically recited machine learning model reads on mathematics such as linear regression performed on a computer. Inputting a table of data into a linear regression algorithm reads on mathematics which may include matrix mathematics (i.e. a table). The step is therefore an abstract idea.
Dependent claims 2-16, 18-22, 25, 32 and 34-47 further characterize the abstract idea and are therefore also judicial exceptions.
Step 2A Prong Two: Consideration of Practical Application
The claims result is step of determining the presence or lack thereof metastatic cancer (claim 1), administering to the subject the treatment determined by the association between decontaminated microbial presence and the metastatic cancer (claims 17, 24, and 26-31), and receiving from the machine-learning model an output indicating the presence or absence of metastatic cancer (claim 33).
The claims do not recite any additional elements that integrate the abstract idea into a practical application.
Determining presence or lack thereof metastatic cancer is an abstract idea and receiving an output of this result is an extra solution activity of data transmission as set forth in MPEP 2106.05(g).
Administering a treatment to a subject does not integrate the abstract idea into any particular treatment but rather recites a step equivalent to “apply it” as described in MPEP 2106.05(f). Furthermore, the process of claim 17 does not integrate analysis steps (a) to (c) into determining a treatment such that a particular treatment can be administered because the process is directed to broadly generating an association between the decontaminated microbial presence and the metastatic cancer. The claimed process does not set forth determining any need for treatment, or selecting a treatment such that a particular treatment can be administered. See rejection under 112(b).
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. administering to the subject the treatment determined by the association between decontaminated microbial presence and the metastatic cancer where in the treatment comprises hormone therapy, a probiotic, an adjuvant that is an antibiotic, antigens, or constituents listed in claim 29, as in claims 17, 24, and 26-31.
2. obtain nucleic acid molecules of a biological sample from a subject, as in claim 33.
3. receiving from the machine-learning model an output indicating the presence or absence of metastatic cancer, as in claim 33.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claims 17, 24, and 26-31 recite routine, conventional, and well understood treatments used in for cancer and microbial infections. The step of obtaining nucleic acid molecules of a biological sample is drawn to routine data gathering as described in MPEP 2106.05(g) and receiving an output is also a routine step which is deemed extra solution activity as described in MPEP 2106.05(g).
Other elements of the method include a processor and non-transient computer readable medium (claim 33) which is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112(b) / 112-2nd paragraph
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-47 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1, 17 and 33 recite removing and comparing “contaminated microbial features” from the microbial presence. The term “contaminated microbial features” is unclear. A review of the specification does not set forth a description or definition of “contaminated microbial features” or describe features of microbes that become contaminated. The specification recites “contaminated microbial features” but does not describe what these features are. The specification sets forth examples of how a sample may become contaminated (Figure 21A) and illustrates that some “taxa” may be discarded (Figure 21B). However the specification does not describe what “features” of the microbes are the contaminated features. It is unclear if these “features” which are contaminated are part of the microbes that are intended to be studied in the sample or if the “features” are other microbes or biological constituents that contaminate the sample, in which case they are not features of the microbes (i.e. contaminated microbial features) but rather general contaminants or other microbes that are deemed to be contaminants and not intended to be studied. Clarification of the term “contaminated microbial features” is needed.
Claims 4-8, 18 and 34 recite “the microbial presence.” There is lack of antecedent basis support for this limitation. Claim 1 from which claims 4-8 depend recite step (a) of detecting a microbial presence and in step (c) a microbial presence in a biological sample. Claim 1 also recites in step (c) the decontaminated microbial presence. It is unclear which microbial presence is being referred to. Claim 17, from which claim 18 depends recites step (a) of detecting a microbial presence and step (c) of the contaminated microbial presence. It is unclear what “the microbial presence” in claim 18 is referring to. Claim 33, recites identify a microbial presence in step (c) and decontaminated microbial presence in step (e). It is unclear which of these “the microbial presence” in claim 34 is referring to.
Claim 17 recites administering to the subject “the treatment determined by the association” between decontaminated microbial presence and the metastatic cancer. There is lack of antecedent basis for this limitation because the claim does not set forth determining a treatment by associating decontaminated microbial presence and the metastatic cancer. The instant limitation sets forth a determination that is not recited or required by the claimed process which renders the claim unclear.
Claim Rejections - 35 USC § 112(d)/ 112-4th Paragraph
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 13, 20 and 36 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claims 13, 20 and 36 recite wherein the step of “removing contaminated microbial features” is omitted. The claims thereby negate one of the steps performed in independent claims 1, 17 and 33, respectively, and therefore become broader than the independent claim from which they depend. Claims 13, 20 and 36 are therefore improper dependent claims.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1-47 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lee et al. (US 2014/0271557; IDS filed 3/8/2023).
Lee et al. teach a process of determining that a subject is likely to have cancer; Lee et al. teach amplifying a microbial DNA sample in a test sample from a subject (Abstract)(i.e. detecting a microbial presence in a biological sample of a subject with cancer), as in claim 1, step (a) and claim 17, step (a).
Lee et al. teach the need for the least amount of contaminating human DNA (par. 0185) and importance of excluding contaminants (par. 0189 and 0205); Lee et al. teach that there may be contamination issues such as additional microbes that interfere with accurate data collection and taking measures to avoid contamination (par. 0212); Lee et al. also teach contamination by human DNA and purifying the sample by removing the human DNA (par. 0265). Based on the teachings of Lee et al. for the need to have a microbial sample from additional contaminating microbes or unwanted DNA and the teachings of Lee et al. for removing contaminating DNA, it would be obvious to one of ordinary skill to detect contaminating species and remove them to arrive at a decontaminated microbial presence (i.e. removing contaminated microbial features from the microbial presence thereby producing a decontaminated microbial presence), as in claim 1, step (b) and claim 17, step (b).
Lee et al. teach determining if a subject has cancer or is at risk of cancer based on the level of microbial DNA in the test sample if the microbial DNA in the test sample is significantly different than the level of microbial DNA in a control sample or standard (par. 0004); embodiments include diagnosing metastatic cancer (par. 0047)(i.e. comparing the microbial presence to a microbial presence of one or more biological samples from one or more subjects with cancer, and thereby generating a microbial-cancer comparison dataset and determining the presence of lack thereof metastatic cancer of the subject from the comparison), as in claim 1, steps (c) and (d) and claim 17, step (c) of generating an association between decontaminated microbial presence and metastatic cancer of the subject.
Regarding claim 1, step (c), Lee et al. specifically teaches comparing microbial DNA levels against a “control” sample and suggests that the control sample is sample without cancer such that a difference between test and control would indicate presence of cancer in the test sample (Figures 4A and 4B). Lee et al. does not specifically teach comparing microbial presence in a test sample against a sample with cancer to determine cancer.
However, Lee et al. does teach (par. 0011) a comparison of a plurality of samples with cancer and their respective microbe levels (Figure 4A). Lee et al. teaches comparing panels of samples with (Figure 4(A)) and without (matched normal Figure 4(B)) cancer (i.e. thereby generating a microbial-cancer comparison dataset), as in claim 1, step (c).
Lee et al. teach administering treatment for breast cancer (par. 0038, 0058, 0068) and various cancers (par. 0092), as in claim 17, step (d).
Lee et al. teach identifying breast cancer tissue (par. 0009), as in claim 2.
Lee et al. teach diagnosing primary or metastatic cancers (par. 0047), as in claim 3.
Lee et al. teach determining abundance of microbial species (par. 0012-0013, Figures 5-6) where qPCR is used (par. 009-0010), as in claims 4 and 6.
Lee et al. teach the microbes may be bacteria, viruses, fungi o rother microscopic organism (par. 0004), as in claim 5 and 18.
Lee et al. teach targeting the 16s V4 domain (par. 0088) and V1-V5 domains (par. 0089) or rDNA but make obvious the rRNA domain by teaching that 16s rRNA can also be targeted (par. 0351), as in claims 7 and 8.
Lee et al. teach diagnosing a plurality of different cancers and tumors (par. 0047), as in claims 9-10 and 25.
Lee et al. teach identifying the genus and species of microbes present in a sample (Figure 4A and B)(i.e. taxonomic assignment), as in claims 11 and 19.
Lee et al. teach the need for the least amount of contaminating human DNA (par. 0185) and importance of excluding contaminants (par. 0189 and 0205); Lee et al. teach that there may be contamination issues such as additional microbes that interfere with accurate data collection and taking measures to avoid contamination (par. 0212), as in claim 12.
Lee et al. suggest that accuracy is improved but does to require decontamination, as in claim 13.
Lee et al. teach that contamination may be filtered out but does not necessitate, as in claims 13 and 20.
Lee et al. teach determining amount of DNA by immunohistochemistry (par. 0054) and determining the presence of human DNA (par. 0185) which would make obvious determining tumor tissue DNA, as in claim 14.
Lee et al. teach extracting test tissue sample (par. 0004) and biopsy (par. 0016), and extracting T cells (i.e. white blood cells) from breast tissue (par. 0020)as in claim 15-16 and 21-22.
Lee et al. teach nonsteroidal anti-inflammatory drugs (par. 0038) which would not be rendered in active by a decontaminated microbial presence, as in claim 23.
Lee et al. teach administering probiotics (par. 0058), as in claim 24.
Lee et al. teach administering vaccine or immunotherapy (par. 0068) and therapeutic agents including antibiotics, immunostimulatory molecules--synthetic or from whole microbes or microbial components (par. 0102), as in claims 26-30.
Lee et al. teach administering a probiotic that degrades hormones linked to breast cancer (par. 0051-52, 58, and 109), as in claim 31.
Lee et al. teach determining breast cancer (Abstract), as in claim 32.
Regarding claims 33-47
Lee et al. teach (par. 0269) that there may be a distinct bacterial and/or viral microbiome associated with breast cancer and these microbes may be present in ductal fluid prior to the development or detection of breast cancer. Lee et al. teach determining an amount of microbial DNA in test sample (Abstract), as in claim 33, step (a).
Lee et al. teach the human DNA contaminates the sample and that human DNA sequences can be filtered out computationally (par. 00265)(i.e. separate microbial nucleic acids from non-microbial nucleic acids); this teaching of Lee et al also suggests that nucleic acid samples were obtained by a computer system, as in claim 33 steps (a) and (b).
Lee et al. teach determining if a subject has cancer or at a risk of cancer based on the level of microbial DNA in the test sample if the microbial DNA in the test sample is significantly different than the level of microbial DNA in a control sample or standard (par. 0004),(i.e. identify microbial presence of the microbial nucleic acid), as in claim 33, step (c).
Lee et al. teach the need for the least amount of contaminating human DNA (par. 0185) and importance of excluding contaminants (par. 0189 and 0205); Lee et al. teach that there may be contamination issues such as additional microbes that interfere with accurate data collection and taking measures to avoid contamination (par. 0212); Lee et al. also teach contamination by human DNA and purifying the sample by removing the human DNA (par. 0265). Based on the teachings of Lee et al. for the need to have a microbial sample from additional contaminating microbes or unwanted DNA and the teachings of Lee et al. for removing contaminating DNA, it would be obvious to one of ordinary skill to detect contaminating species and remove them to arrive at a decontaminated microbial presence (i.e. remove contaminated microbial features of the microbial presence), as in claim 33, step (d)
Lee et al. teach a table of microbes and their respective proportional fractions in normal and malignant samples (Figure 4 A and B and par. 0011)(i.e. produce a table of decontaminated microbial presence), as in claim 33, step (d).
Lee et al. teach comparing levels of microbial presence to control values including other microbes (par. 0085-0088) and performing statistical analysis (par. 0270-0271) with qPCR data to determine metagenomic signatures to serve as classifiers to differentiate DCIS (ductal carcinoma in situ) samples from normal samples using logistic regression models (i.e. which reads on machine learning model because logistic regression is a commonly used machine learning model)(par. 0271)(input decontaminated microbial presence into machine learning model and receive an output that indicates the presence or absence of metastatic cancer), as in claim 33 steps (e ) and (f).
Lee et al. teach the microbes may be bacteria, viruses, fungi o rother microscopic organism (par. 0004), as in claim 34.
Lee et al. teach identifying the genus and species of microbes present in a sample (Figure 4A and B)(i.e. taxonomic assignment), as in claim 35.
Lee et al. teach that contamination may be filtered out but does not necessitate, as in claim 36.
Lee et al. teach (par. 0265) filtering out human DNA using the HMP (human microbiome project) protocol which suggests comparing sequences against references, which is also well known to those of skill in the art. Samples or sample data may be purified by data or physical purification processes that are well known, as in claims 37 and 38.
Lee et al. teach determining amount of DNA by immunohistochemistry (par. 0054) and determining the presence of human DNA (par. 0185) which would make obvious determining tumor tissue DNA, as in claim 39.
Lee et al. teach diagnosing a plurality of different cancers and tumors (par. 0047), as in claims 40-41.
Lee et al. teach extracting test tissue sample (par. 0004) and biopsy (par. 0016), and extracting T cells (i.e. white blood cells) from breast tissue (par. 0020)as in claim 42-43.
Lee et al. teach classifier including using logistic regression to differentiate DCIS samples from normal samples (i.e. discriminate between non-metastatic and metastatic cancerous tissue or blood samples) and suggests application to at least breast cancer (par. 0269) or various cancers (par. 0047) wherein classification or identification of a cancer would include an indication of a tissue of origin, as in claims 44-47.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ram Shukla can be reached on (571) 272-7035. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights