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 Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4, and rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Re. Claims 1, 4, and 7: The claims similarly recite limitations involving analyzing the user data and the diagnosis information with at least one processor according to a machine learning algorithm to learn a marker or pattern of a disease. The specification discloses a predictive disease breath database system that accumulates information regarding the organic compounds in consumer’s breaths and uses the information as input to a machine learning campaign (Paragraphs 0005, 0006). While the applicant states that supervised machine learning may be used to identify profile patterns of compounds that are indicative of early disease (Paragraph 0012), the disclosure does not provide sufficient written description for identifying such a predictive marker. The applicant discloses bootstrapping the system with disease indicators/predictive markers (inputs) extracted from scientific literature that are already well-known for correlating volatile compounds with disease, but does not describe a process or result of the invention itself identifying a novel correlation or predictive marker. The specification further describes the use of a contemporary deep neural network (DNN) in another embodiment for the purpose of identifying patterns and establishing correlations counterintuitive to traditional analog data processing methods (Paragraphs 0009-0011); however, the disclosure also lacks any resultant correlations or patterns drawn from the DNN that would indicate a predictive marker and further lacks any process to identify or extract such information in its output. Additionally, it is well-known in the art of machine learning that the structure of a DNN inherently occludes tracing an output back to its input, and does not allow for an association of how each feature (or set of features) in an input contributed to a prediction – training data is operated on by multiple nonlinear operations as it passes the layers of the DNN, and weights are adjusted until data with the same labels at the training data yields similar output. This occlusion disallows identifying a portion of the input that would be indicative of a predictive marker. It cannot be argued that the identified predictive marker is the prediction (or set of predictions) of the DNN itself since new profile patterns require being processed by the DNN in order to gain such a label or classification. Even if an intuitive feature or portion of the input was identified as being common to a set of profiles (e.g., the presence of a certain compound), the conclusion cannot be drawn that the feature alone was predictive of a disease since the untraceable and counterintuitive associations the DNN makes occludes both the input-output relationships between input variables as well as individual contributions of each. Although the applicant discloses that the dimensions of the system include disease indications and list of volatile marker compounds, these are data which may be integrated into the breath profiles and tagged retroactively (Paragraph 0019), therefore not originating from the extracted chemicals of a patient’s breath as claimed in order to identify a predictive marker.
Furthermore, the implementation of a DNN is inherently computer-based. For computer-implemented inventions, the determination of the sufficiency of the disclosure will require an inquiry into the sufficiency of both the disclosed hardware and the disclosed software due to the interrelationship and interdependence of computer hardware and software. In the present application, while the specification does disclose the computer and relevant parts (Paragraphs 0023-0026), the specification does not disclose the algorithm (e.g., the necessary steps and/or flowcharts ) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. In the context of software steps, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). The simple indication of a database, model, and prediction elements in applicant’s figures is insufficient.
Thus, one of skill in the art would not have recognized applicant had possession of the claimed invention at the time the application was filed.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim of amended claims 1, 2, 4-6, 8, and 9 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of the claims recite steps or instructions for a disease prediction method which are grouped as a mental process or mathematical process. Accordingly, each of the claims recites an abstract idea.
Specifically, independent claims 1, 4, and 7 recite limitation concepts including:
receiving user data defining samples of chemicals extracted from breaths or saliva of a plurality of users over time, the samples including trace levels of the chemicals (data gathering);
storing the user data in memory (extra-solution activity of data gathering);
receiving diagnosis information indicative of diseases diagnosed for the users (data gathering);
analyzing the user data and the diagnosis information with at least one processor according to a machine learning algorithm to learn a marker of a disease based at least on the trace levels of the chemicals, the marker corresponding to a pattern in the trace levels of the chemicals associated with ones of the plurality of users diagnosed with the disease (judgement or evaluation, additional elements and/or mathematical concept);
determining with the at least one processor whether the marker is satisfied by a plurality of samples associated with a user, the plurality of samples defining a history of samples taken from the user over an extended time of at least one year, each of the plurality of samples associated with said user indicative of chemicals extracted from breaths or saliva of said user, wherein the determining comprises determining whether the plurality of samples associated with the user indicate the pattern (judgement or evaluation, additional element);
predicting that said user will likely be afflicted with the disease if the marker is determined to be satisfied by the at least one processor (judgement, evaluation, or observation, additional element); and
providing to at least one user a notice of the predicting by the at least one processor (generic computer function of data output, additional element).
As indicated above, the independent claims recite at least one step or instruction grouped as a mental process or a mathematical concept under the 2019 PEG. Therefore, each of the above claims recites an abstract idea. The step of analyzing particular user data to “learn” a particular marker of the disease can encompass one visually observing patterns in such data and identifying patterns which distinguish certain data from others. Similarly, the step of determining whether the marker is satisfied in a particular set of data based on an identified pattern is a process which can be performed in the mind or by pen and paper practice akin to the process prior. A similar argument can be applied to the step of predicting the user will likely be afflicted with the disease if the marker is determined to be satisfied. Since machine learning, broadly, is an automation of human thinking, any step above which is required to be performed by a machine learning algorithm is merely an automation of mental process steps which can be performed in the mind, and a generic linking of such a mental process to a computer environment.
Alternatively or additionally, the feature of machine learning can be viewed as a mathematical concept since such a feature is merely the creation of mathematical relationships between input and output data (depending on the complexity of the machine learning function claimed – herein, no detail is provided in the claims beyond generic functional recitation steps). These steps describe the concept of using mathematical formula(s) (i.e., “a machine learning algorithm”) based on input of medical data, which corresponds to concepts identified as abstract ideas by the courts, such as in Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989). The concept of the recited steps above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas. In addition, these concept are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial), collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), collection, storage, and recognition of data (Smart Systems Innovations).
The dependent claims merely include limitations that either further define the abstract idea and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed. Particularly, newly added claim 10 merely recites “extracting the samples of chemicals from the breaths or saliva of the plurality of users,” which may be construed as a mere data-gathering step which is extra-solution to the mental process and/or mathematical algorithm of analyzing such gathered data.
Step 2A, Prong 2
The above-identified abstract idea is not integrated into a practical application because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of a memory and processor are generically recited computer elements which do not improve the functioning of a computer, or any other technology or technical field. Furthermore, such additional elements do not serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea is not integrated into a practical application under 2019 PEG.
Accordingly, the claims are each directed to an abstract idea.
Step 2B
None of the claims include additional elements that, when viewed as a whole, are sufficient to amount to significantly more than the abstract idea.
Independent claims describe the use of a machine learning algorithm in broad terms. The use of machine learning algorithms to perform data processing steps (irrespective of the type of data used) is well-known, understood, routine, and conventional, as recognized by at least:
Swiston et al. (U.S. 2018/0000428 A1) – Paragraph 0032: classifiers for predicting an infection;
Nikrad et al. (U.S. 2014/0135302) – use of Naïve Bayes algorithm for classification of a particular disease biomarker;
Fu (US 20030008407 A1) – Abstract; Paragraph 0139: use of neural/fuzzy systems for identifying disease conditions of patients;
Matsumura et al. Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer. PLoS One. 2010 Jan 27;5(1):e8819. doi: 10.1371/journal.pone.0008819. PMID: 20111698; PMCID: PMC2811722. – e.g., Abstract: diagnosis of lung cancer based on odor molecules detected on breath.
Such well-known use of machine learning to automate a mental process is also recited generically by:
Huang, et al. 2006. Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence). Springer-Verlag, Berlin, Heidelberg – Preface, pages 7-8 of 266: machine learning is a well-known tool for analyzing large data sets;
Mitchell, T. (July 2006). The Discipline of Machine Learning [Class lecture notes]. Carnegie Mellon University, CMU-ML-06-108. – Abstract, pages 3 and 5 of 12: machine learning is routinely used for a variety of automated tasks, and has been well-known for at least 50 years.
Dependent claims 2, 5, 6, 8, and 10 recite either additional elements of a memory and a processor, or merely recite steps which further define the abstract idea and data-gathering/data-processing steps.
The above-identified additional elements are generically claimed computer components which enable the mental process to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the processor is conventional (Specification, Paragraph 0026: “The exemplary server 15 depicted by FIG. 6 comprises at least one conventional processor 32, such as a digital signal processor (DSP) or a central processing unit (CPU)…”). Furthermore, it appears that any computer component claimed by Applicant is also recited as a non-specific computer component (Specification, Paragraphs 0024-0025: server is any type of computing device capable of performing the functions described, and a computer-readable medium can be any means that can store or contain a computer program)
Accordingly, in light of Applicant’s specification, the claimed terms memory and processor are reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear from the claims themselves and the specification that these limitations require no improved computer resources and merely utilize already available computers with their already available basic functions to use as tools in executing the claimed process. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
The recitation of the above-identified additional limitations in the claims amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
For at least the above reasons, the claims are directed to applying an abstract idea on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. In other words, none of the claims provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the independent claims do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment (diagnosing disease based levels of chemicals detected on breath or odor). That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, the claims merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself, or (ii) provide a technical solution to a problem in a technical field.
Therefore, none of the claims amounts to significantly more than the abstract idea itself.
Accordingly, the claims are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.
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, 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over:
Swiston et al. (U.S. 2018/0000428 A1) (hereinafter – Swiston)
Nikrad et al. (U.S. 2014/0135302) (hereinafter – Nikrad).
Re. Claims 1, 4, and 7: Swiston teaches a disease prediction method (Paragraph 0033: “… an exemplary system for providing disease classification…”).
Swiston extracts physiological user data via high-resolution physiological waveforms, such as those from electrocardiography, hemodynamics, and temperature (Paragraph 0030), and is silent regarding obtaining user data from samples of chemicals extracted from breaths or saliva.
Nikrad teaches:
receiving user data defining samples of chemicals extracted from breaths or saliva (Paragraph 0084: list of biological sample types used for analysis, including breath, saliva, and additional sample types related thereto) of a plurality of users over time,
the samples including trace levels of the chemicals (Paragraph 0091: control level of target molecule based on statistical average of plurality of subjects with disease/condition);
and storing the user data in memory (Fig. 10: memory 109);
Nikrad teaches analogous art in the technology of applying machine learning algorithms for disease prediction (Abstract).
It would have been obvious to one having skill in the art before the effective filing date to have modified Swiston to include utilizing user data extracted from breaths or saliva as taught by Nikrad, the motivation being that such data obtained from biomarkers and permutations of their combined analysis are physical evidence that can provide a prediction of the presence of a disease as well as its prognosis and effect on the body (Paragraph 0006; Paragraph 0007: lung function decline based on level of specific biomarkers; Figs. 2-9, 13: robust predictors of lung function decline and presence of COPD).
Nikrad, in further describing the implementation of a machine learning algorithm, further teaches:
receiving diagnosis information indicative of diseases diagnosed for the users (Paragraph 0165: supervised learning on labeled data, including individuals having the disease; Paragraph 0203: “A total of 304 samples were collected from 61 COPD patients. COPD diagnosis was determined using the GOLD (global initiative for chronic obstructive disease) standard”);
analyzing the user data and the diagnosis information with at least one processor according to a machine learning algorithm to learn a marker of a disease based at least on the trace levels of the chemicals, the marker corresponding to a pattern in the trace levels of the chemicals associated with the plurality of users diagnosed with the disease (Paragraph 0163: exemplary machine learning pattern classification algorithms which may be used; Paragraph 0179: “… the diagnostic score may be a series of bars that each represent a biomarker value and the pattern of the responses may be compared to a pre-set pattern for determination of the presence or absence of disease;” Paragraph 0216: linear regression applied to identify biomarkers; Paragraph 0225: three different methods to identify candidate biomarkers);
determining with the at least one processor whether the marker is satisfied by a plurality of samples associated with a user, the plurality of samples defining a history of samples taken from the user over an extended period of time of at least one year (Table 2: time points of data extraction, up to 3000+ days; Paragraph 0238: prediction consistent throughout 8 year follow-up period; “Study Inclusion and Exclusion Criteria”),
each of the plurality of samples associated with said user indicative of chemicals extracted from breaths or saliva of said user (see previous citation of Paragraph 0084),
wherein the determining comprises determining whether the plurality of samples associated with the user indicate the pattern (see previous citation of Paragraph 0179);
predicting that said user will likely be afflicted with the disease if the marker is determined to be satisfied by the at least one processor (as an example, Paragraphs 0261, 262: naïve Bayes classification identifying robust prediction of COPD if markers are present).
Swiston further teaches providing to at least one user a notice of the predicting by the at least one processor (Paragraph 0032: “The classifiers will provide a detection indication when the number of classifiers predicting an infection in a given time interval exceeds a threshold, which is referred to as a detection. The classifiers will provide a declaration indication when the number of detection indications exceeds a threshold condition, which is referred to as a declaration. Detection and declaration indications may take any suitable format to indicate to users or elements of the present disclosure that the conditions for detection and declaration have been met”).
Re. Claims 2 and 6: Swiston in view of Nikrad teach the invention according to claims 1 and 4. Swiston further teaches the invention wherein the notice indicates a confidence of the at least one processor in the predicting (Paragraph 0111: “When reporting P.sub.d and P.sub.fa for a study and exposure condition, the 95% confidence interval is reported and is based on normal distributions since the number of trials per study is large (>500 declaration points per class)”).
Re. Claim 5: Swiston in view of Nikrad teach the invention according to claim 4. Swiston further teaches the invention wherein
the diagnosis information indicates that the first user is diagnosed to have a second disease (Paragraph 0032: “In a first step, classifiers are trained on a set of physiological training data for which the patients' physiological states are known. A physiological state may correspond to the progression of an infection within a patient, the whether a patient was ever exposed to an agent… or any suitable classification that may be determined based on physiological data;” the invention of Swiston is capable of identifying features associated with more than one disease; the physiological training data described by Swiston is not restricted to being indicative of only one disease), and
wherein the instructions when executed further cause the at least one processor to: analyze the user data and the diagnosis information according to the machine learning algorithm to learn a second marker of a second disease, wherein the at least one processor learns the second marker based on at least the set of the samples associated with said first user (Paragraph 0086: “In an example, the agent is a first agent, and the training data includes data that is recoded from subjects that were exposed to a second agent that is different from the first agent… classifiers that are trained on exposure to one agent may be used to predict whether exposure to the other agent has occurred;” examiner further notes that providing additional labeled data (e.g., diagnosis information of a second disease for a user) to a machine learning algorithm capable of identifying markers of any single disease in order to identify a second marker of a second disease to be applied to a second user would be trivial – such application of a classification algorithm would be encompassed by the generalized use of the training algorithm on a novel set of data, i.e., second user(s));
determine whether the second marker is satisfied by a set of the samples associated with a second user (Paragraph 0005: “One or more features from the physiological data are extracted… the respective classifier is applied to the one or more features to obtain a classifier output that represents a likelihood that the patient has been exposed to the agent;” this process would be the same as the process applied to identifying a first marker associated with a first user),
each sample of the set of the samples associated with said second user indicative of chemicals extracted from breaths or saliva of said second user (Paragraph 0039: “The physiological data may be recorded by any suitable means including… breath exhalate chemical analysis, and any other suitable physiological measurement”);
predict that said second user will be afflicted with the second disease if the second marker is determined to be satisfied by the set of the samples associated with said second user (Paragraph 0005: “The aggregate patient state classifications are combined across the plurality of classifiers to obtain a combined classification, and an indication that the patient has been exposed to the agent is provided when the combined classification exceeds a second threshold;” this process would be the same as the process applied to identifying a first marker associated with a first user); and
provide to at least one user a notice that said second user will be afflicted with the second disease if the second marker is determined to be satisfied by the set of the samples associated with said second user (Paragraph 0032: “The classifiers will provide a detection indication when the number of classifiers predicting an infection in a given time interval exceeds a threshold, which is referred to as a detection. The classifiers will provide a declaration indication when the number of detection indications exceeds a threshold condition, which is referred to as a declaration. Detection and declaration indications may take any suitable format to indicate to users or elements of the present disclosure that the conditions for detection and declaration have been met”).
Re. Claim 8: Swiston in view of Nikrad teach the invention according to claim 7. Nikrad further teaches further limitations regarding user data samples defining chemicals extracted from breaths or saliva of a plurality of users over time, including the method further comprising extracting such a sample (Paragraph 0084: list of biological sample types used for analysis, including breath, saliva, and additional sample types related thereto, also including use of known extraction methods of obtaining such samples, including phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate biopsy procedure, among others).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over:
Swiston et al. (U.S. 2018/0000428 A1) (hereinafter – Swiston)
Nikrad et al. (U.S. 2014/0135302) (hereinafter – Nikrad)
Fu (US 20030008407 A1) (hereinafter – Fu).
Re. Claim 3: Swiston as modified by Nikrad teaches the invention according to claim 1. Nikrad provides an extensive list of biological materials that may constitute a sample for the purposes of identifying levels of another various target molecules which may be derived from the aforementioned extensive list (Paragraph 0084-0090). Thus, Nikrad indicates that the particular type of biological material to be analyzed via supervised learning techniques is not a limiting factor in order to quantify such material as a biomarker in via supervised learning analysis. Solely for the sake of expediting prosecution, Examiner presents the art of Fu:
Fu teaches highly analogous art in the technology of using machine learning to map extracted chemicals from exhaled breath/odor to predict a disease state of the user (Abstract; Examiner further notes that Fu also discusses the use of trace levels of chemicals in Paragraphs 0036, 0118).
It would have been obvious to one of ordinary skill in the art before effective filing date of the invention to include the use of odor, i.e., aroma, as taught by Fu in the system of Swiston in view of Nikrad, since the claimed invention is merely a combination of old elements (using various biomaterials to identify a marker therein as taught by Nikrad, in view of using specifically breath/odors thereof as taught by Fu), and in the combination each element merely would have performed the same function as it did separately (Nikrad is non-specific regarding the type of biomaterial used in the supervised learning analysis), and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
This is a continuation of applicant's earlier Application No. 16/393,598. All claims are identical to, patentably indistinct from, or have unity of invention with the invention claimed in the earlier application (that is, restriction (including lack of unity) would not be proper) and could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the earlier application. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action in this case. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN XU whose telephone number is (571)272-6617. The examiner can normally be reached Mon-Fri 7:30-5:00.
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, Alexander Valvis can be reached on (571) 272-4233. 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.
/JUSTIN XU/Primary Examiner, Art Unit 3791
Prosecution Insights