DETAILED ACTION
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, 6, 8-9, 11, 12-14 and 17-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 11,996,280. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent is more limited than the claimed invention requiring each of the limitations required in claims 1-3, 6, 8-9, 11, 12-14 and 17-19 of the instant application.
Claims 1-2, 11, 12, 14 and 17-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 11,996,279. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent is more limited than the claimed invention requiring each of the limitations required in claims 1-2, 11, 12, 14 and 17-19 of the instant application.
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 1-19 are 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.
Claim 1 lacks written description for requiring “wherein the data analysis system is further configured to: compile the optical data with unique mass spectral data associated with each selected indexed particle using data fusion; and compare the compiled optical data with a training data set comprising of a knowledge base of known aerosol particles to predict composition.”
Specifically, MPEP 2161.01 recites: “the specification must describe the claimed invention in a manner understandable to a person of ordinary skill in the art in a way that shows that the inventor actually invented the claimed invention at the time of filing. Id.; Ariad, 598 F.3d at 1351, 94 USPQ2d at 1172. ”
Here, the instant specification only defines 1) using data fusion (see for instance [0026], [0031]) 2) analyzing spectra with data fusion and machine learning algorithms (0027) 3) a review of data fusion techniques provided by Castanedo incorporated by reference ([0030]).
However, there is insufficient disclosure as to how data fusion is conducted to compile the specifically claimed data.
Initially, Castanedo was improperly incorporated by reference. Specifically 37 CRF 1.57 (d)-(e) recites:
“(d) "Essential material" may be incorporated by reference, but only by way of an incorporation by reference to a U.S. patent or U.S. patent application publication, which patent or patent application publication does not itself incorporate such essential material by reference. "Essential material" is material that is necessary to:
(1) Provide a written description of the claimed 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 set forth the best mode contemplated by the inventor of carrying out the invention as required by 35 U.S.C. 112(a);
(2) Describe the claimed invention in terms that particularly point out and distinctly claim the invention as required by 35 U.S.C. 112(b); or
(3) Describe the structure, material, or acts that correspond to a claimed means or step for performing a specified function as required by 35 U.S.C. 112(f).
(e) Other material ("Nonessential material") may be incorporated by reference to U.S. patents, U.S. patent application publications, foreign patents, foreign published applications, prior and concurrently filed commonly owned U.S. applications, or non-patent publications. An incorporation by reference by hyperlink or other form of browser executable code is not permitted.”
Here, the inventive concept is disclosed to be the last two clauses of the claim. Specifically, paragraph [0030] teaches “The large amount of data related to each particle in the aerosol beam may then be filtered and analyzed using data fusion protocols in data analysis system 110 to identify the composition and type of particles in real-time and with a high accuracy, sensitivity, and specificity.”
Data fusion is considered to be essential matter and thus incorporation by reference of a non-patent literature is not sufficient to demonstrate possession the claimed subject matter. Therefore, the incorporation by reference is insufficient to demonstrate possession of the claimed “compile the optical data with unique mass spectral data associated with each selected indexed particle using data fusion”
However, even if it was, the cited Castanedo is merely a discussion of data fusion in general. The claim requires the data fusion to specifically “compile the optical data with unique mass spectral data associated with each selected indexed particle”. Castanedo fails to describe indexing particles, optical data or mass spectral data. Therefore, Catanedo is insufficient to provide written description as to how data fusion is used to “compile the optical data with unique mass spectral data associated with each selected indexed particle”.
MPEP 2161.01 further recites:
“ original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. ”
Here, the instant specification merely suggests that data fusion is used, however fails to teach any specific data fusion technique to achieve the claimed result of compiling the optical data with unique mass spectral data associated with each selected indexed particle.
As evidenced by Castanedo data fusion techniques can be classified into three nonexclusive categories including “data association” (see page 1, right column, second to last paragraph). Castanedo continues in the following paragraph by reciting “Section 3 describes the most common methods for data association”. In other words, Castanedo is a broad overview of different non-exhaustive methods of data fusion, however does not resolve the problem of the specific data fusion technique to achieve the claimed result of compiling “the optical data with unique mass spectral data associated with each selected indexed particle”.
In other words, it was recognized by Castanedo that there are any number of data fusion techniques. However, there is no discussion as to which data fusion technique is used and how it is specifically conducted so as to achieve the claimed result of compiling the optical data with unique mass spectral data associated with each selected indexed particle.
Consider Niu et al. (Niu et al., “Individual Micron-Sized Aerosol Qualitative analysis-combined Raman Spectroscopy and Laser-Induced breakdown Spectroscopy by optical trapping in Air”, analytical chemistry, 2023) which teaches single particle mass spectrometry and three data fusion mythologies (early fusion, intermediate fusion and late fusion). Niu et al. notes varying accuracies (see abstract). Figure 12 in Nui shows the data fusion strategies in detail. That is, one of ordinary skill in the art would expect a similar level of detail to demonstrate possession. In contrast, the instant specification provides no details with respect to the data fusion mythology to compile the claimed data.
A different way to reach the same conclusion is to consider all the various ways to perform data fusion implemented as a genus, and then ask if a representative number of species have been disclosed. While as evidenced by Castanedo, there is a non-exhaustive list a of data fusion models, there is no disclose actual data fusion model to achieve the claimed result. The issue is that significant effort is required to determine for a given data fusion technique with the set of data to perform to an acceptable accuracy (see Niu, abstract). Therefore, given that the accuracy varies depending on the type of data fusion model, and there is no disclosure of the specific model used. The issue is raised as to whether there is sufficient disclosure of the data fusion to compile the claimed data to an acceptable accuracy to achieve the claimed result of comparing the compiled data with training data set to predict a composition.
Lastly, it is noted that MPEP 2161.01 (I) recites:
“ It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. ”
Here, there is no disclosure with respect to how the result is achieved of compiling the specifically claimed data using data fusion.
Similarly, with respect to compare the compiled optical data with a training data set comprising of a knowledge base of known aerosol particles to predict composition.”
The instant specification teaches in paragraph [0033] that a supervised learning machine, unsupervised machine learning methods and semi-supervised may be used is used for this step. Additionally, paragraph [0035] teaches support vector machines may also use classification to achieve this step. However, there is no practical disclosure as to how these machine learning algorithms are used to compare optical data with training data set or the accuracy of the prediction of the composition and the details on the effectiveness of the machine learning model is expected from a reduction to practice. Niu et al. is evidence of one machine learning method for prediction (see page 2880, last paragraph in right column teaching stacking model with SVM classifier and supporting information, page s11 teaching the specifics of the stacking classifier). However, as disclosed the instant specification teaches any number of machine learning algorithms with no disclosure of any specific architecture to achieve the claimed results when the accuracy is known to vary dependent on the model (See page 2880, right column, last paragraph and supporting information tables S8 and s9 on page S15). That is, except for the general disclosure of use of a machine learning algorithm to predict a composition via comparison of compiled optical data with a training data and a list of highly general machine learning algorithms, the specification is silent with respect to the specific parameters to achieve the claimed result of comparison of claimed data to predict a composition.
Moreover, as evidenced by Christopoulos et al. (“a machine learning approach to aerosol classification for single particle mass spectrometer” (Submitted with IDS of 22 April 2024))) teaches “the choice of supervised or unsupervised machine learning will depend on the researcher’s use case, and each method has unique advantages and disadvantages…it is noted that comparisons between all machine learning models are sensitive to user defined parameters and algorithm implementation”. This suggests the machine learning algorithm to perform the claimed comparison step is not predictable, as it depends specifically on the user defined parameters, algorithm implementation and researcher’s use case. As the specification is devoid of any of these parameters, specific use case or algorithmic implementation, one of ordinary skill in the art, as evidenced by Christopoulos would not understand the result to be supported by high level discussion of ML algorithms. Moreover, as distinct from Chistopoulos, the instant specification is devoid of any specific steps to perform the claimed result. That is, Chistopoulos teaches a specific method of implementing ML into single particle mass spectrometry, however there is no specific disclosure of the architecture used or how it is implemented to achieve the claimed result.
Therefore, claim 1 fails to meet the written description requirement under 35 USC § 112(a).
Claims 2-11 fail to meet the written description requirement by virtue of their dependencies on rejected claim 1.
Claim 12 requires the same limitations and fails to meet the written description requirement as discussed herein above.
Claims 13-16 fail to meet the written description requirement by virtue of their dependencies on rejected claim 12.
Claim 17 requires the same limitations and fails to meet the written description requirement as discussed herein above.
Claims 18-19 fail to meet the written description requirement by virtue of their dependencies on rejected claim 12.
Additionally, claims 11 and 14 further limit the prediction to a machine leanring engine/method. As discussed above, there is insufficient disclosure of a machine learning engine/method to achieve the claimed result. Therefore, claims 11 and 14 are additionally indefinite as discussed herein above.
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 1-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 1 lacks enabling disclosure for reciting “compile the optical data with unique mass spectral data associated with each selected indexed particle using data fusion; and compare the compiled optical data with a training data set comprising of a knowledge base of known aerosol particles to predict composition”.
MPEP 2161.01 (III) provides examples of enablement issues due to missing information for computer implemented functional limitations. MPEP 2161.01(III) recites:
1) When a claim is not limited to any particular structure for performing a recited function and does not invoke 35 U.S.C. 112(f), any claim language reciting the ability to perform a function per se would typically be construed broadly to cover any and all embodiments that perform the recited function. Because such a claim encompasses all devices or structures that perform the recited function, there is a concern regarding whether the applicant's disclosure sufficiently enables the full scope of protection sought by the claim. In re Swinehart, 439 F.2d 210, 213, 169 USPQ 226, 229 (CCPA 1971);
2) The specification need not teach what is well known in the art. However, applicant cannot rely on the knowledge of one skilled in the art to supply information that is required to enable the novel aspect of the claimed invention when the enabling knowledge is in fact not known in the art. ALZA Corp. v. Andrx Pharms., LLC, 603 F.3d 935, 941, 94 USPQ2d 1823, 1827 (Fed. Cir. 2010) ("ALZA was required to provide an adequate enabling disclosure in the specification; it cannot simply rely on the knowledge of a person of ordinary skill to serve as a substitute for the missing information in the specification."); Auto. Techs. Int’l, Inc. v. BMW of N. Am., Inc., 501 F.3d 1274, 1283, 84 USPQ2d 1108, 1114-15 (Fed. Cir. 2007) ("Although the knowledge of one skilled in the art is indeed relevant, the novel aspect of an invention must be enabled in the patent."). The Federal Circuit has stated that "‘[i]t is the specification, not the knowledge of one skilled in the art, that must supply the novel aspects of an invention in order to constitute adequate enablement.’" Auto. Technologies, 501 F.3d at 1283, 84 USPQ2d at 1115 (quoting Genentech, Inc. v. Novo Nordisk A/S, 108 F.3d 1361, 1366, 42 USPQ2d 1001, 1005 (Fed. Cir. 1997)). See also Idenix Pharms. LLC v. Gilead Scis. Inc., 941 F.3d 1149, 1159-61, 2019 USPQ 2d 415844 (Fed. Cir. 2019). The rule that a specification need not disclose what is well known in the art is "merely a rule of supplementation, not a substitute for a basic enabling disclosure." Genentech, 108 F.3d at 1366, 42 USPQ2d 1005; see also ALZA Corp., 603 F.3d at 940-41, 94 USPQ2d at 1827. Therefore, the specification must contain the information necessary to enable the novel aspects of the claimed invention. Id. at 941, 94 USPQ2d at 1827; Auto. Technologies, 501 F.3d at 1283-84, 84 USPQ2d at 1115 ("[T]he ‘omission of minor details does not cause a specification to fail to meet the enablement requirement. However, when there is no disclosure of any specific starting material or of any of the conditions under which a process can be carried out, undue experimentation is required.’") (quoting Genentech, 108 F.3d at 1366, 42 USPQ2d at 1005).
Under 1) above, the above cited features of claim 1 is not limited by structure and encompasses all devices or structures that perform the recited function raising the question as to whether the claim is enabled for the full scope of the claimed language. As discussed above, the specification only discusses the use of machine learning algorithms to perform the claimed function, however the claim has no requirement for machine learning algorithms. Even if the claim were amended to include a machine learning algorithm, there is no representative number of examples in order to make and use the claimed device. That is, the breath of the claims covers any means of comparing compiled data and training set to predict a composition, wherein the only disclosed suggestion is via machine learning algorithms (which are only disclosed with a high degree of generality). 2) the nature of the invention is to achieve real time identification using data fusion and comparative techniques, however again there is no disclosure of the specific data fusion technique, or comparison methodology or how those techniques are specifically operated 3) the state of the prior art is discussed in paragraph [0006] of the instant specification which requires purification resulting in a long delay in identification. The instant specification teaches the solution to this problem is via data fusion protocols ([0030]) that are used with Machine learning techniques ([0033]) to provide real-time identification. However, given that one of ordinary skill in the art as evidenced by paragraph [0006] would not use such techniques and the data fusion techniques are disclosed with a high degree of generality, there is not enough information disclosed to allow one of ordinary skill in the art to make and use the claimed invention. Moreover, as evidenced by Nui discussed above, data fusion and machine learning techniques result in a variety of accuracies and as evidenced by Castanedo there is any number of data fusion techniques. Therefore, without further disclosure as to the particular methods of data fusion and comparison, one of ordinary skill in the art would understand the actual accuracy to be unpredictable. Moreover, the specification is silent of any direction or working examples that would enable one of ordinary skill in the art to achieve the claimed results of compiling optical data with unique mass spectral data associated with each particle using data fusion and compare the compiled optical data with a training data set to predict composition. Therefore, taken together, there is not enough information disclosed to make and use the device.
Lastly, it is noted that support for data fusion, the specification relies on Castanedo. However, as discussed above the nature of the invention is to perform real-time identification via data fusion. As discussed in MPEP 2161.01 (III) above “applicant cannot rely on the knowledge of one skilled in the art to supply information that is required to enable the novel aspect of the claimed invention when the enabling knowledge is in fact not known in the art. ALZA Corp. v. Andrx Pharms., LLC, 603 F.3d 935, 941, 94 USPQ2d 1823, 1827 (Fed. Cir. 2010)”.
Here, the inventive concept is using data fusion to compile data so as to enable real-time identification, however the specification relies on Castanedo which provide no actual method to compile optical data with unique mass spectral data associated with each particle using data fusion. Instead, Castanedo is evidence of the wide variety of data fusion techniques without resolving the issue of how the claimed results are achieved.
Therefore claim 1 fails to meet the enablement requirement under 35 USC § 112(a).
Claims 2-11 fail to meet the enablement requirement by virtue of their dependencies on rejected claim 1.
Claim 12 requires the same limitations and fails to meet the enablement requirement as discussed herein above.
Claims 13-16 fail to meet the enablement requirement by virtue of their dependencies on rejected claim 12.
Claim 17 requires the same limitations and fails to meet the enablement requirement as discussed herein above.
Claims 18-19 fail to meet the enablement requirement by virtue of their dependencies on rejected claim 12.
Relevant art of interest to the applicant:
The applications associated with USPN 11,996,279 and USPN 11,996,280 suggest many of the limitations of the independent claims however fail to suggest the data fusion and comparison steps of each claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J LOGIE whose telephone number is (571)270-1616. The examiner can normally be reached M-F: 7:00AM-3:00PM.
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/MICHAEL J LOGIE/ Primary Examiner, Art Unit 2881
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