DETAILED ACTION
The Applicant’s response, received 02 April 2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 02 April 2025 has been entered.
Status of the Claims
Claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 are pending.
Claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 are rejected.
Priority
The effective filing date of the claimed invention is 12/02/2016.
Claim Interpretation
Claims 21, 28, and 35 recited the term “disparate.” This term is interpreted to mean input data obtained using fundamentally different measurement technologies (Specification, page 3, ¶ [0005]).
Claims 21, 22, 25, 27, 28, 29, 32, 34, 35, 36, and 39 recite the term “measurement space.” This term is interpreted to mean a unique measurement space for a measurement group that corresponds to a particular measurement technique that is different from that of other measurement groups (Specification, page 19, ¶ [0042]).
Claims 21, 22, 24, 27, 28, 29, 31, 34, 35, 36, and 38 recite the term “latent space.” The term “latent space” is interpreted to be an abstract, lower-dimensional representation of high-dimensional data that simplifies complex data structures to reveal patterns within data.
Claims 22, 24, 25, 29, 31, 32, 36, 38, and 39 recite the term “measurement scale.” This term is interpreted to mean the manner in which variables are defined and categorized, and each measurement scale is further interpreted to have properties that determine how to properly analyze the corresponding data.
Claims 21, 22, 24, 28, 29, 31, 35, 36, and 38 are interpreted as reciting a product-by-process limitation of a “trained predictive model.” The claims are further interpreted to be limited to receiving and using the trained predictive model, and not requiring active steps of training the predictive model.
Claim Objections
The objection to claim 27 in the Office action mailed 19 December 2024 is withdrawn in view of the amendment received 02 April 2025.
Claim Rejections - 35 USC § 112
The amendment received 02 April 2025 has been fully considered, however after further consideration, new grounds of rejection are raised in view of the amendment.
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 21-22, 24-25, 27-29, 31-32, 34-36 and 38-39 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.
Claims 21, 28, and 35, and dependent claims, recite the limitations “a trained predictive model that estimates (i) a first mapping function…and (ii) a second mapping function…” and “computing, by the trained predictive model, the first mapping function” and “computing, by the trained predictive model, the second mapping function” however the “trained predictive model” is described in the specification as having been generated as a result of the determination of one or mapping functions (e.g., see Specification at paras. [0033], [0038], [0044], and [0057]). In particular, the specification (para. [0044]) describes the latent space harmonization module 212 as generating the trained predictive model 222 based on the mapping functions 110 that were used to map data of the training data sets 214 to a shared latent space. The specification does not describe using a trained predictive model to compute the one or more mapping functions, and therefore the claims contain subject matter that is not described in the specification in such a way that is reasonably conveyed to one skilled in the relevant art. Furthermore, the model does not estimate the mapping functions, but instead, the mapping functions are part of the trained model, i.e., training the model estimates the mapping functions.
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 25, 27-29, 31, 32, 34-36, 38, and 39 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 28 and 35 are indefinite for reciting the limitations “computing, by the trained predictive model, the first mapping function” and “computing, by the trained predictive model, the second mapping function” because it is not clear as to whether the trained predictive model is generated as a result of the determination of the first and second mapping functions, or if the trained predictive model is not generated as a result of the determination of the first and second mapping functions, but rather, uses the first and second mapping functions. Therefore, the claims recite contradictory language due to the “receiving” step reciting that the trained predictive model learns the first and second mapping functions, but then recites the trained predictive model computes the first and second mapping functions at the “computing” steps.
Claims 29, 31, 32, 34, 36, 38, and 39 are indefinite for depending from either of claim 28 or 35 and for failing to remedy the indefiniteness of claims 28 and 35.
Claims 25, 32, and 39 are indefinite for reciting “wherein output training data…is quantified in a measurement scale…different from output training data for a…” because the claims from which they depend only recite “a trained predictive model…” but do not recite any training data, and therefore dependent claims 25, 32, and 39 are just describing training data that is not even used in the independent claims, and thus, the limiting effect of dependent claims 25, 32, and 39 on the independent claims from which they depend is not clear (MPEP 2111.04).
Claims 27 and 34 are indefinite for depending from claims 25 and 32, respectively, and for failing to remedy the indefiniteness of claim 25 or 32.
Claims 27 and 34 are indefinite for reciting “wherein the output training data…comprises a single data set…” because the independent claims from which they depend only recite “a trained predictive model…” but do not recite any training data, and therefore dependent claims 27 and 34 are just describing training data that is not even used in the independent claims, and thus, the limiting effect of dependent claims 27 and 34 on the independent claims from which they depend is not clear (MPEP 2111.04).
Claim Rejections - 35 USC § 101
The rejection of claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 under 35 U.S.C. 101 in the Office action mailed 19 December 2024 is maintained with modification in view of the amendment received 02 April 2025.
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 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 21, 22, 24, 25, and 27 are directed to a computing system (machine or manufacture); claims 28, 29, 31, 32, and 34 are directed to a computer-implemented method (process); and claims 35, 36, 38, and 39 are directed to a non-transitory computer-readable storage device (machine or manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claims 21, 28, and 35 recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a trained predictive model (claim 21) (i.e., mental processes);
computing, by the trained predictive model, the first mapping function (i.e., mental processes and mathematical concepts);
computing, by the trained predictive model, the second mapping function (i.e., mental processes and mathematical concepts);
generating a joint, cohesive dataset by applying the first and second learned mapping functions on the new data resulting in output data representing a supervised target variable that is common to the disparate measurement spaces, the output data representative of using a measurement technique of a measurement space of the measurement spaces on the new data (i.e., mental processes and mathematical concepts).
Dependent claims 22, 24, 25, 27, 29, 31, 32, 34, 36, 38, and 39 recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas.
Dependent claims 22, 29, and 36 further recite:
transforming output data generated by the trained predictive model in the shared latent space to output data in a measurement scale of a measurement space of the measurement spaces (i.e., mental processes and mathematical concepts).
Dependent claim 24 further recites:
the trained model is trained on input training data in the shared latent space that shares a common measurement scale (i.e., mental processes).
Dependent claims 31 and 38 further recite:
training the trained predictive model based on input training data in the shared latent space, the input training data shares a common measurement scale (i.e., mental processes and mathematical concepts).
Dependent claims 25, 32, and 39 further recite:
output training data for a first measurement space of the measurement spaces is quantified in a measurement scale that is different from output training data for a second measurement space of the measurement spaces (i.e., mental processes and mathematical concepts).
Dependent claim 27 further recites:
the output training data in the shared latent space comprises a single data set that is mappable to supervised target variables included in each of the measurement spaces, and pairs of input data and output data in the shared latent space have a same ordering of corresponding pairs of input training data and output training data in the training data sets (i.e., mental processes and mathematical concepts).
Dependent claim 34 further recites:
the output training data in the shared latent space comprises a single data set that is mappable to supervised target variables included in each of the measurement spaces, and pairs of input data and output data in the shared latent space preserves, by the first and second mapping functions being monotonic mapping functions, an ordering of corresponding pairs of input training data and output training data in the training data sets (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., the Specification shows an implementation using a linear regression model (page 11, ¶ [0025]) for mapping new input data to unmeasured output data for a given physical principle or phenomenon, which can practically be performed in the human mind with the aid of pen and paper) and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., in the Specification: Equation 1 (page 11, ¶ [0023]); linear regression (page 11, ¶¶ [0023] – [0025]); and Equations 2 & 3 (page 12, ¶¶ [0026] – [0027])) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 22, 24, 25, 27, 29, 31, 32, 34, 36, 38, and 39 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claims 21, 28, and 35 include:
a computing system comprising a processing circuitry and a memory (claim 21);
a computer (claim 28);
a non-transitory computer-readable storage device (claim 35);
a machine, i.e., computer (claim 35);
receiving a trained predictive model (claims 28 and 35); and
receiving new data including data from each of the disparate measurement spaces (claims 21, 28, and 35);
The additional elements of a computing system comprising a processing circuitry and a memory (claim 21); a computer (claim 28); and a non-transitory computer-readable storage device and a machine, i.e., computer (claim 35); invoke a computer and/or computer related hardware merely as a tool for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, does not integrate the judicial exceptions into a practical application (see MPEP 2106.04(d)(1)).
The additional element of receiving data (claims 21, 28, and 35) is a step of gathering data for use in the claimed process, and therefore does not add more than insignificant extra-solution activity to the judicial exception, and thus does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 22, 24, 25, 27, 29, 31, 32, 34, 36, 38, and 39 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 21, 28, and 35 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computing system comprising a processing circuitry and a memory (claim 21); a computer (claim 28); and a non-transitory computer-readable storage device and a machine, i.e., computer (claim 35); and receiving data (claims 21, 28, and 35); are conventional (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
Therefore, when taken alone, all additional elements in claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 21, 22, 24, 25, 27-29, 31, 32, 34-36, 38, and 39 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments/remarks received 02 April 2025 have been fully considered, but are not persuasive.
The Applicant summarizes the state of the art on page 7 (para. 7) and page 8 (paras. 1-4) in the Remarks, and summarizes the invention on page 8 (paras. 5-6) and page 9 (para. 1). The Applicant states on page 9 (bottom) that the claims are necessarily rooted in computer technology and overcome a problem specifically arising in the context of computer-implemented machine learning models by overriding the routine and conventional sequence of events ordinarily performed in generating and operating a computer-implemented machine learning model. The Applicant further states that the specification explains that prior art models are limited to training and operating based on data from a single measurement technique, and that this limits model accuracy and application, however the claimed invention overcomes these problems by learning a mapping of variables to a shared latent space.
These arguments are not persuasive, because first, claims can recite an abstract idea even if they are claimed as being performed on a computer (see MPEP 2106.04(a)(2)(III)(C)). Second, latent space itself is an abstract representation of data used in machine learning and data science to simplify complex data structures in order to reveal patterns within the data, using dimensionality reduction techniques that are grounded in mathematical and statistical concepts. Third, the recited judicial exceptions are not integrated into a practical application at Eligibility Step 2A Prong Two, as discussed in the rejection above, and when the identified additional elements are carried over to Eligibility Step 2B and evaluated for an inventive concept, the claims are deemed to not recite any additional elements that amount to significantly more than the recited judicial exceptions, as discussed in the rejection above. Therefore, the instant claimed improvement over prior art models is a purported improvement to the abstract idea (e.g., learning a mapping of variables to a shared latent space), and not an improvement to computer functionality itself, or an improvement to another technology or technical field.
The Applicant states on page 10 (para. 1) in the Remarks that the claims are not practically performed in the human mind at least because the mapping to the shared latent space is not practically performed in the human mind and the disparate measurement techniques generate data that are not comparable, and thus use of the data is not practically performed in the human mind. The Applicant further states (para. 2) that while a machine learning model is a mathematical concept, the claims limit the machine learning model to an application of machine learning model operations which improves the computer-rooted machine learning model operations as described in the specification.
These arguments are not persuasive, because first, latent space itself is an abstract representation of data used in machine learning and data science to simplify complex data structures in order to reveal patterns within the data, using dimensionality reduction techniques that are grounded in mathematical and statistical concepts to compress information from a high-dimensional space into a more compact, low-dimensional representation. Furthermore, the word “space” in the term latent space refers to a mode of mapping, comparing, or sampling of data points. Second, claims can recite a mental process even if they are claimed as being performed on a computer (MPEP 2106.04(a)(2)(III)(C)). Third, at Eligibility Step 2A: Prong One, examiners evaluate whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula or equation (MPEP 2106.04(a)(2)(I)). A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation (MPEP 2106.04(a)(2)(C)). Furthermore, the instant specification shows the equations required for performing the claims steps, for example at paragraphs [0023] through [0026].
The Applicant states on page 10 (para. 3) in the Remarks that the claims improve a technology and technical field by obtaining large sample sizes of training data due to the cost associated with training data; reducing costs associated with testing the new input data in a laboratory to measure output values; and eliminating the need for costly and time-consuming measurement procedures otherwise required to determine outputs from the new input data; among others.
These arguments are not persuasive, because first, a trained predictive model is an abstract idea, and even a purported improved abstract idea is still an abstract idea and therefore a judicial exception. Second, generating a joint, cohesive dataset by applying learned mapping functions on new data that results in output data is an abstract idea, and even if it is an improvement to the abstract idea, is nonetheless still an abstract idea, and therefore a judicial exception. Third, regarding the argument that the claims improve a technology and technical field by obtaining large sample sizes of training data, it is noted that the claims do not require any particular amount of data, and therefore this argument is not commensurate with the scope of the claims. Fourth, the instant claims do not apply, rely on, or use the judicial exceptions in a manner that imposes a meaningful limit on the judicial exceptions, i.e., the claims do not recite an additional element (or combination of elements) that integrates the exception(s) into a practical application when evaluated using one or more of the considerations introduced at MPEP 2106.04(d) subsection I. Fifth, when the identified additional elements are carried over to Eligibility Step 2B and evaluated for an inventive concept, the claims are deemed to not recite any additional elements that amount to significantly more than the recited judicial exceptions, as discussed in the rejection above. Therefore, the instant claimed improvements described above are a purported improvement to the abstract idea (e.g., learning a mapping of variables to a shared latent space, and using a trained predictive model), and not an improvement to computer functionality itself, or an improvement to another technology or technical field.
Double Patenting
The amendment received 02 April 2025 has been fully considered, however after further consideration, new grounds of rejection are raised in view of the amendment and in view of further consideration.
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 21, 22, 24, 25, 27, 28, 29, 31, 32, 34, 35, 36, 38, and 39 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, 4, 5, and 6 of U.S. Patent No. 10,923,231 in view of Meeds et al. (PeerJ Computer Science, 2015, Vol. 1:e11, doi 10.7717/peerjcs.11, pp. 1-24, previously cited in the Office action mailed 18 June 2024).
Regarding instant independent claims 21, 28, and 35, reference claims 1, 5, 11, and 15 show the limitations of instant claims 21, 28, and 35, but do not show a trained predictive model (claim 21); or receiving a trained predictive model, the trained predictive model trained using gradient descent (claims 28 and 35).
Meeds et al. shows existing, pre-trained models that can be modified by training on new data (page 19, para. 3); and further shows training deep neural networks using gradient descent (Abstract; page 3, para. 1; and page 17, paras. 4 & 5).
Regarding instant dependent claims 22, 29, and 36, reference claim 2 shows the limitations of the instant claims.
Regarding instant dependent claims 24, 31, and 38, reference claim 3 shows the limitations of the instant claims.
Regarding instant dependent claims 25, 32, and 39, reference claim 4 shows the limitations of the instant claims.
Regarding instant dependent claims 27 and 34, reference claim 6 shows the limitations of the instant claims.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify reference claims 1, 11, and 15 by incorporating a trained predictive model as shown by Meeds et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the reference claims with Meeds et al. to arrive at the claimed invention because Meeds et al. shows, e.g., that a researcher could start with a pre-trained classifier, modify it, and easily update the trained model, taking advantage of an existing, generalized model (page 20, para. 3). This modification would have had a reasonable expectation of success because both the reference claims and Meeds et al. show training predictive models.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
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, KARLHEINZ SKOWRONEK can be reached on (571) 272-9047. 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.
/S.W.B./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685