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 .
Preliminary Amendments
This action is in response to preliminary amendments filed March 14th, 2024, in which Claims 1-20 are cancelled, and Claims 21-39 are added. The amendments have been entered, and Claims 21-39 are currently pending.
Specification
The specification is objected to because the amendment to the specification reads “[0000] This patent arises from a continuation of US Patent Application No. 17/140,909 (now US Patent -___) …” This is objected to because a) there is no “this patent”, only a patent application and b) US Patent Application No. 17/140,90 has been abandoned, and thus is not “now” any US Patent.
Claim Interpretation
Claims 27-32 recite a tangible computer readable storage medium, which, according to the specification, [0055], “is expressly defined to … exclude propagating signals and transmission medium.” Therefore, the claim scope is interpreted NOT to include any signal per se embodiments.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 21-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 21 recites an apparatus comprising interface circuitry, i.e. an article of manufacture, and thus falls within the four statutory categories of patentable subject matter. However, Claim 21 further recites to perform first and second comparisons between geographic areas to identify similarities and aggregating ones of trained machine learning models and to create a composite machine learning model, and to determine whether data corresponding to the composite machine learning model for the first geographic area matches at least a portion of data collected for the first geographic area each of which are mental processes capable of being performed in the human mind (aggregating machine learning models and creating an composite model includes embodiments such as averaging their results, see for example [0069-0070], which a human is capable of performing).
The claim further recites additional elements of interface circuitry, machine readable instructions, and at least one processor circuit, limitations such as that the second/third geographic area are associated with a first/second set of trained machine learning models, that the first geographic area is devoid of an associated trained machine learning model, and to facilitate a reduction of computational costs by the aggregation. Implementing an abstract idea on a computer can neither integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself (MPEP 2106.05(f)). Specifying that the areas that are under consideration are associated with models is no more than specifying a particular technological environment or field of use of the mental processes themselves (MPEP 2106.05(h)) which cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself. The limitation of facilitating a reduction of computational costs merely states the desired outcome of the positively recited steps, which by MPEP 2106.05(f) (1) “only the idea of a solution” & 2106.05(f) (3) “the generality of the application of the judicial exception” do not recite a practical application that is an improvement in a computer or technology, at most an improvement in an abstract idea itself, nor significantly more than the abstract idea itself. Thus, the additional elements have been shown 1) not to integrate the abstract idea into a practical application nor 2) neither alone nor in combination (there is no nexus between the additional elements) to provide significantly more than the abstract idea itself.
Dependent claims 22-26 recite only additional mental process steps (Claim 22, to weight the areas; Claim 23, to exclude a subset of models in the aggregation; Claim 24 recites the criterion for deciding to include models; Claim 25, determining matches and populating a dataset; Claim 26, including particular models in the aggregation) but no additional elements which could integrate the abstract idea into a practical application nor provide significantly more. Thus, the dependent claims are also subject-matter ineligible.
Claims 27-32 recite computer storage medium to perform the same abstract ideas of Claim 21-26 and only additionally recite implementing the abstract idea on a computer (MPEP 2106.05(f)) and are thus rejected for reasons set forth in the rejections of Claims 21-26. Claims 33, 35-37, 38, and 39 recite means (interpreted as a computer processor and the associated algorithms to perform the functions) to perform the same abstract ideas of Claim 21, 23-25, 22, and 26 also thus only recite implementing the abstract idea on a computer (MPEP 2106.05(f)) and are thus rejected for reasons set forth in the rejections of Claims 21, 23-25, 22, and 26, respectively.
Dependent Claim 34 only additionally recites the abstract idea to mix a plurality of techniques and no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 21-39 are rejected under 35 U.S.C. 103 as being unpatentable over Reichl et al., “Optimization of a similarity measure for estimating ungauged streamflow” (as cited by the applicant in the Information Disclosure Statement of 9/11/2024).
Regarding Claim 21, Reichl teaches to collect data for a first geographic area, the first geographic area an unknown geographic area (Reichl, pg. 1, Abstract, “an ungauged catchment” is a geographical area lacking a gauge to directly measure stream water flow, e.g. an unknown geographic area; “The focus of this paper is the identification of catchment attributes … to produce the best possible ungauged streamflow predictions given a dataset” see pg. 3, Table 1 & pg. 4, Section 3.2 for specific data collected at each catchment) perform a first comparison between the first geographic area and a second geographic area to identify a first similarity between the first geographic area and the second geographic area based on a criterion (Reichl, pg. 1, Abstract, “to select an ensemble of hydrological models previously identified for similar gauged catchments, where the similarity is based on some combination of important physical catchment attributes … a similarity measure” where “catchments” are geographic areas) the second geographic area associated with a first set of trained machine learning models, (Reichl, pg. 5, 1st column, Eq (1) describes a set of K candidate models at each of M catchments/geographical areas) the first geographic areas is devoid of an associated trained machine learning model (Reichl, pg. 1, Abstract, “an ungauged catchment” is a geographical area lacking a model trained on any measured data for that catchment) perform a second comparison between the first geographic area and a third geographic area to identify a second similarity between the first geographic area and the third geographic area based on the criterion, the third geographic area associated with a second set of trained machine learning models (Reichl, pg. 1, Abstract, “an ensemble of hydrological models previously identified for similar gauged catchments” denotes multiple areas each with their own daily/monthly model set, see also pg. 5, 1st column, last paragraph, “The number of donor catchments,
M
, is later optimized”) cause a reduction of computational costs associated with determining geographical information by, when the first similarity between the first and second geographic areas and the second similarity between the first and third geographic areas are identified based on the criterion, aggregating ones of the first and second sets of trained machine learning models corresponding to the second geographic area and the third geographic area respectively; create a composite machine learning model for the first geographic area from the aggregated ones of the first and second sets of trained machine learning models, the composite machine learning model predictive of the geographic information (Reichl, pg. 1, title and Abstract, “for estimating ungauged streamflow – One approach to predicting streamflow in an ungauged catchment is to select an ensemble of hydrological models previously identified for similar gauged catchments” also see pg. 5, 1st column, Eq (1) describes a composite model where
θ
k
,
i
are parameters for the predictive models
h
at each known geographic area/catchment
i
– note that this is done for the purpose of predicting ungauged streamflow, e.g. reducing the costs in measurement and computation required to gauge all catchments) and determine whether data corresponding to the composite machine learning model for the first geographic area matches at least a portion of the data collected for the first geographic area (Reichl, pg. 12, 2nd column, 1st paragraph, “As a case study, monthly flows were simulated using the SimHyd model for 184 gauged catchments in Australia. 95 catchments were used for optimization and 89 for testing” where “testing” denotes validation/determine whether data corresponding the model matches data collected, see also pg. 13, Fig. 7).
Reichl does not explicitly teach an apparatus comprising: interface circuitry; machine readable instructions, and at least one processor circuit to perform these steps, but Reichl strongly implies that their method is performed on a computer (Reichl, pg. 6, 2nd column, [31], “very large optimization problem,” “function call,” “thorough, but slow, global optimization algorithms,” “computationally expensive”). It therefore would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the invention of Reichl on a computer, in order to perform the required optimization.
Regarding Claim 22, Reichl renders obvious the apparatus of Claim 21 (and thus the rejection of Claim 21 is incorporated). Reichl further teaches to weight the second and third geographic area based on a similarity threshold to the first geographic area, wherein a geographic area with a higher similarity value to the first geographic area is weighted heavier (Reichl, pg. 5, Eqs. (1&2) & 1st column, last paragraph, “The number of donor catchments,
M
” is a similarity threshold).
Regarding Claim 23, Reichl renders obvious the apparatus of Claim 21 (and thus the rejection of Claim 21 is incorporated). Reichl further teaches to exclude a first subset of the second set of trained machine learning models corresponding to the third geographic area, the first subset conflicting with a second subset, the first subset conflicting with a second subset of the first set of trained machine learning models corresponding to the second geographic area (Reichl, pg. 5, 1st column, last paragraph, “in this study we choose only one candidate model from each donor catchment”) wherein the second geographic area has a first affinity and the third geographic area has a second affinity, the first affinity higher than the second affinity (the different areas/catchments have different affinities, call the second area any which has higher similarity than an area mapped to the third area/catchment).
Regarding Claim 24, Reichl renders obvious the apparatus of Claim 21 (and thus the rejection of Claim 21 is incorporated). Reichl further teaches wherein the criterion corresponds to at least one of a type, a geography, an inhabitant lifestyle, demographic, a wealth distribution, or a size (Reichl, pg. 3, Fig. 1 are the similarity attributes/criteria, including both geographies and sizes of values for the catchments/areas).
Regarding Claim 25, Reichl renders obvious the apparatus of Claim 21 (and thus the rejection of Claim 21 is incorporated). Reichl further teaches to, in response to determining that the composite machine learning model represents the first area, populate a first dataset associated with the first geographic area with a second dataset associated with the composite machine learning model (Reichl, pg. 10, 1st column, last paragraph, “The results of the ungauged streamflow predictions are shown in Fig. 7”).
Regarding Claim 26, Reichl renders obvious the apparatus of Claim 21 (and thus the rejection of Claim 21 is incorporated). Reichl further teaches wherein the composite machine learning model includes a third subset of the first set of trained machine learning models corresponding to the second geographic area and a fourth subset of the set of trained machine learning models corresponding to the third geographic area, the third subset different from the fourth subset (Reichl, pg. 5, Eq.(1), with multiple candidate models from each catchment chosen).
Claims 27-32 recite a tangible computer readable storage medium comprising instructions to perform the exact method of the apparatus of Claims 21-26, respectively. As the invention described in the rejection of Claim 21 is performed on a computer, Claims 27-32 are rejected for reasons set forth in the rejections of Claims 21-26, respectively.
Claims 33, 35-37, 38, and 39 recite an apparatus comprising various means to perform the exact method of the apparatus of Claims 21, 23-25, 22, and 26, respectively. As the invention described in the rejection of Claim 21 is performed on a computer, Claims 33, 35-37, 38, and 39 are rejected for reasons set forth in the rejections of 21, 23-25, 22, and 26, respectively.
Regarding Claim 34, the Reichl renders obvious the apparatus of Claim 33 (and thus the rejection of Claim 33 is incorporated). Reichl further teaches to mix a plurality of machine learning techniques to create the composite machine learning model (Reichl, pg. 5, Eq. (1)).
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-39 are rejected on the ground of nonstatutory double patenting as being unpatentable, variously, over Claims 1, 5, and 6 of U.S. Patent No. 10,885,097, in view of Reichl. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the reference patent anticipate the claims of the reference application, other than the limitation the first geographic area is devoid of an associated trained machine learning model of Claim 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the method of the reference patent in such a case. The motivation to do so is to obtain a trained machine learning model for the area in question. See the provided chart for details.
Application 18/339,084
US Patent 10,885,097
Claims 21, 28, 35: An [apparatus /medium/apparatus means] to:
perform a first comparison between the first geographic area and a second geographic area to identify a similarity between the first geographic area and the second geographic area based on a criterion, the second geographic area a known matching geographic area;
the second geographic area associated with a first set of trained machine learning models,
the first geographic area is devoid of an associated trained machine learning model
perform a second comparison between the first geographic area and a third geographic area to identify a second similarity between the first geographic area and the third geographic area based on the criterion, the third geographic area a known matching geographic area;
the third geographic area associated with a second set of trained machine learning models,
cause a reduction of computational costs associated with determining geographic information by,
when the first similarity between the first and second geographic areas and the second similarity between the first and third geographic areas are identified based on the criterion,
aggregating ones of the first and second sets of trained machine learning models corresponding to the second geographic area and the third geographic area respectively to create a composite machine learning model characteristic of the first geographic area
determine whether data corresponding to the composite machine learning model for the first geographic area matches at least a portion of the data collected for the first geographic area
Claim 1: A method, comprising: executing an instruction with at least one processor
a first determination that the second geographic area includes a second data element that matches the first data element of the first geographic area … the second geographic area including a first affinity corresponding to the first geographic area
a first set of machine learning models trained with information representative of a second geographic area
Reichl: Abstract, “ungauged catchment” thus no trained model trained on the catchment data
a second determination that the third geographic area includes a third data element that matches the first data element of the first geographic area … the third geographic area including a second affinity corresponding to the first geographic area
a second set of machine learning models trained with information representative of a third geographic area
reducing computational complexity of profiling the first geographic area
based on a third determination that the first affinity is less than the second affinity
combining … non-conflicting ones of the first set of machine learning models included in the second dataset and the second set of machine learning models included in the third dataset to generate a composite set of machine learning models
determining that the composite set of machine learning models represents the first geographic area
Claims 22, 28, 38: to weight the second and third geographic areas based on a similarity threshold to the first geographic area, wherein a geographic area with a higher similarity is weighted higher
Claim 6: weighted based on similarities … non-conflicting
Claims 23, 29, 35: to exclude a first subset of the set of trained machine learning models corresponding to the third geographic area, the first subset conflicting with a second subset of the first set of trained machine learning models, wherein the second geographic area has a first affinity and the third geographic area has a second affinity, the first affinity higher than the second affinity
Claim 1: the composite set of machine learning models excluding the first one of the first set of machine learning models that conflicted with the second set of machine learning models … a third determination that the first affinity is less than the second affinity
Claims 24, 30, 36: the criterion corresponds to at least one of a type, a geography, an inhabitant lifestyle, a demographic, a wealth distribution, or a size
Claim 5: at least one of a type, a geography, a demographic, and inhabitant lifestyle, a wealth distribution, a size, or a shape
Claims 25, 31, 37: in response to determining that the composite machine learning model of the first geographic area represents the first geographic area, populate a first dataset associated with the first geographic area with a second dataset associated with the composite machine learning model
Claim 1: in response to determining that the composite set of machine learning models represents the first geographic area … inserting … data from the composite set of machine learning models
Claims 26, 32, 38: to determine that second and third geographic areas are known matching geographic areas based on the data collected meeting or exceeding a threshold of information
Claim 1: a first determination that the second geographic area includes a second data element; a second determination that the third geographic area includes a third data element
Claims 27, 33, 39: a third subset of the first set of trained machine learning models corresponding to the second geographic area and a fourth subset of the second set of trained machine learning models corresponding to the third geographic area, the third subset different than the fourth subset
Claim 1: a first set of machine learning models trained with information representative of a second geographic area … a second set of machine learning models trained with information representative of a third geographic area
Claim 34: to mix a plurality of machine learning techniques to create the composite model
Claim 1: combining non-conflicting models
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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