DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to the claimed application filed on June 13, 2023, in which claims 1-20 presented for examination.
Information Disclosure Statement
The information disclosure statement filed June 13, 2023 and September 11, 2023 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
Drawings
The drawings are objected to because Fig.3A and Fig.3B recite item 300. It is not clear as what item 300 is associated with. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
At Step 1:
With respect to subject matter eligibility under 35 USC 101, it is determined that the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
At Step 2A, Prong One:
The limitation “creating, by the computing system, an interpolated value map and a variance map for the geographical area using the plurality of sampling data values” in claims 1 and 11, as drafted, is a process that, under its broadest reasonable interpretation, covers a “mathematical relationship,” which is specifically identified as an exemplar in the “mathematical concepts” grouping of abstract ideas. Moreover, the recited creation can be practically performed in the human mind, and so it also falls into the “mental process” group of abstract ideas. Thus, limitation (c) recites a concept that falls into the “mathematical concept” and “mental process” groups of abstract ideas.
At Step 2A, Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation “receiving, by a computing system, a plurality of sampling data values for a geographical area” amounts to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)) and does not provide integration into a practical application.
The limitation “training, by the computing system, a machine learning model using values of the interpolated value map as ground truth values and evaluating performance of the machine learning model using the variance map” is also an additional element which is configured to carry out the receiving, creating and storing steps, i.e., it is the tool that is used to train machine learning model using values of the interpolated value map as ground truth values and evaluating performance of the machine learning model using the variance map to create an interpolated value map and a variance map. But the limitation “using values of the interpolated value map as ground truth values and evaluating performance of the machine learning model using the variance map” is recited so generically that it represents no more than generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not amount to significantly more than the exception itself, and does not integrate a judicial exception into a practical application. No specific type of machine learning processing or techniques are recited in the claim itself, the steps performed in this "training" are entirely mentally performable processes as explained above, and the specification describes this machine learning in the claim in terms of using generic ML models.
The limitation “storing, by the computing system, the trained machine learning model in a model data store” recites insignificant extra-solution activity such as mere outputting of the result. The mere outputting of data does meaningfully limit the abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
The limitation “non-transitory computer-readable medium and one or more processors” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
At Step 2B:
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (i.e. ML) as a tool are carried over and do not provide significantly more.
With respect to the "receiving" identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the “training ….” the limitation “ is an additional element which is configured to carry out the receiving, creating and storing steps, i.e., it is the tool that is used to train data machine learning model. But the limitation “using a trained machine learning model” is recited so generically that it represents no more than generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not amount to significantly more than the exception itself, and does not integrate a judicial exception into a practical application.
With respect to the "storing…" identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334; i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
The “non-transitory computer-readable medium and one or more processors” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 1 is directed to an abstract idea. The remaining independent claim 11 falls short the 35 USC 101 requirement under the same rationale.
The dependent claims 2-10 and 12-20 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 2 recites “generating, by the computing system, a predicted value for the geographical area using the trained machine learning model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for using the trained machine learning model, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 3 recites “receiving, by the computing system, one or more input values from a user interface; wherein generating the predicted value for the geographical area using the trained machine learning model includes providing the one or more input values received from the user interface as input to the trained machine learning model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving one or more input values from a user interface, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 4 recites “wherein creating the interpolated value map and the variance map includes performing kriging over at least a portion of the plurality of sampling data values to generate both the interpolated value map and the variance map”. This additional element is recited at a high level of generality and would function in its ordinary capacity for performing kriging over at least a portion of the plurality of sampling data values to generate both the interpolated value map and the variance map, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 5 recites “determining a difference between a value predicted by the machine learning model and a corresponding ground truth value of the interpolated value map; and weighting the difference by a variance value of the variance map corresponding to the ground truth value of the interpolated value map”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 6 recites “wherein determining the difference between the value predicted by the machine learning model and the corresponding ground truth value of the interpolated value map, and weighting the difference by the variance value of the variance map corresponding to the ground truth value of the interpolated value map includes performing a log-likelihood comparison”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 7 recites “wherein each sampling data value of the plurality of sampling data values includes a latitude, a longitude, and a timestamp”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 8 recites “wherein each sampling data value of the plurality of sampling data values includes a count value”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 9 recites “wherein the count value is a count of animals detected by a trap”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 10 recites “wherein training the machine learning model includes updating the machine learning model using gradient descent”. This additional element is recited at a high level of generality and would function in its ordinary capacity for updating the machine learning model using gradient descent, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 12 recites “generating, by the computing system, a predicted value for the geographical area using the trained machine learning model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for using the trained machine learning model, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 13 recites “receiving, by the computing system, one or more input values from a user interface; wherein generating the predicted value for the geographical area using the trained machine learning model includes providing the one or more input values received from the user interface as input to the trained machine learning model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for receiving one or more input values from a user interface, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 14 recites “wherein creating the interpolated value map and the variance map includes performing kriging over at least a portion of the plurality of sampling data values to generate both the interpolated value map and the variance map”. This additional element is recited at a high level of generality and would function in its ordinary capacity for performing kriging over at least a portion of the plurality of sampling data values to generate both the interpolated value map and the variance map, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 15 recites “determining a difference between a value predicted by the machine learning model and a corresponding ground truth value of the interpolated value map; and weighting the difference by a variance value of the variance map corresponding to the ground truth value of the interpolated value map”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 16 recites “wherein determining the difference between the value predicted by the machine learning model and the corresponding ground truth value of the interpolated value map, and weighting the difference by the variance value of the variance map corresponding to the ground truth value of the interpolated value map includes performing a log-likelihood comparison”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 17 recites “wherein each sampling data value of the plurality of sampling data values includes a latitude, a longitude, and a timestamp”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 18 recites “wherein each sampling data value of the plurality of sampling data values includes a count value”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 19 recites “wherein the count value is a count of animals detected by a trap”. As drafted, the recited is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 20 recites “wherein training the machine learning model includes updating the machine learning model using gradient descent”. This additional element is recited at a high level of generality and would function in its ordinary capacity for updating the machine learning model using gradient descent, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Riley et al., (hereinafter “Riley) US 20190107521.
As to claim 1, Riley discloses a computer-implemented method of training and using a machine learning model, the method comprising:
receiving, by a computing system, a plurality of sampling data values for a geographical area (see paragraph 10, "field test for a geographical region... field data"; paragraph 12, "sampling");
creating, by the computing system, an interpolated value map and a variance map for the geographical area using the plurality of sampling data values (see paragraph 41, interpolation is one of the operations disclosed in the modules of the system of Riley and thus an interpolated value map is implied in the interpolation transformation operation of the data; a variance map is implied in the variance determination of paragraph 37, "The baseline variance is preferably temporal variance for a given geographic region, but can additionally or alternatively be a spatial variance (e.g., across the geographic region, between the geographic region and an adjacent geographic region, etc.)" implicitly variance and interpolation are based on the plurality of sampling data values as this data is the input for the field test recommendation module and other modules of the system of Riley);
training, by the computing system, a machine learning model using values of the interpolated value map as ground truth values and evaluating performance of the machine learning model using the variance map (at least variance is used for evaluating the model in Riley, paragraph 37, "differences in any suitable parameters can be evaluated according to a field test recommendation and training the field test identification module (machine learning model) using the farm management system data, wherein the values which the differences are based on are implied ground truth values as broadly claimed); and
storing, by the computing system, the trained machine learning model in a model data store (see paragraph 45).
As to claim 2, Riley discloses the claimed generating, by the computing system, a predicted value for the geographical area using the trained machine learning model (see paragraphs [0014], [0037] and [0041]).
As to claim 3, Riley discloses the claimed receiving, by the computing system, one or more input values from a user interface; wherein generating the predicted value for the geographical area using the trained machine learning model includes providing the one or more input values received from the user interface as input to the trained machine learning model ((see paragraph [0045]).
As to claim 4, Riley discloses the claimed wherein creating the interpolated value map and the variance map includes performing kriging over at least a portion of the plurality of sampling data values to generate both the interpolated value map and the variance map (see paragraphs [0036]-[0038]).
As to claim 5, Riley discloses the claimed determining a difference between a value predicted by the machine learning model and a corresponding ground truth value of the interpolated value map; and weighting the difference by a variance value of the variance map corresponding to the ground truth value of the interpolated value map (see paragraphs [0015]-[0017], analyzing a plurality of field tests across a plurality of geographic regions, locations, and users).
As to claim 6, Riley discloses the claimed wherein determining the difference between the value predicted by the machine learning model and the corresponding ground truth value of the interpolated value map, and weighting the difference by the variance value of the variance map corresponding to the ground truth value of the interpolated value map includes performing a log-likelihood comparison (see paragraphs [0016]-[0039]).
As to claim 7, Riley discloses the claimed wherein each sampling data value of the plurality of sampling data values includes a latitude, a longitude, and a timestamp (see paragraph [0039]).
As to claim 8, Riley discloses the claimed wherein each sampling data value of the plurality of sampling data values includes a count value (see paragraphs [0011]-[0012] and [0023]).
As to claim 9, Riley discloses the claimed wherein the count value is a count of animals detected by a trap (see paragraphs [0011]-[0012] and [0023]).
As to claim 10, Riley discloses the claimed wherein training the machine learning model includes updating the machine learning model using gradient descent (see paragraphs [0045]-[0046]).
As to claims 11-20, claims 11-20 are non-transitory computer-readable medium having computer-executable instructions for executing the method of claims 1-10. They are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210110298 A1 (involved in storing instructions to configure the system to pre-process data, by a machine learning module. A prediction for a user is outputted by the machine learn module to a user interface. The prediction is amended through the user interface, by the user to provide an amended prediction. The data associated is pre-processed with the amended prediction, by the machine learning module. The processor predicts a new result based on the data associated with the amended prediction and the trained machine learning model, by the machine learning module).
US 20210110299 (involved in storing instructions that, when executed by a processor, configure the system to pre-process data by a machine learning module. A trained machine learning model is selected by the machine learning module. A result is predicted based on the trained machine learning model by the machine learning module. A prediction (106) is outputted for a user by the machine learn module to a user interface. The prediction is amended by the user to provide an amended prediction through the user interface. The trained machine learning model is retrained based on data associated with the amended prediction by the machine learning module, thus providing a retrained machine learning model, and A new result is predicted based on the data associated with the amended prediction, and the re-trained machine learning model).
US20210097433 (involved in producing predictions, and collect data associated with use of the machine learning model over a window of time, where the data associated with use of the machine learning model comprises input data (116), predictions, and metadata associated with the use of the machine learning model. The data associated with use of the machine learning model is stored. One or more computing devices are configured to implement a machine learning analysis system (170) to retrieve the data associated with the use of model, perform analysis of the data associated with the use of model after the window of time. One or more problems associated with the use of the machine learning model are detected based on the analysis. One or more notifications describing the problems associated with the use of the machine learning model are generated).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571)272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 January 26, 2026