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 .
Claims 1-4, 6, 8-14, and 17 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 29, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s amendment has obviated the objections to the specification. Accordingly, these objections are withdrawn. The 35 U.S.C. 112(f) interpretation of “data storage unit” has been obviated by the amendment, however the 35 U.S.C. 112(f) interpretations of “query-processing unit” and “machine-learning unit” remain (see “Response to Arguments” below).
Claim Objections
Claims 13, 14, and 17 are objected to because of the following informalities: In claim 13, the period following “present, and” should be omitted. Claims 14 and 17 are objected to due to dependency on claim 13. Appropriate correction is required.
Claim Interpretation
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: query-processing unit and machine-learning unit in claims 1-4 and 6.
Because these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4 and 6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which
was not described in the specification in such a way as to reasonably convey to one skilled in the relevant
art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitations “query-processing unit” and “machine-learning unit” which invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed functions and to clearly link the structure, material, or acts to the functions. Therefore, the written description is inadequate to show that the inventor had possession of the claimed invention at the time of filing. Claims 2-4 and 6 are rejected for being dependent on a rejected base claim. See rejections under 35 U.S.C. 112(b) below for further analysis.
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 1-4, 6, and 8-12 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.
Claim 1 recites limitations “query-processing unit” and “machine-learning unit” which invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 2-4 and 6 are rejected for being dependent on a rejected base claim. Examiner recommends amending claim 1 to omit the recitations of “unit” such that the claim instead reads “a processor and memory to store instructions executable by the processor, the instructions in response to execution by the processor implementing: analyzing… training… and storing” in order to overcome this rejection.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 8 recites the limitation “returning a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data”. Both “generating synthetic spatiotemporal data based on the machine-learning model” and “determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried” are previously recited. It is unclear if returning a result value is based on the synthetic spatiotemporal data from the generating step or the determining step. Further, it is unclear if the synthetic spatiotemporal data from the generating step and the synthetic spatiotemporal data from the determining step are meant to refer to the same data set or different data sets.
Claim 8 additionally recites the limitation “the machine-learning unit” which has insufficient antecedent basis in the claims.
Claims 9-12 are rejected due to dependency on claim 8.
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 1-4, 6, 8-14, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”).
Claim 1
Step 1: The claim recites an apparatus, and therefore is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
“…analyzing a predictive spatiotemporal query of a user”; This limitation encompasses, mentally analyzing a predictive spatiotemporal query of a user.
“… generating synthetic spatiotemporal data…”; This limitation encompasses mentally generating synthetic spatiotemporal data.
“…analyzes the predictive spatiotemporal query of the user, thereby extracting information about target data and columns to be queried”; This limitation encompasses mentally analyzing the predictive spatiotemporal query of the user, thereby extracting information about target data and columns to be queried.
“…determines whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried”; This limitation encompasses mentally determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried.
“when synthetic data corresponding to the target data and columns to be queried is not present but a machine-learning model corresponding thereto is present, the machine-learning unit generates synthetic data corresponding to the target data and columns…”; This limitation encompasses, excepting the recitation of generic computer components (the machine-learning unit), mentally generating synthetic data corresponding to the target data and columns when synthetic data corresponding to the target data and columns to be queried is not present but a machine-learning model corresponding thereto is present.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “a query-processing unit for…returning a processing result”, “a data storage unit for storing raw spatiotemporal data and the generated synthetic spatiotemporal data, wherein the raw spatiotemporal data is stored in a form of a table including an identifier column and a position column”, and “the machine learning unit… returns a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data,” however these limitations amount to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)). The claim further recites “a processor and memory to store instructions executable by the processor, the instructions in response to execution by the processor implementing…”, that the “query-processing unit” performs the analyzing steps, that the “machine-learning unit” performs the generating and determining steps, and that the synthetic data is generated “based on the machine-learning model,” however these limitations amount to mere instructions to apply a judicial exception using generic computer components programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “a machine-learning unit for training a machine-learning model in response to a request from the query-processing unit,” however this limitation amounts to merely generally linking the use of the judicial exception to the technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “a query-processing unit for…returning a processing result”, “a data storage unit for storing raw spatiotemporal data and the generated synthetic spatiotemporal data, wherein the raw spatiotemporal data is stored in a form of a table including an identifier column and a position column”, and “the machine learning unit… returns a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data,” limitations, in addition to reciting insignificant extra solution activity, are also directed to the well-understood, routine, and conventional activities of storing and retrieving information in memory (MPEP 2106.05(d)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93) and/or receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2 above. As an ordered whole, the claim is directed to a generic computer that performs a mentally performable process of analyzing a predictive spatiotemporal query and generating synthetic spatiotemporal data. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“…selects a column of the raw spatiotemporal data to be learned”; This limitation encompasses mentally selecting a column of the raw spatiotemporal data to be learned.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the selection is performed by “the machine-learning unit,” however this limitation amounts to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 2.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “the machine-learning unit trains the machine-learning model while changing a condition value for the column to be learned,” however this limitation amounts to merely generally linking the use of the judicial exception to the technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 2.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “the machine-learning unit stores metadata corresponding to training of the machine-learning model, and the metadata includes information about the learned raw spatiotemporal data, information about a condition for the column, and information about a structure of the machine-learning model.” however this limitation amounts to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “machine-learning unit stores metadata” limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
“when synthetic data corresponding to the target data and columns to be queried is not present, the machine-learning unit determines whether a machine-learning model corresponding to the target data and columns to be queried is present”; This limitation encompasses, excepting the recitation of generic computer components (the machine-learning unit), mentally determining whether a machine-learning model corresponding to the target data and columns to be queried is present when synthetic data corresponding to the target data and columns to be queried is not present.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the “machine-learning unit” performs the determining, however this limitation amounts to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2.
Claim 8
Step 1: The claim recites a method, and therefore is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
“determining a structure of a machine-learning model for generating synthetic spatiotemporal data”; This limitation encompasses mentally determining a structure of a machine-learning model for generating synthetic spatiotemporal data.
“generating synthetic spatiotemporal data…”; This limitation encompasses mentally generating synthetic spatiotemporal data.
“analyzing a predictive spatiotemporal query of the user, thereby extracting information about target data and columns to be queried”; This limitation encompasses mentally analyzing the predictive spatiotemporal query of the user by extracting information about target data and columns to be queried.
“determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried”; This limitation encompasses mentally determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried.
“when synthetic data corresponding to the target data and columns to be queried is not present but a machine-learning model corresponding thereto is present, the machine-learning unit generates synthetic data corresponding to the target data and columns…”; This limitation encompasses, excepting the recitation of generic computer components (the machine-learning unit), mentally generating synthetic data corresponding to the target data and columns when synthetic data corresponding to the target data and columns to be queried is not present but a machine-learning model corresponding thereto is present.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the raw spatiotemporal data is stored in a form of a table including an identifier column and a position column” and “returning a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data” however these limitations amount to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)). The claim further recites that the synthetic spatiotemporal data is generated “based on the machine-learning model,” and that “the machine-learning unit” generates synthetic data corresponding to the target data and columns “based on the machine-learning model,” however these limitations amount to mere instructions to apply a judicial exception using generic computer components programmed with a generic class of computer algorithms (MPEP 2106.05(f)). The claim further recites “training the machine-learning model based on raw spatiotemporal data,” however this limitation amounts to merely generally linking the use of the judicial exception to the technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “raw spatiotemporal data is stored in the form of a table” and “returning a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data” limitations, in addition to reciting insignificant extra solution activity, are also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93) and/or receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A Prong 2 above. As an ordered whole, the claim is directed to a mentally performable process of determining a structure of a machine-learning model, generating synthetic spatiotemporal data, analyzing a predictive spatiotemporal query, determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about target data and columns to be queried, and when synthetic data corresponding to the target data and columns to be queried is not present, generating synthetic data corresponding to the target data and columns. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“selecting a column of the raw spatiotemporal data to be learned”; This limitation encompasses mentally selecting a column of the raw spatiotemporal data to be learned.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 8.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 8.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 9.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “training the machine-learning model while changing a condition value for the column to be learned,” however this limitation amounts to merely generally linking the use of the judicial exception to the technological environment of model training (MPEP 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 9.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “storing metadata corresponding to training of the machine-learning model,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The storing metadata limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Claim 12
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 9.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the metadata includes information about the learned raw spatiotemporal data, information about a condition for the column, and information about the structure of the machine-learning model,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The metadata limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Claim 13
Step 1: The claim recites a method, and therefore is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
“analyzing a predictive spatiotemporal query of a user, thereby extracting information about target data and columns to be queried”; This limitation encompasses mentally analyzing a predictive spatiotemporal query of a user by extracting information about target data and columns to be queries.
“determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried”; This limitation encompasses mentally determining whether synthetic spatiotemporal data and a trained machine-learning model are present based on the information about the target data and columns to be queried.
“calculating a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data”; This limitation encompasses mentally calculating a result value for the predictive spatiotemporal query based on the synthetic spatiotemporal data.
“adjusting the result value”; This limitation encompasses mentally adjusting the result value.
“when synthetic data corresponding to the target data and columns to be queried is not present, determining whether a machine-learning model corresponding to the target data and columns to be queried is present”; This limitation encompasses mentally determining whether a machine-learning model corresponding to the target data and columns to be queried is present when synthetic data corresponding to the target data and columns to be queried is not present.
“when the synthetic data corresponding to the target data and columns to be queried is not present but the machine-learning model corresponding thereto is present, generating synthetic data corresponding to the target data and columns…”; This limitation encompasses mentally generating synthetic data corresponding to the target data and columns when the synthetic data corresponding to the target data and columns to be queried is not present but the machine-learning model corresponding thereto is present.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the generating of synthetic data corresponding to the target data and columns is “based on the machine-learning model,” however this limitation amounts to mere instructions to apply a judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2. As an ordered whole, the claim is directed to a mentally performable process of analyzing a predictive spatiotemporal query, determining if synthetic spatiotemporal data and a trained machine-learning model are present, generating synthetic data when synthetic data corresponding to the target data and columns is not present, calculating a result value for the query based on the synthetic data, and adjusting the result value. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 14
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“wherein the synthetic spatiotemporal data is generated based on raw spatiotemporal data…”; This limitation encompasses mentally generating synthetic spatiotemporal data based on raw spatiotemporal data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the raw spatiotemporal data is “stored in the form of a table including an identifier column and a position column,” however this limitation amounts to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “raw spatiotemporal data stored in a form of a table” limitation, in addition to being insignificant extra solution activity, is also directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Claim 17
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“adjusting the result value using a difference between the synthetic spatiotemporal data and the raw spatiotemporal data”; This limitation encompasses mentally adjusting the result value using a difference between the synthetic spatiotemporal data and the raw spatiotemporal data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See analysis of claim 14.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 14.
Response to Arguments
Applicant's arguments filed July 27, 2026, regarding the rejections under 35 U.S.C. 112 have been fully considered but they are not persuasive.
Applicant submits that the interpretation of the claim terms “query-processing unit” and “machine-learning unit” under 35 U.S.C. 112(f) is avoided in view of the amendments (Remarks, page 1). Examiner respectfully disagrees. A general purpose computer (“a processor and memory to store instructions executable by the processor” recited in claim 1) is not sufficient structure for performing the specialized computing functions that the “query-processing unit” and “machine-learning unit” are claimed as performing. No corresponding algorithms for performing these functions are present in the claim. Thus, the 35 U.S.C. 112(f) interpretation of these elements and corresponding 35 U.S.C. 112(a) and 35 U.S.C. 112(b) rejections are maintained.
Applicant's arguments regarding the rejections under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that amended claim 1 does not recite a judicial exception because it directly corresponds to Example 39 of the USPTO SME examples (Remarks, page 2). Examiner respectfully disagrees. While amended claim 1 and Example 39 both recite training a machine learning model, Example 39 is eligible because it does not recite a judicial exception. Amended claim 1 recites mental processes as analyzed in the 35 U.S.C. 101 rejection above. Examiner submits that the “training a machine-learning model in response to a request from the query-processing unit” limitation is not being analyzed as reciting a judicial exception, but rather as generally linking the judicial exception to a technological environment.
Applicant further argues that the claims as amended integrate a practical application therein and improve the functioning of a technology under Step 2A, Prong Two (Remarks, page 3). Examiner respectfully disagrees. The purported improvement provided by paragraphs 100-101 of the specification of “a predictive spatiotemporal analytical query may be supported even when there is a lack of spatiotemporal data” and “the present disclosure may generate spatiotemporal data through machine-learning technology, thereby supporting spatio-temporal-query-processing” amounts to an improvement to an abstract idea of spatiotemporal data analysis rather than to the functioning of a computer or other technology. Further, this purported improvement is provided by the judicial exception itself of generating synthetic spatiotemporal data. The judicial exception alone cannot provide the improvement (MPEP 2106.05(a)).
Applicant’s arguments regarding the rejections under 35 U.S.C. 103 have been fully considered and are persuasive. Accordingly, the rejections under 35 U.S.C. 103 have been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GWYNEVERE A DETERDING whose telephone number is (571)272-7657. The examiner can normally be reached Mon-Fri. 9am-5pm.
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, Kamran Afshar can be reached at (571) 272-7796. 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.
/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
Prosecution Insights