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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 408 (Fig. 4); 812 (Fig. 8); and 814 (Fig. 8). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) 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. 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.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: -Deriving Additional Data Features Based on Semantic Context-.
The disclosure is objected to because of the following informalities: In paragraph [0036], reference character "216" — which throughout the specification designates the data store of the FIG. 2 environment — is used erroneously in three instances to refer to "new data" (which should bear reference character "214"). Specifically, the passage reads "by running the new data 216 again," "the entirety of the new data 216 to identify features," and "additional features associated with the new data 216" — in each instance, "216" should be "214."
Appropriate correction is required.
Claim Objections
Claims 14, 16, and 19 are objected to because of the following informalities: (1) Claim 14 recites "The system claim 13" — the word "of" is omitted and should read "The system of claim 13"; (2) Claim 19 recites "The system claim 13" — the word "of" is omitted and should read "The system of claim 13."
Appropriate correction is required.
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.
Written Description
Claims 6 and 14 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, at the time the application was filed, had possession of the claimed invention.
Claim 6 (and claim 14, which recites a commensurate limitation in system form) recites that the instructions “heuristically determine the metadata based at least in part on the identified subset of the input data.” As used in the specification, the “metadata” carries the semantic context and/or semantic type of a feature — e.g., recognizing that a column of integers represents “temperature” with unit “Fahrenheit degrees” rather than merely integers (¶[0021]). Automatically inferring such semantic metadata from the underlying data values is the central function recited by these claims.
The specification does not describe how this heuristic determination is performed. The only relevant passages restate the function in conclusory terms: ¶[0017] states that “the pre-processing is performed programmatically using heuristics, etc.,” and ¶[0051] states that “[t]he metadata may be heuristically determined based at least in part on the identified subset of the input data” — language that merely repeats the claim limitation. No heuristic algorithm, rule, feature set, decision procedure, flowchart, or worked example is provided for inferring semantic metadata from data. To the contrary, every embodiment that uses semantic metadata presupposes that the semantic type/metadata has already been declared as an input (e.g., “Semantic Type: Timestamp” in the worked tables of ¶¶[0042]–[0043], and the type declarations of ¶[0063]), not heuristically derived from the data.
Because the specification discloses only the desired result (that metadata is heuristically determined) without disclosing the algorithm or steps by which the computer achieves it, it does not reasonably convey that the inventor had possession of the claimed heuristic-determination subject matter at the time of filing. See MPEP § 2163; see also Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671 (Fed. Cir. 2015) (a computer-implemented functional claim requires disclosure of the algorithm achieving the function).
Claims 6 and 14 are therefore rejected for lack of written description.
Enablement
Claims 6 and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
The factors to be considered in determining whether a disclosure meets the enablement requirement of 35 U.S.C. 112(a) have been described in In re Wands, 858 F.2d 731, 737 (Fed. Cir. 1988). See MPEP § 2164.01.
(1) Breadth of the claims: The claims cover heuristically determining metadata for any “identified subset” of any input data.
(2) Nature of the invention: The limitation requires automatically imputing semantic meaning (context/type) to data — a non-trivial inference task that the specification itself frames as the problem the invention solves (¶¶[0019]–[0020]).
(3) State of the prior art: While basic data typing is known, automatic determination of semantic context is presented by the specification as the difficulty being addressed, rather than a routine, off-the-shelf operation.
(4) Level of one of ordinary skill: The level of skill is high (e.g., a data scientist or software engineer); nonetheless, even a skilled artisan requires a disclosed approach to reproduce the specific heuristic determination.
(5) Level of predictability in the art: Although software is generally predictable, the outcome of an undisclosed heuristic that infers semantic meaning from data cannot be predicted from the disclosure.
(6) Amount of direction provided by the inventor: None directed to the heuristic. The specification supplies only the labels “heuristics, etc.” (¶[0017]) and a restatement of the function (¶[0051]); it provides no rules, parameters, features, or steps.
(7) Existence of working examples: There are no working examples of heuristically determining metadata. Every example assumes the semantic type/metadata is pre-declared (¶¶[0042]–[0043], [0063]).
(8) Quantity of experimentation needed: A person of ordinary skill would have to devise, from scratch, the entire heuristic for inferring semantic metadata from arbitrary data — the core inventive function — which constitutes undue experimentation.
Considering the above factors, and in particular the absence of direction (factor 6), the absence of working examples for the heuristic (factor 7), and the resulting quantity of experimentation (factor 8), the specification does not enable a person of ordinary skill in the art to make and use the full scope of the “heuristically determine the metadata” limitation of claims 6 and 14 without undue experimentation.
Claim Rejections - 35 USC § 112
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 5-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 5 and 13, and their respective dependent Claims 6–12 and 14–20, each recite “provide access to the new data as associated with a corresponding subset of the input data.” The term “corresponding” in this phrase is a relative term that renders the claims indefinite because neither the claims nor the specification provides an objective standard or clear referent for what “corresponding” means in this context.
Specifically, Claims 5 and 13 each identify “a subset of the input data” in the first operative step. The final operative step then introduces “a corresponding subset of the input data” using the indefinite article “a,” suggesting this may be a different subset. A PHOSITA cannot determine with reasonable certainty whether (i) “a corresponding subset” is the same subset identified in the first step, (ii) it is a new subset corresponding to the second feature or new data, or (iii) it refers to some other relationship not defined in the claims. The specification at ¶[0052] mirrors this same language without defining the referent of “corresponding.”
For purposes of examination, “a corresponding subset of the input data” is interpreted under BRI to encompass the subset identified in the first processing step (the subset corresponding to the feature having the first semantic type).
Claims 6–12 and 14–20 inherit this indefiniteness through their respective dependency chains.
Claims 6 and 7 (depending from claim 5) and Claims 14 and 15 (depending from claim 13) each recite “the instructions, if executed, that process the input data” to identify a specific set of instructions from the parent claim. This phrase lacks sufficient antecedent basis because each parent claim (Claims 5 and 13) recites two distinct groups of instructions that “process the input data”: (1) instructions to process input data to identify a subset of the input data; and (2) instructions to process the input data to determine a second feature. The relative clause “that process the input data” does not distinguish between these two groups, leaving a PHOSITA unable to determine with reasonable certainty which group of instructions each dependent claim modifies.
For purposes of examination, Claims 6 and 14 are interpreted under BRI to extend the first group of processing instructions, as the added heuristic metadata limitation is most consistent with the first processing step per ¶[0051]. Claims 7 and 15 are interpreted under BRI to extend the second group of processing instructions, as those claims concern determination of the second feature per spec ¶[0052].
Claims 14 and 19 each recite “The system claim 13” rather than “The system of claim 13.” The omission of the preposition “of” renders the dependency phrasing defective under 35 U.S.C. § 112(d), which requires a dependent claim to “contain a reference to a claim previously set forth.” The phrase “The system claim 13” does not grammatically identify claim 13 as the parent claim. Because the scope of a dependent claim is defined by its dependency chain, a claim that does not properly identify its parent fails to particularly and distinctly claim its subject matter and is indefinite under 35 U.S.C. § 112(b). Applicant must correct Claims 14 and 19 to read “The system of claim 13, wherein …”
Appropriate correction is required.
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 idea without significantly more.
CLAIM 1
Step 1: Claim 1 recites “A computer-implemented method” comprising a series of steps, which falls within the statutory category of a process. See MPEP 2106.03. Accordingly, claim 1 satisfies Step 1 of the eligibility analysis.
Step 2A, Prong 1: Claim 1 recites an abstract idea. Specifically, claim 1 recites a mental process — the collection, evaluation, and analysis of data that a person (e.g., a data scientist) can practically perform in the human mind or with pen and paper.
The claim recites the following abstract idea limitations: “processing input data to identify a feature in the input data, the feature corresponding to a subset of the input data and having a semantic type”, “obtaining semantic metadata for the feature, the semantic metadata indicating a first semantic context for the feature”, “identify, in the subset of the input data and based at least in part on a parameter associated with the input data, a first plurality of elements”, and “aggregate the first plurality of elements by generating, in a manner determined at least in part on the first semantic context, a second element derived from a subset of the first plurality of elements and having a different second semantic context, the subset of the first plurality of elements selected based at least in part on the parameter”. These limitations describe the mental acts of observing data, recognizing the meaning (semantic type/context) of a portion of the data, selecting elements of interest, and deriving a new value from those elements — the very manual analysis the specification attributes to human data scientists, who “would look at a dataset and identify which columns of data might be meaningful” and would “elect to use two timestamps… to derive a new… column” (specification ¶¶ [0002]–[0004]). Under the broadest reasonable interpretation the claim covers data of a size a person can analyze by hand. Such observation, evaluation, and judgment fall within the mental-process grouping. See MPEP 2106.04(a)(2). To the extent the “aggregate” limitation is viewed as a calculation, it is a generic combination of values (e.g., an average) that does not set forth or describe any particular mathematical formula, equation, or named algorithm, and is therefore properly treated as part of the pen-and-paper mental process rather than as a separately recited mathematical concept.
Step 2A, Prong 2: The claim recites the following additional elements:
“A computer-implemented method”…The “computer-implemented” environment amounts to mere instructions to apply the abstract idea on a generic computer and uses the computer as a tool to perform the mental process. See MPEP 2106.05(f).
“providing, with the parameter, the second element as associated with the parameter”… This step of “providing… the second element as associated with the parameter” is insignificant post-solution output of the result of the abstract idea. See MPEP 2106.05(g).
The judicial exception is not integrated into a practical application. The additional elements, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application. As mentioned above, the “computer-implemented” environment amounts to mere instructions to apply the abstract idea on a generic computer and uses the computer as a tool to perform the mental process. See MPEP 2106.05(f). The step of “providing… the second element as associated with the parameter” is insignificant post-solution output of the result of the abstract idea.
Applying the improvement analysis of Ex parte Desjardins and MPEP 2106.05(a), the specification does describe asserted benefits — automating the derivation of features “without manual intervention,” creating “a greater number of relevant features” with “lower… defect rates,” and processing features with “O(n) or O(1) scaling” (specification ¶¶ [0004], [0018]–[0021]). However, these asserted benefits are improvements to a data-analysis workflow achieved by automating what the specification itself describes as a manual, human process — not improvements to the functioning of a computer or to any other technology or technical field. Moreover, even assuming a technological improvement were disclosed, the claim does not reflect it: the claim recites the result — generically “processing input data” to identify, derive/aggregate, and provide a derived element — at a high level of generality, and does not recite the components or steps (e.g., the asserted simultaneous processing of a “practically unlimited number of input features”, or the particular mechanism that yields the asserted O(n)/O(1) scaling) that purportedly provide the improvement. A claim that merely claims the idea of a solution or desired outcome, rather than a particular technological way of achieving it, does not integrate the exception. See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016) (the claims must include limitations addressing the asserted improvement); Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea do not confer eligibility). This stands in contrast to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), and Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential), in which the claims recited the specific mechanism that produced the asserted technological improvement. Accordingly, claim 1 does not integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. The only additional elements are the generic “computer-implemented” environment and the “providing” of the result. The specification confirms that the implementing hardware is well-understood, routine, and conventional, describing it in entirely generic terms — generic processors, memory, data stores, web/application servers, and conventional communication networks (specification ¶¶ [0067]–[0078], [0083]). Using a generic computer to perform an abstract idea is well-understood, routine, and conventional. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208 (2014). Outputting or providing the result of the abstract idea is likewise a well-understood, routine, and conventional post-solution activity. See OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359 (Fed. Cir. 2015). Considered as an ordered combination, the additional elements add nothing beyond what they add individually — they merely implement the mental process on a generic computer. Accordingly, claim 1 does not amount to significantly more, does not satisfy Step 2B, and is rejected under 35 U.S.C. 101.
CLAIM 2
Step 1: Claim 2 depends from claim 1 and recites a computer-implemented method, which falls within the statutory category of a process. See MPEP 2106.03.
Step 2A, Prong 1: Claim 2 additionally recites “the parameter comprises a second feature in the input data, the second feature having a second semantic type”. This further limitation merely specifies the nature of the abstractly-recited parameter and is itself part of the mental process of observing and characterizing data. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 3
Step 1: Claim 3 depends from claim 1 and recites a computer-implemented method, which falls within the statutory category of a process. See MPEP 2106.03.
Step 2A, Prong 1: Claim 3 additionally recites “the input data includes the parameter; and the parameter identifies the manner and the subset of the input data to aggregate”. This further limitation merely specifies the source and role of the abstractly-recited parameter and remains part of the mental process of selecting and combining data. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 4
Step 1: Claim 4 depends from claim 1 and recites a computer-implemented method, which falls within the statutory category of a process. See MPEP 2106.03.
Step 2A, Prong 1: Claim 4 additionally recites “the manner is determined based at least in part on the first semantic context and a second semantic context corresponding to a different feature in the input data”. This further limitation merely specifies how the derivation is decided based on the meaning of the data and is itself an additional mental evaluation/judgment. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 5
Step 1: Claim 5 recites “A non-transitory computer-readable storage medium” having stored instructions, which falls within the statutory category of an article of manufacture. See MPEP 2106.03. Accordingly, claim 5 satisfies Step 1.
Step 2A, Prong 1: Claim 5 recites an abstract idea, namely the same mental process of collecting, evaluating, and analyzing data identified for claim 1. The claim recites “process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type”, “obtain metadata for the feature, the metadata being associated with a first semantic context for the feature”, “process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context”, and “generate, from the input data, new data to correspond to the second feature”. These are the mental acts of observing data, recognizing the meaning of a portion of it, determining a related feature, and deriving new data — the manual data-science analysis (specification ¶¶ [0002]–[0004]), performable in the human mind or with pen and paper. See MPEP 2106.04(a)(2).
Step 2A, Prong 2: The claim recites the following additional elements:
“A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system..,”…This amounts to mere instructions to apply the abstract idea on a generic computer and uses the computer as a tool to perform the mental process. See MPEP 2106.05(f).
“provide access to the new data as associated with a corresponding subset of the input data”…This is insignificant post-solution output of the result of the abstract idea. See MPEP 2106.05(g).
The judicial exception is not integrated into a practical application. As mentioned above, the recited medium, instructions, and processors amount to mere instructions to apply the abstract idea on a generic computer and use the computer as a tool. The step of “provid[ing] access to the new data” is insignificant post-solution output.
Applying the improvement analysis of Ex parte Desjardins and MPEP 2106.05(a), the specification does describe asserted benefits — automating the derivation of features “without manual intervention,” creating “a greater number of relevant features” with “lower… defect rates,” and processing features with “O(n) or O(1) scaling” (specification ¶¶ [0004], [0018]–[0021]). However, these asserted benefits are improvements to a data-analysis workflow achieved by automating what the specification itself describes as a manual, human process — not improvements to the functioning of a computer or to any other technology or technical field. Moreover, even assuming a technological improvement were disclosed, the claim does not reflect it: the claim recites the result — generically “processing input data” to identify, derive/aggregate, and provide a derived element — at a high level of generality, and does not recite the components or steps (e.g., the asserted simultaneous processing of a “practically unlimited number of input features”, or the particular mechanism that yields the asserted O(n)/O(1) scaling) that purportedly provide the improvement. A claim that merely claims the idea of a solution or desired outcome, rather than a particular technological way of achieving it, does not integrate the exception. See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016) (the claims must include limitations addressing the asserted improvement); Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea do not confer eligibility). This stands in contrast to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), and Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential), in which the claims recited the specific mechanism that produced the asserted technological improvement. Accordingly, claim 5 does not integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. The additional elements are a generic non-transitory storage medium, executable instructions, generic processors, and the providing of access to the result. The specification confirms that the implementing hardware is well-understood, routine, and conventional, describing it in entirely generic terms — generic processors, memory, data stores, web/application servers, and conventional communication networks (specification ¶¶ [0067]–[0078], [0083]). Using a generic computer to perform an abstract idea is well-understood, routine, and conventional. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208 (2014). Storing instructions on a computer-readable medium and storing and retrieving data in memory are well-understood, routine, and conventional. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306 (Fed. Cir. 2015); OIP Techs., 788 F.3d 1359 (providing/outputting results). Considered as an ordered combination, the additional elements merely implement the mental process on a generic computer. Accordingly, claim 5 does not amount to significantly more, does not satisfy Step 2B, and is rejected under 35 U.S.C. 101.
CLAIM 6
Step 1: Claim 6 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: Claim 6 additionally recites “heuristically determine the metadata based at least in part on the identified subset of the input data”. Heuristically determining the metadata (i.e., the meaning of the data) is the same kind of mental evaluation/judgment as in claim 5 and remains part of the abstract idea.
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 7
Step 1: Claim 7 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: Claim 7 additionally recites “determine the second feature based on information other than the first semantic context”. This further limitation merely specifies the basis for the abstract determination of the second feature and is itself part of the mental process.
Step 2A, Prong 2 and Step 2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 8
Step 1: Claim 8 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: Claim 8 additionally recites “the metadata identifies the first semantic context”. This further limitation merely specifies the content of the abstractly-recited metadata and remains part of the mental process.
Step 2A, Prong 2 and Step 2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 9
Step 1: Claim 9 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: Claim 9 additionally recites “generate, based at least in part on other metadata associated with the feature, an identifier for the second feature”. Generating an identifier (a label) for the derived feature is an act of evaluation/judgment performable mentally and remains part of the abstract idea.
Step 2A, Prong 2 and Step 2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 10
Step 1: Claim 10 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: There a no more additional abstract idea limitations.
Step 2A, Prong 2: The claim recites the following additional elements:
“causing processing of the new data by a machine learning algorithm”…The “machine learning algorithm” that processes the output merely generally links the abstract idea to a technological environment and recites insignificant post-solution use of the output by a generic, unspecified machine-learning algorithm used as a tool. See MPEP 2106.05(h) and 2106.05(g).
The judicial exception is not integrated into a practical application. Although machine-learning operations are not characterized as a mental process, the bare, result-level invocation of a generic machine-learning algorithm as a downstream consumer of the output recites only the idea of processing the result with “a machine learning algorithm” — it names no particular model, architecture, or training operation and imposes no meaningful limit. Accordingly, claim 10 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. For the same reasons discussed in the Step 2B analysis of claim 5, the carried-over additional elements are well-understood, routine, and conventional. With respect to the new element, the “machine learning algorithm” is recited generically and the specification describes the use of machine learning in conventional, off-the-shelf terms — “algorithms (e.g., heuristics, machine learning, etc.)” and “applying a machine learning algorithm to the new data” — without any assertedly unconventional implementation (specification ¶¶ [0018], [0052]). Generic application of a machine learning algorithm to data is well-understood, routine, and conventional. See Alice Corp., 573 U.S. 208; OIP Techs., 788 F.3d 1359. The ordered combination adds nothing more. Accordingly, claim 10 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 11
Step 1: Claim 11 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: There are no more additional abstract idea limitations.
Step 2A, Prong 2: The claim recites the following additional elements:
“cause the computer system to cause processing, by a different computer system, of the new data”….Causing processing of the new data by “a different computer system” is the mere transmission/hand-off of data to a second generic computer; it generally links the abstract idea to a technological environment and constitutes insignificant extra-solution activity, using generic computers as tools.
The judicial exception is not integrated into a practical application. For the same reasons discussed in the Step 2A, Prong 2 analysis of claim 5, the additional elements carried over from claim 5 do not integrate the exception and generally links the abstract idea to a technological environment and constitutes insignificant extra-solution activity, using generic computers as tools. See MPEP 2106.05(h), 2106.05(g), and 2106.05(f). Accordingly, claim 11 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. For the same reasons discussed in the Step 2B analysis of claim 5, the carried-over additional elements are well-understood, routine, and conventional. With respect to the new element, receiving or transmitting data between computer systems over a network is a well-understood, routine, and conventional computer function, and the specification confirms the system is an ordinary “distributed and/or virtual computing system” using conventional network connections (specification ¶¶ [0072], [0067]–[0078]). See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); TLI Communications LLC v. AV Automotive, L.L.C., 823 F.3d 607 (Fed. Cir. 2016); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350 (Fed. Cir. 2014). The ordered combination adds nothing more. Accordingly, claim 11 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 12
Step 1: Claim 12 depends from claim 5 and recites a non-transitory computer-readable storage medium, which falls within the statutory category of a manufacture. See MPEP 2106.03.
Step 2A, Prong 1: Claim 12 additionally recites “determine the second feature using an algorithm identified in a policy as applicable to the feature”. Selecting and applying an algorithm identified by a policy as applicable to the feature is abstract rule-following — a mental act of choosing how to derive data according to stated rules; the generically-recited “algorithm” and “policy” add no specific technology and no new additional element. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step 2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 13
Step 1: Claim 13 recites “A system” comprising “one or more processors” and “memory,” which falls within the statutory category of a machine. See MPEP 2106.03. Accordingly, claim 13 satisfies Step 1.
Step 2A, Prong 1: Claim 13 recites an abstract idea, namely the same mental process of collecting, evaluating, and analyzing data identified for claims 1 and 5. The claim recites “process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type”, “obtain metadata for the feature, the metadata being associated with a first semantic context for the feature”, “process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context”, and “generate, from the input data, new data to correspond to the second feature”. These are mental acts of observation, evaluation, and judgment performable with pen and paper (specification ¶¶ [0002]–[0004]). See MPEP 2106.04(a)(2).
Step 2A, Prong 2: The claim recites the following additional elements:
“one or more processors, and memory that stores computer-executable instructions that, if executed, cause the one or more processors to…”… The recited processors and memory amount to mere instructions to apply the abstract idea on a generic computer and use the computer as a tool. See MPEP 2106.05(f).
“provide access to the new data as associated with a corresponding subset of the input data”. This is insignificant post-solution output. See MPEP 2106.05(g).
The judicial exception is not integrated into a practical application. The recited processors and memory amount to mere instructions to apply the abstract idea on a generic computer and use the computer as a tool and the step of “provid[ing] access to the new data” is insignificant post-solution output. See MPEP 2106.05(g).
Applying the improvement analysis of Ex parte Desjardins and MPEP 2106.05(a), the specification does describe asserted benefits — automating the derivation of features “without manual intervention,” creating “a greater number of relevant features” with “lower… defect rates,” and processing features with “O(n) or O(1) scaling” (specification ¶¶ [0004], [0018]–[0021]). However, these asserted benefits are improvements to a data-analysis workflow achieved by automating what the specification itself describes as a manual, human process — not improvements to the functioning of a computer or to any other technology or technical field. Moreover, even assuming a technological improvement were disclosed, the claim does not reflect it: the claim recites the result — generically “processing input data” to identify, derive/aggregate, and provide a derived element — at a high level of generality, and does not recite the components or steps (e.g., the asserted simultaneous processing of a “practically unlimited number of input features”, or the particular mechanism that yields the asserted O(n)/O(1) scaling) that purportedly provide the improvement. A claim that merely claims the idea of a solution or desired outcome, rather than a particular technological way of achieving it, does not integrate the exception. See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016) (the claims must include limitations addressing the asserted improvement); Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea do not confer eligibility). This stands in contrast to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), and Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential), in which the claims recited the specific mechanism that produced the asserted technological improvement. Accordingly, claim 13 does not integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. The additional elements are generic processors, memory storing instructions, and the providing of access to the result. The specification confirms that the implementing hardware is well-understood, routine, and conventional, describing it in entirely generic terms — generic processors, memory, data stores, web/application servers, and conventional communication networks (specification ¶¶ [0067]–[0078], [0083]). Using a generic computer to perform an abstract idea is well-understood, routine, and conventional. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208 (2014). Storing and retrieving data and instructions in memory is well-understood, routine, and conventional. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306 (Fed. Cir. 2015); OIP Techs., 788 F.3d 1359 (providing/outputting results). Considered as an ordered combination, the additional elements merely implement the mental process on a generic computer. Accordingly, claim 13 does not amount to significantly more, does not satisfy Step 2B, and is rejected under 35 U.S.C. 101.
CLAIM 14
Step 1: Claim 14 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 14 additionally recites “heuristically determine the metadata based at least in part on the identified subset of the input data”. Heuristically determining the metadata (i.e., the meaning of the data) is the same kind of mental evaluation/judgment as in claim 13 and remains part of the abstract idea. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 15
Step 1: Claim 15 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 15 additionally recites “determine the second feature based on information other than the first semantic context”. This further limitation merely specifies the basis for the abstract determination of the second feature and is itself part of the mental process. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 16
Step 1: Claim 16 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 16 additionally recites “the metadata identifies the first semantic context”. This further limitation merely specifies the content of the abstractly-recited metadata and remains part of the mental process. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 17
Step 1: Claim 17 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 17 additionally recites “generate, based at least in part on other metadata associated with the feature, an identifier for the second feature”. Generating an identifier (a label) for the derived feature is an act of evaluation/judgment performable mentally and remains part of the abstract idea. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
CLAIM 18
Step 1: Claim 18 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: There a no more additional abstract idea limitations.
Step 2A, Prong 2: The claim recites the following additional elements:
“cause the system to cause processing of the new data by a machine learning algorithm”. The “a machine learning algorithm” that processes the new data merely generally links the abstract idea to a technological environment and recites insignificant post-solution use of the output by a generic, unspecified machine-learning algorithm used as a tool. See MPEP 2106.05(h) and 2106.05(g).
The judicial exception is not integrated into a practical application. Although machine-learning operations are not characterized as a mental process, the bare, result-level invocation of a generic machine-learning algorithm as a downstream consumer of the output recites only the idea of processing the result with “a machine learning algorithm” — it names no particular model, architecture, or training operation and imposes no meaningful limit. Accordingly, claim 18 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. For the same reasons discussed in the Step 2B analysis of claim 13, the carried-over additional elements are well-understood, routine, and conventional. With respect to the new element, the “machine learning algorithm” is recited generically and the specification describes the use of machine learning in conventional, off-the-shelf terms — “algorithms (e.g., heuristics, machine learning, etc.)” and “applying a machine learning algorithm to the new data” — without any assertedly unconventional implementation (specification ¶¶ [0018], [0052]). Generic application of a machine learning algorithm to data is well-understood, routine, and conventional. See Alice Corp., 573 U.S. 208; OIP Techs., 788 F.3d 1359. The ordered combination adds nothing more. Accordingly, claim 18 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 19
Step 1: Claim 19 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: There are no more additional abstract idea limitations.
Step 2A, Prong 2: The claim recites the following additional elements:
“cause processing, by a different computer system, of the new data”… Causing processing of the new data by “a different computer system” is the mere transmission/hand-off of data to a second generic computer; it generally links the abstract idea to a technological environment and constitutes insignificant extra-solution activity, using generic computers as tools.
The judicial exception is not integrated into a practical application. For the same reasons discussed in the Step 2A, Prong 2 analysis of claim 13, the additional elements carried over from claim 13 do not integrate the exception and generally links the abstract idea to a technological environment and constitutes insignificant extra-solution activity, using generic computers as tools. See MPEP 2106.05(h), 2106.05(g), and 2106.05(f). Accordingly, claim 19 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. For the same reasons discussed in the Step 2B analysis of claim 13, the carried-over additional elements are well-understood, routine, and conventional. With respect to the new element, receiving or transmitting data between computer systems over a network is a well-understood, routine, and conventional computer function, and the specification confirms the system is an ordinary “distributed and/or virtual computing system” using conventional network connections (specification ¶¶ [0072], [0067]–[0078]). See Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); TLI Communications LLC v. AV Automotive, L.L.C., 823 F.3d 607 (Fed. Cir. 2016); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350 (Fed. Cir. 2014). The ordered combination adds nothing more. Accordingly, claim 19 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 20
Step 1: Claim 20 depends from claim 13 and recites a system, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 20 additionally recites “determine the second feature using an algorithm identified in a policy as applicable to the feature”. Selecting and applying an algorithm identified by a policy as applicable to the feature is abstract rule-following — a mental act of choosing how to derive data according to stated rules; the generically-recited “algorithm” and “policy” add no specific technology and no new additional element. It adds no new additional element. See MPEP 2106.04(a)(2).
Step 2A, Prong 2 and Step 2B: There are no more additional elements. Therefore, the analysis from the parent claim is maintained.
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 5, 7-11, 13 and 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Deep Feature Synthesis: Towards Automating Data Science Endeavors to Kanter et al. (hereinafter Kanter).
Per claim 5, Kanter discloses A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least (Kanter: Section III, p. 5…Kanter discloses the Data Science Machine implemented as software logic in Python executing over a MySQL database, i.e., executable instructions run by the processors of a computer system, "We implement the logic for calculating, managing, and manipulating the synthesized features in Python"):
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type (Kanter: Section II.B, p. 2…each column of the input relational data is a feature corresponding to a subset of the data and is identified as having one of an enumerated set of types describing the kind of information it holds, which constitutes a feature having a first semantic type under BRI, "An instance of the entity has features which fall into one of the following data types: numeric, categorical, timestamps and freetext");
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature (Kanter: Section II.B, p. 2…the per-feature type designation describes the meaning and context of the feature’s values, "features which fall into one of the following data types: numeric, categorical, timestamps and freetext"; Section II.C, p. 4…Kanter expressly maintains per-feature metadata characterizing each feature, "The algorithm stores and returns information to assist with later uses of the synthesized feature. This information includes not only feature values, but also metadata about base features and functions that were applied");
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context (Kanter: Section II.B, p. 3…because a feature is of the timestamp context, Kanter’s entity-feature operation determines new distinct features such as weekday and month, each a different kind of value (a different semantic context) than the source timestamp, "Entity features (efeat): Entity features derive features by computing a value for each entry xi,j . These features can be based on the computation function applied element-wise to the array x:,j . Examples include functions that translate an existing feature in an entity table into another type of value, like conversion of a categorical string data type to a pre-decided unique numeric value or rounding of a numerical value. Other examples include translation of a timestamp into 4 distinct features — weekday (1-7), day of the month (1-30/31), month of the year (1-12) or hour of the day (1-24)");
generate, from the input data, new data to correspond to the second feature (Kanter: Section II.B, p. 3…Kanter computes and stores the derived feature’s value for each entry as new data, "Entity features derive features by computing a value for each entry xi,j. These features can be based on the computation function applied element-wise to the array x:,j"); and
provide access to the new data as associated with a corresponding subset of the input data (Kanter: Section II.C, p. 4…the synthesized feature values are stored and returned together with metadata identifying the base features they were derived from, thereby provided in association with the corresponding source subset, "The algorithm stores and returns information to assist with later uses of the synthesized feature. This information includes not only feature values, but also metadata about base features and functions that were applied").
Per claim 7, Kanter discloses claim 5, further disclosing the instructions, if executed, that process the input data, further cause the computer system to determine the second feature based on information other than the first semantic context (Kanter: Section II.B, p. 3…Kanter additionally derives relational features by following inter-entity relationships keyed on an entity identifier, i.e., based on the dataset’s relational structure rather than the source feature’s context alone, "Relational features are applied over the backward relationships ... assembled by extracting all the values for feature j in entity El where the identifier of Ek is ek = i").
Per claim 8, Kanter discloses claim 5, further disclosing the metadata identifies the first semantic context (Kanter: Section II.B, p. 2…the type label assigned to each feature identifies the kind of information the feature represents, "features which fall into one of the following data types: numeric, categorical, timestamps and freetext").
Per claim 9, Kanter discloses claim 5, further disclosing the instructions, if executed, that generates the new data, further cause the computer system to generate, based at least in part on other metadata associated with the feature, an identifier for the second feature (Kanter: Section II.C, p. 4…each synthesized feature is stored with metadata about the base features and functions applied, which names and identifies the derived feature, "The algorithm stores and returns information to assist with later uses of the synthesized feature. This information includes not only feature values, but also metadata about base features and functions that were applied").
Per claim 10, Kanter discloses claim 5, further disclosing the instructions, if executed, that provide access to the new data, further cause the computer system to cause processing of the new data by a machine learning algorithm (Kanter: Section I, p. 1…the synthesized features are fed into the Data Science Machine’s machine-learning pathway to build predictive models, "It starts with a relational database and automatically generates features to be used for predictive modeling").
Per claim 11, Kanter discloses claim 5, further disclosing the instructions, if executed, that provide access to the new data, further cause the computer system to cause processing, by a different computer system, of the new data (Kanter: Section I, p. 1…Kanter packages the synthesized features into submissions to online data science competitions; an online competition necessarily evaluates and scores each submission on a remote competition-platform server, so a different computer system necessarily processes the new data, "Our contributions through this paper are as follows: … (c) produce submissions for online data science competitions").
Claims 13 and 15-19 are substantially similar in scope and spirit as claims 5 and 7-11. Therefore the rejections of claims 5 and 7-11 are applied accordingly.
Claim Rejections - 35 USC § 103
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.
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 1-4, 6, 12, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kanter in view of US Pat. Pub. No. 2018/0165604 A1 to Minkin et al. (hereinafter Minkin).
Per claim 1, Kanter discloses A computer-implemented method (Kanter: Section III, p. 5…a computer-implemented method realized as Python logic over a MySQL database, "We implement the logic for calculating, managing, and manipulating the synthesized features in Python"), comprising:
processing input data to identify a feature in the input data, the feature corresponding to a subset of the input data and having a semantic type (Kanter: Section II.B, p. 2…each column of the input data is identified as a feature having one of an enumerated set of types describing the kind of data it holds, "An instance of the entity has features which fall into one of the following data types: numeric, categorical, timestamps and freetext");
obtaining semantic metadata for the feature, the semantic metadata indicating a first semantic context for the feature (Kanter: Section II.B, p. 2…the type designation obtained for each feature indicates the kind and meaning of the feature’s values, "features which fall into one of the following data types: numeric, categorical, timestamps and freetext");
processing the input data with the obtained semantic metadata to (Kanter: Section III, p. 5…each feature function consults the columns’ types to decide how the data is processed, "The function definition is responsible for determining which columns in the relationship it can be applied to and how to calculate the output value"):
identify, in the subset of the input data and based at least in part on a parameter associated with the input data, a first plurality of elements (Kanter: Section II.B, p. 3…a relational feature gathers the plurality of values of a feature across all related rows keyed on an entity identifier, which is constitutes the parameter, "a collection of values for feature j in related entity El, assembled by extracting all the values for feature j in entity El where the identifier of Ek is ek =| i"); and
aggregate the first plurality of elements by generating, in a manner determined at least in part on the first semantic context, a second element derived from a subset of the first plurality of elements and having a different second semantic context, the subset of the first plurality of elements selected based at least in part on the parameter (Kanter: Section II.B, p. 3…an aggregation function is applied to the keyed collection to produce a single new value of a different kind than the inputs, "Relational features are applied over the backward relationships. They are derived for an instance i …by applying a mathematical function…which is a collection of values for feature j…Some examples of rfeat functions are min, max, and count"; Section III, p. 5…a Filter Object selects the subset of instances to aggregate, "Filter Objects provide a flexible way to select subsets of data for rfeat functions");
providing, with the parameter, the second element as associated with the parameter (Kanter: Section II.B, p. 3 and Fig. 4 — the aggregated value is produced for and stored against the entity instance identified by the parameter, e.g., an average computed per CustomerID, "assembled by extracting all the values for feature j in entity El where the identifier of Ek is ek = i").
Kanter discloses each of the foregoing limitations as set forth above. To the extent that it is argued that Kanter’s enumerated data-type labels (numeric, categorical, timestamp, freetext) do not expressly constitute the claimed first semantic context and different second semantic context, Minkin expressly teaches:
obtaining semantic metadata for the feature, the semantic metadata indicating a first semantic context for the feature (Minkin: ¶[0100]…Minkin teaches enhancing a feature’s ‘Semantic Context’ by trivial semantic mapping of individual source features upon load, supplying the explicit semantic-context metadata that Kanter’s type labels approximate, "Implicit Modeling can include trivial semantic mapping performed using individual Source Features upon a load to enhance Semantic Context");
aggregate the first plurality of elements by generating, in a manner determined at least in part on the first semantic context, a second element ... having a different second semantic context (Minkin: ¶[0096]…Minkin teaches creating new features from source features through aggregations driven by the data’s semantic/analytic context, "Feature Engineering can include creation of new features derived from Source Features that are based on filters, aggregations, and additional calculations"; ¶[0180] — aggregation governed by analytic context, "Schema nudges can change the analytic context for raw data where new metaspace points will be added with the same or different levels of detail, sometimes with an aggregation into smaller rowsets").
Kanter and Minkin are analogous art because they are from the same field of endeavor, specifically automated feature engineering for machine learning over structured and relational data. They address the same problem of automatically deriving and aggregating new features from existing dataset columns without manual data-scientist intervention.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate Minkin’s semantic-context modeling and semantic-context-driven feature engineering into Kanter’s Deep Feature Synthesis so that the selection and manner of each derivation and aggregation is governed by the meaning (semantic context) of the feature. This combines prior-art feature-engineering elements according to their established functions to yield the predictable result of more pertinent automatically-derived features (KSR rationale A).
The suggestion/motivation for doing so would have been Minkin’s express teaching that feature engineering be performed as the "creation of new features derived from Source Features that are based on filters, aggregations, and additional calculations" (Minkin: ¶[0096]), driven by an understanding of the semantic context of the data, which directly motivates governing Kanter’s aggregation primitives by the semantic context of each feature (MPEP § 2143.01).
Per claim 2, Kanter combined with Minkin discloses claim 1. Kanter further teaches the parameter comprises a second feature in the input data, the second feature having a second semantic type (Kanter: Section II.B, p. 3…the grouping parameter is the entity identifier, which is itself a feature/column of the data having an identifier type, "where the identifier of Ek is ek = i").
Per claim 3, Kanter combined with Minkin discloses claim 1. Kanter further teaches the input data includes the parameter; and the parameter identifies the manner and the subset of the input data to aggregate (Kanter: Section III, p. 5…a Filter Object, part of the relational input structure, specifies the subset to aggregate and the condition governing the aggregation, "they provide a way to apply rfeat functions only to instances where a certain condition is true. We call this usage a categorical filter").
Per claim 4, Kanter combined with Minkin discloses claim 1. Kanter combined with Minkin further teaches the manner is determined based at least in part on the first semantic context and a second semantic context corresponding to a different feature in the input data (Kanter: Section III, p. 5…each feature function determines from the columns’ types which columns it applies to and how to compute, "The function definition is responsible for determining which columns in the relationship it can be applied to and how to calculate the output value"; Minkin: ¶[0096]…new features are derived from multiple source features’ semantic context, "creation of new features derived from Source Features that are based on filters, aggregations"). The rationale to combine Minkin with Kanter is the same as the parent claim.
Per claim 6, Kanter combined with Minkin discloses claim 5. Minkin further teaches:
• the instructions, if executed, that process the input data, further cause the computer system to heuristically determine the metadata based at least in part on the identified subset of the input data (Minkin: ¶[0100]…Minkin heuristically infers a feature’s semantic context from the source feature’s own values upon load, e.g., recognizing two numerics as a GPS coordinate or a value such as 20160716 as a date, "Implicit Modeling can include trivial semantic mapping performed using individual Source Features upon a load to enhance Semantic Context. As an example, this can include suggesting two numerics with expected ranges and names that are a GPS coordinate"). The rationale to combine Minkin with Kanter is the same as the parent claim.
--
Per claim 12, Kanter combined with Minkin discloses claim 5. Minkin further teaches the instructions, if executed, that generates the new data, further cause the computer system to determine the second feature using an algorithm identified in a policy as applicable to the feature (Minkin: ¶[0136]… Minkin determines which derivation algorithms are applicable from a feature’s ontology type, e.g., date/time parts enabling time-based algorithms to be applied, "date and time parts such as day, month, year can allow a mapping into autoregressive and other time-based forecasting algorithms to be applied by the system". The rationale to combine Minkin with Kanter is the same as the parent claim.
Claims 14 and 20 are substantially similar in scope and spirit as claims 6 and 12. Therefore the rejections of claims 6 and 12 are applied accordingly.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 10,510,020. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant independent claims 1 (method), 5 (medium), and 13 (system) recite a broadened, generic version of independent claims 9, 17, and 1, respectively, of U.S. Patent No. 10,510,020: each processes input data to identify a feature having a semantic type, obtains semantic metadata indicating a first semantic context for that feature, processes the input data based at least in part on the first semantic context to derive or generate further data (a “second element” / “second feature” / “new data”) having a different semantic context, and provides access to that data. The patented claims recite the very same invention in narrower form—expressly reciting a first feature and a second feature, generation of a tagged set of data, and a third feature derived from the interdependent relationship of a first and a second semantic context. Practicing the patented claims would therefore meet every limitation of the corresponding instant independent claim, which omits these narrowing details. The instant dependent claims add only limitations (heuristic determination of metadata, generation of an identifier, processing of the new data by a machine-learning algorithm or by a different computer system, and selection of an algorithm by a policy) that are obvious variations within the level of ordinary skill in the art and, in several instances, find direct counterparts in the patented claims; a later claim that is merely a broadened or obvious variant of a patented claim is not patentably distinct from it.
Instant Claim 1 ↔ U.S. Pat. No. 10,510,020 Claim 9
Instant Application Claims
U.S. Pat. No. 10,510,020 Claims
A computer-implemented method, comprising:
A computer-implemented method, comprising:
processing input data to identify a feature in the input data, the feature corresponding to a subset of the input data and having a semantic type;
processing input data to identify a first feature and a second feature in the input data, the first feature ... respectively corresponding to a first subset of the input data ..., the first subset of input data having a first semantic type ...;
obtaining semantic metadata for the feature, the semantic metadata indicating a first semantic context for the feature;
obtaining first semantic metadata for the first feature ..., the first ... semantic metadata ... indicating a first semantic context ... for the first feature ...;
identify, in the subset of the input data and based at least in part on a parameter associated with the input data, a first plurality of elements; and
processing the input data with the obtained first semantic metadata and the obtained second semantic metadata to generate a tagged set of data comprising the first subset of the input data, the second subset of the input data, the first semantic metadata, and the second semantic metadata; (the patented second feature/second subset corresponds to the claimed parameter that governs identification within the input data)
aggregate the first plurality of elements by generating, in a manner determined at least in part on the first semantic context, a second element derived from a subset of the first plurality of elements and having a different second semantic context, the subset of the first plurality of elements selected based at least in part on the parameter; and
processing the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature corresponding to a third semantic context ...; generating, from the tagged set of data, new data to correspond to the third feature; (the patented third feature/new data is derived data having a different semantic context, generated based at least in part on the first semantic context)
providing, with the parameter, the second element as associated with the parameter.
providing access to the new data.
Instant Claim 2 ↔ U.S. Pat. No. 10,510,020 Claim 9
the parameter comprises a second feature in the input data, the second feature having a second semantic type.
processing input data to identify a first feature and a second feature in the input data, ... the second subset of input data having a second semantic type;
Instant Claim 3 ↔ U.S. Pat. No. 10,510,020 Claim 9
the input data includes the parameter; and the parameter identifies the manner and the subset of the input data to aggregate.
processing input data to identify a first feature and a second feature in the input data ...; processing the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature ...; (the second feature is part of the input data and governs how the further feature is derived)
Instant Claim 4 ↔ U.S. Pat. No. 10,510,020 Claim 9
the manner is determined based at least in part on the first semantic context and a second semantic context corresponding to a different feature in the input data.
processing the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature corresponding to a third semantic context that indicates an interdependency of both the first semantic context and the second semantic context;
Instant Claim 5 ↔ U.S. Pat. No. 10,510,020 Claim 17
A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type;
process input data to identify a first feature and a second feature in the input data, ... the first subset of input data having a first semantic type ...;
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature;
obtain first semantic metadata for the first feature ..., the first ... semantic metadata ... indicating a first semantic context ... for the first feature ...;
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context;
process the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature corresponding to a third semantic context associated with information about a relationship of both the first semantic context and the second semantic context;
generate, from the input data, new data to correspond to the second feature; and
generate, from the tagged set of data, new data to correspond to the third feature; and
provide access to the new data as associated with a corresponding subset of the input data.
provide access to the new data.
Instant Claim 6 ↔ U.S. Pat. No. 10,510,020 Claim 17
heuristically determine the metadata based at least in part on the identified subset of the input data.
obtain first semantic metadata for the first feature ...; (the patented claim is not limited as to the manner of obtaining the semantic metadata; heuristically determining the metadata from the identified subset is an obvious implementation within ordinary skill)
Instant Claim 7 ↔ U.S. Pat. No. 10,510,020 Claim 17
determine the second feature based on information other than the first semantic context.
process the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature ...; (the patented derivation also relies on the second semantic context, i.e., information other than the first semantic context)
Instant Claim 8 ↔ U.S. Pat. No. 10,510,020 Claim 17
the metadata identifies the first semantic context.
obtain first semantic metadata for the first feature ..., the first ... semantic metadata ... indicating a first semantic context ...;
Instant Claim 9 ↔ U.S. Pat. No. 10,510,020 Claim 17
generate, based at least in part on other metadata associated with the feature, an identifier for the second feature.
generate, from the tagged set of data, new data to correspond to the third feature ...; (generating an identifier for the derived feature from associated metadata is an obvious variant of generating data to correspond to that feature)
Instant Claim 10 ↔ U.S. Pat. No. 10,510,020 Claim 17
cause processing of the new data by a machine learning algorithm.
provide access to the new data. (downstream processing of the provided new data, e.g., by a machine-learning algorithm, is a conventional and obvious use within ordinary skill)
Instant Claim 11 ↔ U.S. Pat. No. 10,510,020 Claim 17
cause processing, by a different computer system, of the new data.
provide access to the new data. (providing the new data to a different computer system for processing is a conventional and obvious use of the provided access)
Instant Claim 12 ↔ U.S. Pat. No. 10,510,020 Claim 17
determine the second feature using an algorithm identified in a policy as applicable to the feature.
process the tagged set of data to derive ... a third feature ...; (selecting the derivation algorithm by a policy applicable to the feature is an obvious implementation choice within ordinary skill)
Instant Claim 13 ↔ U.S. Pat. No. 10,510,020 Claim 1
A system, comprising: one or more processors; and memory that stores computer-executable instructions that, if executed, cause the one or more processors to:
A system, comprising: one or more processors; and memory that stores computer-executable instructions that, if executed, cause the one or more processors to:
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type;
process input data to identify a first feature and a second feature in the input data, ... the first subset of input data having a first semantic type ...;
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature;
obtain first semantic metadata for the first feature ..., the first ... semantic metadata ... indicating a first semantic context ...;
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context;
process the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature corresponding to a third semantic context indicating an interdependent relationship of both the first semantic context and the second semantic context;
generate, from the input data, new data to correspond to the second feature; and
generate, from the tagged set of data, new data to correspond to the third feature; and
provide access to the new data as associated with a corresponding subset of the input data.
provide access to the new data.
Instant Claim 14 ↔ U.S. Pat. No. 10,510,020 Claim 1
heuristically determine the metadata based at least in part on the identified subset of the input data.
obtain first semantic metadata for the first feature ...; (manner of obtaining the metadata is unlimited; heuristic determination is an obvious implementation within ordinary skill)
Instant Claim 15 ↔ U.S. Pat. No. 10,510,020 Claim 1
determine the second feature based on information other than the first semantic context.
process the tagged set of data to derive, based at least in part on the first semantic context and the second semantic context, a third feature ...; (derivation also relies on the second semantic context, i.e., information other than the first semantic context)
Instant Claim 16 ↔ U.S. Pat. No. 10,510,020 Claim 1
the metadata identifies the first semantic context.
obtain first semantic metadata for the first feature ..., the first ... semantic metadata ... indicating a first semantic context ...;
Instant Claim 17 ↔ U.S. Pat. No. 10,510,020 Claim 1
generate, based at least in part on other metadata associated with the feature, an identifier for the second feature.
generate, from the tagged set of data, new data to correspond to the third feature ...; (generating an identifier for the derived feature from associated metadata is an obvious variant)
Instant Claim 18 ↔ U.S. Pat. No. 10,510,020 Claim 1
cause processing of the new data by a machine learning algorithm.
provide access to the new data. (downstream processing of the new data by a machine-learning algorithm is a conventional and obvious use)
Instant Claim 19 ↔ U.S. Pat. No. 10,510,020 Claim 1
cause processing, by a different computer system, of the new data.
provide access to the new data. (providing the new data to a different computer system for processing is a conventional and obvious use)
Instant Claim 20 ↔ U.S. Pat. No. 10,510,020 Claim 1
determine the second feature using an algorithm identified in a policy as applicable to the feature.
process the tagged set of data to derive ... a third feature ...; (selecting the derivation algorithm by a policy applicable to the feature is an obvious implementation choice)
Claims 5-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5-20 of U.S. Patent No. 11,861,465. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant independent claims 5 (medium) and 13 (system) are broader, generic versions of patented claims 5 and 13 — reciting the identical core of processing input data to identify a subset corresponding to a feature having a first semantic type, obtaining metadata associated with a first semantic context, determining from the first semantic context a second feature corresponding to a second semantic context, generating new data corresponding to the second feature, and providing access to that data — while omitting only the patented claims’ additional narrowing limitations, namely that the second semantic context be “mutually exclusive” of the first and that additional data be joined or merged with the new data before access is provided. The entire scope of each patented claim therefore falls within the scope of the corresponding instant claim, so the instant claim is anticipated by (generic to) the patented species. The instant dependent claims 6-12 and 14-20 recite limitations word-for-word identical to patented claims 6-12 and 14-20. A claim that is merely a broadened version of a patented claim is not patentably distinct from it.
Instant Claim 5 ↔ U.S. Pat. No. 11,861,465 Claim 5
Instant Application Claims
U.S. Pat. No. 11,861,465 Claims
A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
A non-transitory computer-readable storage medium having stored thereon executable instructions for deriving additional features within input data by integrating semantic information that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type;
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type;
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature;
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature;
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context;
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context that is mutually exclusive of the first semantic context; (the patented claim adds the narrowing “mutually exclusive” qualifier, which the broader instant claim omits)
generate, from the input data, new data to correspond to the second feature; and
generate, from the input data, new data to correspond to the second feature;
provide access to the new data as associated with a corresponding subset of the input data.
join additional data corresponding to the feature or the second feature with the new data; and provide access to the additional data joined with the new data as associated with a corresponding subset of the input data. (the patented claim adds a narrowing join-additional-data step the instant claim omits; the patented claim’s entire scope thus falls within the broader instant claim)
Instant Claim 6 ↔ U.S. Pat. No. 11,861,465 Claim 6
the instructions, if executed, that process the input data, further cause the computer system to heuristically determine the metadata based at least in part on the identified subset of the input data.
the instructions, if executed, that process the input data, further cause the computer system to heuristically determine the metadata based at least in part on the identified subset of the input data.
Instant Claim 7 ↔ U.S. Pat. No. 11,861,465 Claim 7
the instructions, if executed, that process the input data, further cause the computer system to determine the second feature based on information other than the first semantic context.
the instructions, if executed, that process the input data, further cause the computer system to determine the second feature based on information other than the first semantic context.
Instant Claim 8 ↔ U.S. Pat. No. 11,861,465 Claim 8
the metadata identifies the first semantic context.
the metadata identifies the first semantic context.
Instant Claim 9 ↔ U.S. Pat. No. 11,861,465 Claim 9
the instructions, if executed, that generates the new data, further cause the computer system to generate, based at least in part on other metadata associated with the feature, an identifier for the second feature.
the instructions, if executed, that generates the new data, further cause the computer system to generate, based at least in part on other metadata associated with the feature, an identifier for the second feature.
Instant Claim 10 ↔ U.S. Pat. No. 11,861,465 Claim 10
the instructions, if executed, that provide access to the new data, further cause the computer system to cause processing of the new data by a machine learning algorithm.
the instructions, if executed, that provide access to the new data, further cause the computer system to cause processing of the new data by a machine learning algorithm.
Instant Claim 11 ↔ U.S. Pat. No. 11,861,465 Claim 11
the instructions, if executed, that provide access to the new data, further cause the computer system to cause processing, by a different computer system, of the new data.
the instructions, if executed, that provide access to the new data, further cause the computer system to cause processing, by a different computer system, of the new data.
Instant Claim 12 ↔ U.S. Pat. No. 11,861,465 Claim 12
the instructions, if executed, that generates the new data, further cause the computer system to determine the second feature using an algorithm identified in a policy as applicable to the feature.
the instructions, if executed, that generates the new data, further cause the computer system to determine the second feature using an algorithm identified in a policy as applicable to the feature.
Instant Claim 13 ↔ U.S. Pat. No. 11,861,465 Claim 13
A system, comprising: one or more processors; and memory that stores computer-executable instructions that, if executed, cause the one or more processors to:
A system for deriving additional features within input data by integrating semantic information, comprising: one or more processors; and memory that stores computer-executable instructions that, if executed, cause the one or more processors to:
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type;
process input data to identify a subset of the input data, the subset of the input data corresponding to a feature in the input data, the feature having a first semantic type;
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature;
obtain metadata for the feature, the metadata being associated with a first semantic context for the feature;
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context;
process the input data to determine, based at least in part on the first semantic context, a second feature corresponding to a second semantic context that is mutually exclusive of the first semantic context; (the patented claim adds the narrowing “mutually exclusive” qualifier the instant claim omits)
generate, from the input data, new data to correspond to the second feature; and
generate, from the input data, new data to correspond to the second feature;
provide access to the new data as associated with a corresponding subset of the input data.
obtain additional data corresponding to the feature or the second feature to merge with the new data; and provide access to the additional data merged with the new data as associated with a corresponding subset of the input data. (the patented claim adds a narrowing merge-additional-data step the instant claim omits)
Instant Claim 14 ↔ U.S. Pat. No. 11,861,465 Claim 14
the instructions, if executed, that process the input data, further cause the system to heuristically determine the metadata based at least in part on the identified subset of the input data.
the instructions, if executed, that process the input data, further cause the system to heuristically determine the metadata based at least in part on the identified subset of the input data.
Instant Claim 15 ↔ U.S. Pat. No. 11,861,465 Claim 15
the instructions, if executed, that process the input data, further cause the system to determine the second feature based on information other than the first semantic context.
the instructions, if executed, that process the input data, further cause the system to determine the second feature based on information other than the first semantic context.
Instant Claim 16 ↔ U.S. Pat. No. 11,861,465 Claim 16
the metadata identifies the first semantic context.
the metadata identifies the first semantic context.
Instant Claim 17 ↔ U.S. Pat. No. 11,861,465 Claim 17
the instructions, if executed, that generates the new data, further cause the system to generate, based at least in part on other metadata associated with the feature, an identifier for the second feature.
the instructions, if executed, that generates the new data, further cause the system to generate, based at least in part on other metadata associated with the feature, an identifier for the second feature.
Instant Claim 18 ↔ U.S. Pat. No. 11,861,465 Claim 18
the instructions, if executed, that provide access to the new data, further cause the system to cause processing of the new data by a machine learning algorithm.
the instructions, if executed, that provide access to the new data, further cause the system to cause processing of the new data by a machine learning algorithm.
Instant Claim 19 ↔ U.S. Pat. No. 11,861,465 Claim 19
the instructions, if executed, that provide access to the new data, further cause the system to cause processing, by a different computer system, of the new data.
the instructions, if executed, that provide access to the new data, further cause the system to cause processing, by a different computer system, of the new data.
Instant Claim 20 ↔ U.S. Pat. No. 11,861,465 Claim 20
the instructions, if executed, that generates the new data, further cause the system to determine the second feature using an algorithm identified in a policy as applicable to the feature.
the instructions, if executed, that generates the new data, further cause the system to determine the second feature using an algorithm identified in a policy as applicable to the feature.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to generating data features based on semantic context.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN CHEN whose telephone number is (571)272-4143. The examiner can normally be reached M-F 10-7.
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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.
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/ALAN CHEN/Primary Examiner, Art Unit 2125
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