Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are presented for examination.
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 6, 7, 19 and 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 pre-AIA the applicant regards as the invention.
Claim(s) 6, 7, 19 and 20, each recite(s) “the one or more time series insights”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “the one or more time series insights” to be “one or more time series insights”.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim(s) 1-8 is/are method type claim. Claim(s) 9-13 is/are system type claim(s). Claim(s) 14-20 is/are product type claim(s). Therefore, claims 1-20 is/are directed to either a process, machine, manufacture or composition of matter.
Independent claim(s):
Step 2A Prong 1:
Regarding claim(s) 1, 9 and 14, this/these claim(s) recite(s)
generating a model template by translating the SME input...., wherein the model template specifies one or more components of the time series (mental process. One can evaluate the SME input and generate a model template via mind);
generating, ... a machine learning model configured based on the model template, wherein the machine learning model defines a multilayer neural network having one or more component definition layers ... extract the one or more components from time series data input (mental process and math. One can mentally generate a machine learning model and define layers of the model via mind and can determine components of a time series using math);
determining, with respect to a decision ... a component-wise contribution of each of the one or more components to the decision (mental process. One can determine contribution of a component towards the decision via mind).
Step 2A Prong 2:
Regarding claim(s) 1, 9 and 14, this judicial exception is not integrated into a practical application.
Additional elements:
Regarding claim(s) 9 and 14, this/these claim(s) recite(s) processor and memory to perform the step of determining (mere instructions stored in a generic memory component to apply the exception using a generic computer component).
Regarding claim(s) 1, 9 and 14, this/these claim(s) further recite(s)
receiving subject matter expert (SME) input..., wherein the SME input characterizes one or more aspects of a time series (insignificant extra solution activity of mere data gathering),
via a computer-user interface of a computer (generic computer user interface being used as a tool)
using a rule-based translator implemented by the computer (mere instructions to apply the exception using generic computer),
by the computer (generic computer user interface being used as a tool),
one or more component definition layers configured to extract the one or more components from time series data input corresponding to an instantiation of the time series
a decision generated by the machine learning model based on the time series data input (insignificant extra solution activity of mere data gathering to apply the abstract idea),
outputting ... the component-wise contribution of at least one of the one or more components (insignificant extra solution activity presenting information).
The additional element(s) as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B:
Regarding claim(s) 1,0 and 14 this/these claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Regarding claim(s) 9 and 14, this/these claim(s) recite(s) processor and memory to perform the step of determining (mere instructions stored in a generic memory component to apply the exception using a generic computer component).
Regarding claim(s) 1, 9 and 14, this/these claim(s) further recite(s)
receiving subject matter expert (SME) input..., wherein the SME input characterizes one or more aspects of a time series (insignificant extra solution activity of mere data gathering, this insignificant extra solution activity is well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362),
via a computer-user interface of a computer (generic computer user interface being used as a tool)
using a rule-based translator implemented by the computer (mere instructions to apply the exception using generic computer),
by the computer (generic computer user interface being used as a tool),
one or more component definition layers configured to extract the one or more components from time series data input corresponding to an instantiation of the time series
a decision generated by the machine learning model based on the time series data input (insignificant extra solution activity of mere data gathering to apply the abstract idea, this insignificant extra solution activity is well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362),
outputting ... the component-wise contribution of at least one of the one or more components (insignificant extra solution activity presenting information, this insignificant extra solution activity is well understood routine and conventional activity, see Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
The additional element(s) as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
Step 2A Prong 1, Dependent claims:
Regarding claim(s) 2, 10 and15, this/these claim(s) recite(s) wherein the generating the model template by translating the SME input includes mapping domain-specific keywords extracted from the SME input to model-specific keywords identifying the one or more components,
Regarding claim(s) 3, 11 and 16, this/these claim(s) recite(s) wherein the model template specifies a modeling strategy and wherein the generating the machine learning model is based, at least in part, on the modeling strategy,
Regarding claim(s) 4, 12 and 17, this/these claim(s) recite(s) wherein the one or more components comprise a plurality of components, and wherein the determining a component-wise contribution for each of the plurality of components includes decomposing the time series data input to identify each of the plurality of components,
Regarding claim(s) 5, 13 and 18, this/these claim(s) recite(s) wherein the one or more components comprise a plurality of components and wherein the model template specifies a positioning of each the plurality of components relative to one another within the time series,
Regarding claim(s) 6 and 19, this/these claim(s) recite(s) ... generate a forecast based on the time series data input, and wherein the one or more time series insights identifies a contribution of an exogenous factor,
Regarding claim(s) 7 and 20 this/these claim(s) recite(s) .... anomaly detection, and wherein, in response to detecting an anomaly based on the time series data input, the one or more time series insights identifies a component as a likely cause of the anomaly,
Regarding claim(s) 8, this/these claim(s) recite(s) .... machine learning models, each configured based on a respective one of the multiple model templates, and further comprising generating a contrastive explanation of differences between the machine learning models
The above limitations appear to be practically implementable in the human mind and is understood to be a recitation of mental processes.
Step 2A Prong 2, Dependent claims:
Regarding claim(s) 6 and 19, this/these claim(s) recite(s) the machine learning model is configured to generate (mere instructions to apply the exception using generic computer, high level recitation of a machine learning model),
Regarding claim(s) 7 and 20, this/these claim(s) recite(s) wherein the machine learning model is configured for (mere instructions to apply the exception using generic computer, high level recitation of a machine learning model),
Regarding claim(s) 8 this/these claim(s) recite(s) wherein the model template comprises multiple model templates and the machine learning model comprises multiple machine learning models (mere instructions to apply the exception using generic computer, high level recitation of a machine learning models and templates).
Step 2B, Dependent claims:
Regarding claim(s) 6 and 19, this/these claim(s) recite(s) the machine learning model is configured to generate (mere instructions to apply the exception using generic computer, high level recitation of a machine learning model),
Regarding claim(s) 7 and 20, this/these claim(s) recite(s) wherein the machine learning model is configured for (mere instructions to apply the exception using generic computer, high level recitation of a machine learning model),
Regarding claim(s) 8 this/these claim(s) recite(s) wherein the model template comprises multiple model templates and the machine learning model comprises multiple machine learning models (mere instructions to apply the exception using generic computer, high level recitation of a machine learning models and templates).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rangapuram (US 11281969 B1), in view of Polleri (US 20210081720 A1) and Schwiep (US 20220292308 A1).
Rangapuram was cited in the IDS filed on 9/25/2023.
Regarding claim 1, Rangapuram teaches a computer-implemented method, comprising (Rangapuram Col23, line61-Col24, line49, method performed by computer executed instructions stored in memory by processor):
receiving subject matter expert (SME) input via a computer-user interface of a computer, wherein the SME input characterizes one or more aspects of a time series (Rangapuram Col5, lines6-24, Col16, lines1-10, client or user (SME) may provide input through graphical user interface (GUI) for various parameters (aspects) of time series to be used by model);
generating a model template ...based on... the SME input ...wherein the model template specifies one or more components of the time series (Rangapuram Col5, line46- Col6, line15, Col17, line47- Col18, line9, model template may be generated for user request based on existing templates, model template may specify time series information (components such as structural characteristics, domain or measurements));
generating, by the computer, a machine learning model configured based on the model template, wherein the machine learning model defines a multilayer neural network having one or more component definition layers configured to extract the one or more components from time series data input corresponding to an instantiation of the time series (Rangapuram Col6, lines 6-20, Col13, line5- Col14, line23, based on template- machine learning model (MLM) is generated, MLM extracts time series information based on template using multiple layers);
determining... a decision generated by the machine learning model based on the time series data input...and... the one or more components (Rangapuram Col8, line56- Col 9, line18, Col23, lines23-60, MLM makes a decision (for e.g. a recommendation) using time series and time series components (such as item demand(s)).
Rangapuram does not specifically teach generating a model template by translating the SME input using a rule-based translator implemented by the computer, wherein the model template specifies one or more components of the time series; determining, with respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision; and
outputting via the computer-user interface the component-wise contribution of at least one of the one or more components
However Polleri teaches generating a model template by translating the SME input using a rule-based translator implemented by the computer, wherein the model template specifies one or more components of the time series (Polleri [106] data can be time series (stream), Polleri [55, 62-64, 70, 71] model template meta-data and format (rules) are analyzed and template is adjusted (template generation- data schema associated with time series (stream), which can broadly be considered part of a template), template specifies transformations and metrics (components) of time series, Polleri Abstract invention allows existing templates to be used even when input data is misaligned by adjusting template information).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Polleri of generating a model template by translating the SME input using a rule-based translator implemented by the computer, wherein the model template specifies one or more components of the time series, into the invention suggested by Rangapuram; since both inventions are directed towards using templates to generate MLMs for time series analysis, and incorporating the teaching of Polleri into the invention suggested by Rangapuram would provide the added advantage of allowing existing templates to be used even when input data is misaligned, and the combination would perform with a reasonable expectation of success (Polleri [Abstract, 55, 62-64, 70, 71, 106]).
Rangapuram and Polleri do not specifically teach determining, with respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision; and
outputting via the computer-user interface the component-wise contribution of at least one of the one or more components.
However Schwiep teaches determining, with respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision; and outputting via the computer-user interface the component-wise contribution of at least one of the one or more components (Schwiep [51, 94, 95, 115, 121] time series components (such as characteristics and distributions of features and time series) and their respective impact (contribution) may be provided as insights and explanations to users, Schwiep [24, 109] these may be provided in graphical user interface, this assists user with model interpretation).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Schwiep of determining, with respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision; and outputting via the computer-user interface the component-wise contribution of at least one of the one or more components, into the invention suggested by Rangapuram and Polleri; since both inventions are directed towards using MLM(s) to make decisions using time series components, and incorporating the teaching of Schwiep into the invention suggested by Rangapuram and Polleri would provide the added advantage of assisting users with model interpretation, and the combination would perform with a reasonable expectation of success (Schwiep [51, 94, 95, 115, 121, 24, 109]).
Regarding claim 2, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above. Rangapuram does not specifically teach wherein the generating the model template by translating the SME input includes mapping domain-specific keywords extracted from the SME input to model-specific keywords identifying the one or more components
However Polleri teaches wherein the generating the model template by translating the SME input includes mapping domain-specific keywords extracted from the SME input to model-specific keywords identifying the one or more components (Polleri [27, 62-64, 70, 71, 106] template keywords (using domain ontology) may be mapped to user input to determine and generate template).
Regarding claim 3, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above.
Rangapuram further teaches wherein the model template specifies a modeling strategy and wherein the generating the machine learning model is based, at least in part, on the modeling strategy (Rangapuram Col5, line46- Col6, line15, Col17, line47- Col18, line9, model template may be generated for user request based on existing templates, model template may specify (strategy) time series information (components such as structural characteristics, domain or measurements))
Regarding claim 4, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above. Rangapuram does not specifically teach wherein the one or more components comprise a plurality of components, and wherein the determining a component-wise contribution for each of the plurality of components includes decomposing the time series data input to identify each of the plurality of components
However Schwiep teaches wherein the one or more components comprise a plurality of components, and wherein the determining a component-wise contribution for each of the plurality of components includes decomposing the time series data input to identify each of the plurality of components ((Schwiep [73, 94, 95] time series may be decomposed into components (such as characteristics and distributions of features and time series), Schwiep [16, 51, 73, 94, 95, 115, 117, 121] time series components and their respective impact (contribution) may be provided as insights and explanations to users).
Regarding claim 5, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above.
Rangapuram further teaches wherein the one or more components comprise a plurality of components and wherein the model template specifies a positioning of each the plurality of components relative to one another within the time series (Rangapuram Col 9, lines 43-50, model (which is based on template) may specify generating forecast every week based on time series for data collected over a time window of the previous N weeks).
Regarding claim 6, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above.
Rangapuram further teaches wherein the machine learning model is configured to generate a forecast based on the time series data input, and wherein the one or more time series insights identifies a contribution of an exogenous factor (Rangapuram Col 9, lines 28-50, model may generate forecast for K weeks or T days into the future, every week based on time series for data collected over a time window of the previous N weeks, Rangapuram Col 8, lines 61-64 factors used for forecasts may include insights based external factors).
Regarding claim 7, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above. Rangapuram does not specifically teach wherein the machine learning model is configured for anomaly detection, and wherein, in response to detecting an anomaly based on the time series data input, the one or more time series insights identifies a component as a likely cause of the anomaly
However Schwiep teaches wherein the machine learning model is configured for anomaly detection, and wherein, in response to detecting an anomaly based on the time series data input, the one or more time series insights identifies a component as a likely cause of the anomaly ((Schwiep [16, 51, 73, 94, 95, 115, 117, 121] time series components (such as characteristics and distributions of features and time series) and their respective impact (contribution) may be provided as insights and explanations to users, Schwiep [29] insight may be anomaly detected in component(s)).
Regarding claim 8, Rangapuram, Polleri and Schwiep teach the invention as claimed in claim 1 above. Rangapuram does not specifically teach wherein the model template comprises multiple model templates and the machine learning model comprises multiple machine learning models, each configured based on a respective one of the multiple model templates, and further comprising generating a contrastive explanation of differences between the machine learning models
However Polleri teaches wherein the model template comprises multiple model templates and the machine learning model comprises multiple machine learning models, each configured based on a respective one of the multiple model templates, and further comprising generating a contrastive explanation of differences between the machine learning models (Polleri [26, 63, 64, 3, 10, 11] multiple models and templates may be available for use, based on requirements, problem to be solved may be defined and request keywords, metrics may be determined for model(s), metrics may be used to determine best model for requirements and problems to be solved).
Claim 9 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Rangapuram further teaches a system, comprising: a processor configured to initiate operations (Rangapuram Col 23, line 61-Col 24, line 49, method performed by computer executed instructions stored in memory by processor).
Claim(s) 10-13 is/are dependent on claim 9 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2-4 respectively, and is/are rejected under the same rationale.
Claim 14 is directed towards a product storing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Rangapuram further teaches a computer program product, the computer program product comprising: one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor to cause the processor to initiate operations (Rangapuram Col 23, line 61-Col 24, line 49, method performed by computer executed instructions stored in memory by processor).
Claim(s) 15-20 is/are dependent on claim 14 above, is/are directed towards a product storing instructions similar in scope to the instructions performed by the method of claim(s) 2-7 respectively, and is/are rejected under the same rationale.
Conclusion
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
Prosecution Insights