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 .
DETAILED ACTION
The action is in response to claims dated 3/20/2024
Claims pending in the case: 1-20
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 a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
determine a plurality of values associated with a prediction matrix based on a first output of a trained machine learning model, wherein the plurality of values associated with the prediction matrix are values representing an error value between a predicted classification value for each event of a plurality of events and a ground truth value for each event of the plurality of events (Determining error from expectation is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) ,
tune a set of reference measures to provide an adjustment to a predicted classification value of a prospective output of the trained machine learning model (This is adjusting parameters and is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.),
wherein, when tuning the set of reference measures to provide the adjustment to the predicted classification value of the prospective output of the trained machine learning model, …: adjust the predicted classification value of the prospective output of the trained machine learning model to reduce one or more lower error values in the plurality of values associated with the prediction matrix (This is adjusting parameters and is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
A system, comprising: at least one processor (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
wherein the plurality of values associated with the prediction matrix comprise:upper error values for the plurality of events and lower error values for the plurality of events, wherein the upper error values comprise error values associated with the predicted classification value for the plurality of events being greater than the ground truth value for the plurality of events, and wherein the lower error values comprise error values associated with the predicted classification value for the plurality of events being less than the ground truth value for the plurality of events (Determining output based on input specification amounts to a mere instruction to apply the abstract idea. This additional element is mere instructions to apply an exception because they recite no more than an idea of a solution or outcome. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f)).;
apply the set of reference measures to determine a predicted classification value of a second output of the trained machine learning model, wherein the second output of the trained machine learning model comprises a prediction for an event (This high level recitation of the machine learning model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 8 and 15, are similar in scope as claim XX and therefore rejected under the same rationale. The additional elements of non-transitory computer-readable medium in claim 8 also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (This is a high level recitation of generic computer components for applying a result of the abstract idea. The computer is used merely as a tool to implement an existing process. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2-3, 5-7 pertain to, data being used and calculating values (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.)
Dependent claim 4 pertain to, training a model (This high level recitation of training of the model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
The dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Dependent Claims XXX, are similar in scope as claim XX and therefore rejected under the same rationale. The additional elements of ….. also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Hence these claims are rejected as being abstract.
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.
Claim(s) 1, 7-8, 14-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Spalt (US 20200409323).
Spalt cited in applicant IDS.
Regarding Claim 1, Spalt teaches, A system, comprising: at least one processor (Spalt: [14, 61]) programmed or configured to:
determine a plurality of values associated with a prediction matrix based on a first output of a trained machine learning model (Spalt: Fig. 4: [97, 103, 134, 138]: Input matrix generation and update based on model output), wherein the plurality of values associated with the prediction matrix are values representing an error value between a predicted classification value for each event of a plurality of events and a ground truth value for each event of the plurality of events (Spalt: [105, 108-109]: adjust based on predicted values and a desired range (error value)),
wherein the plurality of values associated with the prediction matrix comprise:
upper error values for the plurality of events and lower error values for the plurality of events (Spalt: [108-109]: adjust based on predicted values and a desired range),
wherein the upper error values comprise error values associated with the predicted classification value for the plurality of events being greater than the ground truth value for the plurality of events (Spalt: [108-109]: adjust based on predicted values and a desired range), and
wherein the lower error values comprise error values associated with the predicted classification value for the plurality of events being less than the ground truth value for the plurality of events (Spalt: [43, 108-109]: adjust based on predicted values and a desired range);
tune a set of reference measures to provide an adjustment to a predicted classification value of a prospective output of the trained machine learning model (Spalt: Fig. 4, [96-97]: tune matrix parameters),
wherein, when tuning the set of reference measures to provide the adjustment to the predicted classification value of the prospective output of the trained machine learning model, the at least one processor is programmed or configured to: adjust the predicted classification value of the prospective output of the trained machine learning model to reduce one or more lower error values in the plurality of values associated with the prediction matrix (Spalt: Fig. 4, [43, 96-97, 102-104, 108-109]: dynamically adapt to changes due to seasonality, equipment function etc.); and
apply the set of reference measures to determine a predicted classification value of a second output of the trained machine learning model, wherein the second output of the trained machine learning model comprises a prediction for an event (Spalt: [41, 103]: predict future values);
Although Spalt does not recite the terms upper error and lower error, Spalt teaches, adjusting based on an evaluation of whether a value is within a desired range. It would be obvious to one skilled in the art that such an evaluation involves upper and lower error with respect to the upper and lower bounds of the range. Thus the limitations as claimed are found to be obvious over the teachings in the cited prior art.
Regarding claim 7, Spalt teaches the invention as claimed in claim 1 above and, wherein the at least one processor is further programmed or configured to:
calculate a lower error rate based on the upper error values for the plurality of events, the lower error values for the plurality of events, and correct prediction values for the plurality of events (Spalt: [105, 108-109]: adjust based on predicted values and a desired range – deviation from upper and lower bounds resulting in the respective error values); and
wherein, when tuning the set of reference measures to provide the adjustment to the predicted classification value of the prospective output of the trained machine learning model, the at least one processor is programmed or configured to: tune the set of reference measures to provide the adjustment to the predicted classification value of the prospective output of the trained machine learning model based on the lower error rate (Spalt: [105, 108-109]: adjust based on predicted values and a desired range – adjusting for a range may be done by considering the error to the lower value).
Regarding Claim(s) 8, 14, this/these claim(s) is/are similar in scope as claim(s) 1 and 7 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 15, 20, this/these claim(s) is/are similar in scope as claim(s) 1 and 7 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 2-6, 9-13, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Spalt (US 20200409323) in view of Gunes (US 20190370684).
Gunes cited in applicant IDS.
Regarding claim 2, Spalt teaches the invention as claimed in claim 1 above and, wherein the set of reference measures comprises a reference measure vector with a set of values, wherein the second output of the trained machine learning model comprises an output vector with a set of values (Spalt: [133-134]: Values representing events with time (vector)), and
wherein, when applying the set of reference measures to determine the predicted classification value of the output of the trained machine learning model, the at least one processor is programmed or configured to: multiply the set of values of the output vector by the set of values of the reference measure vector to provide an adjusted output vector (Spalt: [137-138]: adjust parameter based on model output – adjusting parameters may be done by any mathematical equation);
It is to be noted that given all the relevant data fields that may be used, a user may choose to use in the analysis, multiplication, addition, ratios, percentages or other forms of weights generated using the data to achieve the same goal. The mere mention of multiplication in this claim will not distinguish the claimed invention from the prior art in terms of patentability;
Although it is obvious that input and outputs may be represented by vectors, Spalt do not specifically teach, vectors;
Gunes teaches, input and output in the form of vectors (Gunes: [26-27, 97-98]: vector representations in multiclass SVMs);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Spalt and Gunes because the prior arts pertain to tuning of model output and combination would enable tuning vector representations of the input based on model output. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve the accuracy of machine learning models (see Gunes [2]).
Regarding claim 3, Spalt and Gunes teach the invention as claimed in claim 2 above and, wherein the at least one processor is further programmed or configured to:
determine the predicted classification value of the output of the trained machine learning model based on the adjusted output vector (Spalt: Fig. 4, [41, 43, 96-97, 102-104, 108-109]: dynamically adapt to changes) (Gunes: Fig. 2B [109-110, 126-133]: output based on adjustments).
Regarding claim 4, Spalt teaches the invention as claimed in claim 1 above and Gunes further teaches, wherein the at least one processor is further programmed or configured to: train a multi-class deep learning model based on a training dataset used to generate the trained machine learning model, wherein the training dataset comprises a plurality of data instances associated with the plurality of events (Gunes: [22, 45, 54]: classification model like SVM which may be a multi class model).
The same motivation to combine stated above applies.
Regarding claim 5, Spalt teaches the invention as claimed in claim 1 above and Gunes further teaches, wherein the set of reference measures comprises a number of values that is equal to a number of a plurality of class labels associated with the output of the trained machine learning model (Gunes: [37]: “The target variable may be a label or other value that is considered to result from the associated observation vector values such as a characteristic associated with the observation vector values”).
The same motivation to combine stated above applies.
Regarding claim 6, Spalt and Gunes teach the invention as claimed in claim 5 above and, wherein each reference measure in the set of reference measures has a value between 0 and 1 (Gunes: [45]: binary pattern recognition), and wherein values of the reference measures in the set of reference measures are equal to 1 when summed together (Spalt: [137-138]: adjust reference parameters).
This appears to a choice of a mathematical representation that does not rely on any technical consideration and therefore does not contribute to a technical effect and to the solution of an objective technical problem.
Regarding Claim(s), 9-13 this/these claim(s) is/are similar in scope as claim(s) 2-6 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 16-19 this/these claim(s) is/are similar in scope as claim(s) 2-5 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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