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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 2, 2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1, 3, 5, 8, 10, 12, 15, 16, and 18 change “machine based” to “machine-based”
The dependent claims 2, 4, 6-7, 9, 11, 13-14, 17, and 19-20 are objected for being either directly or indirectly dependent on the objected claims since they carry the same deficiencies as the objected claims 1, 3, 5, 8, 10, 12, 15, 16, and 18.
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0063] – change “may also optimized using” to “may also be optimized using”
Paragraph [0007] –change “implemented services using tree based models” to “implemented services using support vector machine based inference models”
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: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step2A Prong 1: The claim recites, inter alia:
[I]dentifying an occurrence of a condition that indicates an inference is necessary to…: This limitation is a mental process because it deals with identifying when a condition is met.
[O]btaining the inference using the inference model: This limitation is seen as a mental process as it deals with selecting a model from a group of inference models.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[P]roviding computer implemented services using inference models… provide the computer implemented services… and providing the computer implemented services using the inference: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[O]btaining an inference model of the inference models: Data Gather- Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
the inference model being a support vector machine based inference model that is based on a soft margin and a debiasing term: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[P]roviding computer implemented services using inference models… provide the computer implemented services… and providing the computer implemented services using the inference: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[O]btaining an inference model of the inference models: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
the inference model being a support vector machine based inference model that is based on a soft margin and a debiasing term: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception. The claim merely integrates routine computer implementation such as “providing computer implemented services” with generic data acquisition like “obtaining an inference model” and applying it to the support vector machine algorithm. Combining these elements describes the process of using a known mathematical tool with standard computing equipment.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 2
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[O]btaining the inference model comprises: reading the inference model from storage: This limitation is a mental process as it involves reading a model from something stored.
Step 2A Prong 2 and Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 3
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
[O]btaining the inference model comprises: prior to identifying the occurrence: This limitation is seen as a mental process as it deals with identifying an occurrence of a condition and using that occurrence to select an inference model.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
[T]raining an instance of the support vector machine based inference model using training data: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
[T]raining an instance of the support vector machine based inference model using training data: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Claim 4
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 3 which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value; and at least one bias feature value associated with the at least one feature value: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value; and at least one bias feature value associated with the at least one feature value: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Claim 5
Step 1 A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 4 that depends on claim 3, which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
wherein training the instance of the support vector machine based inference model comprises: obtaining, based on the training data and an objective function based in part on the debiasing term and the soft margin, a decision boundary: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
wherein training the instance of the support vector machine based inference model comprises: obtaining, based on the training data and an objective function based in part on the debiasing term and the soft margin, a decision boundary: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Claim 6
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 5 that depends on claim 4 and then 3, which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
the debiasing term incentivizes a uniform distribution of the records with respect to the bias features across the decision boundary in the objective function: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
the debiasing term incentivizes a uniform distribution of the records with respect to the bias features across the decision boundary in the objective function: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Claim 7
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 6 that depends on claim 5, then 4, and then 3, which recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
the objective function comprises a weight that scales a level of the incentive for the uniform distribution of the records: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
the objective function comprises a weight that scales a level of the incentive for the uniform distribution of the records: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Claim 8
Step 1: The claim recites an apparatus; therefore, it is directed to the statutory category of apparatus.
Step2A Prong 1: The claim recites, inter alia:
[I]dentifying an occurrence of a condition that indicates an inference is necessary to…: This limitation is a mental process because it deals with identifying when a condition is met.
[O]btaining the inference using the inference model: This limitation is seen as a mental process as it deals with selecting a model from a group of inference models.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[P]roviding computer implemented services using inference models… provide the computer implemented services… and providing the computer implemented services using the inference: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[O]btaining an inference model of the inference models: Data Gather- Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
[T]he inference model being a support vector machine based inference model that is based on a soft margin and a debiasing term: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[P]roviding computer implemented services using inference models… provide the computer implemented services… and providing the computer implemented services using the inference: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[O]btaining an inference model of the inference models: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
[T]he inference model being a support vector machine based inference model that is based on a soft margin and a debiasing term: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception. The claim merely integrates computer implementation like “non-transitory machine-readable medium” and “providing computer implemented services” with generic data acquisition such as “obtaining an inference model” and applying it to the support vector machine algorithm. Combining these elements describes the process of using a known mathematical tool with standard computing equipment.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 9 is an apparatus claim that recites identical limitations to method claim 2. Therefore, claim 9 is
rejected using the same rationale as claim 2.
Claim 10 is an apparatus claim that recites identical limitations to method claim 3. Therefore, claim 10 is
rejected using the same rationale as claim 3.
Claim 11 is an apparatus claim that recites identical limitations to method claim 4. Therefore, claim 11 is
rejected using the same rationale as claim 4.
Claim 12 is an apparatus claim that recites identical limitations to method claim 5. Therefore, claim 12 is
rejected using the same rationale as claim 5.
Claim 13 is an apparatus claim that recites identical limitations to method claim 6. Therefore, claim 13 is
rejected using the same rationale as claim 6.
Claim 14 is an apparatus claim that recites identical limitations to method claim 7. Therefore, claim 14 is
rejected using the same rationale as claim 7.
Claim 15
Step 1: The claim recites an apparatus; therefore, it is directed to the statutory category of apparatus.
Step2A Prong 1: The claim recites, inter alia:
[I]dentifying an occurrence of a condition that indicates an inference is necessary to…: This limitation is a mental process because it deals with identifying when a condition is met.
[O]btaining the inference using the inference model: This limitation is seen as a mental process as it deals with selecting a model from a group of inference models.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[P]roviding computer implemented services using inference models… provide the computer implemented services… and providing the computer implemented services using the inference: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
[O]btaining an inference model of the inference models: Data Gather- Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
[T]he inference model being a support vector machine based inference model that is based on a soft margin and a debiasing term: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[P]roviding computer implemented services using inference models… provide the computer implemented services… and providing the computer implemented services using the inference: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
[O]btaining an inference model of the inference models: Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
[T]he inference model being a support vector machine based inference model that is based on a soft margin and a debiasing term: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception. The claim merely integrates computer implementation like “data processing system” and “providing computer implemented services” with generic data acquisition such as “obtaining an inference model” and applying it to the support vector machine algorithm. Combining these elements describes the process of using a known mathematical tool with standard computing equipment.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 16 is an apparatus claim that recites identical limitations to method claim 3. Therefore, claim 16 is
rejected using the same rationale as claim 3.
Claim 17 is an apparatus claim that recites identical limitations to method claim 4. Therefore, claim 17 is
rejected using the same rationale as claim 4.
Claim 18 is an apparatus claim that recites identical limitations to method claim 5. Therefore, claim 18 is
rejected using the same rationale as claim 5.
Claim 19 is an apparatus claim that recites identical limitations to method claim 6. Therefore, claim 19 is
rejected using the same rationale as claim 6.
Claim 20 is an apparatus claim that recites identical limitations to method claim 7. Therefore, claim 20 is
rejected using the same rationale as claim 7.
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.
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 1-3, 5, 8-10, 12, 15-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lian (“Divide-and-Conquer for Debiased l1-norm Support Vector Machine in Ultra-high Dimensions”, 2018) in view of Gauci (“US 10831339 B2”) and Fortney (“US 11881311 B1”).
Regarding claim 1,
Lian teaches [a] method (Page 2 Paragraph 3, “we focus on distributed estimation of l1 penalized linear SVM coefficients using multiple computing machines. The simplest and most popular approach in data parallelism is averaging: each machine uses a part of the data and obtains a local estimator using the standard estimation methods and sends it back to the master machine which combines the local estimators by simple averaging into an aggregated estimator.”)
the method comprising… obtaining an inference model of the inference models, the inference model being a support vector machine based inference model that is based on… and a debiasing term obtaining the inference using the inference model (Pg. 3 Under Section 2.1, “We begin with the basic setup of SVM for binary classification. We observe a simple random sample (xi , yi), i = 1, . . . , N, from an unknown distribution P(x, y). Here yi ∈ {−1, 1} is the class label and xi = (xi1, . . . , xip) T is the p-dimensional features… The standard linear SVM estimates the parameters by solving… where L is the hinge loss function L(y, t) = max{0, 1 − yt} and λ is the regularization parameter which changes with N”, Page 4 Above Equation 3, “While averaging will reduce the standard deviation of the estimator, it generally cannot reduce the bias. Thus it is important to apply a debiasing mechanism before we aggregate estimators from different machines.”);
Lian does not teach providing computer implemented services using inference models …identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence… and providing the computer implemented services using the inference.
Gauci, in the same field of endeavor, teaches providing computer implemented services using inference models (Paragraph 3 of Gauci, “In light of the triggering event, the contextual information and historical information may be utilized to identify a set of one or more applications for a user. The identified application may then be suggested to the user by providing a user interface in a manner different than how, when, or where the identified application is normally accessed (e.g., on a home screen), thereby giving the user the option to run the application if desired.”)
…identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence… and providing the computer implemented services using the inference (See Figure 1 and Paragraph 21, “A triggering event can be an event induced by a user and/or an external device. For instance, the triggering event can be when an accessory device is connected to the mobile device.”, Paragraph 22 of Gauci, “A prediction model can identify the associated application, where the prediction model may be selected for the specific triggering event.”, Paragraph 23, “ the providing of a user interface for a user to select to run the application. The user interface may be provided in various ways, such as by displaying on a screen of the device, projecting onto a surface, or providing an audio interface.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Gauci's computer-implemented framework for providing services based on triggers with Lian's specific implementation details of a support vector machine-based inference model. A person in the skilled art would have been motivated to modify Lian’s debiased SVM-based inference model with Gauci’s computer-implemented framework for providing services based on event triggers in order to upgrade the static research method of Lian into a functioning, real time computer-implemented service (Paragraph 3 of Gauci).
Lian and Gauci do not teach a soft margin.
Fortney, in the same field of endeavor, teaches a soft margin (Paragraph 95 of Fortney, “Similar to soft-margin SVMs, some observations may be allowed to lie on the ‘wrong’ side of the margin”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian and Gauci’s teaching with Fortney’s soft margin in order to improve robustness to outliers and the practical applicability of Lian’s SVM-based inference model with computer implemented services (Paragraph 93 of Fortney).
Regarding claim 2,
Lian does not teach obtaining the inference model comprises: reading the inference model from storage.
Gauci teaches obtaining the inference model comprises: reading the inference model from storage (Paragraph 73, “the prediction engine 302 may send the suggested application 304 to an expert center module 320. In embodiments, the event manager 320 may be a section of code that manages what is displayed on a device, e.g., on a lock screen, when a search screen is opened, or other screens.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teaching of the support vector machine based inference model with Gauci’s teaching of reading the inference model from the storage in order to implement Lian’s technique of receiving the model from the storage to provide computer implemented services (Paragraph 3 of Gauci).
Regarding claim 3,
Lian teaches obtaining the inference model comprises: prior to identifying the occurrence: training an instance of the support vector machine based inference model using training data (Page 2 Paragraph 3, “In this paper, we focus on distributed estimation of l1 penalized linear SVM coefficients using multiple computing machines. The simplest and most popular approach in data parallelism is averaging: each machine uses a part of the data and obtains a local estimator using the standard estimation methods and sends it back to the master machine which combines the local estimators by simple averaging into an aggregated estimator.”).
Regarding claim 5,
Lian teaches training the instance of the support vector machine based inference model comprises: obtaining, based on the training data (Pg.3 Under Section 2.1, “We begin with the basic setup of SVM for binary classification. We observe a simple random sample (xi , yi), i = 1, . . . , N, from an unknown distribution P(x, y).”)
and an objective function based in part on the debiasing term and the soft margin, a decision boundary (See first Equation On Page 3 Section 2.1,
PNG
media_image1.png
66
287
media_image1.png
Greyscale
Where Equation 1 teaches the standard soft-margin SVM objective with hinge loss and regularization,
See Equation 2 On Page 4,
PNG
media_image2.png
57
210
media_image2.png
Greyscale
Where Equation 2 teaches the population level minimizer that adjusts the estimator known as the debiased target parameter (Beta_0).
Page 4 Above Equation 3, “Due to the penalty term, the penalized estimator is generally biased (i.e. shrunk towards zero). Conceptually, λ controls the trade-off between bias and standard deviation of the estimator.”
The decision boundary is mentioned throughout the process of training, especially when it talks about the hinge loss gradient S(Beta) which determines the boundary, and the Beta_0 which is the population minimizer. These two terms discuss how the decision boundary moves.)
Regarding claim 8,
Lian does not teach a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations.
Gauci, in the same field of endeavor, teaches [a] non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations (Paragraph 123, “One or more processors 818 are configurable to process various data formats for one or more application programs 834 stored on medium 802.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teaching of a support vector machine-based inference model with Gauci’s teaching of storing program instructions on a computer-readable medium. A person of ordinary skill in the art would have modified Lian's debiased SVM method to incorporate Gauci’s generic computer system, the data storage, and processing operations in order to upgrade the static research method of Lian into a functioning, real time computer-implemented service (Paragraph 3 of Gauci).
The remaining of claim 8 is an apparatus claim that recites identical limitations to method claim 1. Therefore, claim 8 is rejected using the same rationale as claim 1.
Claim 9 is an apparatus claim that recites identical limitations to method claim 2. Therefore, claim 9 is
rejected using the same rationale as claim 2.
Claim 10 is an apparatus claim that recites identical limitations to method claim 3. Therefore, claim 10 is
rejected using the same rationale as claim 3.
Claim 12 is an apparatus claim that recites identical limitations to method claim 5. Therefore, claim 12 is
rejected using the same rationale as claim 5.
Regarding claim 15,
Lian does not teach a data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations.
Gauci, in the same field of endeavor, teaches [a] data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations (Paragraph 123 of Gauci, “One or more processors 818 are configurable to process various data formats for one or more application programs 834 stored on medium 802.”, Paragraph 124 of Gauci, “One or more processors 818 communicate with computer-readable medium 802 via a controller 820. Computer-readable medium 802 can be any device or medium that can store code and/or data for use by one or more processors 818. Medium 802 can include a memory hierarchy, including cache, main memory and secondary memory.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teaching of a support vector machine-based inference model with Gauci’s disclosure of a data processing system, computer-readable medium, and processors. A person of ordinary skill in the art would have been motivated to modify Lian’s debiased SVM method with Gauci’s hardware architecture in order to upgrade the static research method of Lian into a functioning, real time computer-implemented service (Paragraph 3 of Gauci).
The remaining of claim 15 is an apparatus claim that recites identical limitations to method claim 1. Therefore, claim 15 is rejected using the same rationale as claim 1.
Claim 16 is an apparatus claim that recites identical limitations to method claim 3. Therefore, claim 16 is
rejected using the same rationale as claim 3.
Claim 18 is an apparatus claim that recites identical limitations to method claim 5. Therefore, claim 18 is
rejected using the same rationale as claim 5.
Claims 4, 6, 11, 13, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lian (“Divide-and-Conquer for Debiased l1-norm Support Vector Machine in Ultra-high Dimensions”, 2018) in view of Gauci (“US 10831339 B2”), Fortney (“US 11881311 B1”), and Prabhu (“US 20210004700 A1”).
Regarding claim 4,
Lian teaches the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value (Page 3 Under Section 2.1, “We observe a simple random sample (xi , yi), i = 1, . . . , N, from an unknown distribution P(x, y). Here yi ∈ {−1, 1} is the class label and xi = (xi1, . . . , xip) T is the p-dimensional features.”);
Lian does not teach at least one bias feature value associated with the at least one feature value.
Prabhu, in the same field of endeavor, teaches at least one bias feature value associated with the at least one feature value (Paragraph 33 of Prabhu, “Sampling bias can include different types of sampling biases such as class bias and feature bias.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teaching of training data used in training with Prabhu’s teaching of using a bias feature value in order to mitigate and improve unfairness with the model’s prediction performance (Paragraph 0004 of Prabhu).
Regarding claim 6,
Lian teaches the decision boundary in the objective function and the debiasing term (Page 4 Above Equation 2, “Let β0 = (β01, . . . , β0p) T be the true parameter, which is defined as the minimizer of the population hinge loss,”).
Lian does not teach a uniform distribution of the records with respect to the bias features.
Prabhu, in the same field of endeavor, teaches a uniform distribution of the records with respect to the bias features (Paragraph 0033 of Prabhu, “Sampling bias can include different types of sampling biases such as class bias and feature bias… In particular, let P denote the true distribution of labels, {circumflex over (P)} the sample distribution and C the total number of classes. Since P follows a uniform distribution…”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teachings with Prabhu’s teaching of using a uniform distribution associated with bias feature values in order to mitigate and improve unfairness with the model’s prediction performance (Paragraph 0004 of Prabhu).
Claim 11 is an apparatus claim that recites identical limitations to method claim 4. Therefore, claim 11 is
rejected using the same rationale as claim 4.
Claim 13 is an apparatus claim that recites identical limitations to method claim 6. Therefore, claim 13 is
rejected using the same rationale as claim 6.
Claim 17 is an apparatus claim that recites identical limitations to method claim 4. Therefore, claim 17 is
rejected using the same rationale as claim 4.
Claim 19 is an apparatus claim that recites identical limitations to method claim 6. Therefore, claim 19 is
rejected using the same rationale as claim 6.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lian (“Divide-and-Conquer for Debiased l1-norm Support Vector Machine in Ultra-high Dimensions”, 2018) in view of Gauci (“US 10831339 B2”), Fortney (“US 11881311 B1”), and Francavilla (“US 20240369662 A1”).
Regarding claim 7,
Lian teaches the objective function (See Equation 2 on Page 4).
Lian does not teach a weight that scales a level of the incentive for the uniform distribution of the records.
Francavilla, in the same field of endeavor, teaches comprises a weight that scales a level of the incentive for the uniform distribution of the records (Para 0112, “Because there may not be a uniform tissue distribution within each voxel, the weights may be dynamically adjusted to model different kinds of distributions inside each voxel in order find the distributions that minimize the error.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teachings with Francavilla’s teaching of scaling a weight in a uniform distribution in order to control the classification accuracy with the improvement of the debiasing constraint (Paragraphs 0051 and 0112 of Francavilla).
Claim 14 is an apparatus claim that recites identical limitations to method claim 7. Therefore, claim 14 is
rejected using the same rationale as claim 7.
Claim 20 is an apparatus claim that recites identical limitations to method claim 7. Therefore, claim 20 is
rejected using the same rationale as claim 7.
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 1-4, 8-11, and 15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 8-11, and 15 of copending Application No. 18/160,588 in view of Lian (“Divide-and-Conquer for Debiased l1-norm Support Vector Machine in Ultra-high Dimensions”, 2018) and Fortney (“US 11881311 B1”). Although the claims at issue are not identical, they are not patentably distinct from each other because all of the limitations of the instant application’s claims are contained in their counterpart claims of the reference patent, with the exception that the reference patent recites a “tree based inference model” and its counterparts, and the instant application recites a “support vector machine based inference model” with its counterparts. This is not considered to be a significant distinction; however, they imply the same meaning as the instant application of using the inference model to provide computer implemented services as seen in paragraph 16 of the reference application. A comparison chart of the claims follows, followed by an analysis.
Instant Application
Reference Application (18/160,588)
1. A method for providing computer implemented services using inference models, the method comprising: identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence: obtaining an inference model of the inference models, the inference model being
a support vector machine based inference model that is based on a soft margin and a debiasing term;
obtaining the inference using the inference model; and providing the computer implemented services using the inference.
A method for providing computer implemented services using inference models, the method comprising: identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence: obtaining an inference model of the inference models, the inference model being a
tree based inference model based on a splitting rule that partitions training data used to obtain the inference model for predive ability: for labels of the training data, and adversely for bias features of the training data;
obtaining the inference using the inference model; and providing computer implemented services using the inference.
2. The method of claim 1, wherein obtaining the inference model comprises: reading the inference model from storage.
2. The method of claim 1, wherein obtaining the inference model comprises: reading the inference model from storage.
3. The method of claim 1, wherein obtaining the inference model comprises: prior to identifying the occurrence: training an instance of the
support vector machine
based inference model using training data.
3. The method of claim 1, wherein obtaining the inference model comprises: prior to identifying the occurrence: training an instance of the
tree
based inference model using the training data.
4. The method of claim 3, wherein the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value; and at least one bias feature value associated with the at least one feature value.
4. The method of claim 3, wherein the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value; and at least one bias feature values associated with the at least one feature value.
8. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for providing computer implemented services using inference models, the operations comprising: identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence: obtaining an inference model of the inference models, the inference model being a
support vector machine based inference model that is based on a soft margin and a debiasing term;
obtaining the inference using the inference model; and providing the computer implemented services using the inference.
8. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for providing computer implemented services using inference models, the operations comprising: identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence: obtaining an inference model of the inference models, the inference model being a
tree based inference model based on a splitting rule that partitions training data used to obtain the inference model for predive ability: for labels of the training data, and adversely for bias features of the training data;
obtaining the inference using the inference model; and providing computer implemented services using the inference.
9. The non-transitory machine-readable medium of claim 8, wherein obtaining the inference model comprises: reading the inference model from storage.
9. The non-transitory machine-readable medium of claim 8, wherein obtaining the inference model comprises: reading the inference model from storage.
10. The non-transitory machine-readable medium of claim 8, wherein obtaining the inference model comprises: prior to identifying the occurrence: training an instance of the
support vector machine based inference model
using a training data.
10. The non-transitory machine-readable medium of claim 8, wherein obtaining the inference model comprises: prior to identifying the occurrence: training an instance of the
tree based inference model
using the training data.
11. The non-transitory machine-readable medium of claim 10, wherein the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value; and at least one bias feature value associated with the at least one feature value.
11. The non-transitory machine-readable medium of claim 10, wherein the training data comprises: records, and each of the records comprises: at least one feature value; at least one label value associated with the at least one feature value; and at least one bias feature values associated with the at least one feature value.
15. A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for providing computer implemented services using inference models, the operations comprising: identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence: obtaining an inference model of the inference models, the inference model being a
support vector machine based inference model that is based on a soft margin and a debiasing term;
obtaining the inference using the inference model; and providing the computer implemented services using the inference.
15. A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for providing computer implemented services using inference models, the operations comprising: identifying an occurrence of a condition that indicates an inference is necessary to provide the computer implemented services; based on the occurrence: obtaining an inference model of the inference models, the inference model being a
tree based inference model based on a splitting rule that partitions training data used to obtain the inference model for predive ability: for labels of the training data, and adversely for bias features of the training data;
obtaining the inference using the inference model; and providing computer implemented services using the inference.
Claims 1, 3, 8, 10, and 15 are essentially identical to their counterparts in the reference application, with the limitations of “a support vector machine” and “a support vector machine based inference model that is based on a soft margin and a debiasing term”. Lian teaches a support vector machine based inference model that is based on a soft margin and a debiasing term (Pg. 3 Under Section 2.1, “We begin with the basic setup of SVM for binary classification. We observe a simple random sample (xi , yi), i = 1, . . . , N, from an unknown distribution P(x, y). Here yi ∈ {−1, 1} is the class label and xi = (xi1, . . . , xip) T is the p-dimensional features… The standard linear SVM estimates the parameters by solving… where L is the hinge loss function L(y, t) = max{0, 1 − yt} and λ is the regularization parameter which changes with N”, Page 4 Above Equation 3, “While averaging will reduce the standard deviation of the estimator, it generally cannot reduce the bias. Thus it is important to apply a debiasing mechanism before we aggregate estimators from different machines.”);
Lian does not teach a soft margin.
Fortney, in the same field of endeavor, teaches a soft margin (Paragraph 95 of Fortney, “Similar to soft-margin SVMs, some observations may be allowed to lie on the ‘wrong’ side of the margin”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Lian’s teaching with Fortney’s soft margin in order to improve robustness of outliers and the practical applicability of Lian’s SVM-based inference model with computer implemented services (Paragraph 93 of Fortney).
It would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified the reference application claims to use a SVM inference model rather than a tree-based model in order to make more accurate predictions with large datasets (Pg. 1 Paragraph 2 of Lian and Paragraph 2 of Fortney).
Similarly, claims 8-11 are rejected since they are apparatus claims that recite identical limitations as claims 1-4 in the reference application.
Conclusion
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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