DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Application filed on 11/17/2023. Claims 1-17 are pending in the case. Claims 1, 16, and 17 are independent claims.
Claim Rejections - 35 U.S.C. § 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 1-17 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-15 and 17-D are directed towards the statutory category of a machine. Claim 16 is directed towards the statutory category of a process.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
assign a knowledge label to at least one training sample among the multiple training samples based on the knowledge base (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A medical information processing apparatus comprising processing circuitry configured to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
acquire multiple training samples, each of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, and an effect label of the event (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g));
acquire a knowledge base independent from the multiple training samples (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)); and
train, based at least on the at least one training sample to which the knowledge label is assigned, a model that infers an effect of each type of an event, the at least one training sample to which the knowledge label is assigned including the feature amount, the type label, the effect label, and the knowledge label (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A medical information processing apparatus comprising processing circuitry configured to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
acquire multiple training samples, each of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, and an effect label of the event (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer);
acquire a knowledge base independent from the multiple training samples (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer); and
train, based at least on the at least one training sample to which the knowledge label is assigned, a model that infers an effect of each type of an event, the at least one training sample to which the knowledge label is assigned including the feature amount, the type label, the effect label, and the knowledge label (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the processing circuitry is configured to train the model through multi-task training comprising estimation of the knowledge label and estimation of the effect value (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the processing circuitry is configured to train the model through multi-task training comprising estimation of the knowledge label and estimation of the effect value (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
the knowledge label includes a recommended type of the event and a degree of recommendation (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
the loss function includes a first loss function and a second loss function, wherein the first loss function represents a regression error between an estimated effect value of each type of the event and the effect label, and the second loss function represents a crossed entropy error between an estimated recommendation probability of each type of the event and the knowledge label (mathematical concept and/or mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the processing circuitry is configured to train the model so as to reduce a loss assessed by a loss function (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the processing circuitry is configured to train the model so as to reduce a loss assessed by a loss function (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
convert the estimated effect value of each type of the event to the estimated recommendation probability (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
change a first weight on the first loss function and a second weight on the second loss function of each of the training samples according to the degree of recommendation included in the knowledge label (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
the loss function includes: a third loss function that represents a classification error between an estimated type of the event and the type label; and a fourth loss function that penalizes non- orthogonality of a latent variable corresponding to the estimated type and a latent variable corresponding to the estimated effect value (mathematical concept and/or mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 8:
2A Prong 1: This claim is directed to a judicial exception.
generate an integrated label that integrates the type label and the knowledge label (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
train the model based on an integrated sample that includes the feature amount and the integrated label (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
train the model based on an integrated sample that includes the feature amount and the integrated label (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 9:
2A Prong 1: This claim is directed to a judicial exception.
generate an artificial sample not having the type label (mental process); and
assign the knowledge label to the artificial sample (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
train the model based on the at least one training sample to which the knowledge label is assigned and the artificial sample (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
train the model based on the at least one training sample to which the knowledge label is assigned and the artificial sample (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 10:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
acquire the artificial sample from an externally provided facility or pseudo-generate the artificial sample (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
acquire the artificial sample from an externally provided facility or pseudo-generate the artificial sample (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 11:
2A Prong 1: This claim is directed to a judicial exception.
determine whether or not to adopt the artificial sample based on a distance between the artificial sample and the multiple training samples in a data space (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 12:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
acquire a target feature amount representing a condition relating to a target subject (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g));
infer an effect value of each type of an event performed on the target subject based on the target feature amount and the model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
acquire a target feature amount representing a condition relating to a target subject (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer);
infer an effect value of each type of an event performed on the target subject based on the target feature amount and the model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 13:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
infer the effect value of each type of an event performed on the target subject and a recommended type of an event performed on the target subject (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
infer the effect value of each type of an event performed on the target subject and a recommended type of an event performed on the target subject (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 14:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
causes the effect value to be displayed on a display (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
causes the effect value to be displayed on a display (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 15:
2A Prong 1: This claim is directed to a judicial exception.
the recommended type includes an unknown label (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 16:
2A Prong 1: This claim is directed to a judicial exception.
A medical information processing method comprising (mental process);
assigning a knowledge label to at least one training sample among the multiple training samples based on the knowledge base (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
acquiring multiple training samples, each of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, and an effect label of the event (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g));
acquiring a knowledge base independent from the multiple training samples (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g));
training, based at least on the at least one training sample to which the knowledge label is assigned, a model that infers an effect of each type of an event, the at least one training sample to which the knowledge label is assigned including the feature amount, the type label, the effect label, and the knowledge label (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
acquiring multiple training samples, each of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, and an effect label of the event (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer);
acquiring a knowledge base independent from the multiple training samples (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer);
training, based at least on the at least one training sample to which the knowledge label is assigned, a model that infers an effect of each type of an event, the at least one training sample to which the knowledge label is assigned including the feature amount, the type label, the effect label, and the knowledge label (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 17:
2A Prong 1: This claim is directed to a judicial exception.
at least one of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, an effect label of the event, and a knowledge label based on a knowledge base independent from the multiple training samples (mental process);
infer an effect of each type of an event performed on the target subject based on the target feature amount and the model (mental process);
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A medical information processing apparatus comprising processing circuitry configured to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
acquire a model trained based on multiple training samples (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g));
acquire a target feature amount representing a condition relating to a target subject (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A medical information processing apparatus comprising processing circuitry configured to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
acquire a model trained based on multiple training samples (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer);
acquire a target feature amount representing a condition relating to a target subject (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
Claim Rejections - 35 U.S.C. § 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 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 of this title, 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, 3, 16, and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over De Bruin et al. (U.S. Pat. App. Pub. No. 2014/0279746, hereinafter De Bruin) in view of Ratner et al. (Ratner, Alexander J., Christopher M. De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. "Data programming: Creating large training sets, quickly." Advances in neural information processing systems 29 (2016), hereinafter Ratner).
As to independent claim 1, De Bruin teaches:
A medical information processing apparatus comprising processing circuitry configured to (Title and abstract):
acquire multiple training samples, each of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, and an effect label of the event (Claim 5, "a first level training dataset comprising a plurality of records comprising measured patient related data from a large number of patients, the measured patient related data including clinical and/or laboratory data, diagnoses of presence or absence of known disorders and data relating to patient treatment response");…
train, based at least on the at least one training sample to which the knowledge label is assigned, a model that infers an effect of each type of an event, the at least one training sample to which the knowledge label is assigned including the feature amount, the type label, the effect label, and the knowledge label (Paragraph 21, "using the treatment-planning and diagnosis datasets in a learning or training procedure to construct the processors and models and find their unknown parameters which are used for assessment of the patient, medical/clinical diagnosis and treatment planning models").
While De Bruin discusses knowledge based system (see De Bruin at paragraph 43), De Bruin does not appear to expressly teach acquire a knowledge base independent from the multiple training samples; and assign a knowledge label to at least one training sample among the multiple training samples based on the knowledge base.
Ratner teaches acquire a knowledge base independent from the multiple training samples (Page 2, "utilize existing knowledge bases"); and assign a knowledge label to at least one training sample among the multiple training samples based on the knowledge base (Page 4, "an external structured knowledge base is used to label a few objects").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the determining patient treatment response of De Bruin to include the labeling techniques of Ratner to help reduce the cost of training set creation (see Ratner at abstract).
As to dependent claim 3, De Bruin further teaches the knowledge label includes a recommended type of the event and a degree of recommendation (Paragraph 69, "recommended treatments with associated response probabilities, and optionally, a list of diagnostic possibilities rank-ordered by probability or likelihood, is then sent to the physician in a timely fashion (100-103).").
As to independent claim 16, De Bruin teaches:
A medical information processing method comprising (Title and abstract):
acquiring multiple training samples, each of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, and an effect label of the event (Claim 5, "a first level training dataset comprising a plurality of records comprising measured patient related data from a large number of patients, the measured patient related data including clinical and/or laboratory data, diagnoses of presence or absence of known disorders and data relating to patient treatment response");…
training, based at least on the at least one training sample to which the knowledge label is assigned, a model that infers an effect of each type of an event, the at least one training sample to which the knowledge label is assigned including the feature amount, the type label, the effect label, and the knowledge label (Paragraph 21, "using the treatment-planning and diagnosis datasets in a learning or training procedure to construct the processors and models and find their unknown parameters which are used for assessment of the patient, medical/clinical diagnosis and treatment planning models").
While De Bruin discusses knowledge based system (see De Bruin at paragraph 43), De Bruin does not appear to expressly teach acquiring a knowledge base independent from the multiple training samples; assigning a knowledge label to at least one training sample among the multiple training samples based on the knowledge base.
Ratner teaches acquiring a knowledge base independent from the multiple training samples (Page 2, "utilize existing knowledge bases"); assigning a knowledge label to at least one training sample among the multiple training samples based on the knowledge base (Page 4, "an external structured knowledge base is used to label a few objects").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the determining patient treatment response of De Bruin to include the labeling techniques of Ratner to help reduce the cost of training set creation (see Ratner at abstract).
As to independent claim 17, De Bruin teaches:
A medical information processing apparatus comprising processing circuitry configured to (Title and abstract):
acquire a model trained based on multiple training samples, at least one of the multiple training samples including a feature amount representing a condition of a subject, a type label of an event performed on the subject, an effect label of the event… (Claim 5, "a first level training dataset comprising a plurality of records comprising measured patient related data from a large number of patients, the measured patient related data including clinical and/or laboratory data, diagnoses of presence or absence of known disorders and data relating to patient treatment response");…
infer an effect of each type of an event performed on the target subject based on the target feature amount and the model (Paragraph 69, "inference process (104-106). This process will generate a report consisting of the response-probabilities associated with a range of possible treatments for the condition diagnosed, and optionally, a list of diagnostic possibilities rank-ordered by likelihood").
De Bruin does not appear to expressly teach a knowledge label based on a knowledge base independent from the multiple training samples; acquire a target feature amount representing a condition relating to a target subject.
Ratner teaches a knowledge label based on a knowledge base independent from the multiple training samples(Page 2, "utilize existing knowledge bases"); acquire a target feature amount representing a condition relating to a target subject (Page 4, "an external structured knowledge base is used to label a few objects").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the determining patient treatment response of De Bruin to include the labeling techniques of Ratner to help reduce the cost of training set creation (see Ratner at abstract).
Claim 2 is rejected under 35 U.S.C. § 103 as being unpatentable over De Bruin in view of Ratner and Kim et al. (U.S. Pat. App. Pub. No. 2016/0247501, hereinafter Kim).
As to dependent claim 2, the rejection of claim 1 is incorporated.
De Bruin does not appear to expressly teach train the model through multi-task training comprising estimation of the knowledge label and estimation of the effect value.
Kim teaches train the model through multi-task training comprising estimation of the knowledge label and estimation of the effect value (Paragraph 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the determining patient treatment response of De Bruin to include the transfer learning techniques of Kim to provide a more accurate, a more efficient, and a more reliable sequence tagging system (see Kim at paragraph 23).
Subject Matter Allowable over the Prior Art
Claims 4-15 are allowable over the prior art and would be allowed if they were to overcome the outstanding 101 rejection.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shi et al. (Shi, Claudia, David Blei, and Victor Veitch. "Adapting neural networks for the estimation of treatment effects." Advances in neural information processing systems 32 (2019)) teaches The use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected outcome and the probability of treatment (propensity score) for each unit. Second, we plug these fitted models into a downstream estimator of the effect. Neural networks are a natural choice for the models in the first step. The question we address is: how can we adapt the design and training of the neural networks used in the first step in order to improve the quality of the final estimate of the treatment effect? We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects. The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties ‘out-of-the-box’.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Casey R. Garner/Primary Examiner, Art Unit 2123
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