DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are currently pending examination.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted by Applicant on 5/28/2024 has been considered.
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 a judicial exception (abstract idea) without significantly more.
Regarding claim 1,
Step 1: Claim 1 is directed towards a method.
Step 2A, Prong 1: Claim 1 recites the following limitations:
generating, by the one or more processors and using a first machine learning model, a prediction output for the training entity; (i.e., generating a prediction output is a limitation that can be done in the human mind)
generating, by the one or more processors and using a composite loss function, a composite loss metric for the first machine learning model that is based on (i) a first loss metric based on a comparison between the prediction output and a plurality of reward measures and (ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output; (i.e., this limitation is a mathematical process in view of paragraphs [0075], [0076], [0077], and [0134] of Applicant’s specification)
modifying, by the one or more processors, one or more model parameters of the first machine learning model based on the composite loss metric. (i.e., this limitations is a mathematical process in view of paragraphs [0075], [0135], and [0142] of Applicant’s specification)
Hence, the claim recites an abstract idea.
Step 2A, Prong 2: Claim 1 recites the additional elements of “a computer-implemented method, the computer-implemented method comprising:”, “by the one or more processors and using a first machine learning model”. These limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)). Furthermore, the limitation of “receiving, by one or more processors, a plurality of training tuples for a training entity;” (see MPEP 2106.05(g)) has been understood as insignificant extra-solution activity consisting of mere data transmission. Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “a computer-implemented method, the computer-implemented method comprising:”, “by the one or more processors and using a first machine learning model” are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)) Furthermore the limitation of “receiving, by one or more processors, a plurality of training tuples for a training entity;” (see MPEP 2106.05(g)) has been understood as insignificant extra-solution activity consisting of mere data transmission. As such, it is re-evaluated under Step 2B to verify if it is more than what the courts have considered to be well-understood, routine and conventional activity in the field. The court decisions cited in MPEP 2106.05(d)(II) have concluded that mere data transmission (as it is recited in the present claim) is a well-understood routine and conventional activity in the field well supported under Berkheimer. (See, i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2,
Step 2A, Prong 1: Claim 2 recites an abstract idea as inherited from claim 1. Claim 2 further recites the following limitations:
wherein the composite loss metric is further based on a hyper-parameter indicative of a deviation allowance from one or more observed actions. (i.e., this limitation is a mathematical concept in view of paragraphs [0075] and [0149] of Applicant’s specification)
Hence, the claim further recites an abstract idea.
Step 2A, Prong 2: Claim 2 does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 2 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the claim as a whole, the claim is not patent eligible.
Regarding claim 3,
Step 2A, Prong 1: Claim 3 recites an abstract idea as inherited from claims 1 and 2.
Step 2A, Prong 2: Claim 3 recites the additional element of “wherein the one or more observed actions are manually defined by a domain policy.” This additional element merely generally links the use of the judicial exception to a particular technological environment of field of use. (see MPEP 2106.05(h)). Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 3 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of “wherein the one or more observed actions are manually defined by a domain policy.” merely generally links the use of the judicial exception to a particular technological environment of field of use. (see MPEP 2106.05(h)). Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 4,
Step 2A, Prong 1: Claim 4 recites an abstract idea as inherited form claim 1.
wherein the second loss metric comprises an imitation loss… (i.e., this limitation is a mathematical concept in view of paragraph [0077] of Applicant’s specification)
Hence the claim further recites an abstract idea.
Step 2A, Prong 2: Claim 4 recites the additional element of “and the imitation output is generated by a second machine learning model that is previously trained using an imitation loss function.” This limitation is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. (see MPEP 2106.05(f)). Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 4 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of “and the imitation output is generated by a second machine learning model that is previously trained using an imitation loss function.” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. (see MPEP 2106.05(f)). Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 5,
Step 2A, Prong 1: Claim 5 recites an abstract idea as inherited from claims 1 and 4. Claim 5 further recites the following limitation:
wherein the imitation loss function is based on a comparison between a training output and an observed action identified by a domain policy (i.e., this limitations is a mathematical concept in view of paragraph [0077] of Applicant’s specification)
Hence the claim further recites an abstract idea.
Step 2A, Prong 2: Claim 5 does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 5 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 6,
Step 2A, Prong 1: Claim 6 recites an abstract idea as inherited from claim 1.
Step 2A, Prong 2: Claim 6 recites the additional elements of “wherein: (i) the plurality of training tuples corresponds to a training temporal sequence for the training entity, (ii) the training temporal sequence defines an evaluation time period with a plurality of time segments, and (iii) a training tuple of the plurality of training tuples comprises a state token, an action token, and an outcome token for the training entity at a time segment of the plurality of time segments.” These additional elements are mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)) Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 6 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “wherein: (i) the plurality of training tuples corresponds to a training temporal sequence for the training entity, (ii) the training temporal sequence defines an evaluation time period with a plurality of time segments, and (iii) a training tuple of the plurality of training tuples comprises a state token, an action token, and an outcome token for the training entity at a time segment of the plurality of time segments.” are mere instructions to apply the exception using generic computer components. (see MPEP 2106.05(f)) Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 7,
Step 2A, Prong 1: Claim 7 recites an abstract idea as inherited from claims 1 and 6.
Step 2A, Prong 2: Claim 7 recites the additional elements of “wherein the state token is indicative of a state for the training entity, the action token is indicative of one or more action combinations for the training entity, and the outcome token is indicative of one or more outcomes for the training entity.” These additional elements merely generally link the use of the judicial exception to a particular technological environment or field of use. (See MPEP 2106.05(h)) Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 7 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “wherein the state token is indicative of a state for the training entity, the action token is indicative of one or more action combinations for the training entity, and the outcome token is indicative of one or more outcomes for the training entity.” merely generally link the use of the judicial exception with a particular technological environment or field of use. (see MPEP 2106.05(h)) Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 8,
Step 2A, Prong 1: Claim 8 recites an abstract idea as inherited from claims 1, 6, and 7.
Step 2A, Prong 2: Claim 8 recites the additional elements of “wherein the one or more action combinations correspond to one or more of a plurality of actions defined by an action space data object and the prediction output comprises a probability score for an action of the plurality of actions.” These additional elements merely generally link the use of the judicial exception to a particular technological environment or field of use. (See MPEP 2106.05(h)) Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “wherein the one or more action combinations correspond to one or more of a plurality of actions defined by an action space data object and the prediction output comprises a probability score for an action of the plurality of actions.” merely generally link the use of the judicial exception with a particular technological environment or field of use. (see MPEP 2106.05(h)) Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 9,
Step 2A, Prong 1: Claim 9 recites an abstract idea as inherited form claims 1, 6, and 7. Claim 9 further recites the following limitation:
wherein the plurality of reward measures is based on the one or more outcomes. (i.e., this limitation is a mathematical concept in view of paragraph [0078] of Applicant’s specification)
Step 2A, Prong 2: Claim 9 does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 9 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 10,
Step 2A, Prong 1: Claim 10 recites an abstract idea as inherited from claim 1. Claim 10 further recites the following limitations:
wherein the first loss metric comprises an expected outcome loss that is generated based on the prediction output, an importance weight for the training entity, and a discounted reward for the training entity that is based on an aggregation of the plurality of reward measures. (i.e., this limitation is a mathematical concept in view of paragraphs [0075] and [0076] of Applicant’s specification)
Hence the claim recites an abstract idea.
Step 2A, Prong 2: Claim 10 does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 11,
Step 2A, Prong 1: Claim 11 recites an abstract idea as inherited from claim 1.
Claim 11 further includes:
“generating one or more action tokens, each representing a unique combination of actions from an available action space” (which is a mental process. One can mentally generate token with unique combination of action by use of pen and paper.)
“filtering the one or more action tokens according to exclusion criteria” (which is a mental process. One can mentally filter tokens by use of pen and paper.)
“imputing one or more excluded tokens by replacing one or more filtered action tokens with one or more similar non-excluded action tokens” (which is a mental process. One can mentally impute/replace filtered token with similar tokens by use of pen and paper.)
Hence the claim recites an abstract idea.
Step 2A, Prong 2: Claim 11 does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 12,
Step 1: Claim 12 is directed towards a system.
Step 2A, Prong 1: Claim 12 recites the same and/or similar limitations as claim 1 and is rejected under the same rationale as claim 1 which is hereby incorporated by reference.
Step 2A, Prong 2: Claim 12 recites the additional elements of “A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:” These additional elements These limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)) Furthermore the limitation of “receiving, by one or more processors, a plurality of training tuples for a training entity;” (see MPEP 2106.05(g)) has been understood as insignificant extra-solution activity consisting of mere data transmission. Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 12 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:” are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)) Furthermore the limitation of “receiving, by one or more processors, a plurality of training tuples for a training entity;” (see MPEP 2106.05(g)) has been understood as insignificant extra-solution activity consisting of mere data transmission. As such, it is re-evaluated under Step 2B to verify if it is more than what the courts have considered to be well-understood, routine and conventional activity in the field. The court decisions cited in MPEP 2106.05(d)(II) have concluded that mere data transmission (as it is recited in the present claim) is a well-understood routine and conventional activity in the field well supported under Berkheimer. (See, i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 13,
Claim 13 recites the same and/or analogous limitations as claim 2. Therefore it is rejected under the same rationale as claim 2.
Regarding claim 14,
Claim 14 recites the same and/or analogous limitations as claim 3. Therefore it is rejected under the same rationale as claim 3.
Regarding claim 15,
Claim 15 recites the same and/or analogous limitations as claim 4. Therefore it is rejected under the same rationale as claim 4.
Regarding claim 16,
Claim 16 recites the same and/or analogous limitations as claim 5. Therefore it is rejected under the same rationale as claim 5.
Regarding claim 17,
Claim 17 recites the same and/or analogous limitations as claim 6. Therefore it is rejected under the same rationale as claim 6.
Regarding claim 18,
Claim 18 recites the same and/or analogous limitations as claim 7. Therefore it is rejected under the same rationale as claim 7.
Regarding claim 19,
Claim 19 recites the same and/or analogous limitations as claim 8. Therefore it is rejected under the same rationale as claim 8.
Regarding claim 20,
Step 1: Claim 20 is directed towards one or more non-transitory computer-readable storage media.
Step 2A, Prong 1: Claim 20 recites the same and/or similar limitations as claim 1 and is rejected under the same rationale as claim 1 which is hereby incorporated by reference.
Step 2A, Prong 2: Claim 20 recites the additional elements of “one or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:” These additional elements These limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)) Furthermore the limitation of “receiving, by one or more processors, a plurality of training tuples for a training entity;” (see MPEP 2106.05(g)) has been understood as insignificant extra-solution activity consisting of mere data transmission. Hence the claim does not recite additional elements that integrate the judicial exception into a practical application. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
Step 2B: Claim 12 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “one or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:” are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the judicial exception using generic computer components. (see MPEP 2106.05(f)) Furthermore the limitation of “receiving, by one or more processors, a plurality of training tuples for a training entity;” (see MPEP 2106.05(g)) has been understood as insignificant extra-solution activity consisting of mere data transmission. As such, it is re-evaluated under Step 2B to verify if it is more than what the courts have considered to be well-understood, routine and conventional activity in the field. The court decisions cited in MPEP 2106.05(d)(II) have concluded that mere data transmission (as it is recited in the present claim) is a well-understood routine and conventional activity in the field well supported under Berkheimer. (See, i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Hence the claim lacks limitations which amount to significantly more than the judicial exception or an inventive concept, and is rejected. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
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 non-obviousness.
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, 2, 6, 7, 8, 9, 12, 13, 17, 18, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al., “Decision Transformer: Reinforcement Learning via Sequence Modeling”, (2021) in view of Chhaya et al. (US 20230121711 A1, filed Oct. 14, 2021 and published Apr. 20, 2023), and Truong et al. (U.S. Patent No. 11615208 filed Oct. 4, 2018 and published Mar. 28, 2023)
Regarding claim 1, Chen teaches a computer-implemented method, the computer-implemented method comprising:
receiving, by one or more processors, a plurality of training tuples for a training entity (Chen, pg. 3, Section 2.1 teaches we consider learning in a Markov decision process (MDP) by the tuple (S, A, P, R). The MDP tuple consists of states, actions, transition dynamics, and a reward function);
generating, by the one or more processors and using a first machine learning model, a prediction output for the training entity (Chen, pg. 3-4, Section 3, teaches we feed the last K timesteps into Decision Transformer, for a total of 3K tokens (one for each modality : return-to-go, state, or action). To obtain token embeddings, we learn a linear layer for each modality…The tokens are then processed by a GPT model, which predicts future action tokens via autoregressive modeling.);
However, Chen does not distinctly disclose:
generating, by the one or more processors and using a composite loss function, a composite loss metric for the first machine learning model that is based on (i) a first loss metric based on a comparison between the prediction output and a plurality of reward measures and (ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output;
modifying, by the one or more processors, one or more model parameters of the first machine learning model based on the composite loss metric.
Nevertheless, Chhaya teaches:
generating, by the one or more processors and using a composite loss function, a composite loss metric for the first machine learning model that is based on (i) a first loss metric based on a comparison between the prediction output and a plurality of reward measures… (Chhaya, [0078] teaches during the training period, the computing system 200 may provide a training information that is used to train the neural network 121. As part of the processing in 440, one or more optimization techniques (e.g., back propagation techniques) may be used to iteratively train the neural network 121 while optimizing one or more of the combined loss functions, the causal modelling loss function, the causal loss function, and the metric loss function. As part of the optimization, weights and biases associated with different layers of the neural network 121 may be changed such that the errors in the prediction of the output text and the predicted stage identifier for the output text is minimized. [Note: optimizing the combined loss such that the errors in the prediction of the output text and the predicted stage identifier for the output text is minimized is being understood as comparison between the prediction output and a plurality of reward measures] In some examples, the computing system 200 trains the neural network 121 over the training period by optimizing model 213 parameters to minimize the causal language modelling loss function, the causal loss function, and the metric loss function. In another example, the computing system 200 trains the neural network 121 over the training period by optimizing the model 213 parameters to minimize the combined loss function. In an example, over the training period, the computing system 200 trains the neural network 121 according to hyperparameters information 119 retrieved from the memory storage subsystem 127. The computing system 200 may optimize one or more model 213 parameters relevant to determining the generated output text or determining the predicted stage identifier for the generated output text over the training period in accordance with the hyperparameters information 119 in order to minimize the causal loss function, the metric loss function, or the combined loss function. For example, model 213 parameters of the neural network 211 comprise one or more of weight matrices, bias terms, or other model 213 parameters described in equations 5, 6, 7, 8, 9, and/or 10.)
modifying, by the one or more processors, one or more model parameters of the first machine learning model based on the composite loss metric (Chhaya, [0078] teaches in some examples, the computing system 200 trains the neural network 121 over the training period by optimizing model 213 parameters to minimize the causal language modelling loss function, the causal loss function, and the metric loss function. In another example, the computing system 200 trains the neural network 121 over the training period by optimizing the model 213 parameters to minimize the combined loss function. In an example, over the training period, the computing system 200 trains the neural network 121 according to hyperparameters information 119 retrieved from the memory storage subsystem 127. The computing system 200 may optimize one or more model 213 parameters relevant to determining the generated output text or determining the predicted stage identifier for the generated output text over the training period in accordance with the hyperparameters information 119 in order to minimize the causal loss function, the metric loss function, or the combined loss function.);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the Decision Transformer, as taught by Chen, with the features of the combined loss of generative transformer network, as taught by Chhaya. Certain embodiments described herein improve the performance of automated content generation systems by, for example, implementing a cause-effect analysis to predict content features that have a significant impact on a target entity’s stage and training a generative pre-trained transformer (“GPT”) network (e.g. a GPT-2 network) using these causally-significant features. Also, certain embodiments described herein improve the performance of automated content generation systems by, for example, using a GPT network that includes a causal loss function to capture a relationship between text and a stage of one or more stages toward a goal. Thus, the GPT network’s predicted text content can be more useful than those provided by conventional solutions, particularly in contexts where, in order to be effective, one or more characteristics (e.g. a type, a style, a grammar, a format, or other characteristic) of generated text content varies among stages in a multi-stage objective. (Chhaya, [0016])
However, the combination does not distinctly disclose …(ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output;
Nevertheless, Truong teaches …(ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output (Truong, Col. 4, lines 60-67 teaches in some aspects, training the recurrent neural network using the training sequence and the label sequence can include estimating a label by applying a subset of the training sequence to the recurrent neural network and comparing the estimated label to an actual label in the label sequence, the actual label corresponding to the subset of the training sequence. The recurrent neural network can be updated according to a loss function based on a result of the comparison.; Truong, Col. 20, lines 38-50 teaches FIG. 7 depicts a process 700 for training a classifier for generation of synthetic data, consistent with disclosed embodiments. According to process 700, a data sequence 701 can include preceding samples 703, current sample 705, and subsequent samples 707. In some embodiments, data sequence 701 can be a subset of a training sequence, as described above with regard to FIG. 6. Data sequence 701 may be applied to recurrent neural network 709. In some embodiments, neural network 709 can be configured to estimate whether current sample 705 is part of a sensitive data portion of data sequence 701 based on the values of preceding samples 703, current sample 705, and subsequent samples 707.; Truong, Col. 31, lines 53-56 teaches in various embodiments, the performance criteria can include prediction metrics. The prediction metrics can enable a user to determine whether data models perform similarly for both synthetic and actual data.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the Decision Transformer as taught by Chen in view of Chhaya, to further include the systems and methods for synthetic data generation, as taught by Truong. Because the synthetic data more accurately models the underlying actual data, a data model trained using this improved synthetic data may perform better processing the actual data. (Truong, Col. 15, lines 51-54)
Regarding claim 2, the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 1, and the combination further teaches wherein the composite loss metric is further based on a hyper-parameter indicative of a deviation allowance from one or more observed actions (Chhaya [0078] teaches during the training period, the computing system 200 may provide a training information that is used to train the neural network 121. As part of the processing in 440, one or more optimization techniques (e.g., back propagation techniques) may be used to iteratively train the neural network 121 while optimizing one or more of the combined loss functions, the causal modelling loss function, the causal loss function, and the metric loss function. As part of the optimization, weights and biases associated with different layers of the neural network 121 may be changed such that the errors in the prediction of the output text and the predicted stage identifier for the output text is minimized. In some examples, the computing system 200 trains the neural network 121 over the training period by optimizing model 213 parameters to minimize the causal language modelling loss function, the causal loss function, and the metric loss function. In another example, the computing system 200 trains the neural network 121 over the training period by optimizing the model 213 parameters to minimize the combined loss function. In an example, over the training period, the computing system 200 trains the neural network 121 according to hyperparameters information 119 retrieved from the memory storage subsystem 127. The computing system 200 may optimize one or more model 213 parameters relevant to determining the generated output text or determining the predicted stage identifier for the generated output text over the training period in accordance with the hyperparameters information 119 in order to minimize the causal loss function, the metric loss function, or the combined loss function.).
Motivation to combine same as stated in claim 1.
Regarding claim 6, the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 1, and the combination further teaches wherein: (i) the plurality of training tuples corresponds to a training temporal sequence for the training entity (Chen, pg. 3, Section 2.1 teaches we consider learning in a Markov decision process (MDP) by the tuple (S, A, P, R). The MDP tuple consists of states, actions, transition dynamics, and a reward function); (Chen, pg. 3-4, Section 3, teaches we feed the last K timesteps into Decision Transformer, for a total of 3K tokens (one for each modality : return-to-go, state, or action)), (ii) the training temporal sequence defines an evaluation time period with a plurality of time segments (Chen, Figure 1, teaches Decision Transformer architecture. States, actions and returns are fed into modality specific linear embeddings and a positional episodic timestep encoding is added.; Chen, pg. 3, Section 2.1 further teaches we use st,at, and rt to denote the state, action and reward at timestep t, respectively.), and (iii) a training tuple of the plurality of training tuples comprises a state token, an action token, and an outcome token for the training entity at a time segment of the plurality of time segments (Chen, Figure 1, teaches Decision Transformer architecture. States, actions and returns are fed into modality specific linear embeddings and a positional episodic timestep encoding is added.; Chen, pg. 3, Section 2.1 further teaches we use st,at, and rt to denote the state, action and reward at timestep t, respectively.).
Regarding claim 7, the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 6, and the combination further teaches wherein the state token is indicative of a state for the training entity, the action token is indicative of one or more action combinations for the training entity, and the outcome token is indicative of one or more outcomes for the training entity (Chen, Figure 1 teaches decision transformer architecture. States, actions, and returns are fed into modality specific linear embeddings and a positional episodic timestep encoding is added. Tokens are fed into a GPT architecture which predicts actions autoregressively using a causal self-attention mask.; Chen, pg. 2 further teaches “Illustrative example” – we train a GPT model to predict next token in a sequence of returns-to-go (sum of future rewards), states, and actions; Chen, pg. 3-4 teaches 3k tokens (one for each modality: return-to-go, state, or action).
Regarding claim 8, the combination of the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 7, and the combination further teaches wherein the one or more action combinations correspond to one or more of a plurality of actions defined by an action space data object and the prediction output comprises a probability score for an action of the plurality of actions (Chen Figure 1 teaches plurality of actions at, at-1; Chen, Abstract teaches by conditioning an autoregressive model on the desired return (reward), past states, and actions, our Decision Transformer model can generate future actions that achieve the desired return.; Chen, pg. 3, Section 3, teaches we would like the model to generate actions based on future desired returns, rather than past rewards; Chen, pg. 8, Section 5.4 teaches we find that the transformer continuously updates reward probability based on events during the episode.).
Regarding claim 9, the combination of the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 7, and the combination further teaches wherein the plurality of reward measures is based on the one or more outcomes (Chen, Abstract teaches by conditioning an autoregressive model on the desired return (reward), past states, and actions, our Decision Transformer model can generate future actions that achieve the desired return.; Chen, pg. 2 further teaches “Illustrative example” – we train a GPT model to predict next token in a sequence of returns-to-go (sum of future rewards), states, and actions. Training only on random walk data -with no expert demonstrations – we can at test time generate optimal trajectories by adding a prior to generate highest possible returns.; Chen, pg. 3, Section 3, teaches we would like the model to generate actions based on future desired returns, rather than past rewards; Chen, pg. 8, Section 5.4 teaches we find that the transformer continuously updates reward probability based on events during the episode.; See also equation (2) in pg. 3).
Regarding claim 12,
Claim 12 recites the same and/or analogous limitations as claim 1, thus it is rejected under the same rationale and motivation as claim 1.
Truong further teaches a computing system comprising memory and one or more processors communicatively coupled to the memory (Truong, Col. 3, lines 31-36 teaches the disclosed embodiments may include a cloud computing system for generating data models. The cloud computing system can include at least one processor and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the cloud computing system to perform the following operations.)
Regarding claim 13,
Claim 13 recites the same and/or analogous limitations as claim 2, thus it is rejected under the same rationale and motivation as claim 2.
Regarding claim 17,
Claim 17 recites the same and/or analogous limitations as claim 6, thus it is rejected under the same rationale and motivation as claim 6.
Regarding claim 18,
Claim 18 recites the same and/or analogous limitations as claim 7, thus it is rejected under the same rationale and motivation as claim 7.
Regarding claim 19,
Claim 19 recites the same and/or analogous limitations as claim 8, thus it is rejected under the same rationale and motivation as claim 8.
Regarding claim 20,
Claim 20 recites the same and/or analogous limitations as claim 1, thus it is rejected under the same rationale and motivation as claim 1.
Truong further teaches “one or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to” (The cloud computing system can include at least one processor and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the cloud computing system to perform the following operations.)
Claim 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Chhaya, Truong, as applied to claim 2, and further in view of Robinson et al. (US 20200311601 A1, filed Mar. 29, 2019 and published Oct. 1, 2020)
Regarding claim 3, the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 2, however, the combination does not distinctly disclose wherein the one or more observed actions are manually defined by a domain policy.
Nevertheless, Robinson teaches wherein the one or more observed actions are manually defined by a domain policy (Robinson, [0068] teaches various embodiments of the present invention address technological problems related to degradations in accuracy of machine learning systems resulting from differences between training data and inference input data by using post-training rules to develop new rule-based features (e.g., rule-based prediction scores and/or rule-based predictions) for the machine learning model. Through enabling such rule-based prediction capabilities and integrating such capabilities with intelligent machine learning models, various embodiments of the present invention allow users (such as SMEs) to create ad-hoc prediction rules ranked by confidence weights for each rule that a prediction algorithm can use as partial input in addition to its other machine learning input features. Because some users, such as SMEs, are generally well-trained and understand the prediction domain intimately, such users are able to specify logical rules that can have high degrees of accuracy and can proper account for post-training discoveries and changes in data. Thus, even after training and deployment of a prediction system, new prediction rules can be manually created and modified by end users to generalize classification capabilities without the need to incur costs associated with re-training the machine learning model. Moreover, the prediction rules may be weighed with user-defined and/or automatically-generated weights which indicates measures of predictive confidence in and/or predictive priority of the respective prediction rules.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the Decision Transformer, as taught by Chen in view of Chhaya, and Truong, to further include the rule-based machine learning prediction, as taught by Robinson, as even after training and deployment of a prediction system, new prediction rules can be manually created and modified by end users to generalize classification capabilities without the need to incur costs associated with re-training the machine learning model. (Robinson [0078])
Regarding claim 14,
Claim 14 recites the same and/or analogous limitations as claim 3, thus it is rejected under the same rationale and motivation as claim 3.
Claim 4, 5, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Chhaya, and Truong, as applied to claim 1, and further in view of Agarwal et al. (US 20220019888 A1, filed Jul. 20, 2020 and published Jan. 20, 2022)
Regarding claim 4, the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 1, however, the combination does not distinctly disclose wherein the second loss metric comprises an imitation loss and the imitation output is generated by a second machine learning model that is previously trained using an imitation loss function.
Nevertheless, Agarwal teaches wherein the second loss metric comprises an imitation loss and the imitation output is generated by a second machine learning model that is previously trained using an imitation loss function (Agarwal [0019] teaches during the training, the system computes a policy similarity metric between two observations, each from a different training environment, and uses a contrastive loss function that is based on the policy similarity metric to train the representation neural network, e.g., while also training the policy neural network and the representation neural network through reinforcement learning or imitation learning. Training the representation neural network using the contrastive loss function enables the policy output to be generalizable across different environments.; See also Fig. 1 teaching Policy Neural Network 102 and Representation Neural Network 112 withing action selection system 100).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the Decision Transformer, as taught by Chen in view of Chhaya, and Truong, to further include the training of multiple neural networks, as taught by Agarwal, as the training the representation neural network using the contrastive loss function enables the policy output to be generalizable across different environments (Agarwal, [0006], [0019])
Regarding claim 5, the combination of Chen in view of Chhaya, Truong, and Agarwal teaches all of the limitations of claim 4, and Agarwal further teaches wherein the imitation loss function is based on a comparison between a training output and an observed action identified by a domain policy (Agarwal [0019] teaches during the training, the system computes a policy similarity metric between two observations, each from a different training environment, and uses a contrastive loss function that is based on the policy similarity metric to train the representation neural network, e.g., while also training the policy neural network and the representation neural network through reinforcement learning or imitation learning. Training the representation neural network using the contrastive loss function enables the policy output to be generalizable across different environments; Agarwal, Abstract further teaches a method includes: obtaining a first observation of a first training environment; obtaining a plurality of second observations of a second training environment; for each second observation, determining a respective policy similarity metric between the second observation and the first observation. [Note: similarity metric, as taught by Agarwal, understood to read on “comparison” as claimed])
Motivation to combine same as stated in claim 4.
Regarding claim 15,
Claim 15 recites the same and/or analogous limitations as claim 4, thus it is rejected under the same rationale and motivation as claim 4.
Regarding claim 16,
Claim 16 recites the same and/or analogous limitations as claim 5, thus it is rejected under the same rationale and motivation as claim 5.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Chhaya, Truong, as applied to claim 1, and further in view of Bennet et al. (U.S. Patent No. 10755816, filed May 6, 2019 and published Aug. 25, 2020)
Regarding claim 10, the combination of Chen in view of Chhaya, Truong teaches all of the limitations of claim 1, and the combination further teaches wherein the first loss metric comprises an expected outcome loss that is generated based on the prediction output (Chen, pg. 4, Training, teaches we sample minibatches of sequence length K from the dataset. The prediction head corresponding to the input token st is trained to predict at – either with cross-entropy loss for discrete actions or mean-squared error for continuous actions – and the losses for each timestep are averaged.), …
However, the combination does not distinctly disclose …an importance weight for the training entity, and a discounted reward for the training entity that is based on an aggregation of the plurality of reward measures.
Nevertheless Bennet teaches …an importance weight for the training entity (Bennet, Col. 29, lines 20-23 teaches Enhanced utility conceptualization—embodiments having alternative ways to estimate utility of a given action (e.g. different methods for weighting costs and outcomes).; Bennet, Col. 3, lines 5-42 teaches The patient agent includes a plurality of health status information at a plurality of times. The doctor agent includes a module that receives rewards/utilities, and a module to select patient treatments in order to maximize overall utilities. The decision-outcome nodes are updated according to a transition model. [Note: maximizing the overall utilities in Bennet understood to teach a loss based on… as claimed]), and a discounted reward for the training entity that is based on an aggregation of the plurality of reward measures (Bennet, Col. 5, lines 66-67 and Col. 6, lines 1-33 teaches modeling of dynamic sequential decision making in medicine involves several techniques...Problems can be either finite horizon or infinite horizon. In either case, utilities/rewards of various decisions may be undiscounted or discounted, where discounting increases the importance of short-term utilities/rewards over long-term ones.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the Decision Transformer, as taught by Chen in view of Chhaya, and Truong, to further include the clinical decision-making artificial intelligence, as taught by Bennet, given that the given the multi-agent design, the system may be modeled on an individual, personalized treatment basis (including genetics), i.e. “personalized medicine.” given the multi-agent design, the system may be modeled on an individual, personalized treatment basis (including genetics), i.e. “personalized medicine.” As such, each patient agent may thus maintain their own individualized transition model, which may then be passed into the physician agent at the time of decision-making for each patient. This is a significant advantage over a “one-size-fits-all” approach to healthcare, both in terms of quality as well as efficiency. (Bennet, Col. 29, lines 24-39)
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over of Chen in view of Chhaya, Truong, as applied to claim 1, and further in view of Prince et al. (US 20120324113 A1, filed Apr. 19, 2012 and published Dec. 20, 2012)
Regarding claim 11, the combination of Chen in view of Chhaya, and Truong teaches all of the limitations of claim 1, however, the combination does not distinctly disclose wherein the plurality of training tuples is previously generated by: generating one or more action tokens, each representing a unique combination of actions from an available action space; filtering the one or more action tokens according to exclusion criteria; and imputing one or more excluded tokens by replacing one or more filtered action tokens with one or more similar non-excluded action tokens.
Nevertheless, Prince teaches wherein the plurality of training tuples is previously generated by: generating one or more action tokens, each representing a unique combination of actions from an available action space; filtering the one or more action tokens according to exclusion criteria; and imputing one or more excluded tokens by replacing one or more filtered action tokens with one or more similar non-excluded action tokens (Prince, [0242] teaches according to one embodiment, each SSDM [i.e., server side defined modification] token indicates an action to take, where the action may be specified in the corresponding modification rule. An exclude action will exclude the content represented by the token from the response to the visitor. An obfuscate action will replace the content represented by the token with a script, which when executed, generates the replaced content to prevent automated bots from easily being able to read that content. At block 1940, the proxy server 120 determines whether the token indicates an exclude action.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the Decision Transformer, as taught by Chen in view of Chhaya, and Truong, to further include the token exclude actions, as taught by Prince. The script operates such that human users will be able to read the content when the script is executed, yet it is difficult for automated bots to read that content. Thus, defining portions of the content as wrapped in a SSDM token with an obfuscate action effectively hides that content for those visitors that trigger the modification rule. (Prince, [0244])
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Shazeer et al. (US 20220374608 A1) – [0010] The method includes obtaining a plurality of training tuples, each training tuple comprising an example contextual text string comprising one or more contextual text tokens, one or more example intermediate text strings comprising one or more intermediate text tokens, and an example output text string comprising one or more output text tokens. The method includes, for each training tuple: inputting at least a portion of the training tuple to a language model; receiving a predicted next token as an output of the language model, the predicted next token generated by the language model by processing the portion of the training tuple; evaluating a loss function that compares the predicted next token generated by the language model with an actual next token included in the training tuple; and modifying one or more values of one or more parameters of the language model based on the evaluation of the loss function.
Shi et al. (US 20240028907 A1) - [0025] To generate the simulated training data 103, the example constrained GAN 200 includes the example training data transformer (generator) 100. The example training data transformer (generator) 100 is trained (e.g., its taps, connection weights, coefficients, etc. adjusted, adapted, etc.) using the simulated training data 118 (y) and the random noise 126 (z) as inputs, and a combination of a distortion loss 210 and a realness loss 212 as a combined loss feedback.
Mallinson et al. (U.S. Patent No. 12626050) teaching combined loss function.
Melnychuk et al., “Causal Transformer for Estimating Counterfactual Outcomes” (July, 2022)
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