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 office action is in response to claims in application 18/336,160 filed on 4/17/2024.
The instant application claims benefit to provisional application #63/459,995 with a priority date of 4/17/2023.
The Pre-Grant publication # 2024/0420585A1 is published on 12/19/2024.
Claims 1-20 are pending.
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 (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is a computer system (1-8) and to a process (claim 9-20). Thus fall within one of the four statutory categories (Step 1: YES).
Claims 1 , 8 and 15 are directed to a computing system for improving a progress of knowledge transfer process. This involves a receiving of an assigned learning task, decomposing the assigned learning task into a plurality of subtasks based on a predefined relationship to a learning goal, evaluating, using a learning algorithm. This also provided response related to each learning subtask to generate a continuous score for each provided response based on a difference between the provided response and an expected response. Finally the system converts each continuous score to a binary value to obtain a set of binary values and generates, based on the set of binary values, a diagnostic report that reflects a knowledge transfer progress related to the assigned learning task. The activities of receiving decomposing learning task, generating of scores and report falls within the “Certain Method of Organizing Human Activity” groupings of abstract ideas subject to the 2019 Revised Patent Subject Matter Eligibility Guidance. Evaluating, counting of score accounts for mental process. counting to convert binary value are part of mental process. The process also obtain a set of binary values, uses deep learning algorithm to provide response based on a difference between the provided response and an expected response and generates, based on the set of binary values. Hence are mathematical concepts. Accordingly, the claims recite one or more groupings of abstract ideas (Step 2A: Prong 1 YES).
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, a computer readable storage device coupled to the processor, storage device containing program code executable by the processor via the memory device to implement a method. They do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interconnected network”, “a processor’, “a memory’, "network remote storage", "automatically categorizing and scoring content” and “ delivering a report to a user”” are merely use of generic computer functions and computer parts to apply or use of judicial exception for a stipulated outcome. Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art. Fig.1,2 of the instant specification block diagram for a hardware/ software in a standard computational environment with network module implement the process claimed here. As an example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
Claims 2-7.9-14, 16-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, taking the claimed elements individually yields no difference from taking them in combination because each element simply performs its respective function as discussed above. In other words, these claims merely apply an abstract idea to a programmable processor or computer and do not improve the performance of the process or computer itself or provide a technical solution to a problem in a technical field. They do not effect a transformation of a particular article to a different state or thing, the underlying computing elements remain the same. Instead, the additional features like converting scores, identifying predefined threshold is a model-indicated threshold or a manual threshold. The using of a cognitive diagnostic model learning paradigm organizing knowledge transfer learning tasks by learning goals, converting of a binary value base and response thereof are again merely amount to a calculations or an instruction to apply the abstract idea using generic, functional, and conventional components well-known in the art and cannot provide an inventive concept. Therefore, the claims appears that claims 1-20 are not patent-eligible under 35 USC 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20240144676 A1 Arroyo et al. ( Arroyo filed 10/28/2026).
Claim 1. Arroyo teaches a method for improving a progress of knowledge transfer process (Para 0146 Data and/or instructions may be transferred e.g., shared by writing to and/or reading from the shared memory), comprising:
receiving an assigned learning task (Para 0005 machine-learning (ML) and/or artificial intelligence (AI) model framework receiving a query regarding a store observation image and outputting a response to the query for an assigned learning task; Para 0160 to at least obtain first metadata associated with a set of store dictionaries, select ones of the set of store dictionaries);
decomposing the assigned learning task into a plurality of subtasks based on a predefined relationship to a learning goal (Fig.10 element 1015; Para 0160 obtain second metadata, a plurality of subtasks, associated with a set of question templates, select or decompose ones of the set of question templates for use based on the associated second metadata, generate question-answer pairs using the selected ones of the set of store dictionaries);
evaluating, using a deep learning algorithm (Para 0021 a training algorithm is used to train a model to operate in accordance with patterns and/or associations based on, for example, training data) , a provided response related to each learning subtask to generate a continuous score for each provided response based on a difference between the provided response and an expected response (Fig.11 elements 1112 response by generating question-answer pairs;Fig.12 element 1212 score by calculating accuracy loss i.e. a difference between the provided response and an expected response),
converting each continuous score to a binary value to obtain a set of binary values (Para 0040 data stored in the datastore may be in any data format such as binary data);
and
generating, based on the set of binary values, a diagnostic report that reflects a knowledge transfer progress related to the assigned learning task (Para 0021 series of nodes and connections within the model to transform or transfer associated metadata knowledge input data into output data).
Claim 2. Arroyo teaches the method of claim 1, further comprising converting, by the processor, each continuous score to a ‘1’ when the continuous score is greater or equal to a predefined threshold value or a ‘0’ when the continuous score is less than the predefined threshold (Para 0030 score is greater or equal to a predefined threshold value; Para 0040 binary format);
Claim 3. Arroyo teaches the method of claim 2, wherein the predefined threshold is a model-indicated threshold or a manual threshold (Para 0059, 0094 model or manual score is greater or equal to a predefined threshold).
Claim 4. Arroyo teaches the method of claim 1, further comprising decomposing the assigned learning task into the plurality of subtasks using a cognitive diagnostic model learning paradigm (Fig.10 element 1015 providing query response subtasks; Para 0160 obtain second metadata as a plurality of subtasks, associated with a set of question templates, selected or decomposed set of question templates for use based on the associated second metadata, generated question-answer pairs using the selected ones of the set of store dictionaries).
Claim 5. Arroyo teaches the method of claim 4, wherein the cognitive diagnostic model learning paradigm organizes knowledge transfer learning tasks by learning goals (Para 0019 Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and/or other artificial machine-driven logic, enables machines e.g., computers, logic circuits, etc. to use a model to process input data to generate an output based on patterns and/or associations previously learned by the model via a training process i.e. when result transferred to output(s) consistent with an organized patterns and/or associations in models) for learning goals).
Claim 6. Arroyo teaches the method of claim 1, wherein each continuous score is converted to a binary value based on a threshold in a learning algorithm (Para 0040 data stored in the datastore may be in any data format such as binary data value).
Claim 7. Arroyo teaches the method of claim 1, wherein each provided response is an answer to a related learning subtask (Para 0066 generation of question-answer pairs in related learning subtask increase efficiency of query response provision and/or improve a response accuracy).
Claim 8. . Arroyo teaches the computing system for improving a progress of knowledge transfer process, comprising: a processor; a memory device coupled to the processor; and a computer readable storage device coupled to the processor, wherein the storage device contains program code executable by the processor via the memory device to implement a method, the method comprising: receiving an assigned learning task; decomposing the assigned learning task into a plurality of subtasks based on a predefined relationship to a learning goal; evaluating, using a deep learning algorithm, a provided response related to each learning subtask to generate a continuous score for each provided response based on a difference between the provided response and an expected response; converting each continuous score to a binary value to obtain a set of binary values; and generating, based on the set of binary values, a diagnostic report that reflects a knowledge transfer progress related to the assigned learning task (Fig.10 element 1015; Para 0160 obtain second metadata, a plurality of subtasks, associated with a set of question templates, select or decompose ones of the set of question templates for use based on the associated second metadata, generate question-answer pairs using the selected ones of the set of store dictionaries; Para 0021 a training algorithm is used to train a model to operate in accordance with patterns and/or associations based on, for example, training data; (Fig.11 elements 1112 response by generating question-answer pairs;Fig.12 element 1212 score by calculating accuracy loss i.e. a difference between the provided response and an expected; Para 0040 binary data);
Claim 9. Arroyo teaches the computing system of claim 8, further comprising converting, by the processor, each continuous score to a ‘1’ when the continuous score is greater or equal to a predefined threshold value or a ‘0’ when the continuous score is less than the predefined threshold (Para 0030 score is greater or equal to a predefined threshold value; Para 0040 binary format);
Claim 10. Arroyo teaches the computing system of claim 9, wherein the predefined threshold is a model-indicated threshold or a manual threshold (Para 0059, 0094 model or manual score is greater or equal to a predefined threshold).
Claim 11. Arroyo teaches the computing system of claim 8, further comprising decomposing the assigned learning task into the plurality of subtasks using a cognitive diagnostic model learning paradigm (Fig.10 element 1015 providing query response subtasks; Para 0160 obtain second metadata as a plurality of subtasks, associated with a set of question templates, selected or decomposed set of question templates for use based on the associated second metadata, generated question-answer pairs using the selected ones of the set of store dictionaries).
Claim 12. Arroyo teaches the computing system of claim 11, wherein the cognitive diagnostic model learning paradigm organizes knowledge transfer learning tasks by learning goals (Para 0019 Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and/or other artificial machine-driven logic, enables machines e.g., computers, logic circuits, etc. to use a model to process input data to generate an output based on patterns and/or associations previously learned by the model via a training process i.e. when result transferred to output(s) could be a learning goal consistent with an organized patterns and/or associations in models).
Claim 13. Arroyo teaches the computing system of claim 8, wherein each continuous score is converted to a binary value based on a threshold in a learning algorithm (Para 0040 data stored in the datastore may be in any data format such as binary data value).
Claim 14. Arroyo teaches the computing system of claim 8, wherein each provided response is an answer to a related learning subtask (Para 0066 generation of question-answer pairs in related learning subtask increase efficiency of query response provision and/or improve a response accuracy).
Claim 15. Arroyo teaches a computer program product for improving a progress of knowledge transfer process, comprising, the computer program product comprising a computer readable storage device, and program instructions stored on the computer readable storage device, to: receive an assigned learning task; decompose the assigned learning task into a plurality of subtasks based on a predefined relationship to a learning goal; evaluate, using a deep learning algorithm, a provided response related to each learning subtask to generate a continuous score for each provided response based on a difference between the provided response and an expected response; convert each continuous score to a binary value to obtain a set of binary values; and generate, based on the set of binary values, a diagnostic report that reflects a knowledge transfer progress related to the assigned learning task (Fig.10 element 1015; Para 0160 obtain second metadata, a plurality of subtasks, associated with a set of question templates, select or decompose ones of the set of question templates for use based on the associated second metadata, generate question-answer pairs using the selected ones of the set of store dictionaries; Para 0021 a training algorithm is used to train a model to operate in accordance with patterns and/or associations based on, for example, training data; (Fig.11 elements 1112 response by generating question-answer pairs;Fig.12 element 1212 score by calculating accuracy loss i.e. a difference between the provided response and an expected; Para 0040 binary data);
Claim 16. Arroyo teaches the computer program product of claim 15, further comprising program instructions stored on the computer readable storage device to convert, by the processor, each continuous score to a ‘1’ when the continuous score is greater or equal to a predefined threshold value or a ‘0’ when the continuous score is less than the predefined threshold (Para 0030 score is greater or equal to a predefined threshold value; Para 0040 binary format).
Claim 17. Arroyo teaches the computer program product of claim 16, wherein the predefined threshold is a model-indicated threshold or a manual threshold (Para 0059, 0094 model or manual score is greater or equal to a predefined threshold).
Claim 18. Arroyo teaches the computer program product of claim 15, further comprising program instructions stored on the computer readable storage device to decompose the assigned learning task into the plurality of subtasks using a cognitive diagnostic model learning paradigm (Fig.10 element 1015 providing query response subtasks; Para 0160 obtain second metadata as a plurality of subtasks, associated with a set of question templates, selected or decomposed set of question templates for use based on the associated second metadata, generated question-answer pairs using the selected ones of the set of store dictionaries).
Claim 19. Arroyo teaches the computer program product of claim 18, wherein the cognitive diagnostic model learning paradigm organizes knowledge transfer learning tasks by learning goals (Para 0019 Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and/or other artificial machine-driven logic, enables machines e.g., computers, logic circuits, etc. to use a model to process input data to generate an output based on patterns and/or associations previously learned by the model via a training process i.e. when result transferred to output(s) could be a learning goal consistent with an organized patterns and/or associations in models).
Claim 20. Arroyo teaches the computer program product of claim 15, wherein each continuous score is converted to a binary value based on a threshold in a learning algorithm (Para 0040 data stored in the datastore may be in any data format such as converted to binary data based on threshold).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12481899 B2 Paiement; Jean-Francois et al.
Automated machine learning model explanation generation
US 12254388 B2 McGrath; Rory et al.
Generation of counterfactual explanations using artificial intelligence and machine learning techniques
US 12481913 B2 Barr; Brian et al.
Automatic generation of attribute sets for counterfactual explanations
US 20220114451 A1 Lacewell; Chaunté W. et al.
METHODS AND APPARATUS FOR DATA ENHANCED AUTOMATED MODEL GENERATION
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/S.Z/Examiner, Art Unit 3715 July 25, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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