DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-25 are presented for examination on the merits.
Examiner’s Note
2. Claim 10-17, recites “a computer program product comprising a computer readable program stored on a computer readable storage medium …” Applicant clarifies in Paragraph 0047 as “A computer readable storage medium, as that term is used in the present invention, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.” Therefore, examiner confirms that the definition provided in applicant’s disclosure excludes transitory mediums.
1ids Information Disclosure Statement
3. The Information Disclosure Statement (IDS) submitted on 03/25/2024 has been fully considered except Item 7 (see line through). Item 7 reference publication date has not been provided.
Claim Rejections - 35 USC § 102
4. 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.
5. 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)(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.
6. Claims 1-6, 8-14, 17-19 and 21-23 rejected under 35 U.S.C. 102(a)(2) as being anticipated by Peng (WO 2024123664 A1)
As to claim 1, Peng discloses in confusion matrix estimation in distributed computation environments having claimed:
a. a computer-implemented method comprising: executing a machine learning (ML) model operable to perform a ML task comprising generating a ML output responsive to a ML input read on ¶ [0191] Figure 5 is a block diagram of an example processing flow for using machine- learned model(s) 1 to process input(s) 2 to generate output(s) 3. Machine-learned model(s) 1 can be or include, for instance, a prediction model of prediction system 102;
b. wherein the ML output comprises encoded domain information associated with a domain read on ¶ 0194, (For example, some example sequence processing models in the text domain are referred to as “Large Language Models,” or LLMs. See, e.g., PaLM 2 Technical Report, GOOGLE, https://ai.google/static/documents/palm2techreport.pdf (n.d.). Other example sequence processing models can operate in other domains, such as image domains, see, e.g., Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ARXIV:2010.11929v2 (Jun.3, 2021), audio domains, see, e.g., Agostinelli et al., MusicLM: Generating Music From Text, ARXIV:2301.11325v1 (Jan.26, 2023), biochemical domains, see, e.g., Jumper et al., Highly accurate protein structure prediction with AlphaFold, 596 Nature 583 (Aug.26, 2021), by way of example. Sequence processing model(s) can process one or multiple types of data simultaneously. Sequence processing model(s) can include relatively large models (e.g., more parameters, computationally expensive, etc.), relatively small models (e.g., fewer parameters, computationally lightweight, etc.), or both);
c. wherein the encoded domain information is encoded in a computer-code-based domain-specific data structure read on ¶ [0200] An output sequence can include one or multiple portions or elements. In an example content generation configuration, an output sequence can include multiple elements corresponding to multiple portions of a generated output sequence (e.g., a textual sentence, values of a discretized waveform, computer code, etc.). In an example classification configuration, an output sequence can include a single element associated with a classification output. For instance, an output “vocabulary” can include a set of classes into which an input sequence is to be classified. For instance, a vision transformer block can pass latent state information to a multilayer perceptron that outputs a likely class value associated with an input image; and
d. wherein the ML task is associated with the domain read on ¶ [0194] Machine-learned model(s) 1 can include a sequence processing model. Sequence processing model(s) can include one or multiple machine-learned model components configured to ingest, generate, or otherwise reason over sequences of information. For example, some example sequence processing models in the text domain are referred to as “Large Language Models,” or LLMs. See, e.g., PaLM 2 Technical Report, GOOGLE, https://ai.google/static/documents/palm2techreport.pdf (n.d.). Other example sequence processing models can operate in other domains, such as image domains, see, e.g., Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ARXIV:2010.11929v2 (Jun.3, 2021), audio domains, see, e.g., Agostinelli et al., MusicLM: Generating Music From Text, ARXIV:2301.11325v1 (Jan.26, 2023), biochemical domains, see, e.g., Jumper et al., Highly accurate protein structure prediction with AlphaFold, 596 Nature 583 (Aug.26, 2021), by way of example. Sequence processing model(s) can process one or multiple types of data simultaneously).
As to claim 2, Peng further discloses:
a. wherein the ML input comprises pre-encoded domain information read on ¶ [0202] Input(s) 2 can generally include or otherwise represent various types of data. Input(s) 2 can include one type or many different types of data. Output(s) 3 can be data of the same type(s) or of different types of data as compared to input(s) 2. Output(s) 3 can include one type or many different types of data. [0203] Example data types for input(s) 2 or output(s) 3 include natural language text data, software code data (e.g., source code, object code, machine code, or any other form of computer-readable instructions or programming languages), machine code data (e.g., binary code, assembly code, or other forms of machine-readable instructions that can be executed directly by a computer's central processing unit), assembly code data (e.g., low-level programming languages that use symbolic representations of machine code instructions to program a processing unit), genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values, such as voltage or other absolute or relative level measurement values from a real or artificial input, such as from an audio sensor, light sensor, displacement sensor, etc.), and the like. Data can be raw or processed and can be in any format or schema).
As to claim 3, Peng further discloses:
a. wherein: the pre-encoded domain information comprises a natural language question comprising a natural language data structure; and the ML output is responsive to the natural language question of the ML input read on ¶ [0202] Input(s) 2 can generally include or otherwise represent various types of data. Input(s) 2 can include one type or many different types of data. Output(s) 3 can be data of the same type(s) or of different types of data as compared to input(s) 2. Output(s) 3 can include one type or many different types of data. [0203] Example data types for input(s) 2 or output(s) 3 include natural language text data, software code data (e.g., source code, object code, machine code, or any other form of computer-readable instructions or programming languages), machine code data (e.g., binary code, assembly code, or other forms of machine-readable instructions that can be executed directly by a computer's central processing unit), assembly code data (e.g., low-level programming languages that use symbolic representations of machine code instructions to program a processing unit), genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values, such as voltage or other absolute or relative level measurement values from a real or artificial input, such as from an audio sensor, light sensor, displacement sensor, etc.), and the like. Data can be raw or processed and can be in any format or schema).
As to claim 4, Peng further discloses:
a. wherein the ML model comprises a large language model (LLM) operable to understand a domain-specific syntax of the computer-code-based domain-specific data structure read on ¶ [0194] Machine-learned model(s) 1 can include a sequence processing model. Sequence processing model(s) can include one or multiple machine-learned model components configured to ingest, generate, or otherwise reason over sequences of information. For example, some example sequence processing models in the text domain are referred to as “Large Language Models,” or LLMs. See, e.g., PaLM 2 Technical Report, GOOGLE, https://ai.google/static/documents/palm2techreport.pdf (n.d.). Other example sequence processing models can operate in other domains, such as image domains, see, e.g., Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ARXIV:2010.11929v2 (Jun.3, 2021), audio domains, see, e.g., Agostinelli et al., MusicLM: Generating Music From Text, ARXIV:2301.11325v1 (Jan.26, 2023), biochemical domains, see, e.g., Jumper et al., Highly accurate protein structure prediction with AlphaFold, 596 Nature 583 (Aug.26, 2021), by way of example).
As to claim 5, Peng further discloses:
a. wherein the ML model comprises a generative model read on ¶ [0071] Prediction system 102 can generate predictions using one or more prediction models. A prediction model can be or include one or multiple machine-learned models. The prediction model can operate locally on prediction system 102. Local operation can reduce a latency of prediction. Local operation can reduce an amount of data to be transmitted over a network. For instance, if a prediction model is implemented on a centralized server, then inputs can be transmitted over a network to the server and outputs can be transmitted over a network from the server. In contrast, local deployment of a prediction model within an operational environment that already has access to the inputs can facilitate direct processing of the inputs and avoid some amount of additional network traffic. Advantageously, example implementations of the present disclosure can facilitate local operation of prediction models while allowing the prediction models to be evaluated with respect to data distributed over multiple systems without directly distributing the data itself).
As to claim 6, Peng further discloses:
a. wherein the encoded domain information comprises encoded synthetic data read on ¶ [0260] In some implementations, the task can be a data generation task. Machine- learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of data (e.g., data from various data domains, such as sensor data, image data, multimodal data, statistical data, etc.). The desired data can be, for instance, synthetic data for training other machine-learned models. The context can include arbitrary data type(s). Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent data that aligns with the desired data. For instance, machine-learned model(s) 1 can be configured to generate data values for populating a dataset. Values for the data object(s) can be selected based on the context (e.g., based on a probability determined based on the context)).
As to claim 8, the claim is interpreted and rejected a to claim 1.
As to claim 9, the claim is interpreted and rejected a to claims 3 and 4.
As to claim 10, the claim is interpreted and rejected a to claim 1.
As to claim 11, the claim is interpreted and rejected a to claims 3 and 4.
As to claim 12, Peng further discloses:
a. computer-implemented method comprising: executing a machine learning (ML) model operable to perform a ML task comprising generating a ML output responsive to a ML input; wherein the ML output comprises encoded domain information associated with a domain; wherein the encoded domain information is encoded in a domain-specific programming language data structure; wherein the ML model is operable to understand a domain-specific syntax of the domain-specific programming language data structure; and wherein the ML task is associated with the domain read on ¶ 200 & ¶ 203, (an output sequence can include one or multiple portions or elements. In an example content generation configuration, an output sequence can include multiple elements corresponding to multiple portions of a generated output sequence (e.g., a textual sentence, values of a discretized waveform, computer code, etc.). In an example classification configuration, an output sequence can include a single element associated with a classification output. For instance, an output “vocabulary” can include a set of classes into which an input sequence is to be classified. For instance, a vision transformer block can pass latent state information to a multilayer perceptron that outputs a likely class value associated with an input image. Example data types for input(s) 2 or output(s) 3 include natural language text data, software code data (e.g., source code, object code, machine code, or any other form of computer-readable instructions or programming languages), machine code data (e.g., binary code, assembly code, or other forms of machine-readable instructions that can be executed directly by a computer's central processing unit), assembly code data (e.g., low-level programming languages that use symbolic representations of machine code instructions to program a processing unit), genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values, such as voltage or other absolute or relative level measurement values from a real or artificial input, such as from an audio sensor, light sensor, displacement sensor, etc.), and the like. Data can be raw or processed and can be in any format or schema).
As to claim 13, Peng further discloses:
a. wherein the ML model is trained to perform the ML task using domain-specific training information encoded in the domain-specific programming language data structure read on ¶ 0206, (Figure 6 is a block diagram of an example model development platform 12 that can facilitate creation, adaptation, and refinement of example machine-learned models (e.g., machine-learned model(s) 1, sequence processing model(s), etc.). Model development platform 12 can provide a number of different toolkits that developer systems can employ in the development of new or adapted machine-learned models).
As to claim 14, Peng further discloses:
a. wherein the ML input comprises a natural language question having a natural language data structure read on ¶ 0260, (the task can be a data generation task. Machine- learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of data (e.g., data from various data domains, such as sensor data, image data, multimodal data, statistical data, etc.). The desired data can be, for instance, synthetic data for training other machine-learned models. The context can include arbitrary data type(s). Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent data that aligns with the desired data. For instance, machine-learned model(s) 1 can be configured to generate data values for populating a dataset. Values for the data object(s) can be selected based on the context (e.g., based on a probability determined based on the context)).
As to claim 17, Peng further discloses:
a. wherein: the ML model comprises a generative model; and the encoded domain information comprises encoded synthetic data read on ¶ 0260, (the task can be a data generation task. Machine- learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of data (e.g., data from various data domains, such as sensor data, image data, multimodal data, statistical data, etc.). The desired data can be, for instance, synthetic data for training other machine-learned models. The context can include arbitrary data type(s). Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent data that aligns with the desired data. For instance, machine-learned model(s) 1 can be configured to generate data values for populating a dataset. Values for the data object(s) can be selected based on the context (e.g., based on a probability determined based on the context)).
As to claim 18, the claim is interpreted and rejected a to claim 12.
As to claim 19, Peng further discloses:
a. wherein the multiple data structures are selected from a group consisting of tables, charts, images, and video read on ¶ 0202 & ¶ 0203 (input(s) 2 can generally include or otherwise represent various types of data. Input(s) 2 can include one type or many different types of data. Output(s) 3 can be data of the same type(s) or of different types of data as compared to input(s) 2. Output(s) 3 can include one type or many different types of data. Example data types for input(s) 2 or output(s) 3 include natural language text data, software code data (e.g., source code, object code, machine code, or any other form of computer-readable instructions or programming languages), machine code data (e.g., binary code, assembly code, or other forms of machine-readable instructions that can be executed directly by a computer's central processing unit), assembly code data (e.g., low-level programming languages that use symbolic representations of machine code instructions to program a processing unit), genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values, such as voltage or other absolute or relative level measurement values from a real or artificial input, such as from an audio sensor, light sensor, displacement sensor, etc.), and the like. Data can be raw or processed and can be in any format or schema).
As to claim 21, Peng further discloses:
a. wherein the ML model comprises a large language model (LLM) read on ¶ 0194, (Machine-learned model(s) 1 can include a sequence processing model. Sequence processing model(s) can include one or multiple machine-learned model components configured to ingest, generate, or otherwise reason over sequences of information. For example, some example sequence processing models in the text domain are referred to as “Large Language Models,” or LLMs. See, e.g., PaLM 2 Technical Report, GOOGLE, https://ai.google/static/documents/palm2techreport.pdf (n.d.). Other example sequence processing models can operate in other domains, such as image domains, see, e.g., Dosovitskiy et al., An Image is Worth 16x16 Words).
As to claim 22, the claim is interpreted and rejected a to claim 14.
As to claim 23, the claim is interpreted and rejected a to claim 17.
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. Claims 7, 16 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over
Peng in view of Wilcox (US 20250258875 A1).
As to claim 7, Peng does not explicitly disclose wherein the encoded domain information represents one or more new material designs.
However, Wilcox cures in accelerating and automating reading and research using artificial intelligence the deficiency by teaching that it may be beneficial wherein
a. The computer-implemented method of claim 5, wherein the encoded domain information represents one or more new material designs read on ¶ 0116, (a client system 101 and/or 3rd-party systems 140 may automate the generation of new educational material by sending an educational text corpus of all of the desired information for a student or students to learn to the ingestion process 122a. This ingestion process 122a breaks down the documents within the educational material into outlines and knowledge graphs and stores them in processed information storage 203 and associated information storage 123. The reorganization process 122c may generate new documents (optionally using querying process 122b, and either self-guided or guided by configurations from a client system 101 and/or 3rd-party systems 140) and sends the new documents back to client system 101 and/or 3rd-party systems 140 for educational use).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of invention to incorporate the conceptual calculator system and method of wilcox in view of peng in order to provide a conceptual calculator system and method that does not suffer from the complications and limitations of the known techniques.
As to claim 16, Wilcox further teaches:
a. wherein the ML model comprises a large language model (LLM) operable to understand a domain-specific syntax of the domain-specific programming language data structure read on ¶ 0095, ( FIG. 7 illustrates a detailed process flow diagram for converting a document (such as PDF/Text/HTML/Markdown 172, Audio 173, or Video 174) into a sequence of cited and referenceable sentences in machine-readable format useful for producing infinite outlines and knowledge graphs in a manner such that every piece of information can be linked back to its original source(s) without hallucination (The Verified Referenced Encoder, or VRE) 178. The VRE process 178 thus reduces hallucinations that may otherwise occur in AI and LLMs).
As to claim 24, Wilcox further teaches:
a. using a validation module to validate the synthetic data read on ¶ 0037, (These can then be traced to the outlines used to produce content via DIGEST, where they can be updated and then re-digested. These digested materials may then be ruminated upon to re-train the AI with the new knowledge, enabling it to stay up to date with as many synthetic examples as it needs to overcome its previous training. This provides a workaround for a lack of adaptability to new or evolving information).
Allowable Subject Matter
10. Claims 15, 20 and 25 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. However, an updated search will need to be performed after the next response from Applicant.
Citation of pertinent Prior Arts
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i. Fisher (US 20250307465 A1) discloses in a systems and methods for large language model (LLM)-agnostic data anonymization. Data anonymization includes data obfuscation (and data deobfuscation) to protect confidential information a user is going to send to an LLM service or application programming interface (API). Encryption can be used for data obfuscation and particularly, for securing data from unauthorized access. Likewise, decryption can be used for data de-obfuscation, and
ii. Yuan (US 20250292074 A1) discloses in a question is received at a first language machine learning model. In response to an external machine learning model providing a response to the question and a confidence determination for the response that exceeds a predetermined threshold, the response is injected into the first language machine learning model so that the response overwrites a vector state layer output of the first language machine learning model that provides another response to the question and without modifying original parameters of the first language machine learning model, where the external machine learning model was trained with training material with which the first language machine learning model was not trained.
Conclusion
12. If the claimed invention is amended, Applicant is respectfully requested to indicate the portion(s) of the specification, which dictate(s) the structure/description relied upon to assist the Examiner in proper interpretation of the amended language and also to verify and ascertain the metes and bounds of the claimed invention. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fekadeselassie Girma whose telephone number is (571) 270-5886. The examiner can normally be reached on Monday thru Friday, 8:30 – 5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached on (571) 272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Fekadeselassie Girma/
Primary Examiner Art Unit 2689
Prosecution Insights