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 .
Claim Objections
Claims 3-4, 7-8, 11-12, 15-16 and 19-20 are objected to because of the following informalities: claim 3 recite “wherein the relationship is one of the following… a nominal value, and a differential value”, also claim 4 recite, “wherein the unstructured data text comprises at least one of the following:… computer code, and Optical Character Recognized (OCRed) text images”, where it should be claim 3 recite “wherein the relationship is one of the following… a nominal value, or a differential value”, also claim 4 recite, “wherein the unstructured data text comprises at least one of the following:… computer code, or Optical Character Recognized (OCRed) text images”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the term “a computer readable storage medium” can be directed to a transitory signal, carrier wave, or similar embodiment capable of storing information.
Claims 17-20 would be directed to an appropriate article of manufacture within the meaning of 35 U.S.C. 101 if the media would only reasonably be interpreted by one of ordinary skill in the art as covering embodiments which are articles produced from raw or prepared materials and which are structurally and functionally interconnected to the program in such a manner as to enable the program to act as a computer component and realize its functionality.
Regarding Claim 17, regarding the claimed a computer readable storage medium, under a recent precedential opinion, the scope of the recited “a computer readable storage medium” encompasses transitory media such as signals or carrier waves, where, as here the Specification does not limit the computer-readable storage media to non-transitory forms. See Ex parte Mewherter, 107 USPQ2d 1857, 1862. The claim in using the term “a computer readable storage medium”, in Applicants’ Specification, allows for the a computer readable storage medium to be signals.
Applicants are advised to amend the claim by prefacing the term “a computer readable storage medium” in claim 8 with “non-transitory”, , to recite “non-transitory a computer readable storage medium ". This would render Claim 17 statutory under 35 U.S.C. 101 based on the latest guidance available to the examiner.
Regarding Dependent Claims 18-20, fail to cure the deficiency of independent Claim 17, and therefore are also rejected under 35 USC § 101 as being directed to non-statutory subject matter for the same reason addressed above.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 1.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a system, which is a machine.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“inferring from the unstructured data text the topic; filtering the unstructured data text to identify passages mentioning the topic; instructing (Mental processes- concept of observation and evaluation for identifying information topics from texts and summarizing to organizing knowledge, wherein the execution of the LLM is merely using the computer as a tool to implement an abstract idea).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application.
“receiving unstructured data text, the unstructured data text including a mention to a topic;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform acts; and instructing execution of a Large Language Model (LLM) for the passages mentioning the topic to infer knowledge of at least one entity associated with the topic”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “instructing
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 3.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “wherein the relationship is one of the following: a role, an attribute, a sentiment, a binary value, a relevance value, a nominal value, and a differential value”: This limitation merely further limiting the abstract idea of claim 1.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 4.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “wherein the unstructured data text comprises at least one of the following: text, charts, spreadsheets, messages, computer code, and Optical Character Recognized (OCRed) text images”: This limitation merely further limiting the abstract idea of claim 1.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claims 5-8
Step 1: The claims recite a system; therefore, they fall into the statutory category of machines.
Step 2A Prong 1: The claims recite the same mental processes as claims 1-4, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis, mirrors that of claims 1-4, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, mirrors that of claims 1-4, respectively.
Claims 9-12
Step 1: The claims recite a method; therefore, they fall into the statutory category of process.
Step 2A Prong 1: The claims recite the same mental processes as claims 1-4, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis, mirrors that of claims 1-4, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, mirrors that of claims 1-4, respectively.
Claims 13-16
Step 1: The claims recite a method; therefore, they fall into the statutory category of process.
Step 2A Prong 1: The claims recite the same mental processes as claims 1-4, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis, mirrors that of claims 1-4, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, mirrors that of claims 1-4, respectively.
Claims 17-20
Step 1: The claims recite a computer program product comprising a computer readable storage medium; See rejection above.
Step 2A Prong 1: The claims 17-20 recite the same mental processes as claims 1-4, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Claims 17-20 recite generic computer components, namely “computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform operations”. As before, the mere recitation that the method is to be performed on a generic computer amounts to a mere instruction to apply the exception on the computer. See MPEP § 2106.05(f). With that exception, the analysis mirrors that of claims 1-4, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, with the one exception noted above, mirrors that of claims 1-4, respectively.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by West et al. (“Symbolic Knowledge Distillation: from General Language Models to Commonsense Models”, Proceedings of the 2022, Human Language Technologies, pages 4602- 4625).
Regarding claim 1.
West teaches a computing system, comprising: a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform acts (see page 4603, “Our proposed methodology parallels knowledge distillation (Hinton et al., 2015), a method for compressing a large or complicated teacher distribution Pt into a smaller/simpler student distribution Ps. Key to knowledge distillation2 is the notion of minimizing the cross-entropy between Pt and Ps:”) comprising:
receiving unstructured data text, the unstructured data text including a mention to a topic (see page 4606, section 3.3, “Generating ATOMIC inferences requires reasoning about events and relations together. We design verbalization templates for each relation, with iterative design and small-scale verification by the authors5 e.g. we prompt the xNeed relation as follows: What needs to be true for this event to take place? . . . Event : X goes jogging Prerequisites: For this to happen, X needed to wear running shoes . . . Event : X looks at flowers Prerequisites: For this to happen, The language of this template implies the relation specific task, both "Prerequisites:" and beginning with "for this to happen" suggest the xNeed relation. As well, we include an xNeed-specific . We use 10 few-shot examples for each prompt.”, also see page 4610, “Past automatic data mainly use extractive approaches, e.g. syntactic parsing (Zhang et al., 2020a) or pattern matching (Li et al., 2020) from unstructured text”);
inferring from the unstructured data text the topic (see page 4603, “We first go machine–to corpus (§3), by decoding from GPT-3, then improve our knowledge with a specialized critic model (§4), and finally distill this knowledge into an efficient commonsense model (§5), going corpus–to–machine.”, also see page 4604 figure 1, automatic inference generated for events/topic, also see page 4605, “3.2 Event Generation Events are context-free premises in ATOMIC involving PersonX (and sometimes a second PersonY) in various scenarios. These events form heads in knowledge graph triples. We generate events by filling in the elements of our template: 1. Event: X overcomes evil with good 2. Event: X does not learn from Y . . . 10. Event: X looks at flowers 11. The format is simple, as events are generated un conditionally. We use 100 high-quality events from the ATOMIC20 20 corpus for our prompt, selected to avoid grammatical or logical errors, and minimize semantic overlap.”);
filtering the unstructured data text to identify passages mentioning the topic (see page 4604, “We demonstrate symbolic knowledge distillation on the ATOMIC if-then resource (Sap et al., 2019). This follows an event-relation-inference (triple) for mat. The corpus links events (e.g. X attacks Y) to relations, e.g. Hindered By which describes what might hinder an event. For a relation/event, the goal is to generate a resulting inference, e.g. X attacks Y Hindered By X is restrained. Of the 23 relations from the most recent version ATOMIC20 20–we limit our investigation to 7 relations that correspond to causal commonsense knowledge: xAttr (how X is perceived after event), xReact (how X reacts in response to event), xEffect (what X does after event), xIntent (X’s intent in event), xWant (what X wants after event), xNeed (what X needed for event to take place) and Hindered By. We describe how verbalization is applied to ATOMIC data in 2 steps: generating underlying events (heads), then full examples (inference given event)”, also see section 3.2 and 3.3, “Generating ATOMIC inferences requires reasoning about events and relations together. We design verbalization templates for each relation, with iterative design and small-scale verification by the authors5 e.g. we prompt the xNeed”, also see page 4608, “While the loose teacher (GPT-3 alone) results in a viable commonsense knowledge graph, evaluation shows this isn’t a perfect commonsense teacher. Thus, we multiply in a critic model, to filter lower-quality knowledge, correcting the teacher (§2.1). With modest supervision (a small-scale hu man evaluation) we train a classifier to predict and discriminate unacceptable examples. We multiply this with the loose teacher §3, creating a critical teacher product of experts. In practice this means filtering ATOMIC10x to create new corpora that are higher quality, yet still larger scale than human authored ATOMIC…What gets filtered out? We qualitatively identify two types of filtered triples: 1) logical misalignments, events/inferences joined in an inconsistent manner. Recognizing these requires understanding events-inference interactions, e.g., X cannot find his shirt as a result X is wearing a shirt; 2) awkward phrasings, in which events/inferences are individually incoherent e.g. PersonX has a fire in the bath–resulting triples are invalid as the event is implausible…”);
instructing execution of a Large Language Model (LLM) for the passages mentioning the topic to infer knowledge of at least one entity associated with the topic (see page 4608, “Symbolic knowledge distillation requires a strong teacher model to maximize the quality of the generated knowledge graph and resulting student model (§5). While the loose teacher (GPT-3 alone) results in a viable commonsense knowledge graph, evaluation shows this isn’t a perfect commonsense teacher. Thus, we multiply in a critic model, to filter lower-quality knowledge, correcting the teacher (§2.1)…We find GPT-3 produces both independent awkwardly-phrased events/inferences (filtered by X-only models) and logical misalignments. The classifier, trained on validated knowledge triples, helps in both cases. The EMAP of our full model (identifies only awkward phrasings) achieves 87% AP, and our full model (which additionally identifies logical misalignments) improves to 94% AP.”);
and distilling the inferred knowledge of the at least one entity associated with the topic into distilled inferred knowledge (see page 4605, “Symbolic knowledge distillation begins by going machine–to–corpus, i.e. generating many commonsense facts, which results in a commonsense knowledge graph. §2.1 frames this as sampling to estimate the knowledge distillation objective–a student commonsense model learns from the generations of a teacher (GPT-3).”, also see page 4609, “The final step of symbolic knowledge distillation trains a compact model on the generated natural language knowledge graph. Our base model is GPT2-XL trained on all of ATOMIC10x: we denote this model by COMETDIS TIL . We additionally train the model on critical versions of ATOMIC10x–critlow denotes training on the corpus achieving 91.5% ac curacy, and crithigh on the 96.4% accuracy corpus. Models are trained for 1 epoch, with default parameters using the Hugging face Transformers library”).
Regarding claim 2.
West discloses the computing system of claim 1, wherein:
West further discloses instructing execution of the LLM for the passages mentioning the topic and the entity further infers a relationship between the at least one entity and the topic (see page 4604, “We demonstrate symbolic knowledge distillation on the ATOMIC if-then resource (Sap et al., 2019). This follows an event-relation-inference (triple) for mat. The corpus links events (e.g. X attacks Y) to relations, e.g. Hindered By which describes what might hinder an event. For a relation/event, the goal is to generate a resulting inference, e.g. X attacks Y Hindered By X is restrained. Of the 23 relations from the most recent version ATOMIC20 20–we limit our investigation to 7 relations that correspond to causal commonsense knowledge: xAttr (how X is perceived after event), xReact (how X reacts in response to event), xEffect (what X does after event), xIntent (X’s intent in event), xWant (what X wants after event), xNeed (what X needed for event to take place) and Hindered By. We describe how verbalization is ap plied to ATOMIC data in 2 steps: generating under lying events (heads), then full examples (inference given event)”, also see section 3.2 and 3.3, “Generating ATOMIC inferences requires reasoning about events and relations together. We design verbalization templates for each relation, with iterative design and small-scale verification by the authors5 e.g. we prompt the xNeed”); and
distilling the inferred knowledge further includes distilling the relationship between the at least one entity and the topic (see page 4605, “Symbolic knowledge distillation begins by going machine–to–corpus, i.e. generating many commonsense facts, which results in a commonsense knowledge graph. §2.1 frames this as sampling to estimate the knowledge distillation objective–a student commonsense model learns from the generations of a teacher (GPT-3).”, also see page 4609, “The final step of symbolic knowledge distillation trains a compact model on the generated natural language knowledge graph. Our base model is GPT2-XL trained on all of ATOMIC10x: we denote this model by COMETDIS TIL . We additionally train the model on critical versions of ATOMIC10x–critlow denotes training on the corpus achieving 91.5% ac curacy, and crithigh on the 96.4% accuracy corpus. Models are trained for 1 epoch, with default parameters using the Hugging face Transformers library”).
Regarding claim 3.
West discloses the computing system of claim 2, wherein:
West further discloses wherein the relationship is one of the following: a role, an attribute, a sentiment, a binary value, a relevance value, a nominal value, and a differential value (see page 4604, “We demonstrate symbolic knowledge distillation on the ATOMIC if-then resource (Sap et al., 2019). This follows an event-relation-inference (triple) for mat. The corpus links events (e.g. X attacks Y) to relations, e.g. Hindered By which describes what might hinder an event. For a relation/event, the goal is to generate a resulting inference, e.g. X attacks Y Hindered By X is restrained. Of the 23 relations from the most recent version ATOMIC20 20–we limit our investigation to 7 relations that correspond to causal commonsense knowledge: xAttr (how X is perceived after event), xReact (how X reacts in response to event), xEffect (what X does after event), xIntent (X’s intent in event), xWant (what X wants after event), xNeed (what X needed for event to take place) and Hindered By. We describe how verbalization is applied to ATOMIC data in 2 steps: generating underlying events (heads), then full examples (inference given event)”, also see section 3.2 and 3.3, “Generating ATOMIC inferences requires reasoning about events and relations together. We design verbalization templates for each relation, with iterative design and small-scale verification by the authors5 e.g. we prompt the xNeed”).
Regarding claim 4.
West discloses the computing system of claim 1, wherein:
West further discloses wherein the unstructured data text comprises at least one of the following: text, charts, spreadsheets, messages, computer code, and Optical Character Recognized (OCRed) text images (see page 4606, section 3.3, “Generating ATOMIC inferences requires reasoning about events and relations together. We design verbalization templates for each relation, with iterative design and small-scale verification by the authors5 e.g. we prompt the xNeed relation as follows: What needs to be true for this event to take place? . . . Event : X goes jogging Prerequisites: For this to happen, X needed to wear running shoes . . . Event : X looks at flowers Prerequisites: For this to happen, The language of this template implies the relation specific task, both "Prerequisites:" and beginning with "for this to happen" suggest the xNeed relation. As well, we include an xNeed-specific . We use 10 few-shot examples for each prompt.”, also see page 4610, “Past automatic data mainly use extractive approaches, e.g. syntactic parsing (Zhang et al., 2020a) or pattern matching (Li et al., 2020) from unstructured text”).
Claims 5-8 recites similar limitation to the recited limitation of claims 1-4. Therefore the rejection of claims 1-4 above applies equally here.
Claims 9-12 recites a method to perform the system recited in claims 1-4. Therefore the rejection of claims 1-4 above applies equally here.
Claims 13-16 recites a method to perform the system recited in claims 1-4. Therefore the rejection of claims 1-4 above applies equally here.
Claims 17-20 recites a computer program product to perform the system recited in claims 1-4. Therefore the rejection of claims 1-4 above applies equally here.
Related prior art:
Huang et al. (“LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking”, MM’22, October 10–14, 2022, pages 4083-4091) teaches LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross modal alignment by predicting whether the corresponding image patch of a text word is masked. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre trained model for both text-centric and image-centric Document AI tasks.
WU et al. (US 20220398271 A1) teaches generates a query that references an entity based upon an ontology of a knowledge graph and a query pattern and identifies at least one passage from amongst a plurality of passages stored in a passage repository based upon the query and at least one ranking model. The computing system identifies potential answers to the query based upon the at least one passage, the query, and a machine reading comprehension model.
Conclusion
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/IMAD KASSIM/Primary Examiner, Art Unit 2129
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