CTNF 18/748,681 CTNF 81209 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1–11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if: STEP 1 : the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or STEP 2 : the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: STEP 2A (PRONG 1) : Does the claim recite an abstract idea, law of nature, or natural phenomenon? STEP 2A (PRONG 2) : Does the claim recite additional elements that integrate the judicial exception into a practical application? STEP 2B : Does the claim recite additional elements that amount to significantly more than the judicial exception? Using the two-step inquiry, it is clear that the claims are directed toward non-statutory subject matter, as shown below: STEP 1 : Do the claims fall within one of the statutory categories ? Yes. All claims fall within a statutory category under § 101. Claim 1: method Claims 10 and 11: Device and non-transitory computer-readable storage. STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea ? Yes, the claims are directed to an abstract idea. With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas: Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion). Claims 1-11 are a mathematical concept, that is mathematical relationships, mathematical formulas or equations, mathematical calculations and, therefore, an abstract idea. With regard to independent claims 1 and 10-11, the method/computer-readable media (or computer implemented functionality) recites the steps of: Claims 1 and 10-11 are directed to the abstract idea of collecting, organizing, and analyzing relational data (nodes, edges, names, and queries) to generate an output. Mental Process/Mathematical Concept: The steps of recognizing a node, an edge, or a name, and determining an expression associated with them, constitute data manipulation that can be performed in the human mind or by a human with pen and paper. "Method for Machine Learning": Merely reciting "machine learning" or a "model" is insufficient. The claim describes using generic models ("a first model," "a second model") to "recognize" and "determine expressions," which are functional, high-level descriptions of data processing rather than a specific improvement in the functionality of the computer or the AI algorithm itself. Abstract Input/Output: The input (digital image or symbolic graph description) and the output (an answer to a question) are data-driven, representing a business method or analytical process rather than a physical technical solution. These limitations, under their broadest reasonable interpretation, cover applying mathematical algorithms and/or calculations. The use of a computer or processing device include no more than applying the exception using a generic computer or computer component. The limitations are not directed to an improvement in the computer itself or a computer component and therefore cannot provide an inventive concept. STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application ? No, the claim does not recite additional elements that integrate the judicial exception into a practical application. With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application: an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; an additional element effects a transformation or reduction of a particular article to a different state or thing; and an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Even though the claims are implemented via machine learning models (which generally require a computer), they do not integrate the abstract idea into a practical application, as the claim does not impose a meaningful limit on the abstract idea. No Specific Technical Problem/Solution: The claims are generic. They do not improve the functioning of the computer, nor do they solve a technical challenge in the field of graph analysis (e.g., improving model latency, accuracy, or reducing training data). Applying Generic ML to New Data Environment: generic machine learning techniques to new data environments (a graph) is insufficient. The steps of recognizing, determining, and answering—are functional steps. The "Model" is a "Black Box": The claim says "a first/second/third model" that is "configured to" do X. This is simply using a conventional model as a black box tool, which does not transform the abstract idea into a patent-eligible application STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception. With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements: adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present. The following computer functions have been recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality): receiving or transmitting data over a network. See MPEP 2106.05(d)(II). The claim fails to include an inventive concept that is "significantly more" than the abstract idea itself. Generic Machine Learning Models: The claim relies on "a first model," "a second model," and "a third model" without defining any technical improvement to the models themselves (e.g., a new neural network architecture, a novel training method, or optimized memory usage). The Supreme Court and Federal Circuit have recently affirmed that merely applying conventional machine learning to new data environments (e.g., graphs/images instead of text) is patent ineligible. Result-Oriented Functional Language: The claim describes what the models do (recognize, determine, associate) rather than how they do it (the technical implementation). The steps merely describe the result of receiving input, performing standard association, and outputting an answer. Routine Data Manipulation: The steps of associating a name with a node and detecting an edge between nodes, even with a model, are routine and conventional data manipulation tasks. The dependent claims do not overcome the 101 rejection. As such, the claims are rejected under 101. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Liang (GraphVQA: Language-Guided Graph Neural Networks for Scene Graph Question Answering) in view of Zheng (Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition) . Regarding claim 1, Liang teaches a computer-implemented method for machine learning, comprising the following steps (abstract): providing an input in the form of a digital image or a symbolic description of a graph, wherein the input includes a first node, a first name, a second node, a second name, and an edge between the first node and the second node (see figure 1 and section 1); determining, with a first model, depending on the input, the edge with the first node and the second node, wherein the first model is configured to recognize that the edge is between the first node and the second node wherein the first model is associates the edge with the first node and the second node depending on the input; (see section 3.2, embedding edge features with edge type and eij connected node I to node j), determining, with a second model, depending on the input, associates the first node with the first name and associates the second node with the second name, wherein the second model is configured to recognize the first name and that the first name is associated to the first node, wherein the second model is configured to recognize the second name and that the second name is associated with the second node, wherein the second model is configured to determine associates the first node with the first name and that associates the second node with the second name depending on the input (see section 3.2, associating node names with nodes by using word embeddings of the object name and attributes); providing a question that includes the first name and the second name (figure 1, section 1); determining, with a third model, depending on the question, the first name and the second name, wherein the third model is configured to recognize the first name and the second name in the question and to determine includes the first name and the second name depending on the question; and determining an answer to the question depending on the expressions (section 3.1, question parsing module and answers are provided in section 1). Liang does not expressly teach an expression from the question. Zheng teaches an expression from the question (see section 2.1, each edge is called a triple and vertices of the graph). It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Liang the ability to represent relationships in a graph using expressions as taught by Zheng. The reason is to allow the system to have explicit relationships in written format. Regarding claims 10-11, see the rejection of claim 1. Allowable Subject Matter Claims 2-9 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. The dependent claims have features that are not found in the prior art, including using electrical connections to help determine the state. Also the specifics of the type of questions is not found in the prior art. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HADI AKHAVANNIK whose telephone number is (571)272-8622. The examiner can normally be reached 9 AM - 5 PM Monday to Friday. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HADI AKHAVANNIK/Primary Examiner, Art Unit 2676 Application/Control Number: 18/748,681 Page 2 Art Unit: 2676 Application/Control Number: 18/748,681 Page 3 Art Unit: 2676 Application/Control Number: 18/748,681 Page 4 Art Unit: 2676 Application/Control Number: 18/748,681 Page 5 Art Unit: 2676 Application/Control Number: 18/748,681 Page 6 Art Unit: 2676 Application/Control Number: 18/748,681 Page 7 Art Unit: 2676 Application/Control Number: 18/748,681 Page 8 Art Unit: 2676 Application/Control Number: 18/748,681 Page 9 Art Unit: 2676 Application/Control Number: 18/748,681 Page 10 Art Unit: 2676
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