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 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.
Claim 20 is rejected under 35 U.S.C. 101 because the claim is drawn to a “signal” per se as recited in the preamble an as such is non-statutory subject matter. In paragraph [0080] of the as filed specification, the term “computer-readable storage device” is not defined as to what the scope of the term is meant to encompass. Hence, one of ordinary skilled in the art can interpret such a term to include transitory signals and non-transitory signals. It does not appear that a claim reciting a signal encoded with functional descriptive material falls within any of the categories of patentable subject matter set forth in § 101. First, a claimed signal is not a “process” under § 101 because it is not a series of steps. The other three § 101 classes of machine, compositions of matter and manufactures “relate to structural entities and can be grouped as ‘product’ claims in order to contrast them with process claims”. 1 D. Chisum, Patents § 102 (1994) . The applicant’s as filed specification presents a broad definition as to what the computer-readable storage device covers and is being made to include transitory and non-transitory signals. The applicant’s as filed specification in paragraph [0080] refers to the “storage device”. Hence, it appears that claims appear to be drawn towards transitory signals, which is not subject matter eligible. In order to overcome the present rejection, the applicant is advised to ament the claims by using the following terminology: “non-transitory computer-readable storage device”. Such example terminology has been also found in the official Gazette 1351 OG 212.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 14, and 20 relate to the statutory category of method/process and machine/apparatus. The independent claims 1, 14, and 20 recite “receiv(ing) an explanation request comprising a feature and a machine learning (ML) prediction corresponding to the feature; obtain(ing) context information based on the request; generat(ing), using a first generative ML model instance applied to the feature and the ML prediction, at least two response variations; determin(ing), using a second generative ML model instance applied to the context information and the at least two response variations, a ranking of the at least two response variations according to relevance; determin(ing) an explanation of the prediction based on the ranking of the at least two response variations; and perform(ing) a physical and/or logical operation based on the explanation.
The limitations of claims 1, 14, or 20 of “receiv(ing)…”, “obtain(ing)…”, “generat(ing)…”, “determin(ing)…”, “determin(ing)…”, and, “perform(ing)…” as drafted covers mental activity. More specifically, for claim 1, a human, after receiving a request for a prediction, can come up with at least two alternative predictions for the topic being asked about. The human knowing from previous experience, how the different predictions will rank. The human will then perform functionality based on the ranking and why the prediction was selected.
This judicial exception is not integrated into a practical application. In particular, claims 14 and 20 recite the additional elements of “processing unit”, “memory”, and “processor” which are recited generally in the specification. For example, in paragraph [0047] of the as filed specification, there is a description of using a general purpose computing system. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Also, the additional element of “generative ML model” in claims 1, 14, and 20 are recited generally in the specification. For example, in paragraph [0015] of the as filed specification, there are examples of a generative ML model. However, the structure disclosed in the specification has not been incorporated into the claims. Without the structure, the additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do 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 using a computer as a general computer is noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2 and 15, the claims relate to selecting the selecting the ML model. The claim relates to a mental activity of how the alternative predictions are determined. No additional limitations are present.
With respect to claims 3 and 16, the claims relate to performing the function determined by the prediction and why the prediction was selected. The claims relate to a mental activity of determining why function needs to be performed and performing the function.
With respect to claims 4 and 17, the claims relate to modifying the prediction. The claims relate to a mental activity of changing the prediction based on what the parameters are. No additional limitations are present.
With respect to claims 5-7, 18, and 19, the claims relate to preventing an unauthorized function. The claims relate to a mental activity of making sure that unauthorized access is not given based on the prediction. No additional limitations are present.
With respect to claim 8, the claim relates to determining alternative predictions and why. The claim relates to a mental activity of why and how there are more than one prediction. No additional limitations are present.
With respect to claims 9 and 10, the claims relate to determining if the highest ranking relevant response meets the criteria. If not, determine two more responses. The claims relates to a mental activity of going thru the responses until they meet the criteria. No additional limitations are present.
With respect to claim 11, the claim relates to determining the similarity between the ranked responses. The claim relates to a mental activity of determining how the ranked responses are related and similar to each other. No additional limitations are present.
With respect to claims 12 and 13, the claims relate to how to generate the relevant responses. The claims relate to a mental activity of how the relevant responses are presented to the user. No additional limitations are present.
Allowable Subject Matter
Claims 1-20 would be allowed if the 35 USC 101 rejections above are overcome.
The following is a statement of reasons for the indication of allowable subject matter: Claims 1, 14, and 20 of the current application teach similar subject matter as the prior art of Araujo et al. (US 10,733,292), Kursun (US 2021/0042420), and Shao et al. (US 2022/0400131). The prior art alone or in combination teaches “receiving an explanation request comprising a feature and a machine learning (ML) prediction corresponding to the feature; obtaining context information based on the request; generating, using a first generative ML model instance applied to the feature and the ML prediction, at least two response variations; and performing a physical and/or logical operation based on the explanation” as recited in claims 1 and 20 and “a processing unit; a memory coupled to the processing unit and configured to store executable instructions which, upon execution by the processing unit, are configured to cause the processing unit to: receive an explanation request comprising a feature and a machine learning (ML) prediction corresponding to the feature; obtain context information based on the request; generate, using a first generative ML model instance applied to the feature and the ML prediction, at least two response variations; and perform a physical and/or logical operation based on the explanation”.
Araujo et al teaches “These requests may be provided as structured or unstructured request messages, natural language questions, or any other suitable format for requesting an operation to be performed by the cognitive system. In some illustrative embodiments, the requests may be in the form of input data sets that are to be classified in accordance with a cognitive classification operation performed by a machine learning, neural network, deep learning, or other artificial intelligence based model that is implemented by the cognitive system. The input data sets may represent various types of input data depending upon the particular implementation, such as audio input data, image input data, textual input data, or the like” (col. 15, lines 5-13) and “In general, such cognitive systems are able to perform various ones and/or combinations of the following functions: (1) navigate the complexities of human language and understanding; (2) ingest and process vast amounts of structured and unstructured data; (3) generate and evaluate hypothesis; (4) weigh and evaluate responses that are based only on relevant evidence; (5) provide situation-specific advice, insights, and guidance; (6) improve knowledge and learn with each iteration and interaction through machine learning processes; (7) enable decision making at the point of impact (contextual guidance); (8) scale in proportion to the task; (9) extend and magnify human expertise and cognition; (10) identify resonating, human-like attributes and traits from natural language; (11) deduce various language specific or agnostic attributes from natural language; (12) high degree of relevant recollection from data points (images, text, voice) (memorization and recall); (13) predict and sense with situational awareness that mimic human cognition based on experiences; and (14) answer questions based on natural language and specific evidence” (col. 17, lines 10-31).
Kursun teaches “In the first phase, the system may use an unsupervised learning model to extract common and emerging patterns. In the second phase, the system may use a supervised learning and/or machine learning model to extract a sequence of events. The system may further establish correlations and relationships between both transactional and non-transactional events and patterns. For instance, the system may detect an emerging unauthorized access pattern by integrating and/or correlating client comments (e.g., regarding compromised authentication credentials) with a suspicious login, unauthorized transaction, use of a particular attack vector, or the like. Once the known and emerging threat patterns have been identified, the system may update and/or revise its processes to address the threat (e.g., patch exploits, use different encryption standards, or the like) (page 3, paragraph [0041]).
Shao et al. teaches “In some embodiments, risk interpretation module 130 employs a trained machine learning model such as a Bidirectional Encoder Representations from Transformers (BERT) model to read text inputs and produce predictions (i.e., classifications of risk dimensions and severity levels of risk) for risk reports” (page 4, paragraph [040]) and “Given a text description of issues or warmings, which can be obtained from risk reports, risk mitigation module 132 may encode the natural language descriptions into a machine-understandable format that can be used to infer problem symptoms, causes, troubleshooting activities, and resolution actions. In some embodiments, risk mitigation module 132 employs a generative adversarial network (GAN) for generating natural language suggestions for actions to mitigate risk” (page 4, paragraph [0041]).
The prior art alone or in combination fails to teach “determining, using a second generative ML model instance applied to the context information and the at least two response variations, a ranking of the at least two response variations according to relevance; determining an explanation of the prediction based on the ranking of the at least two response variations” as recited in claims 1 and 20 and “determine, using a second generative ML model instance applied to the context information and the at least two response variations, a ranking of the at least two response variations according to relevance; determine an explanation of the prediction based on the ranking of the at least two response variations” as recited in claim 14.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Endler (US 2020/0279050) discloses generating and monitoring fictitious data entries to detect breaches.
Pandey et al. (US 11,289,075) discloses routing of natural language inputs to speech processing applications.
Agarwal et al. (US 2022/0156489) discloses machine learning techniques for identifying logical sections in unstructured data.
Jeong et al. (US 2022/0197923) discloses building big data on unstructured cyber threat information analyzing unstructured cyber threat information.
Rao et al. (US 2022/0198156) discloses predictive monitoring of a software application framework.
Sengupta et al. (US 2023/0061731) discloses significance based prediction from unstructured text.
Conclusion
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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