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 .
This office correspondence is in response to the application filed on February 8, 2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDSs) submitted on 02/08/2024(2) was filed with the mailing date of the instant application on 02/08/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 they are directed to a judicial exception without significantly more.
Step 1 (Statutory Categories)
The four categories of statutory subject matter are: (1) a process, (2) a machine, (3) a manufacture and (4) a composition of matter. MPEP § 2106.03.
These claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. (2014).
Independent claims 1, 8, and 15, recite a series of steps and, therefore, is a process that are directed to the abstract idea because they cover the concepts of a mental process (process in the human mind) including grouping of certain methods of observing, organizing human activity. Hence, the steps in the independent claims fall within the mental process grouping of abstract idea.
Claims 1-20 are directed to a method, a system, and an apparatus, and the underlying invention is merely to provide notification and determine to switch modes, and is therefore an abstract idea (Analysis: Step 2A-Prong 1). The claimed invention is not directed to patent eligible subject matter. Based upon consideration of all of the relevant factors with respect to the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The underlying invention is merely providing services and notification and determine to switch modes, and is therefore an abstract idea. The claim recites the limitation of generating a notification for a computing environment. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are merely instructions to implement the abstract idea and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry (e.g. generating, determining, and modification of related data). There is nothing in the claim element precludes the step from practically being performed in the mind. For example, transmitting information indicating properties of the computing resources, the claim encompasses simply transmitting information of resources in his/her mind. The mere nominal recitation of a generic performance and does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
The claim recites additional elements of determining to switch from single mode to grouped mode, generating group of notifications, and followed by modification step. The claims do not recite any limitations that improve the functioning of a computer or to any other technology or technical field. The generating step is recited at a high level of generality (i.e., as a general means of gathering notification resources to use in the determining and modification step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation is no more than mere instructions to apply the exception using a generic computer. Subject Matter Eligibility Examples: Abstract Ideas 2019-01-07 13 The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component. Accordingly, even in combination, 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 (2A – Prong 2).
Therefore, claim fails to provide an inventive concept (2B). As discussed with respect to Step 2A Prong 2, the additional element in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B. Here, the receiving step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the example does not provide any indication other than a generic, off the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐ understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the generating, determining, and modification steps are well-understood, routine, conventional activity is supported under Berkheimer Option 2. For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
Claims 2-7, 9-14, and 16-20 recites further collection of properties of the computing resources. The information collected do not add any significant more to the Judicial Exception as they do not add any improvement to the computer system or a technology field. Hence, the claims do not add significant more.
In light of the explanation and evidence provided above, the Examiner asserts that the claimed invention is directed in view of those case laws are directed towards the abstract idea. Lacking significantly more for the remainder of the claim, the invention is nothing more than an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. (US Publication 2025/0156898), in view of Kozhaya et al. (US Publication 2023/0289276)
As per claim 1, Crabtree discloses a network computer-implemented method comprising: generating, by a first machine learning model, a single notification for a computing environment (paragraphs 86, 114-115, 209: a notification service: receive alerts during operation); in response to receiving user responses to a string of the single notification and other single notifications for the computing environment, determining to switch from a single mode to a group mode (paragraphs 114-116, 209: group focused preferences in machine learning models); based on the user responses to the string of the single notification and the other single notifications for the computing environment, generating, by one or more machine learning model, a group of notifications for the computing environment (paragraphs 158-160); and causing at least one modification to the computing environment in accordance with at least one affirmative user response to the group of notifications (paragraphs 111, 209, 418: modification to meet the preferences of users or to specific context from a session). Although, Crabtree discloses AI driven techniques to deliver personalized, contextually relevant user experiences, he fails to expressly disclose generating, by a second machine learning model, a group of notifications for the computing environment.
However, it the same field of endeavor, Kozhaya elaborately discloses the claimed limitation of generating, by a second machine learning model, a group of notifications for the computing environment (paragraphs 26-27, 39-41).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention was made to have incorporated Kozhayas’ teaching of intelligently optimization of machine learning model with the teachings of Crabtree. One would be motivated to dynamically select and execute different generative AI models based on the nature of the query, thus enhancing computing optimization.
As per claim 2, Crabtree discloses the computer-implemented method wherein a one or more machine learning determines the switch from the single mode to the group mode (paragraphs 158-160). Although, Crabtree discloses AI driven techniques to deliver personalized, contextually relevant user experiences, he fails to expressly disclose wherein a third machine learning model determines the switch from the single mode to the group mode.
However, it the same field of endeavor, Kozhaya elaborately discloses the claimed limitation of wherein a third machine learning model determines the switch from the single mode to the group mode (paragraphs 26-27, 39-41).
The same motivation that was utilized in the combination of claim 1 applies equally as well to claim 2.
As per claim 3, Crabtree discloses the computer-implemented method wherein a third machine learning model determines to make another switch from the group mode back to the single mode based on further user responses during the group mode (paragraphs 158-160; Kozhaya: paragraphs 26-27, 39-41).
The same motivation that was utilized in the combination of claim 1 applies equally as well to claim 2.
As per claim 4, Crabtree discloses the computer-implemented method wherein generating the group of notifications for the computing environment comprises: ranking groups of notifications for the computing environment; and outputting a highest ranked group of notifications as the group of notifications (paragraphs 135, 174, 418).
As per claim 5, Crabtree discloses the computer-implemented method wherein generating the group of notifications for the computing environment is based, at least in part, on the group of notifications having a highest likelihood of acceptance (paragraphs 309, 418).
As per claim 6, Crabtree discloses the computer-implemented method wherein: a fourth machine learning model generates a recommendation acceptance probability matrix comprising a probability of acceptance for each past single notification and each past group of notifications; and the second machine learning model generates the group of notifications for the computing environment based, at least in part, on the recommendation acceptance probability matrix (paragraphs 242, 298, 320, 401).
As per claim 7, Crabtree discloses the computer-implemented method wherein the at least one modification to the computing environment improves a functioning of at least one of a software resource and a hardware resource in the computing environment (paragraphs 143, 205-, 279-281).
Claim 8 is an Independent claim with similar limitation but different in preamble and hence are rejected based on the rejection provided in claim 1.
Claims 9-14 are listed all the same elements of claims 2-7, respectively. Therefore, the supporting rationales of the rejection to claims 2-7 apply equally as well to claims 9-14, respectively.
Claim 15 is an Independent claim with similar limitation but different in preamble and hence are rejected based on the rejection provided in claim 1.
Claims 16-20 are listed all the same elements of claims 2-6, respectively. Therefore, the supporting rationales of the rejection to claims 2-6 apply equally as well to claims 16-20, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sankaran et al. (US Publication 2020/0074347) discloses suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.
Yasin Hajizadeh (US Patent 12,664,446) discloses Machine learning model host system recommendations may be generated using multi-objective optimization. A machine learning model and an example payload of an inference request used to generate an inference using the machine learning model may be identified for a machine learning model host system recommendation. Multi-objective optimization may be iteratively performed that at starts from an initial set of host systems as a recommendation set of host systems for the host system recommendation, minimizes resource utilization, and maximizes inference throughput for the example payload. A Pareto front is determined for host systems in a recommendation set according to a mapping function to objective space that takes as input respective configuration parameters for different host systems being considered in a recommendation set of host systems to generate the respective inference throughput values and resource utilization values. The host system recommendation may be provided based on the Pareto front of host systems.
Singh et al. (US Publication 2025/0182494 A1) detecting occluded objects within images or other sensor data representations for autonomous or semi-autonomous systems and applications is described herein. Systems and methods described herein may determine when objects are occluded at portions of images using various techniques. For example, an image may be processed in order to determine classifications associated with objects depicted by the image and, the classifications, along with labels that are projected on the image using a map, may then be used to determine whether one or more of the objects are occluded in the image. For another example, a map may be used to determine first distances to points within an environment and a point cloud may be used to determine second distances to the points within the environment. The distances may then be used to determine whether one or more objects are occluded within the image
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARZANA B HUQ whose telephone number is (571)270-3223. The examiner can normally be reached Monday - Friday: 8:30-5:30 ET.
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/FARZANA B HUQ/Primary Examiner, Art Unit 2455
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