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 .
Claims 1-20 have been examined.
Claim Objections
Claim 3 is objected to because of the following informalities: the claim is missing an ending period. 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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 20 appears to be a system of software alone, lacking the necessary physical components (hardware) to constitute a machine or a manufacture under 101. Since claim 20 is clearly not a process or a composition of matter, it appears to fail to fall within a statutory category and thus non-statutory.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 11-16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Novotny (US 2021/0042638) in view of Soroush (US 2020/0053116).
Per Claim 1:
Novotny teaches configuring a data structure to implement the custom AI workflow; wherein the data structure comprises a plurality of interconnected nodes representing functions performed by components of the multi-layer technology stack, at least some of the bottom layer nodes comprise sensors including at least one camera, and at least one node performs an AI task; configuring the functions performed by the nodes, and determining data flows between the nodes; and in response to an outcome of the AI task, automatically reconfiguring the data structure ([0027] As some non-limiting examples, the AI engine could use extracted usage patterns of end users to create a process, recommend a process template, or recommend creating a quick action on a record based on the click paths of the end users. The AI engine can recommend simpler design patterns by looking at metadata and generating recommendations on how to improve overall processes whether it be by modifying an existing process or flow within the application, by creating a new process or flow within the application or separate from the application, by creating a new workflow or modifying an existing workflow to create a modified workflow, by modifying permissions or profiles, by creating a new set of permissions or profiles, by modifying existing platform features (e.g., objects, tabs, custom applications, custom workflows, lightning flows, validation rules, etc.), by creating new platform features (e.g., objects, tabs, custom applications, custom workflows, lightning flows, validation rules, etc.), by changing how data is funneled into a cloud computing platform, etc. The cloud computing platform allows a developer or system administrator to develop custom data models and applications for desktop and mobile environments. These recommendations can suggest the best way to modify existing applications on the platform, and thus guide administrators and developers to maximize the efficiency of their metadata designs. The AI engine can also track whether end users are uploading data sets to the cloud computing platform, and if so, provide recommendations to developers to avoid the need for uploading data sets to the cloud computing platform. To explain further, most, if not all, major cloud computing platforms have some type of data transfer application that allows an end user to manually upload data to the desired end point (platform). The process is time consuming and prone to errors, that is why it is generally not recommend, but a lot of customers extract, transform, load (ETL) data if they are unfamiliar with how application programming interface (API) calls work. As one non-limiting example of an extract, transform, load (ETL) process, an end user might extract a data set from a SQL database in the form a CSV document, and then modify the data in the CSV document, then upload the data set to the platform via data loader or some other data transfer application. As such, the click paths end users take throughout can guide development teams on how to improve their existing applications. [0127] … The input system 912C can include any suitable combination of input devices, such as one or more touchscreen interfaces, keyboards, mice, trackballs, scanners, cameras, or interfaces to networks. [0144] In some implementations, the pods 1040 and 1044 (interpreted as nodes) perform the core data processing and service functions provided by the on-demand database service environment. Each pod can include various types of hardware or software computing resources. An example of the pod architecture is discussed in greater detail with reference to FIG. 10B. In some implementations, communication between the pods 1040 and 1044 is conducted via the pod switches 1032 and 1036. The pod switches 1032 and 1036 can facilitate communication between the pods 1040 and 1044 and client machines communicably connected with the cloud 1004, for example via core switches 1020 and 1024. Also, the pod switches 1032 and 1036 may facilitate communication between the pods 1040 and 1044 and the database storage 1056. In some implementations, the load balancer 1028 can distribute workload between the pods 1040 and 1044. Balancing the on-demand service requests between the pods can assist in improving the use of resources, increasing throughput, reducing response times, or reducing overhead. The load balancer 1028 may include multilayer switches to analyze and forward traffic. [0145] In some implementations, access to the database storage 1056 is guarded by a database firewall 1048. The database firewall 1048 can act as a computer application firewall operating at the database application layer of a protocol stack. The database firewall 1048 can protect the database storage 1056 from application attacks such as structure query language (SQL) injection, database rootkits, and unauthorized information disclosure. In some implementations, the database firewall 1048 includes a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. The database firewall 1048 can inspect the contents of database traffic and block certain content or database requests. The database firewall 1048 can work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.). Novotny does not explicitly teach configuring a multi-layer graph or wherein configuring the multi-layer graph comprises selecting the nodes in the graph.
However, Soroush teaches configuring a multi-layer graph; and wherein configuring the multi-layer graph comprises selecting the nodes in the graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include configuring a multi-layer graph; and wherein configuring the multi-layer graph comprises selecting the nodes in the graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 2:
The rejection of claim 1 is incorporated, and Novotny further teaches the data structure comprises an app layer, a cloud layer, a device layer and a sensor layer, and the sensor layer contains the bottom layer nodes and the camera; at least one individual node is configured by workflow control packages that specify the function of the individual node, but without fully specifying lower layer nodes that provide data flow to the individual node; and the individual node analyzes the workflow control packages and generates and transmits additional workflow control packages resulting from the analysis to the lower layer nodes (par. 0027 and 0127). Novotny does not explicitly teach a multi-layer graph.
However, Soroush teaches a multi-layer graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include a multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 3:
The rejection of claim 1 is incorporated, and Novotny further teaches wherein: interfaces to layers comprise standardized application programming interfaces (APIs), and at least one individual node is configured by workflow control packages transmitted to the individual node via the standardized API to the layer containing the individual node; and the method implements concurrent custom AI workflows for a plurality of applications, the concurrent custom AI workflows sharing components from the same multi-layer technology stack (par. 0027)
Per Claim 4:
The rejection of claim 1 is incorporated, and Novotny further teaches wherein the nodes are expressed using a standard format, the standard format comprising: a data input for receiving data flow from lower layer nodes; a data output for sending data flow to higher layer nodes; a description of the function performed by the node; a feedback input for receiving Al-triggered feedback from other nodes, wherein the AI-triggered feedback reconfigures the function implemented by the node; and a feedback output for sending AI-triggered feedback to other nodes, wherein the AI-triggered feedback is generated in response to an AI function performed by the node (par. 0027 and 0144).
Per Claim 5:
The rejection of claim 1 is incorporated, and Novotny further teaches wherein the data structure performs multiple AI functions, and automatically reconfiguring the data structure comprises: in response to the outcome of the AI task, automatically changing the AI functions performed by the data structure (par. 0027 and 0145). Novotny does not explicitly teach a multi-layer graph.
However, Soroush teaches a multi-layer graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include a multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 11:
The rejection of claim 1 is incorporated, and Novotny further teaches in response to the outcome of the AI task, automatically changing the components performing functions in the data structure (par. 0027 and 0144). Novotny does not explicitly teach wherein automatically reconfiguring the multi-layer graph comprises: automatically changing the components performing functions in the multi-layer graph.
However, Soroush teaches wherein automatically reconfiguring the multi-layer graph comprises: automatically changing the components performing functions in the multi-layer graph (par. 0067-0069).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include wherein automatically reconfiguring the multi-layer graph comprises: automatically changing the components performing functions in the multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 12:
The rejection of claim 1 is incorporated, and Novotny further teaches in response to the outcome of the Al task, automatically changing the functions performed by the nodes of the data structure (par. 0027 and 0127). Novotny does not explicitly teach automatically reconfiguring the multi-layer graph comprises automatically changing the functions performed by the nodes of the multi-layer graph.
However, Soroush teaches wherein automatically reconfiguring the multi-layer graph comprises automatically changing the functions performed by the nodes of the multi-layer graph. (par. 0067-0069).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include automatically reconfiguring the multi-layer graph comprises automatically changing the functions performed by the nodes of the multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 13:
The rejection of claim 1 is incorporated, and Novotny further teaches in response to the outcome of the AI task, automatically changing the data flow between nodes in the data structure (par. 0144). Novotny does not explicitly teach wherein automatically reconfiguring the multi-layer graph comprises automatically changing the data flow between nodes in the multi-layer graph.
However, Soroush teaches wherein automatically reconfiguring the multi-layer graph comprises automatically changing the data flow between nodes in the multi-layer graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include wherein automatically reconfiguring the multi-layer graph comprises automatically changing the data flow between nodes in the multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 14:
The rejection of claim 1 is incorporated, and Novotny further teaches in response to the outcome of the AI task, automatically changing which cameras are performing image capture (par. 0127 and 0144). Novotny does not explicitly teach wherein automatically reconfiguring the multi-layer graph.
However, Soroush teaches automatically reconfiguring the multi-layer graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include automatically reconfiguring the multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 15:
The rejection of claim 1 is incorporated, and Novotny further teaches in response to the outcome of the AI task, automatically changing settings for cameras performing image capture (par. 0082, 0127, and 0144). Novotny does not explicitly teach wherein automatically reconfiguring the multi-layer graph.
However, Soroush teaches automatically reconfiguring the multi-layer graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include automatically reconfiguring the multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 16:
The rejection of claim 1 is incorporated, and Novotny further teaches in response to the outcome of the AI task, automatically changing a processing and/or analysis of captured images (par. 0082 and 0149). Novotny does not explicitly teach wherein automatically reconfiguring the multi-layer graph.
However, Soroush teaches automatically reconfiguring the multi-layer graph (par. 0053-0054).
It would have been obvious to one having ordinary skill in the computer art before the effective filing date of the claimed invention to modify the method disclosed by Novotny to include automatically reconfiguring the multi-layer graph using the teaching of Soroush. The modification would be obvious because one of ordinary skill in the art would be motivated to improve security in a networked system (Soroush, par. 0003).
Per Claim 18:
The rejection of claim 1 is incorporated, and Novotny further teaches wherein the AI task comprises an event detection based on understanding a context of images captured by the cameras in the multi-layer technology stack (par. 0149).
Per Claim 19:
This is a medium version of the claimed method discussed above (claim 1, respectively), wherein all claim limitations also have been addressed and/or covered in cited areas as set forth above. Thus, accordingly, this claim is also obvious.
Per Claim 20:
This is a system version of the claimed method discussed above (claim 1, respectively), wherein all claim limitations also have been addressed and/or covered in cited areas as set forth above. Thus, accordingly, this claim is also obvious.
Allowable Subject Matter
Claims 6-10 and 17 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smith (US 2025/0308219) teaches a method for generating images by trained neural network.
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/QAMRUN NAHAR/Primary Examiner, Art Unit 2199