DETAILED ACTION
Remarks
Claims 1-23 have been examined and rejected. This Office Action is responsive to the amendment filed on 10/02/2025, which has been entered in the above identified application.
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 1-23 are presented for examination.
Response to Amendment
Applicant’s amendment filed on 10/02/2025 has been entered. Claims 1 and 12 have been amended. Claims 21-23 are cancelled. Claims 1-20 are pending in the application.
Claim Objections
Claims 6 and 8 are objected to because of the following informalities:
Claim 6 [line 2]: “correspond” should be “corresponds”
Claim 8 [line 2]: “is” should be “are”
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 6, it is unclear what “the domain of variables” [line 1] refers to. Claim 6 is depended on claim 5, however, claim 5 recited “a domain of computation variables”. For the purposes of examination, the examiner will interpret this limitation as “the domain of computation variables” to make it consistent with the limitation in claim 5.
With respect to claim 15, it is unclear what “the active machine learning algorithm” [line 3] refers to. Claim 15 is depended on claim 12. However, claim 12 never recited an active machine learning algorithm. For the purposes of examination, the examiner will interpret this limitation as “an active machine learning algorithm”.
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 the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a system and claim 12 is drawn to a method that executes an AI application. Therefore, each of these groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1 and 12 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1 and 12 recite a method of querying an external machine learning component that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to query or to respond to a machine learning (ML) component. Therefore, the step of querying an external ML component is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1 and 12 recite further a method of combining the raw data outputs from the external machine learning component with context data gathered by the first computer to form combined context data that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to combine two difference types of data. Therefore, the step of combining raw data outputs with context data is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1 and 12 recite further a method of querying an active machine learning component on a different computer with the combined context data and the raw data to output a suggested next step to be executed by the computer that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to query or to respond to a ML component. Therefore, the step of querying an active ML component is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1 and 12 recite further a method of querying a rules database of the different computer to select a rule that corresponds to the augmented data set that includes the suggested next step for the active machine learning component that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to query or to respond to a database to select a rule. Therefore, the step of querying a rules database to select a rule is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Claims 1 and 12 recite further a method of receiving raw data outputs from the external machine learning component, the raw data outputs resulting from computer implemented computations directed to a first computer for a first business process that fails to integrate the abstract idea into a practical application. The step of receiving raw data is a form of insignificant input and output solution activities, where receiving raw data outputs is necessary for all uses of the judicial exception. 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 (MPEP 2106.05(g)).
Claims 1 and 12 recite further a method of transmitting the raw data outputs to a context component stored on the first computer that fails to integrate the abstract idea into a practical application. The step of transmitting data to another component is a form of insignificant input and output solution activities, where transmitting raw data outputs is necessary for all uses of the judicial exception. 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 (MPEP 2106.05(g)).
Claims 1 and 12 recite further a method of transmitting the suggested next step back to a respective context component of the different computer for adding to the combined context data and forming an augmented data set that fails to integrate the abstract idea into a practical application. The step of transmitting suggested next step is a form of insignificant input and output solution activities, where transmitting the suggested next step back to a respective context component is necessary for all uses of the judicial exception. 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 (MPEP 2106.05(g)).
Claims 1 and 12 recite further a method of using the computer, implementing an automated output for a different business process with the different computer according to the rule that was selected that fails to integrate the abstract idea into a practical application. The step of implementing an automated output is a form of insignificant input and output solution activities, where implementing an automated output for a business process is necessary for all uses of the judicial exception. 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 (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that these additional elements of receiving raw data outputs; transmitting raw data outputs; transmitting the suggested next step back to a respective context component; and implementing an automated output for a business process to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1 and 12 are not patent eligible.
Dependent claims
Claims 2-11 and 13-20 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 1 and 12, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen. Therefore, claims 2-11 and 13-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-11 are drawn to a system and claims 13-20 are drawn to a method that executes an AI application. Therefore, each of these groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claim 2 recites further the mental process by the active machine learning component comprises a machine learning computer program that has been trained by iteratively learning a series of historical results that have previously resulted from combinations of historical context data and historical selections of rules that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 3 recites further the mental process by the active machine learning component predicts outcomes for the Al application by iteratively evaluating the augmented data set, the suggested next step, and the automated output for a plurality of combinations of context data from the computer and raw data outputs from the external machine learning component that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 13 recites further the mental process by a feedback loop in which the active machine learning component iteratively calculates suggested next steps and sequentially transmits the suggested next steps to the context component for combining with the augmented data set that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 14 recites further the mental process by mapping selected rules to items in the augmented data set that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 15 recites further the mental process by receiving raw data outputs from the external machine learning component that have been calculated from a domain of variables that are distinct from the different business process utilizing the active machine learning algorithm that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 16 recites further the mental process by training the active machine learning component to iteratively learn a series of historical results that have previously resulted from combinations of historical context data that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 17 recites further the mental process by retrieving context data directly from a business transaction completed at least in part by the computer and storing the context data in the context component that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 19 recites further the mental process by initiating the different business process simultaneously with the first business process providing raw data outputs that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 20 recites further the mental process by updating the rule after evaluating the automated output and a corresponding system result that based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 4 recites further the insignificant extra solution activities by the computer implemented computations of the external machine learning component are independent of the active machine learning component. 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 (MPEP 2106.05(g)).
Dependent claim 5 recites further the insignificant extra solution activities by the computer implemented computations of the external machine learning component are directed to a domain of computation variables that is distinct from the Al application. 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 (MPEP 2106.05(g)).
Dependent claim 6 recites further the insignificant extra solution activities by the domain of variables applicable to the external machine learning component correspond to a first business process and the automated output from the Al application corresponds to a different business process. 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 (MPEP 2106.05(g)).
Dependent claim 7 recites further the insignificant extra solution activities by the context data set and the augmented data set comprise data from a plurality of communication channels. 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 (MPEP 2106.05(g)).
Dependent claim 8 recites further the insignificant extra solution activities by the automated output and a corresponding system result is stored in a database of historical results for use in training the active machine learning component. 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 (MPEP 2106.05(g)).
Dependent claim 9 recites further the insignificant extra solution activities by the external machine learning component is an intent classifier comprising at least one conversation input. 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 (MPEP 2106.05(g)).
Dependent claim 10 recites further the insignificant extra solution activities by the external machine learning component is a sentiment classifier comprising at least one conversation input. 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 (MPEP 2106.05(g)).
Dependent claim 11 recites further the insignificant extra solution activities by the context data received from the computer comprises at least one of a transcript of a communication, customer information, customer service agent data, or customer service agent action data. 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 (MPEP 2106.05(g)).
Dependent claim 18 recites further the insignificant extra solution activities by the context data comprises data inputs from multiple communications channels. 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 (MPEP 2106.05(g)).
As such, dependent claims 2-11 and 13-20 are not patent eligible.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nitz et al (US 20140052681 A1) hereafter Nitz, in view of Lee et al (US 20050054381 A1) hereafter Lee.
With respect to claim 1, Nitz teaches a system that utilizes an artificial intelligence (Al) application on a first computer (the learning module 114 applies artificial intelligence (AI)-based machine learning (ML) algorithms to continuously improve the knowledge base 130, and adapt the knowledge base to the user’s daily routine. User’s information is stored in computer memory, as well as the suggested actions generated by the system those may be considered later by the user [par. 0023, 0080, 0101]), comprising:
an external machine learning component in data communication over a network with the first computer, wherein the external machine learning component utilizes computer implemented computations to generate raw data outputs that are transmitted to the first computer (the user activity knowledge base 130 includes raw data, computer code, arguments or parameters of real-time inputs 116, these are considered as associations between or among instances of system characterizations. Such associations may be generated by the engine 110 with AI models. Combining with the stored user-specific information component, the knowledge base may generate activity rules, activity patterns, rhythm and routine templates, etc. to the inference engine [par. 0057, 0058 and FIG. 1]);
a context component of the first computer calculating a context data set, wherein the context component also receives the raw data outputs from the external machine learning component (the system includes a current situation and context inference engine (or inference engine) 110 to receive real-time inputs as well as aspects from the knowledge base to be applied to the real-time inputs. The inference engine applies AI methods and algorithms to infer context scenarios to the user [par. 0021-0025 and FIG. 1]);
a different computer comprising a processor connected to computer memory in data communication over the network with the Al application of the first computer (user’s information is stored in computer memory, as well as the suggested actions generated by the system those may be considered later by the user. Candidate actions or actions include the presenting or sending of suggestions to the user or to other electronic devices. Other candidate actions include activities that can be performed by the mobile device on behalf of the user such as executing a software process, sending a message, communicating with other electronic devices. Real-time inputs 116 may be automatically generated by various electronic components of a mobile device or received by a mobile device from other devices or systems over a wireless network [par. 0023, 0027-0029, 0044, 0101]);
wherein a respective context component of the active machine learning component queries a rules database and selects a rule that corresponds to the augmented data set that different computer implements an automated output according to the rule that was selected (the knowledge base includes templates, rules and patterns which the system uses to interpret and determine the context of the real-time inputs, and to generate appropriate candidate actions relating to user’s current situation. Templates, rules and patterns are determined based on historical data, research or crowd-sourcing techniques. These parameters can be automatically modified and adapted to user’s situation overtime by a context learning module 114 [par. 0058, 0059 and FIG. 1]).
However, Nitz does not particularly disclose an active machine learning component executed by the different computer and in data communication with the context component of the first computer, wherein the active machine learning component uses the context data set and the raw data outputs to transmit a suggested next step to the different computer for adding to the context data set and the raw data and forming an augmented data set.
In the same field of endeavor, Lee teaches an active machine learning component executed by the different computer and in data communication with the context component of the first computer, wherein the active machine learning component uses the context data set and the raw data outputs to transmit a suggested next step to the different computer for adding to the context data set and the raw data and forming an augmented data set (a user interface installed on any type of computational device that is associated with a learning module, which employs an adaptive learning process that is iterative. The leaning module learns an appropriate action to perform for each iteration. An action is selected based on previous iteration’s computation. The adaptive system interacts with the data/mechanisms of mobile information device in order to provide an adaptive user interface. Logic 430 is able to communicate with the knowledge base 102, and information storage 432 includes data about the actions of mobile information device, user information and so on [par. 0099-0101, 0146-0148 and FIG. 1 & 4]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of employing a user interface that makes suggestions to users based on prior experience and/or preprogrammed patterns as suggested by Lee into the concept of applying a user activity knowledge base to real-time inputs to determine a current situation of the user as suggested by Nitz because both of the systems addressing the process of suggesting the next action based on the inputs and the outputs from the knowledge base. Doing so would be desirable because the concept of Nitz would be more efficient by iteratively learning to adapt to the current situation of the user, such that for each iteration the module may be able to learn the appropriate action to perform, wherein an action is selected based on previous iteration’s computation (Lee, [par. 0098-0102]).
With respect to claim 2, the combination of Nitz and Lee teaches wherein the active machine learning component comprises a machine learning computer program that has been trained by iteratively learning a series of historical results that have previously resulted from combinations of historical context data and historical selections of rules (Lee, the key features of the learning module are adaptive learning process – the learning process is iterative, low memory consumption, and fast interaction – at each iteration an action is selected based on previous iteration’s computation. At least a pattern is selected from a group consisting of a pattern determined according to at least one previous interaction of the user with the user interface. The pattern is selected according to a plurality of rules and/or world environment state [par. 0071, 0098-0102]).
With respect to claim 3, the combination of Nitz and Lee teaches wherein the active machine learning component predicts outcomes for the Al application by iteratively evaluating the augmented data set, the suggested next step, and the automated output for a plurality of combinations of context data from the computer and raw data outputs from the external machine learning component (Lee, the actions are selected from the priority queue order. The priority of an action is determined according to a calculation which includes a plurality of parameters, according to the chosen parameters those based on the predicted probability for the success of an action, and also according to the user preferences [par. 0204, 0307]).
With respect to claim 4, the combination of Nitz and Lee teaches wherein the computer implemented computations of the external machine learning component are independent of the active machine learning component (Nitz, the knowledge base is a separate and independent component of the system, which generate data outputs for the inference engine after applying templates, rules and patterns from the real-time inputs [par. 0022, 0056-0059]).
With respect to claim 5, the combination of Nitz and Lee teaches wherein the computer implemented computations of the external machine learning component are directed to a domain of computation variables that is distinct from the Al application (Nitz, user-specific preferences or policies are some of the computation variables that is taken into consideration when determining how to perform certain actions. These variables are may be called knowledge stores that contains or references data, arguments, parameters and/or machine-executable predictive models [par. 0025, 0053-0058]).
With respect to claim 6, the combination of Nitz and Lee teaches wherein the domain of variables applicable to the external machine learning component correspond to a first business process and the automated output from the Al application corresponds to a different business process (Nitz, the method automatically generates the plurality of possible contexts that relates to an often-repeated situation. The knowledge base is a separate component to the system. Without the knowledge base, the system may generate outputs based on the current stored user-specific information and the real-time inputs. However, the knowledge base is provided to calculate a more persistent and more appropriate action based on the real-time inputs to assist the users [par. 0007, 0008]).
With respect to claim 7, the combination of Nitz and Lee teaches wherein the context data set and the augmented data set comprise data from a plurality of communication channels (Nitz, the real-time input data may be generated from multiple communication channels such as video cameras, audio speakers, other output devices, other peripheral devices and at least one network interface [par. 0118-0126]).
With respect to claim 8, the combination of Nitz and Lee teaches wherein the automated output and a corresponding system result is stored in a database of historical results for use in training the active machine learning component (Lee, the target of learning is to improve the potential performance of the AI reasoning system by generalization over experiences. The input of a learning algorithm will be the experiment and the output would be modifications of the knowledge base according to the results. In iterative learning process, the reasoning system receives the current state of the world, outputs the action to be performed, and receives feedback on the action selected [par. 0010, 0106]).
With respect to claim 9, the combination of Nitz and Lee teaches wherein the external machine learning component is an intent classifier comprising at least one conversation input (Nitz, the rhythm and routine template 132 includes data relating to personalized “rhythm” data and to anticipated or actual schedules or routines of the mobile user. The rhythm details can be changed in response to real-time inputs [par. 0063-0065]).
With respect to claim 10, the combination of Nitz and Lee teaches wherein the external machine learning component is a sentiment classifier comprising at least one conversation input (Nitz, the rhythm data also relates to the user’s “tempo” of activity during a certain period of time. The tempo of activity refers to aspects of the user’s lifestyle that are associated with motion or pace. The inference engine may derive tempo information from real-time location data or stored calendar or communications information [par. 0063-0065]).
With respect to claim 11, the combination of Nitz and Lee teaches wherein the context data received from the computer comprises at least one of a transcript of a communication, customer information, customer service agent data, or customer service agent action data (Nitz, a user interface is introduced that employs an AI agent to communicate with the user. For example, the user missed the train and the AI agent helps with rescheduling the user’s meeting [par. 0016-0018, 0110-0117 and FIG. 4-6]).
With respect to claim 12, it is a computer implemented method that is corresponding to the system of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 13, the combination of Nitz and Lee teaches a feedback loop in which the active machine learning component iteratively calculates suggested next steps and sequentially transmits the suggested next steps to the context component for combining with the augmented data set (Lee, the reasoning system receives the current state, outputs the action and receives feedback on the selected action. Based on the feedback, the reasoning system updates the knowledge base. The AI agent may react to the feedback that has been provided [par. 0106, 0189, 0190]).
With respect to claim 14, the combination of Nitz and Lee teaches mapping selected rules to items in the augmented data set (Nitz, the rules from the user activity knowledge base may be retrieved and generated based on the stored user-specific information. If the user’s current situation and/or context is uncertain or the number of real-time inputs 116 and/or stored information 118 may be large, the inference engine may use the knowledge base to determine which inputs and/or information 116 and 118 are likely to be the most relevant to the present situation. Rules may be applied to the user-specific information to determine the situation [par. 0022 and FIG. 1]).
With respect to claim 15, the combination of Nitz and Lee teaches receiving raw data outputs from the external machine learning component that have been calculated from a domain of variables that are distinct from the different business process utilizing the active machine learning algorithm (Nitz, real-time inputs are taken as inputs into the user activity knowledge base to generate an appropriate action to the inference engine. Each of the knowledge base stores may contain data, arguments, parameters and machine-executable predictive models and algorithms that can be applied by the inference engine. Templates, rules and patterns are applied to interpret and determine the context of the real-time inputs to generate context-appropriate candidate actions relating to user’s current situation [par. 0007, 0008, 0056-0059]).
With respect to claim 16, the combination of Nitz and Lee teaches training the active machine learning component to iteratively learn a series of historical results that have previously resulted from combinations of historical context data (Lee, the key features of the learning module are adaptive learning process – the learning process is iterative, low memory consumption, and fast interaction – at each iteration an action is selected based on previous iteration’s computation. At least a pattern is selected from a group consisting of a pattern determined according to at least one previous interaction of the user with the user interface. The pattern is selected according to a plurality of rules and/or world environment state [par. 0071, 0098-0102]).
With respect to claim 17, the combination of Nitz and Lee teaches retrieving context data directly from a business transaction completed at least in part by the computer and storing the context data in the context component (Nitz, the inference engine is designed to integrate multiple sources and types of information about a mobile device user’s virtual and physical existence in ways that are personalized and helpful to the user. The engine makes intelligent inferences about the user’s current activity in the physical world based on many sources and types of data that are available to the mobile device [par. 0021-0025]).
With respect to claim 18, the combination of Nitz and Lee teaches wherein the context data comprises data inputs from multiple communications channels (Nitz, the real-time input data may be generated from multiple communication channels such as video cameras, audio speakers, other output devices, other peripheral devices and at least one network interface [par. 0118-0126]).
With respect to claim 19, the combination of Nitz and Lee teaches initiating the different business process simultaneously with the first business process providing raw data outputs (Nitz, user is required to initiate the location-based services that is available in the mobile device in order for the system to receive real-time inputs. The inference engine is configured as an intelligent software agent that can, with user’s permission, continuously assess the user’s current situation and context based on the real-time inputs and the stored information, and initiate the generation and presentation or execution of candidate actions where and when appropriate [par. 0001, 0027, 0090]).
With respect to claim 20, the combination of Nitz and Lee teaches updating the rule after evaluating the automated output and a corresponding system result (Nitz, method 300 may update the knowledge base based on user’s responses according to candidate actions. Likelihood scores or probabilities associated with templates, rules or patterns may be updated based on a selection or other action performed by the user in response to a suggestion [par. 0109]).
Response to Arguments
The examiner respectfully acknowledges the applicant’s amendments to claims 1, 6, 8 and 12.
Applicant’s amendments filed on 10/02/2025 regarding claims 6, 8 and 21 under Claim Objections have been fully considered but the objections still remain (see Claim Objections above).
Applicant’s amendments filed on 10/02/2025 regarding claims 6 and 15 under 35 USC 112(b) have been fully considered but the rejections still remain (see rejections above).
Applicant’s arguments filed on 10/02/2025 regarding claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argued that “The Applicant submits that the claims in this submission do not fit within any of the identified Groups and is not an abstract idea that merits being labeled as falling within a judicial exception. The Examiner considers all of the claim elements to fit within the scope of mental processes, but the Examiner has not considered certain aspects of the independent claims. In particular, the amended claims set forth above utilize context data and even raw data created within Al processes of a first computer, with a different computer having its own respective Al processes utilizing the context data and the raw data. This context data and raw data are provided over a recited network. These kinds of data resources are not relevant to steps that can be considered mental processes, especially when even raw data from a first computer's Al resources are sent over to the different computer. A human is incapable of understanding, much less utilizing, raw digital data as recited. Accordingly, the Applicant respectfully submits that the claim set does not fall within the mental process judicial exception.”
Examiner respectfully disagrees.
Based on what is recited in claim 1, the broadest reasonable interpretation (BRI) in view of Specification of the claim limitations “querying an active machine learning component on a different computer with the combined context data and the raw data to output a suggested next step to be executed by the computer” encompass a mentally performable process of gathering data (raw and context data), with pen and paper, by applying human understanding or an internal set of rules to decide and to suggest the next step. This particular limitation, among other limitations that recite mental processes above, does not explicitly describe the mental processes in detail; rather they implement a process that mimics a mental task. This limitation is closely associated with the result of a mental process or the automation of a human decision-making step. In similar cases, a human could perform the mental steps of gathering data, applying their understanding or an internal set of rules (machine learning model/rules database), and/or deciding or suggesting a next step.
Applicant argued that “Next, the analysis should include an explanation of how the claim amendments herein present embodiments of the disclosure that show the alleged abstract method in a "practical application" that removes any abstract claim from being directed to the judicial exception … The Applicant refers to USPTO guidelines to argue that the claim amendments herein present embodiments of the disclosure that show the alleged abstract correlation method in a "practical application" that removes any abstract claim from being directed to the judicial exception. This is particularly true when the claim integrates an alleged judicial exception into other claim elements that show a "particular" machine that is integral to the claim. The particular machine in this matter lies in the network access point from which the system can share the content data and the raw data for a separate Al function at a "different" computer. The "particular" machine in the form of the data transmission points also provide geographic diversity for the various Al processes that do not have to be duplicated at every location.”
Examiner respectfully disagrees.
The claim, as a whole, fails step 2A – Prong Two. All the limitations describe the use of generic, conventional computer technology to manage and process data. The steps such as querying, transmitting, receiving, combining data and using standard components like databases and computer are routine computer activities. The claim lacks any limitations that describe a specific, non-conventional technical solution or an improvement to a technological process. The implementation is entirely conventional. The claim focuses on facilitating or automating a business process and outputting a suggested next step. Automating an abstract idea (like a business method or a mental process) with generic computers is the classic example that would not integrate the abstract idea into a practical application. The integration must be meaningful instead of merely using a generic computer to implement the abstract idea is not enough. The claim, as a whole, does not provide the specific “something extra” that transforms the abstract idea into a patent-eligible application, as required by the Alice framework.
Therefore, claims 1 and 12 are not patent eligible for at least the reasons above. Dependent claims 2-11 and 13-20, those are either directly or indirectly depended on independent claims 1 and 12, are not patent eligible for the same reasons.
Applicant’s arguments filed on 10/02/2025 regarding claims 1-20 under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicant argued that “The Applicant has amended the claim set to reinforce the concept that multiple computers connected over a network can share context data that has been created by Al resources that were originally used only for a first computer in a first business sector. The cited combination of references uses individual Al programs and algorithms in only one location. None of the cited references, either alone or in combination can show transmitting context data and raw data from a first computer to a different computer for use in Al processes that are unique to the different computer. The Applicant respectfully submits that the obviousness rejection should be reconsidered.”
Examiner respectfully disagrees.
Nitz teaches some examples of communication networks and/or systems wherein real-time inputs may be obtained [par. 0027-0029, 0043-0045]. Real-time inputs 116 may be provided via communication networks include sensor data transmitted by other devices and information or content transmitted to the mobile device by network-based systems. Moreover, the system also employs one or more artificial intelligence models [par. 0056-0059] for the knowledge base to include associations between or among instances of system-level characterizations such as raw data of real-time inputs. The knowledge base includes stored activity-related templates, rules and patterns those may be used to interpret and determine the context of the real-time inputs 116. A mobile device may engage in one or more ways of communication with any one or more of the various sources of real-time inputs 116, to send or receive information from other devices and/or systems. An example of bidirectional communication between different systems may include a system interfacing with embedded systems such as in-vehicle systems [par. 0086-0088].
Conclusion
THIS ACTION IS MADE FINAL. See MPEP 706.07(a). Applicant is remined of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filled within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143