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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), filed on 12/17/2025 in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/17/2025 has been entered. Claims 1-20 are pending.
Response to Arguments
2. Applicant's arguments are moot in light of the new ground of rejections set forth below.
Claim Rejections - 35 USC § 112
3. 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.
4. 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.
5. Claims 1-20 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.
a) Claim 1 recites “determine whether to issue one or more control operations to the distributed application or to a cloud provider, and when issuance of control operations is determined, issue one or more control operations to the distributed application or a cloud provider”. This limitation recites “a cloud provider” twice. First of all, it is unclear whether the two recited “a cloud provider” are the same cloud provider or different cloud providers. Applicant is required to clarify. For the sake of the examination, Examiner assumes any possibility. Secondly, it is unclear whether and how each of the recited “a cloud provider” relates to the previously recited “a cloud infrastructure”. Applicant is required to clarify. For the sake of the examination, Examiner assumes any relationship. Claims 2-20 are similarly rejected.
b) Claim 3 recites “controller of claim 1 wherein the population of ccomprises” the scope of which cannot be definitely determined. Applicant is required to clarify. For the sake of the examination, Examiner assumes any entity(es).
c) claim 4 recites “receives input from the distributed-application-or-cloud-infrastructure controller; generates internal input data from the received input and inputs the generated input data to the agents of the population; receives output-data responses from the agents of the population; generates output data from the received output-data responses; and outputs the generated output data to the controller”. The recited “the distributed-application-or-cloud-infrastructure” lacks sufficient antecedent basis therefore the scope of this term and the subsequently recited “the controller” cannot be definitely determined. Applicant is required to clarify. For the sake of the examination, Examiner assumes any controller. Claim 18 is similarly rejected.
d) claim 5 recites “each artificial-life agent in the population of agents comprises:…” the scope of which cannot be definitely determine. In addition, the scope of the subsequently recited “the agent” cannot be definitely determined either. For the sake of the examination, Examiner assumes any agent. Claim 6-14 and 19- is similarly rejected.
e) claim 9 recites “the artificial agent”, “an artificial-life agent”, “the agent”, “the artificial-life agent”, wherein the relationship among these claimed terms cannot be definitely determined, and wherein “the artificial agent” and “the agent” lack sufficient antecedent basis. Applicant is required to clarify. For the sake of the examination, Examiner assumes any relationship. Claim 7 also recites “the found agent”, “the found artificial-life agent”, wherein the relationship between the claimed terms cannot be definitely determine. Applicant is required to clarify. For the sake of the examination, Examiner assumes any relationship.
f) claim 10 recites “the artificial-life agent” which lacks sufficient antecedent basis. For the sake of the examination, Examiner assumes any agent.
Claim Rejections - 35 USC § 103
6. 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 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.
7. 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 of this title, 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.
8. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
9. Claims 1-4, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 2023/0214925) in view of GREENWOOD (WO2016/000035) and Shabtai (US 2022/0230070).
As to claim 1, Cela discloses a controller comprising:
one or more computer systems, each containing one or more processors. one or more memories, and one or more data-storage devices; and processor instructions, stored in one or more of the one or more memories that, when and executed by one or more of the one or more processors. control the one or more computer systems to implement the controller, the controller (See citation below) periodically
receiving operational-state information from a distributed application or cloud infrastructure controlled by the controller (See [3237], “In embodiments, the configured system services 23420 function to configure a set of systems (e.g., the set of transactional systems 23430) corresponding to the configured market orchestration system 23400 to perform a set of services based on intelligence determined for the configured system services 23420. Like configured intelligence services 23410, configured system services 23420 provide data storage, library management, data handling, and/or data processing services that are tailored to requirements associated with a particular market orchestration system 23400 (e.g., in response to data requests and/or directed market transactions by the EAL 23300). In some examples, the configured system services 23420 uses the configured intelligence service 23410 to generate decisions relating to configurations of the set of transactional systems 23430”; [3229], “The configured intelligence service 23410 may include an intelligence service controller 23412 and a set of artificial intelligence (AI) modules 23414. When the configured intelligence service 23410 receives an intelligence request (e.g., from a transactional system 23430 or from the configured system services 23420), the request may include any specific/required data to process the request. In response to the request and the specific data, one or more implicated AI modules 23414 perform the intelligence task and output an “intelligence response”. Here, the configured system services 23420 is equivalent to a distributed-application-or-cloud-infrastructure controller, and the set of systems (such as the transactional systems 23430) corresponding to the configured market orchestration system 23400 to perform a set of services is equivalent to a distributed application. The data included in the request necessary to process the request are equivalent to operation-state information, being part of the initial state information of the processing/operation that processes the request. It is to be noted that the claim does not require a specific operation or state. Also see [0107]),
using the received operational-state information to generate input data that the controller inputs to a population of agents (See [3229], “The configured intelligence service 23410 may include an intelligence service controller 23412 and a set of artificial intelligence (AI) modules 23414. When the configured intelligence service 23410 receives an intelligence request (e.g., from a transactional system 23430 or from the configured system services 23420), the request may include any specific/required data to process the request. In response to the request and the specific data, one or more implicated AI modules 23414 perform the intelligence task and output an “intelligence response.” Examples of responses from AI modules 23414 may include a decision (e.g., a control instruction, a proposed action, machine-generated text, and/or the like), a prediction (e.g., a predicted meaning of a text snippet, a predicted outcome associated with a proposed action, a predicted fault condition, an anticipated state of an entity or workflow relevant to a transaction (such as a future price, interest rate, or conversion rate), and/or the like), a classification (e.g., a classification of an object in an image, a classification of a spoken utterance, a classified fault condition based on sensor data, and/or the like), a recommendation (e.g., a recommendation for an action to optimize a transaction parameter), and/or other suitable outputs of an artificial intelligence system”, wherein the one or more implicated AI modules 23414 are a population of artificial-life agents),
receiving, from the population of agents, recommendations for control operations (see citation in the preceding limitation), and
using the recommendations for control operations to
determine whether to issue one or more control operations to the distributed application or to a cloud provider, and when issuance of control operations is determined, issue one or more control operations to the distributed application or a cloud provider (see 112 rejection and Examiner’s interpretation therein. See citation in the preceding limitations, also see [2335], “For example, the intelligent services system 20243 may receive an intelligence request and marketplace offering data from the market orchestration system platform 20500. In response to the request and the received data, the artificial intelligence system may generate an identification of a poor health indicator (such as an absence of new offerings) and may then, based at least in part on the identification, output a control instruction to the market orchestration system platform 20500 to deploy an engine for automated discovery and linking of offerings from other marketplaces for presentation in the marketplace.”).
However, Cella does not expressly disclose wherein a first agent of the agents comprises a genome that includes genes, each gene having a gene code and a gene value, the gene values encoded using Gray encoding to limit mutation impact on gene values. GREENWOOD discloses a concept of an agent to comprise a genome that includes genes, each gene having a gene code and a gene value, the gene values encoded using Gray encoding to limit mutation impact on gene values ([0290], “The <p-TEA encodes candidate values of interest as genes on a chromosome. These genes are typically encoded in an analogous fashion to GA chromosomes, namely using an encoding scheme that can include: Gray Code, conventional binary…”. See Page 237, Table 6 for gene code and gene value, and [0964], “The chromosomes were encoded using Gray code to prevent Hamming walls forming during the mutation process”).
Before the effective filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Cella with GREENWOOD. The suggestion/motivation of the combination would have been to prevent Hamming walls forming during the mutation process (GREENWOOD, [0964]).
Cella does not expressly disclose the first agent being associated with an energy state based on a projected resource utilization and a target utilization. Shabtai discloses a concept of a first agent being associated with an energy state based on a projected resource utilization and a target utilization ([0057], “f) assigning rewards to the usage of computing resources being below a predetermined level and penalties to the usage of computing resources exceeding the predetermined level”, wherein the usage of computing resources reflects projected resource utilization, and predetermined level [of usage of computing resources] reflects a target utilization, and wherein the reward state based on the usage of computing resources and predetermined level is an energy state, which is associated with a tested detector. See [0146]-[0147]; [0192]; [0128]-[0129], wherein the analysis of the detector’s cost/resource utilization are for testing purpose to project the real-world situation therefore is a type of projection. It is to be noted that the claimed limitation does not require a specific type to project resource utilization therefore Examiner interprets as any type of projection).
Before the effective filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Cella and Shabtai. The suggestion/motivation of the combination would have been to regarding agents for efficiently using resources (Shabtai, [0057]).
As to claim 17, see similar rejection to claim 1.
As to claim 20, see similar rejection to claim 1.
As to claim 2, Cella discloses the controller of claim 1 wherein the distributed application or cloud infrastructure controlled by the controller is one of:
a distributed application running in multiple execution environments provided by a distributed computer system ([0402], “Services may be distributed across a number of devices, and/or functions of a service may be performed by one or more devices cooperating to perform the given function of the service”);
cloud-infrastructure provided by a cloud-computing facility; and
a distributed application running in cloud-infrastructure provided by a cloud-computing facility.
As to claim 3, Cella discloses the controller of claim 1 wherein the population of agents comprises a manager and multiple agents, the manager and multiple agents each running within one or more execution environments provided by a computer system or a distributed computer system (see 112 rejection and Examiner’s interpretation therein. See [3229], “The configured intelligence service 23410 may include an intelligence service controller 23412 and a set of artificial intelligence (AI) modules 23414. When the configured intelligence service 23410 receives an intelligence request (e.g., from a transactional system 23430 or from the configured system services 23420), the request may include any specific/required data to process the request. In response to the request and the specific data, one or more implicated AI modules 23414 perform the intelligence task and output an “intelligence response.” Examples of responses from AI modules 23414 may include a decision (e.g., a control instruction, a proposed action, machine-generated text, and/or the like), a prediction (e.g., a predicted meaning of a text snippet, a predicted outcome associated with a proposed action, a predicted fault condition, an anticipated state of an entity or workflow relevant to a transaction (such as a future price, interest rate, or conversion rate), and/or the like), a classification (e.g., a classification of an object in an image, a classification of a spoken utterance, a classified fault condition based on sensor data, and/or the like), a recommendation (e.g., a recommendation for an action to optimize a transaction parameter), and/or other suitable outputs of an artificial intelligence system”, wherein the intelligence service controller is equivalent to a population-of-artificial-life-agents manager. See also [3230]-[3231], wherein the intelligence service controller includes a management module).
As to claim 4, Cella discloses the controller of claim 3 wherein the manager
receives input from the distributed-application-or-cloud-infrastructure controller; generates internal input data from the received input and inputs the generated input data to the agents of the population; receives output-data responses from the agents of the population; generates output data from the received output-data responses; and outputs the generated output data to the controller (see 112 rejection and Examiner’s interpretation therein. See citation in rejection to claim 1, e.g., [3229]-[3231]).
As to claim 18, see similar rejection to claim 4.
10. Claims 5, 7, 9-13, 15-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cella-GREENWOOD-Shabtai, as applied to claim 1 above, and in view of Ng. et al (US 6470261).
As to claim 5, Cella discloses the controller of claim 1 wherein each artificial-life agent in the population of agents (see 112 rejection and Examiner’s interpretation therein) comprises:
a classifier that classifies internal input data by generating a classification that identifies one of a set of classification models maintained by the classifier (see citation in rejection to claim 1 and [3282], “In some implementations, machine learning models can be or include one or more classifier models such as, for example, linear classification models; quadratic classification models; etc. Machine learning models may be or include one or more regression models such as, for example, simple linear regression models; multiple linear regression models; logistic regression models; stepwise regression models; multivariate adaptive regression splines; locally estimated scatterplot smoothing models; etc.”);
a predictor that generates output data from a prediction model corresponding to the identified classification model ([2519], “predictive analytics… a fuzzy classifier”);
control logic that controls operation of the agent, including receiving internal input data from a manager, invoking the classifier to generate a classification for the internal input data, invoking the predictor to generate output data based on the classification, and forwarding the generated output data in a response to the manager (see 112 rejection and Examiner’s interpretation therein. See citation in rejection to claim 1, e.g., [3229]-[3231]. See also [2519], “predictive analytics… a fuzzy classifier);
a blueprint containing control parameters ([0014]; [0783]; [0793]), but does not expressly disclose a genome containing one or more pairs of chromosomes. Ng discloses a concept of a genome containing one or more pairs of chromosomes in an artificial intelligence algorithm (col. 10, lines 45-65).
Before the effective filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Cella-GREENWOOD-Shabtai and Ng. The suggestion/motivation of the combination would have been to breed from parents (Ng, col. 10, lines 45-65).
As to claim 19, see similar rejection to claim 5.
As to claim 7, Cella-GREENWOOD-Shabtai-Ng discloses the controller of claim 5 wherein the predictor generates output data from a prediction model corresponding to the identified classification model by one of:
randomly selecting one or more actions and encoding the selected actions in the output data as control recommendations (Cella, [3342], “The artificial intelligence modules 23504 can include a random forest, in which each of one or more decision trees analyses an input data according to different criteria, and an output of the random forest is based on a consensus of the decision trees”); and
selecting a set of actions for which the sum of reward estimates contained in the prediction model is greater than, or equal to, the sum of reward estimates contained in the prediction model for any other set of actions and encoding the selected actions in the output data as control recommendations (Cella, [1580], “the agent must discover correct actions by trial-and-error so as to maximize some notion of long-term reward. Specifically, in a system employing RL, there exist two entities: (1) an environment and (2) an agent. The agent is a computer program component that is connected to its environment such that it can sense the state of the environment as well as execute actions on the environment. On each step of interaction, the agent senses the current state of the environment, s, and chooses an action to take, a. The action changes the state of the environment, and the value of this state transition is communicated to the agent by a reward signal, r, where the magnitude of r indicates the desirability of an action. Over time, the agent builds a policy, π, which specifies the action the agent will take for each state of the environment”).
As to claim 9 Cella-GREENWOOD-Shabtai-Ng discloses the controller of claim 5 wherein, when the artificial agent is directed by the manager to mate with another agent (see 112 rejection and Examiner’s interpretation therein), the agent
searches for a candidate agent with which to mate by computing scores for candidate agents based on the values of one or more of the candidate agents' state variables (See Cella for agents which are equivalent to artificial nodes. See Ng, col. 8, lines 10-22, wherein the selected parents are equivalent to a candidate artificial node and another candidate artificial node, and wherein the nodes’ individual fitness (for producing children) are equivalent to state variables); and
when an artificial-life agent with which to mate is found (see 112 rejection and Examiner’s interpretation therein. In addition, since the following limitations are conditional on this “when…” condition which does not necessarily happen, the following limitations are not given patentable weight. Alternatively, see citations below), the agent mates with the found agent by
generating duplicated genomes for the artificial-life agent and the found artificial-life agent (col. 10, lines 65-67),
probabilistically altering the one or more chromosome pairs in the duplicated genomes (col. 11, lines 1-8).
randomly selecting one chromosome from each pair of chromosomes of the agent and the found agent to create a new genome for a child agent (col. 11, lines 1-8),
generating a new blueprint from the child agent's genome (col. 11, lines 1-8; col. 7, lines 50-60), and
when the blueprint is viable, creating a child agent that includes the new genome and the new blueprint (since the following limitations are conditional on this “when…” condition which does not necessarily happen, the following limitations are not given patentable weight. Alternatively, see col. 11, lines 10-16, “the resultant chromosome is decoded into the neural network's design parameters to construct the network for later evaluation of the network's performance. This chromosome is referred as the neural network design candidate”; col. 7, lines 50-60).
As to claim 10, Cella-GREENWOOD-Shabtai-Ng discloses controller of claim 5 wherein, when the agent is directed by the manager to purge classification models, the artificial-life agent removes a portion of classification models maintained by the classifier (see 112 rejection and Examiner’s interpretation therein. In addition, this limitation is conditional on the “when…” condition which does not necessarily happen. Therefore the limitation is not given patentable weight. Alternatively, see Cella, [[3851]; [1825]; [2556]).
As to claim 11, Cella-GREENWOOD-Shabtai-Ng discloses the controller of claim 5 wherein a chromosome includes multiple genes, each gene comprising a gene code that specifies the type of gene and a gene value that specifies the value of a control parameter (Ng, col. 10, lines 45-65, wherein each gene can be considered a type with its identifier being equivalent to a code).
As to claim 12, Cella-GREENWOOD-Shabtai-Ng discloses the controller of claim 11 wherein the blueprint is generated by, for each possible gene code. selecting a gene with the gene code from the pairs of chromosomes in the agent's genome (Ng, col. 7, lines 50-60; col. 10, lines 45-65).
As to claim 13, Cella-GREENWOOD-Shabtai-Ng discloses the controller of claim 11 wherein each pair of chromosomes can be altered by one or more of: crossover events (Ng, col. 10, lines 45-65); cut_and_splice events: and single-bit mutations.
As to claim 15, Cella-GREENWOOD-Shabtai-Ng discloses the controller of claim 5 wherein the internal input data includes one or more of:
an indication of the current number of virtual machines allocated to the distributed application or cloud infrastructure;
an indication of the data-storage capacity allocated to the distributed application or cloud infrastructure (Cella, [3335], “if the condition is a storage capacity of a device that exceeds a storage capacity threshold, the RPA module 23516 can perform the workflow based on a severity of the storage capacity condition (e.g., a remaining storage capacity of the device). The RPA module 23516 can perform a workflow based on a data source, such as one or more files of a file system, one or more rows or records of a database, or one or more messages received by a network interface. If the RPA module 23516 is performing a workflow in response to one or more events, the RPA Module 23516 can perform the workflow based on one or more details of the event. For example, if the RPA module 23516 is performing a second workflow in response to a completion of a first workflow on the same device or another device, the RPA module 23516 can perform the workflow based on a date or time of the completion of the first workflow, a result of the first workflow, and/or an output of the first workflow. The RPA module 23516 can perform a workflow based on one or more contextual details. For example, the RPA module 23516 can perform a workflow based on a detected number and identities of humans who are present in the proximity of a device. The RPA module 23516 can perform a workflow based on data associated with an application executing on the device. For example, if the RPA module 23516 performs the workflow based on a loading of a web page, the RPA module 23516 can perform the workflow based on data scraped from the contents of the web page. The RPA module 23516 can perform the workflow based on observation of human actions that involve interactions with hardware elements, with software interfaces, and with other elements. Observations may include field observations as humans perform real tasks, as well as observations of simulations or other activities in which a human performs an action with the explicit intent to provide a training data set or input for the RPA module 23516, such as where a human tags or labels a training data set with features that assist the RPA module 23516 in learning to recognize or classify features or objects, among many other examples”, wherein selecting different workflows or workflow branches is equivalent to selecting a classification model);
an indication of the networking capacity allocated to the distributed application or cloud infrastructure;
an indication of the utilization of the current number of virtual machines allocated to the distributed application or cloud infrastructure;
an indication of the utilization of the data-storage capacity allocated to the distributed application or cloud infrastructure;
an indication of the utilization of the networking capacity allocated lo the distributed application or cloud infrastructure:
an indication of the current number of users using the distributed application or cloud infrastructure;
an indication of the maximum number of virtual machines that can be allocated to the distributed application or cloud infrastructure;
an indication of the maximum data-storage capacity that can allocated to the distributed application or cloud infrastructure: and
an indication of the maximum networking capacity that can be allocated to the distributed application or cloud infrastructure.
As to claim 16, Cella-Ng discloses the controller of claim 5 wherein internal input data includes one or more of:
an indication of the number of virtual machines that should be allocated to the distributed application or cloud infrastructure;
an indication of the data-storage capacity that should be allocated to the distributed application or cloud infrastructure (see citation in rejection to claim 15);
an indication of the networking capacity that should be allocated to the distributed application or cloud infrastructure;
an indication of whether the maximum allocatable virtual machines should be increased;
an indication of whether the maximum allocatable data-storage capacity should be increased; and
an indication of whether the maximum allocatable network capacity should be increased.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUA FAN whose telephone number is (571)270-5311. The examiner can normally be reached on 9-6.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Umar Cheema can be reached at 571-270-3037. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUA FAN/Primary Examiner, Art Unit 2458