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
This action is in response to the preliminary amendment filed on 09/18/2023.
Claims 3-4 are amended by the applicants.
Claims 1-5 are pending.
Examiner’s Note
Please note that Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed 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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1, this claim is within at least one of the four categories of patent eligible subject matter as it is directing to a method claim under Step 1.
1. (Original) A computer implemented method for deploying a composite software application including a plurality of software components, the method comprising:
accessing a classifier trained to classify characteristics of one or more consuming entities to a class identifying a set of software components and a configuration for at least a subset of the identified components;
generating the composite software application using a default set of software components each having a default configuration for deployment to a consumer of the generated software component;
receiving characteristics for the consumer;
determining a set of software components and a configuration for at least a subset of the determined components by executing the classifier based on the received characteristics; and
adjusting the composite software application for deployment to the consumer based on the determined set of software components and configurations.
Regarding claim 1, the limitations “generating the composite software application using a default set of software components each having a default configuration for deployment to a consumer of the generated software component,” “determining a set of software components and a configuration for at least a subset of the determined components… based on the received characteristics” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. For example, a person is capable of using default software components with the aid of pen and paper to generate composite software application to deploy to consumer. In same manner, a person is capable of identifying based on the received characteristics with the aid of pen and paper determining software component and configuration for deployment. Therefore, these limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the additional elements “by executing the classifier” is recited at a high-level of generality such that it amounts no more than mere instructions for executing some execution model to produce the outcome which merely using generic computing equipment to execute the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “accessing a classifier trained to classify characteristics of one or more consuming entities to a class identifying a set of software components and a configuration for at least a subset of the identified components,” “receiving characteristics for the consumer,” and “adjusting the composite software application for deployment to the consumer based on the determined set of software components and configurations” do nothing more than to add insignificant extra solution activity to the judicial exception of merely accessing/gathering/adjusting data for deployment. See MPEP § 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “performing one or more model training experiments to generate a set of trained models” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception, thus, cannot amount to an inventive concept. See MPEP 2105.06(f). For the additional elements “accessing a classifier trained to classify characteristics of one or more consuming entities to a class identifying a set of software components and a configuration for at least a subset of the identified components,” “receiving characteristics for the consumer,” and “adjusting the composite software application for deployment to the consumer based on the determined set of software components and configurations” the courts have recognized storing and retrieving information in memory as a well‐understood, routine, and conventional functions in a merely generic manner (e.g., at a high level of generality) or an insignificant extra-solution activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)) thus, cannot amount to an inventive concept. See MPEP 2106.05(d). Accordingly, the claims are not patent eligible under 35 USC 101.
2. (Original) The method of claim 1 wherein the default set of software components is determined based on an characteristics of the consumer.
The limitations for this claim further recite an additional mental process under Step 2A, Prong 1.
3. (Currently Amended) The method of claim 1 wherein the characteristics of the consumer comprises characteristics including one or more of: a temporal context; an environmental context; attributes of the consumer; a profile of the consumer; a set of software components in the software application; and a configuration of one or more of the software components in the software application.
The limitations for this claim further recite an additional insignificant extra solution activity under step 2A, Prong 2.
4. (Currently Amended) A computer system including a processor and memory storing computer program code for performing the steps of the method of claim 1.
The limitations, amount to no more than mere instructions to apply the exception using generic computer and/or mere computer components to carry out the exception under Step 2A, Prong 2.
5. (Currently Amended) A computer program element comprising computer program code to, when loaded into a computer system and executed thereon, cause the computer to perform the steps of a method as claimed in claim 1.
The limitations, amount to no more than mere instructions to apply the exception using generic computer and/or mere computer components to carry out the exception under Step 2A, Prong 2.
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.
Claim(s) 1-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over USPN 20090007088 to Fischer et al. in view of USPN 20070016615 to Mohan et al.
Per claim 1:
Fischer discloses:
1. (Original) A computer implemented method for deploying a composite software application including a plurality of software components, the method comprising:
generating the composite software application using a default set of software components each having a default configuration for deployment to a consumer of the generated software component (Paragraph [0045, 0048] “generate a composite application by using one or more of such templates (which has default components) as a base for the structure and function of such composite application… instantiation component 21, as the components are needed for instantiating the composite application, and searches artifacts (i.e., default) and templates based on meta-data supplied with the invocation… registry component 26 allows Independent Software Vendors 34 to deploy their templates and artifacts into the Artifact Registry Service 22”);
receiving characteristics for the consumer (Paragraph [0044] “independent software vendor (i.e., consumer) 34 may be represented for example by a portal… which are collected (i.e., received) in the artifact (i.e., characteristics) registry 32 of the respective service 22… register their respective artifact 36 in this artifact registry 32”);
determining a set of software components and a configuration for at least a subset of the determined components by executing the classifier based on the received characteristics (Paragraph [0078] “As a result of more search hits the look-up component looks up all components that have a pre-specified (i.e., determined software components) functionality and preferably performs a selection in order to obtain one or more special components which fit best the functionality requirement from step 720”); and
adjusting the composite software application for deployment to the consumer based on the determined set of software components and configurations (Paragraph [0068,0069] “a first step 510 the independent software vendor 34 updates any given predetermined component which is assumed now to be in use in a certain composite application… a business user or a portal administration user can decide whether or not to adopt the new artifact. In case they do adopt the new one, the new version of the artifact is downloaded” also see Paragraph [0030] “automatically deploying the composite application using at least one of said selected components”).
Fischer does not explicitly disclose accessing a classifier trained to classify characteristics of one or more consuming entities to a class identifying a set of software components and a configuration for at least a subset of the identified components.
However, Mohan discloses in an analogous computer system accessing a classifier (Paragraph [0629] “intelligent component 5802”) trained to classify characteristics of one or more consuming entities to a class identifying a set of software components (Paragraph [0693,0694] “classification (explicitly and/or implicitly trained) schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines…)… classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class)”)and a configuration for at least a subset of the identified components (Paragraph [0692] “intelligent component 5802 can be utilized by the model architecture component 5302 and/or the dynamic engine 5304 to facilitate representing a business rule with at least one node to automatically generate a dynamic composite application”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the method of accessing a classifier trained to classify characteristics of one or more consuming entities to a class identifying a set of software components and a configuration for at least a subset of the identified components as taught by Mohan into the method of generating composite applications and deploying to the consumer as taught by Fischer. The modification would be obvious because of one of ordinary skill in the art would be motivated to add/incorporate the features of accessing a classifier trained to classify characteristics of one or more consuming entities to a class identifying a set of software components and a configuration for at least a subset of the identified components to provide an efficient technique to classify characteristic of consumer entities so as efficiently creating software components and implement business rules as suggested by Mohan (paragraph [0002-0007]).
Per claim 2:
Fischer discloses:
2. (Original) The method of claim 1 wherein the default set of software components is determined based on an characteristics of the consumer (Paragraph [0045-0046] “generate a composite application by using one or more of such templates (which has default components) as a base for the structure and function of such composite application… the components are needed for instantiating the composite application, and searches artifacts and templates based on meta-data supplied with the invocation”).
Per claim 3:
Fischer discloses:
3. (Currently Amended) The method of claim 1 wherein the characteristics of the consumer comprises characteristics including one or more of: a temporal context; an environmental context; attributes of the consumer; a profile of the consumer; a set of software components in the software application; and a configuration of one or more of the software components in the software application (Since this appears to be MARKUSH type language requiring at a minimum just one from the list, Fischer teaches Paragraph [0022] “different composite applications or artifacts are offered by different vendors a decision which composite application or artifact to use for building up a new”).
Per claim 4:
Fischer discloses:
4. (Currently Amended) A computer system including a processor and memory storing computer program code for performing the steps of the method of claim 1 (Paragraph [0093] “data processing system… storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements”).
Per claim 5:
Fischer discloses:
5. (Currently Amended) A computer program element comprising computer program code to, when loaded into a computer system and executed thereon, cause the computer to perform the steps of a method as claimed in claim 1 (Paragraph [0091] “a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Related cited arts:
Ang, Kenneth Li-Minn, and Jasmine Kah Phooi Seng. "Application specific internet of things (ASIoTs): Taxonomy, applications, use case and future directions." IEEE Access 7 (2019): pp. 56577-56590.
Liu, Yongkang, et al. "Wireless network design for emerging IIoT applications: Reference framework and use cases." Proceedings of the IEEE 107.6 (2019): pp. 1166-1192.
Jacques-Silva, Gabriela, et al. "Building user-defined runtime adaptation routines for stream processing applications." arXiv preprint arXiv:1208.4176 (2012). pp. 1826-1837.
US12393773 - Systems and methods for automatically populating documents about special entities are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include receiving user data, extracting a list of entities associated with the user and a list of events that occurred between the user and the entities, transforming metadata for the events associated with entities of interest into vectorized embeddings, selectively classifying, using a binary classifier model, ones of the entities as special entities and ones of the events as special events for a set of documents, assigning, using a multi-class classifier model, one of a plurality of categories to each special event associated with each special entity, each of the categories mapping to a corresponding section within the set of documents, and populating, for each special entity, the corresponding sections within the set of documents based on the categories.
US20190236492 - This disclosure relates to initial learning of a classifier for automating extraction of structured data from unstructured or semi-structured data. In one embodiment, a method is disclosed, comprising: identifying at least one expected relation class associated with at least one expected relation data; populating at least one expected name entity data from the at least one identified expected relation class; generating training data by tagging the at least one expected relation data and the at least one identified expected relation class with unstructured or semi-structured data; generating feedback data for a relation data and relation class, using a convergence technique on the tagged training data; retuning a NE classifier cluster and a relation classifier cluster by continuously tagging new training data or generating new cascaded expression for a deterministic classifier and a statistical classifier; and extracting the structured data when the NE classifier cluster and the relation classifier cluster converge.
US12591598 - A computer implemented method for determining entity attributes. The method comprises determining one or more entity identifiers, determining an entity server address of the entity based on the one or more entity identifiers, wherein the entity server address points to an entity server; verifying the entity server address transmitting a message for request for information to the entity server address, receiving entity information from the entity server; and providing, to a machine learning model, the received entity information. The machine learning model is trained to generate a numerical representations of entities based on the entity information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Satish Rampuria whose telephone number is 571-272-3732. The examiner can normally be reached on Monday-Friday from 8:30 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do, can be reached at telephone number 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Satish Rampuria/Primary Examiner, Art Unit 2193
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