DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a non-final Office action in response to Applicant’s RCE submission filed on 11/3/2025 (with claim set dated 10/7/2025). Currently claims 1-10, 12-17, 19-20 are pending and claims 1, 8, and 15 are independent. Claims 1, 4, 7, 8, 15 have been amended from the previous claim set dated 7/21/2025. No claims have been added and claims 11 and 18 have been cancelled.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/3/2025 has been entered.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action. In light of the amendments, the 35 U.S.C. 112(b) rejections and objections of claim 7 are withdrawn. Additionally, and in light of the amendments, the 35 U.S.C. 101 rejections are withdrawn because of the included limitation “dynamically retaining the cognitive model…wherein the dynamically retraining is based on feedback received as customer demand information” which demonstrates a controlling feature and overcomes the 101 rejection within the Step 2A (Prong 1) analysis.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-10, 12, 14-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chitrapura et al. (US 11295375 B1) in view of Cillis et al. (US 20180165385 A1)
Regarding claims 1, 8, 15 (Amended), Chitrapura discloses a method (Chitrapura ABS - A platform identifies and matches software application programs for a business user based on his/her context such as industry, location, size, etc. by providing nuanced and personalized guidance for the business user to define the business problem and the capabilities needed to solve the problem) comprising: receiving, by one or more processors of a computer system, training inputs related to technology use cases and associated services (Chitrapura Fig. 2 – 212, 214, 216, 218, 220, 222); training, by a cognitive integration engine of the one or more processors of the computer system, a cognitive model from the received training inputs (Chitrapura CLM 1 - the machine learnt model is trained with training data characterized by a set of reviews where each data for each review of the set of reviews is organized in accordance with a context vector for a company); receiving, by one or more processors of a computer system, a demand for composite services from a customer including functional requirements (Chitrapura Fig. 1A – COL 4 ROW 2 - The business user 103 will interact with the platform 10 to satisfy various business needs, which are problems or business processes that the business user 103 needs to solve in his/her role for their business. This includes in particular, searching for software applications by business need instead of category and features); determining, by the cognitive integration engine of the one or more processors of the computer system, a selection of composite services for the customer based on an output generated by the cognitive model (Chitrapura Fig. 3D – COL 15 ROW 56 - An expanded version of the graph 307 of FIG. 3A is shown in FIG. 3D where software application programs identified by the platform 10 in response to the user 103's query is provided plotted against right fit on vertical dimension and satisfaction of users with the software application program on the horizontal axis. The user 103 is permitted to sort the results by other criteria as shown at 322); and recommending, by the one or more processors of the computer system, the selection of composite services to the customer based on the output generated by the cognitive model (Chitrapura COL 2 ROW 7 - A user interface provides recommendations of completeness of business needs based on the machine learnt model by collecting a business context of the user and finding software application programs most relevant to the user, employing the machine learnt model to predict satisfaction of the user with each of the software application programs as a function of business needs and business context of the user, and asking the user to select other relevant processes that the software application program needs to address to complete the user's business need) and dynamically retraining the cognitive model, having generated the output of the selection of the composite services for the customer, based on an evolution of services as features and capabilities that are added or changed to the services, wherein the dynamically retraining is based on feedback received as customer demand information (Chitrapura Fig. 5 - Chitrapura COL 17 ROW 49 - FIG. 5 is a flow diagram illustrating an embodiment of active learning performed by platform 10 which implements a rapid relevance learning system in the form of a self-improving ranking system that enables rapid active learning of (query-result) relevance models… The platform 10 then at 510 re-learns, in one embodiment via supervised learning, the ranking models using a combination of both example and feature based feedback).
Chitrapura lacks demand for composite services from a customer including non-functional requirements and a recommendation of the selection of composite services to the customer, wherein the recommendation comprises the functional requirements of installing an operating system, hardening the operating system, and creating a virtual machine, wherein the recommendation comprises the non-functional requirements of latency requirements, wherein the installing of the operation system is for consumption by the customer.
Cillis, from the same field of endeavor, teaches demand for composite services from a customer including non-functional requirements (Cillis ABS - Components of an initial deployment pattern of a new system are identified. Target non-functional requirements (NFRs) and target service levels of the new system are determined) and a recommendation of the selection of composite services to the customer (Cillis ABS - A recommendation for deploying the new system using the new deployment pattern is generated), wherein the recommendation comprises the functional requirements of installing an operating system, hardening the operating system, and creating a virtual machine (Cillis ¶2 - Deployment patterns are used as instructions by cloud orchestration tools to automate the accurate construction of virtual IT and communications systems including compute, network, and storage systems), wherein the recommendation comprises the non-functional requirements of latency requirements (Cillis ¶2 - NFRs are requirements associated with characteristics of the system, including the security, availability, response time, throughput, and latency)), wherein the installing of the operation system is for consumption by the customer (Cillis ¶52 - the service provider can create, maintain, support, etc. a computer infrastructure that performs the process steps for one or more customers).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the program identification methodology/system of Chitrapura by including the IT deployment techniques of Cillis because Cillis discloses “an automated deployment pattern optimization that advantageously (1) provides an automated discovery and enhancement of new deployment patterns during a design phase (Cillis ¶8)”. Additionally, Chitrapura further details “Additionally, feedback from experts may be collected on the relative importance of matching on business needs, company context (industry, company-size, department), non-functional requirements (integrations, compliance, customer service, ease of use), vendor qualification (maturity, revenue), overall customer satisfaction for a product, volume of evidence associated with a product. The feedback collected by the foregoing operations can be used by the platform 10 to improve matching of business requirements to software application program (Chitrapura COL 18 Row 35)” so it would be obvious to consider including the additional IT deployment techniques that Cillis discloses because it optimizes the recommendation disclosed in Chitrapura by taking into account hardware deployments.
Regarding claims 2, 9, 16, Chitrapura in view of Cillis discloses the training inputs for each technology use case comprise: at least functional and/or non-functional requirement; at least one list of available services; at least one list of planned services; at least one list of available application program interfaces (APIs); at least one list of planned APIs; and inputs for asset identification and prioritization (Chitrapura Fig. 1A – Chitrapura COL 5 ROW 14 - Clean data and models 114 refer to the data/models that are passed on from the back-end 102 (offline environment) to the front-end 101 serving layer to address various use cases (e.g., match score of a software for a business need, top industries associated with a software, software company details)… Mixed data 118 comprises data of uneven quality obtained from heterogeneous sources (e.g., blogs, reviews, company website, experts, users). Offline model building pipeline 120 builds various models or scores from raw data, such as for example, a customer satisfaction score of a software application program for a particular business context and needs, peer view score for a software application program and software similarity, and the results are provided to clean data and models 116. Crawlers and adapters 122 fetch online or syndicated web content, extract the relevant information and store it to the data dump 124 which is a collection of data (mostly raw webpages) from various online sources. Websites, API, search engines 123 represents sources of data from which the platform 10 collects information. IE (Information extraction) pipeline 126 extracts the relevant information from raw pages and enriches this information by annotating fields of interest such as authors, associating sentiment and identifying concepts (e.g., business activities) and relationships of interest (activity-channel). Manual data entry module 130 allows human editors 132 to provide supervision or guidance on various predicates such as relevance of a software application program for a business need, sentiment expressed in a sentence etc, which can then be used to train new automated predictive models 128 using machine learning methods).
Regarding claims 3, 10, 17, Chitrapura in view of Cillis discloses generating, by the cognitive integration engine of the one or more processors of the computer system, a contextual technical services hierarchy for the customer (Chitrapura Table 1 - COL 8 ROW 35 - The process hierarchy preferably takes a format similar to the hierarchy shown in the table below in which a high-level business function is the top level (column A) of the hierarchy, followed by multiple sub-levels of the hierarchy (columns B, C, D, E, F)).
Regarding claim 4, Chitrapura in view of Cillis discloses the cognitive model is trained on training data comprising: technology management uses cases including human actor types, human actor action types, and human actor non-functional requirement types; technical service assets including technical service asset functional action operational types and asset non-functional operation types; hardware assets; and APIs (Chitrapura Fig. 1A – Chitrapura COL 5 ROW 14 - data entry module 130 allows human editors 132 to provide supervision or guidance on various predicates such as relevance of a software application program for a business need, sentiment expressed in a sentence etc, which can then be used to train new automated predictive models 128 using machine learning methods - Chitrapura COL 9 ROW 15).
Regarding claims 5, 12, 19, Chitrapura in view of Cillis discloses recommending, by the one or more computer processors of the computer system, changes to existing service offerings or the creation of new service offerings based on customer demand information acquired and analyzed by the cognitive integration engine (Chitrapura COL 13 ROW 14 - Computing a process affinity matrix based on process co-occurrence (220). The platform 10 provides a machine learnt model that predicts other business needs for a business user given his/her expressed needs. The platform 10 embodies the recognition that a business user 103 may not be able to completely specify his/her business need as he/she may be unaware of available software application programs to solve his/her needs and also the user 103 typically may not know the latest trends, such as newly released software application programs and reviews of such programs).
Regarding claims 7, 14, Chitrapura in view of Cillis discloses the cognitive integration engine generates the cognitive model using text analytics artificial intelligence and machine learning models (Chitrapura COL 2 ROW 2- A machine learnt model is employed to predict user perception of suitability of one or more software application programs as a function of business need and business context. The model also predicts related business needs for a business user given expressed needs of the business user), wherein the functional requirements further comprise installing OpenShift and configuring a Pod (Chitrapura COL 1 ROW 45- With rapid growth in cloud-based technology solutions known as Software As A Service (SAAS), technology vendors can deliver solutions across the globe to their customers and customers are keen to discover such ready to use solutions).
Cillis further teaches the non-functional requirements further comprise a microservice having predefined transactions per day with a predefined latency (Cillis ¶2 - non-functional requirements (NFRs) and target service levels (e.g., target levels for availability and fluctuations in transaction throughput over time) and how to design infrastructure to achieve the NFRs and the target service levels. NFRs are requirements associated with characteristics of the system, including the security, availability, response time, throughput, and latency)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the program identification methodology/system of Chitrapura by including the IT deployment techniques of Cillis because Cillis discloses “an automated deployment pattern optimization that advantageously (1) provides an automated discovery and enhancement of new deployment patterns during a design phase (Cillis ¶8)”. Additionally, Chitrapura further details “Additionally, feedback from experts may be collected on the relative importance of matching on business needs, company context (industry, company-size, department), non-functional requirements (integrations, compliance, customer service, ease of use), vendor qualification (maturity, revenue), overall customer satisfaction for a product, volume of evidence associated with a product. The feedback collected by the foregoing operations can be used by the platform 10 to improve matching of business requirements to software application program (Chitrapura COL 18 Row 35)” so it would be obvious to consider including the additional IT deployment techniques that Cillis discloses because it optimizes the recommendation disclosed in Chitrapura by taking into account hardware deployments.
Claims 6, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chitrapura et al. (US 11295375 B1) in view of Cillis et al. (US 20180165385 A1) further in view of Arseneault et al. (US 20220317985 A1)
Regarding claims 6, 13, 20, Chitrapura in view of Cillis discloses a method (Chitrapura ABS - A platform identifies and matches software application programs for a business user based on his/her context such as industry, location, size, etc. by providing nuanced and personalized guidance for the business user to define the business problem and the capabilities needed to solve the problem).
Chitrapura in view of Cillis lacks dynamically extending, by the one or more processors of the computer system, the cognitive model across an ecosystem of technology partners by integrating with exposed composite business or technical service assets of the technology partners.
Arseneault, from the same field of endeavor, teaches dynamically extending, by the one or more processors of the computer system, the cognitive model across an ecosystem of technology partners by integrating with exposed composite business or technical service assets of the technology partners (Arseneault Fig. 2 - ¶35 - The software artifacts 202 may represent a variety of existing software entities. In some configurations, the existing software entities may be OSS entities…¶13 - In some circumstances, companies or individual developers contribute to OSS development because collective efforts may often lead to better quality software and also because an ecosystem built on top of OSS typically attracts higher adoption of software. Furthermore, use of OSS often expands the available software entities that developers may choose from for their specific software development projects).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the program identification methodology/system of Chitrapura by including the software recommendation techniques of Arseneault because Arseneault discloses “In particular, the disclosed embodiments include aspects to recommend software entities for use in software development. Such features may improve the quality of the developed software by identifying which software entities may help satisfy the requirements of the associated software application. Further, such features may decrease the amount of time used to develop software applications by reducing the amount of time used to identify which software entities may be used (Arseneault ¶15)”. Additionally, Chitrapura further details “A platform identifies and matches software application programs for a business user based on his/her context (Chitrapura ABS)” so it would be obvious to consider including the additional software recommendation techniques that Arseneault discloses because it improves the efficiency of identifying useful software services to recommend.
Response to Arguments
Applicant's arguments filed 11/3/2025 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
As mentioned above and in light of the amendments, the 35 U.S.C. 112(b) rejections and objections of claim 7 are withdrawn.
Additionally, and also in light of the amendments, the 35 U.S.C. 101 rejections are withdrawn because of the included limitation “dynamically retaining the cognitive model…wherein the dynamically retraining is based on feedback received as customer demand information” which demonstrates a controlling feature and overcomes the 101 rejection within the Step 2A (Prong 1) analysis.
Regarding the 35 USC § 103 rejections on the previous Office Action, Applicant amended the independent claims to further limit the claims with respect to dynamically retraining the model. In light of this amendment, Examiner agrees that the cited references did not specifically cite to this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, the previously cited prior art was found to does teach these limitations and is now cited. As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624