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 communication is a final office action in response to application filed on 11/05/2025. Claims 1-21 are pending.
Response to Argument
Applicant’s argument directed to claim and spec objection are persuasive. The objections are withdrawn.
Applicant’s argument directed to 101 rejection has been fully considered but are not persuasive. Please see the reasons below.
Applicant first argues that monitoring using API requires computer and can not be performed in human mind similar to example 37 and therefore making the claims eligible. Examiner respectfully disagree with Applicant’s conclusion for at least the reason that API and its usage is analyzed in step 2A prong 2 as an additional element, example 37 does not address how an additional element such as API should be analyzed in further step. Further, example 37 can not be grouped into certain methods of organizing human activities but the claimed invention can properly grouped as certain methods of organizing human activities. Therefore, the argument is not persuasive.
Applicant goes on to argue that the claimed invention is a technical solution to a technical problem that is complex in managing SAAS, which is shown at least in 0013 of specification.
Examiner respectfully disagree that claimed invention requires a complex problem as suggested in 0013 of specification. Examiner notes, for example, claim 1 discuss having a functional fit based on a criteria that is broad enough to encompass cost. A model is used to evaluate SaaS applications based on organizational need, organizational utilization, budget, and the criteria of cost. These are broad enough to encompass a rule-based decision making system based on optimization of cost without considering various changing factors as discussed in 0013. For illustration purpose only, the claimed invention can be broadly interpreted as comparing cloud storage costs for a period of a year regarding whether to switch from current provider to a different provider based on current percentage of usage given a known budget. A problem as illustrated above is not so complex that is a technology problem.
Similarly, Applicant’s additional argument based on multiple dimension and machine learning module would not be persuasive as the current scope of the claim is broader and therefore a lot less complex where these functionality are merely generally applying the abstract idea to a particular technical field of machine learning.
Examiner notes that if claim is amended to incorporate additional detail showcasing the complexity of the problem managing SaaS applications as suggested in 0013, the above analysis would not be applicable anymore.
Applicant’s argument directed to 103 rejection is fully considered but are not persuasive.
Applicant argues that prior arts cited does not teach monitoring SaaS application API data traffic for a period, examiner disagree as it is at least taught in Mehmanapazir’s 0057 where an agent using API is tasked to collect customer-specific and product-specific information. Mehmanapazir goes on to discuss data retrieved are segmented by time periods for training purpose (0066-0067). Therefore, the limitation is taught by prior art.
Applicant then argues that prior art does not teach the limitation of evaluating replacement SaaS by the updated machine learning model for at least the reason that the input to the reference engine is a customer profile, not a functional fit of an application. Examiner respectfully disagree with Applicant’s claim construction as the limitation merely discuss an updated machine learning model being used to evaluate replacement SaaS application that has certain characteristic (having the criteria). There is no requirement that the input to this model is a functional fit. In prior art, models are updated and used to evaluate SaaS application based on criteria (e.g., cost, utilization based on actual usage) and therefore the combination meets the claim.
Examiner would agree that prior arts does not discuss inputting functional fit into a machine learning model to evaluate an SaaS application. This limitation, however, is not currently claimed.
Similarly, functional fit, as currently claimed, does not require network security. Cybersecurity is an element in a Markush grouping and therefore claim need not include it.
Examiner would also agree Mehmanpanzir is based on usage and not network security as well. However, current cope of the claim is not limited to network security.
Further arguments are based on similarity, please see above.
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 1-21 are rejected under 35 U.S.C. 101 because they recite an abstract idea without significantly more.
Step 2A prong 1
As per claim 1, examiner believes the following limitations recites an abstract idea.
determining current [software-as-a-service (SaaS)] applications being used by an organization;
generating a [machine learning] model for the organization that defines a functional fit based at least in part on criteria of the organization;
monitoring … traffic associated with the SaaS application;
determining the organization's SaaS applications utilization over an interval of time based on the data traffic associated with the SaaS applications;
obtaining criteria for each of the current SaaS applications including at least a cost, a renewal date, uplift, or cybersecurity;
updating [the machine learning] model, … the updating based on current needs of the organization, the organization's SaaS applications utilization, a SaaS budget, and the criteria;
evaluating … replacement [SaaS] applications by [the machine learning model] when one of the current [SaaS] applications does not comply with the functional fit according to the SaaS application current utilization and the replacement [SaaS] applications also having criteria; and
automatically generating new contract terms [by the machine learning model] for any of the replacement [SaaS] applications.
The above limitations, when viewed as a group, describes a series of steps to evaluate application (for context, these are SaaS applications) and generate contract terms for replacement applications. Examiner notes generating a model is broad enough to encompass as rules that should be met (e.g., cheapest on the market or no more than an amount such as $10k). Therefore, these are all describing a series of steps to generate new contract terms, which is interpersonal relationship or business relationship or fundamental economic practices. All of which falls into certain methods of organizing human activities. Therefore, claim 1 recites an abstract idea.
Step 2A prong 2
Claim 1 includes additional elements that the applications are SaaS applications and the model being machine learning models and a processor. SaaS application and processor is merely limiting the abstract idea to a particular field of use. As per machine learning model, it is just being used to generally linking the abstract idea to a particular field of use (machine learning) at this level of breadth (see response to argument above discussing the complexity of problem claimed invention is solving). Even viewed as an ordered combination, they’re still merely generally linking the abstract idea into particular field of use. Therefore, the additional elements do not integrate the abstract idea into practical application. Therefore claim 1 is directed to an abstract idea.
Step 2B
As discussed above in step 2A prong 2, which the analysis can still apply, the additional elements, whether viewed individually or as an ordered combination are merely generally linking the abstract idea to a particular field of use. They would not provide an inventive concept. Therefore, claim 1 is not eligible.
Claims 2-10, 21 merely further limit the abstract idea by introducing additional rules to be performed. Therefore, they would still recite the same abstract idea. Some dependent claims include further additional elements such as “electronic document” or “workflow application”. However, they’re recited in substantially similar manner as the additional elements discussed in claim 1 where they’re simply merely limited abstract idea to particular field of use. Therefore, the analysis for step 2A prong 2 and step 2B would be substantially similar and the conclusion would remain.
Claims 11-20 are also substantially similar to claims 1-10 except for positively reciting computer hardware such as processor and memory and being in different statutory categories. The step 2A prong 1 analysis would remain substantially similar. The generic computer hardware can also be analyzed similarly as merely generally linking the abstract idea to computer technology. Additionally, they’re also merely being used as instructions to implement the abstract idea on generic computer. In any case, the step 2A prong 2 and step 2B would still arrive at the same conclusion. Therefore, these claims are still not eligible.
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.
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.
Claim(s) 1-5, 7-15, 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chitrapura (US 11295375) in view of Mehmanpazir (US 20210166251)
As per claim 1, Chitrapura discloses a computer implemented method for assessing software-as-a-service (SaaS) applications and determining contract terms for SaaS replacement applications, the method comprising:
generating a machine learning model for the organization that defines a functional fit based at least in part on criteria of the organization (see at least Chitrapura 21:34-45, “At 1012, a report generation model is generated by combining the master report schema, NLG models corresponding to each element and the variable bindings, text summarization models.” see also, 11:49-61, “Estimate the right fit for each software application fora given set of processes …” See also 4:49-54 where cost is a factor to be optimized in relation to business needs.);
obtaining criteria for each of the current SaaS applications including at least a cost, a renewal date, uplift, or cybersecurity (see at least Chitrapura, 11:49-61, “Estimate the right fit for each software application fora given set of processes …” See also 4:49-54 where cost is a factor to be optimized in relation to business needs. See also 18:35-45 shows non-functional requirement includes compliance and integration, which are cybersecurity factors. Examiner notes the list is interpreted as a list of alternatives when read in light of specification.);
updating the machine learning model, using the processor, the updating based on current needs of the organization,
evaluating, using a processor, replacement SaaS applications by the machine learning model
automatically generating new contract terms for any of the replacement SaaS applications (see Fig. 3B, where product pricing information is displayed).
Chitrapura does not explicitly disclose
determining current software-as-a-service (SaaS) applications being used by an organization;
monitoring, using a processor, the SaaS applications utilizing Application Program Interfaces (APIs) by tracking data traffic associated with the SaaS applications;
determining the organization's SaaS applications utilization over an interval of time based on the data traffic associated with the SaaS applications;
updating model based on the organization's SaaS applications utilization
evaluation of replacement SaaS occurs when one of the current SaaS does not comply with functional fit
Mehmanpazir, however, teaches
determining current software-as-a-service (SaaS) applications being used by an organization (see at least Mehmanpazir, 0189, “a customer profile for tenant 506b may indicate that actual usage 516 is substantially less than subscribed usage 512. The customer profile may be input into the insight engine to determine a renewal probability for tenant” See also 0196 that the provider is an SaaS provider);
monitoring, using a processor, the SaaS applications utilizing Application Program Interfaces (APIs) by tracking data traffic associated with the SaaS applications ( 00557, API is used to collect customer specific information and product specific information 0066-0067, time periods are used to segment data collected);
determining the organization's SaaS applications utilization over an interval of time based on the data traffic associated with the SaaS applications (see at least Mehmanpazir, 0189, “a customer profile for tenant 506b may indicate that actual usage 516 is substantially less than subscribed usage);
updating model based on the organization's SaaS applications utilization (0133-0134, insight is periodically monitored and retrained based on current information of customer)
evaluation of replacement SaaS occurs when one of the current SaaS applications does not comply with the functional fit according to the SaaS applications current utilization (see at least Mehmanpazir, 0189, “a customer profile for tenant 506b may indicate that actual usage 516 is substantially less than subscribed usage 512. The customer profile may be input into the insight engine to determine a renewal probability for tenant”)
Therefore, it would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine Mehmanpazir’s application of evaluating SaaS currently being used with Chitrapura’s SaaS service recommendation for the purpose of allowing user to receive better services (Mehmanpazir, 0131, noting services offered can be increased when renewal probability is low, which can be a result of bad functional fit per 0189).
As per claim 2, Chitrapura further discloses the method according to claim 1, further comprising generating a dynamic playbook that is used to optimize a selection process for replacing or retaining SaaS applications (see at least Chitrapura, 5:35-42, noting model is automated predictive model and the model is part of process that is used to optimize a selection process).
As per claim 3, Chitrapura further discloses the method according to claim 2, wherein at least a portion of the criteria of the current SaaS applications are defined by a user of the organization (see at least 22:13-24, business users may collaborate to identify requirements).
As per claim 4, Chitrapura further discloses the method according to claim 3, wherein generating the dynamic playbook includes generating weighted metrics for the criteria of the replacement SaaS applications and the criteria of the current SaaS applications (see at least 22:13-24, business users may collaborate to identify requirements including user specified weights).
As per claim 5, Chitrapura further discloses the method according to claim 1, further comprising activating an automated workflow application configured to procure a replacement SaaS application in accordance with an algorithmic workflow (see at least Chitrapura, Fig.4 4 where query kicks off the entire flow 402-418. See Fig. 3C for illustration of report generated as a result).
As per claim 7, Chitrapura further discloses the method according to claim 1, further comprising:
selecting one of the replacement SaaS applications based on utilization data (see at least 21:34-45, “At 1012, a report generation model is generated by combining the master report schema, NLG models corresponding to each element and the variable bindings, text summarization models.”).
Chitrapura does not but Mehmanpazir teaches monitoring performance data for the current SaaS applications to calculate utilization thereof (see at least Mehmanpazir, 0189, “a customer profile for tenant 506b may indicate that actual usage 516 is substantially less than subscribed usage 512. The customer profile may be input into the insight engine to determine a renewal probability for tenant”).
The rationale to combine would persist.
As per claim 8, Chitrapura further discloses the method according to claim 1, further comprising:
monitoring performance data (16:27-31, user reviews is performance data);
predicting data based on the performance data during an interval of time to form forecasted data (see at least Chitrapura, 18:41-47, “The feedback collected by the foregoing operations can be used by the platform 10 to improve matching of business requirements to software application program” See also 17:14-21 where time period is part of factors used by model); and
selecting one of the replacement SaaS applications as a function of the forecasted data (see at least Chitrapura, 18:41-47, “The feedback collected by the foregoing operations can be used by the platform 10 to improve matching of business requirements to software application program. A similar methodology can be applied to improve matching of vendor companies to potential customers.”).
As per claim 9, Chitrapura further discloses the method according to claim 1, wherein the criteria include at least one key performance indicator ("KPI") metric (see at least Chitrapura, 21:10-16, business needs such as goals and activities are a type of KPI metric).
As per claim 10, Chitrapura further discloses the method according to claim 9, wherein the criteria include data representing terms in an electronic document constituting requirements associated with an exchange of data (see Fig. 3B, where product pricing information is displayed. Examiner notes product pricing represent terms in a document that constitute cost requirement associated with using the SaaS, which involves exchange of data).
Claims 11-15, 17-19 includes limitations substantially similar to claims 2-5, 7-8, 10 and would be rejected under similar rationale set forth above.
As per claim 20, Chitrapura does not but Mehmanpazir teaches the system according to claim 11, wherein the processor is configured to:
evaluate invoices and subsequent payments for a customer (see at least Mehmanpazir, 0038, contract and its payment history are evaluated);
evaluate payment history for the customer (see at least Mehmanpazir, 0038, payment history are evaluated); and
generate a suggestion to shorten a time span for payment of an invoice based on the payment history (see at least Mehmanpazir, 0131, minimal contractual terms are part of incentive. Also, see 0027, contract terms can be either yearly or half-year basis).
The rationale to combine would persist.
As per claim 21, Chitrapura does not but Mehmanpazir teaches the method according to claim 1, wherein the contract terms include renewal dates, uplift, and vendor (0131 where contract terms can include things like “decreasing price, increasing services offered, decreasing minimum contractual term” for a particular salesperson for a vendor. Examiner notes a reduction in price is a negative uplift)
The rationale to combine would persist.
Claim(s) 6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chitrapura (US 11295375) in view of Mehmanpazir (US 20210166251), further in view of Groth (US 20220292220)
As per claim 6, Chitrapura further discloses the method according to claim 5, wherein activating the automated workflow application comprises:
identifying end users configured to approve to select a vendor of the replacement SaaS application (see at least Chitrapura, 22:20-25, permit business users on a team to obtain approvals.); and
Chitrapura does not but Groth teaches
receiving data representing approvals to select the vendor of the replacement SaaS application from the end users, wherein at least one approval is performed automatically (see at least Groth, 0310, approval can be automated based on user preference).
Therefore, it would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine Groth’s auto approval with Chitrapura’s steps to obtain approvals for the purpose of obtaining resources needed by team members based on approving person’s preference.
Claim 16 can be similarly rejected as claim 6.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded 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 filed 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE CHEN whose telephone number is (571)270-5499. The examiner can normally be reached Monday-Friday, 8:30 AM -5:00 PM Eastern.
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GEORGE CHEN
Primary Examiner
Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628