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), 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims X are canceled.
Claims X are new.
Claims 1-20 are pending and have been examined.
This action is in reply to the papers filed on 04/10/2025 (effective filing date 07/26/2018).
Information Disclosure Statement
The information disclosure statement(s) submitted: 08/28/2025, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. Independent claims X, Y, and Z all contain the same inventive scope. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method and computer readable medium for implementing intelligent, adaptive electronic procurement systems.
Claim 1 recites [a] computer-implemented method, comprising: maintaining, by a processor, a database containing buyer data and supplier data, receiving, by the processor, a query inputted using a user device associated with a buyer account; computing, by the processor, a context for the query that includes a level of an organizational hierarchy in which the buyer account fits; selecting a first cognitive advisor and a second cognitive advisor from a group of cognitive advisors based on the context, the first cognitive advisor being a first digital model having a first set of input parameters and first output data and the second cognitive advisor being a second digital model having a second set of input parameters and second output data that are different from the first set of input parameters and first output data; executing the first cognitive advisor, wherein the first cognitive advisor outputs a first recommendation of one or more products or supplier accounts based on the level of the organizational hierarchy in which the buyer account fits; executing the second cognitive advisor to generate a second recommendation; and sending, by the processor, instructions to the user device for displaying a summary of the first recommendation and the second recommendation and detail related to a selected one of the first recommendation and the second recommendation.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-10 recite a method and, therefore, are directed to the statutory class of a process. Claims 11-20 recite a non-transitory storage media and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. A computer-implemented method, comprising:
No additional elements are positively claimed.
maintaining, by a processor, a database containing buyer data and supplier data, receiving, by the processor, a query inputted using a user device associated with a buyer account;
This limitation includes the step(s) of: maintaining, by a processor, a database containing buyer data and supplier data, receiving, by the processor, a query inputted using a user device associated with a buyer account.
But for the processor, database, and/or user device, this limitation is directed to processing and/or communicating known information (e.g., receiving and transmitting information) to facilitate implementing intelligent, adaptive electronic procurement systems which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
maintaining, by a processor, a database containing …, receiving, by the processor, a query inputted using a user device…
Additional elements (processor, database, user device) are merely communicating or transmitting known information.
computing, by the processor, a context for the query that includes a level of an organizational hierarchy in which the buyer account fits;
This limitation includes the step(s) of: computing, by the processor, a context for the query that includes a level of an organizational hierarchy in which the buyer account fits.
But for the processor this limitation is directed to processing known information to facilitate implementing intelligent, adaptive electronic procurement systems which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
computing, by the processor, a context for the query…
selecting a first cognitive advisor and a second cognitive advisor from a group of cognitive advisors based on the context, the first cognitive advisor being a first digital model having a first set of input parameters and first output data and the second cognitive advisor being a second digital model having a second set of input parameters and second output data that are different from the first set of input parameters and first output data;
This limitation includes the step(s) of: selecting a first cognitive advisor and a second cognitive advisor from a group of cognitive advisors based on the context, the first cognitive advisor being a first digital model having a first set of input parameters and first output data and the second cognitive advisor being a second digital model having a second set of input parameters and second output data that are different from the first set of input parameters and first output data.
No additional elements are positively claimed.
This limitation is directed to making a selection to facilitate implementing intelligent, adaptive electronic procurement systems which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
executing the first cognitive advisor, wherein the first cognitive advisor outputs a first recommendation of one or more products or supplier accounts based on the level of the organizational hierarchy in which the buyer account fits;
This limitation includes the step(s) of: executing the first cognitive advisor, wherein the first cognitive advisor outputs a first recommendation of one or more products or supplier accounts based on the level of the organizational hierarchy in which the buyer account fits.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate implementing intelligent, adaptive electronic procurement systems which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
executing the second cognitive advisor to generate a second recommendation; and
This limitation includes the step(s) of: executing the second cognitive advisor to generate a second recommendation.
No additional elements are positively claimed.
This limitation is directed to processing known information in order to facilitate implementing intelligent, adaptive electronic procurement systems which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
sending, by the processor, instructions to the user device for displaying a summary of the first recommendation and the second recommendation and detail related to a selected one of the first recommendation and the second recommendation.
This limitation includes the step(s) of: sending, by the processor, instructions to the user device for displaying a summary of the first recommendation and the second recommendation and detail related to a selected one of the first recommendation and the second recommendation.
But for the processor and/or user device, this limitation is directed to communicating known information to facilitate implementing intelligent, adaptive electronic procurement systems which may be categorized as any of the following:
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
sending, by the processor, instructions to the user device for displaying a summary…
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of implementing intelligent, adaptive electronic procurement systems, which, pursuant to MPEP 2106.04, is aptly categorized as a mental process and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: a processor, database, and user device for implementing the method claims (Claims 1-10, method claims), a processor, database, storage media, and user device for implementing the CRM claims (Claims 11-20, CRM claims). Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The claims also recite additional technical elements including: a processor, database, and user device for implementing the method claims (Claims 1-10, method claims), a processor, database, storage media, and user device for implementing the CRM claims (Claims 11-20, CRM claims). These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data. Furthermore, Applicant’s Specification (PGPub. 2025/0238476 [0071; 0336; 0339]) refers to a general computer system, but they do not include any technically-specific computer algorithm or code.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent CRM claim 11 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer components, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-10 and 12-20 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims maintaining a database, receiving a query, selecting a model (e.g., selecting a cognitive advisor), executing the model, and sending or displaying a recommendation, etc… Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from implementing intelligent, adaptive electronic procurement systems. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
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 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 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840.
19/174,982 – Claim 11. Weis et al. 2015/0032543 One or more non-transitory storage media storing instructions which, when executed by one or more processors (Weis et al. 2015/0032543 [0007; 0057; 0062; 0101]), cause performing a method comprising: maintaining, by a processor, a database containing buyer data and supplier data (Weis et al. 2015/0032543 [0047 – Database 120 may store transaction data generated as part of sales activities conducted over the processing network including data relating to merchants, account holders or customers, issuers, acquirers, purchases made. Database 120 may also store account data including at least one of a cardholder name, a cardholder address, an account number, and other account identifiers. Database 120 may also store merchant data including a merchant identifier that identifies each merchant registered to use the network, and instructions for settling transactions including merchant bank account information. Database 120 may also store purchase data associated with items being purchased by a cardholder from a merchant, and authorization request data.][0096; Fig. 9]), receiving, by the processor, a query inputted on a user device associated with a buyer account (Weis et al. 2015/0032543 [0018 – a user can input a search…][0031 – receiving search preferences from a candidate consumer inputted using a recommender app stored on a user computing device…][Fig. 2; 0043 - a plurality of computer devices including a user device][0047 – account holders][0059 – user system 202 includes an input device 220 for receiving input from user]); computing, by the processor, a context for the query that includes a level of an organizational hierarchy in which the buyer account fits (Weis et al. 2015/0032543 [0025 - system sorts the merchant list in descending order with the merchant having the highest ratio of local and unique transactions to total transactions at the top]); selecting a first cognitive advisor and a second cognitive advisor from a group of cognitive advisors based on the context (Weis et al. 2015/0032543 [0101]), the first cognitive advisor being a first digital model having a first set of input parameters and first output data and the second cognitive advisor being a second digital model having a second set of input parameters and second output data that are different from the first set of input parameters and output data (Weis et al. 2015/0032543 [0018 – input… output… MA computer system is in communication with a user device having a merchant recommender application (also referred to as "recommender app") stored thereon such that a user can input a search location to be considered by the MA computer system, and view output from the MA computer system]); executing the first cognitive advisor, wherein the first cognitive advisor outputs a first recommendation of one or more products or supplier accounts based on the level of the organizational hierarchy in which the buyer account fits (Weis et al. 2015/0032543 [0002 - methods and systems for recommending merchants and, more particularly, to methods and systems for recommending merchants to a potential consumer based at least in part on the a search location provided by the potential customer…][Fig. 8; 0016 - method of recommending merchants to a candidate customer using the merchant analytic computer system shown in FIG. 2 coupled to a user device having a merchant recommender application][0043 – recommender application][0074 – recommender module]); executing the second cognitive advisor to generate a second recommendation (Weis et al. 2015/0032543 [0018 - output includes recommendations for merchants that are most transacted with by local residents][Fig. 2; 0043 - payment processing system that includes a merchant analytic (MA) computer system 121 configured to provide merchant recommendation data to a computing device using a merchant recommender application][0050 - displayed along with merchant recommendations through recommender app]); and sending, by the processor, instructions to the user device for displaying a summary of the first recommendation and the second recommendation and detail related to a selected one of the first recommendation and the second recommendation (Weis et al. 2015/0032543 [0007 - computer-executable instructions embodied thereon for recommending at least one…][Fig. 7; 0015 - method of recommending merchants to a candidate customer using the merchant analytic computer system shown in FIG. 2 coupled to a user device having a merchant recommender application stored thereon][0018 – display a list of recommended merchants based on a search…]).
Weis et al. 2015/0032543 may not expressly disclose the “cognitive advisor” features, however Kaguma et al. 2018/0253814 teaches (Kaguma et al. 2018/0253814 [0053 – a cognitive advisor module configured to implement at least a portion of the response plan]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Kaguma et al. 2018/0253814. One of ordinary skill in the art would have been motivated to do so to to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
Weis et al. 2015/0032543 may not expressly disclose the “organizational hierarchy” features, however Carson et al. 2007/0112840 teaches (Carson et al. 2007/0112840 [0059 - weighted sum of the click through rate at the one or more levels of the organizational hierarchy associated with a given advertisement may be used to calculate an historical clickability score for a given advertisement displayed in response to a given query][0051 - the clickability engine 108 calculates an actual clickability score for the one or more query/advertisement pairs, wherein an actual clickability score for a given query/advertisement pair comprises a numerical value indicating the normalized rate at which the advertisement was selected in response to the query in a given context]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Carson et al. 2007/0112840. One of ordinary skill in the art would have been motivated to do so to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
19/174,982 – Claim 1. A computer-implemented method, comprising:
maintaining, by a processor, a database containing buyer data and supplier data, receiving, by the processor, a query inputted using a user device associated with a buyer account;
computing, by the processor, a context for the query that includes a level of an organizational hierarchy in which the buyer account fits;
selecting a first cognitive advisor and a second cognitive advisor from a group of cognitive advisors based on the context, the first cognitive advisor being a first digital model having a first set of input parameters and first output data and the second cognitive advisor being a second digital model having a second set of input parameters and second output data that are different from the first set of input parameters and first output data;
executing the first cognitive advisor, wherein the first cognitive advisor outputs a first recommendation of one or more products or supplier accounts based on the level of the organizational hierarchy in which the buyer account fits;
executing the second cognitive advisor to generate a second recommendation; and
sending, by the processor, instructions to the user device for displaying a summary of the first recommendation and the second recommendation and detail related to a selected one of the first recommendation and the second recommendation.
Claim 1, has similar limitations as of Claim 11, therefore it is REJECTED under the same rationale as Claim 11.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840; in view of Kinnucan et al. 2008/0092111.
19/174,982 – Claim 12. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, the method further comprising: determining an execution order between the first cognitive advisor and the second cognitive advisor, wherein executing the first cognitive advisor and executing the second cognitive advisor are performed in the execution order (Weis et al. 2015/0032543 [Fig. 4; 0057 – executable instructions][0097 - processing component 910 assists with execution of computer-executable instructions associated with the system]).
Weis et al. 2015/0032543 may not expressly disclose the “execution order between” features, however Kinnucan et al. 2008/0092111 teaches (Kinnucan et al. 2008/0092111 [0060 - modeling environment 300 may determine an order of execution based on a relationship between…] Further, the graphical modeling environment 300 may determine an order of execution based on a relationship between elements in a graphical model. To determine the order of execution, the elements in the graphical model may specify the existence of any input dependencies, such that the output of the element cannot be computed before the input to the element is available. This approach enables the graphical modeling environment 300 to determine the order of execution without requiring the graphical modeling environment 300 to know the details of equations internal to the elements.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Kinnucan et al. 2008/0092111. One of ordinary skill in the art would have been motivated to do so to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
19/174,982 – Claim 2. The computer-implemented method of claim 1, further comprising: determining an execution order between the first cognitive advisor and the second cognitive advisor, wherein executing the first cognitive advisor and executing the second cognitive advisor are performed in the execution order.
Claim 2, has similar limitations as of Claim 12, therefore it is REJECTED under the same rationale as Claim 12.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840; in view of Janjua et al. 2019/0235988.
19/174,982 – Claim 13. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, wherein executing the first cognitive advisor and executing the second cognitive advisor are performed in parallel (Weis et al. 2015/0032543 [Fig. 4; 0057 – executable instructions][0097 - processing component 910 assists with execution of computer-executable instructions associated with the system]).
Weis et al. 2015/0032543 may not expressly disclose the “parallel execution” features, however Janjua et al. 2019/0235988 teaches (Janjua et al. 2019/0235988 [0007 - comprising the first segment model, the second segment model, and the parallel execution dependency definition may be generated][0127 - a first segment model is executing in parallel with a second segment model]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Janjua et al. 2019/0235988. One of ordinary skill in the art would have been motivated to do so to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
19/174,982 – Claim 3. The computer-implemented method of claim 1, wherein executing the first cognitive advisor and executing the second cognitive advisor are performed in parallel.
Claim 3, has similar limitations as of Claim 13, therefore it is REJECTED under the same rationale as Claim 13.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840; in view of Dziuba et al. 2016/0092990.
19/174,982 – Claim 14. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, the method further comprising: determining a display order among the first cognitive advisor and the second cognitive advisor (Weis et al. 2015/0032543 [0025 - system sorts the merchant list in descending order…][0026 - system sorts the merchant list according to a search location input … wherein the list is based on the merchant list sorted by the number of local and unique transactions relative to …][0031 - sorting the merchant list in descending order based on the calculated ratio…][0074 - system 121 sorts the merchant list in accordance with search preference information 606 and displays a recommended merchant list…]), the summary of the first recommendation and the second recommendation being in an accordion format with a first section and a second section, respectively for the first recommendation and the second recommendation arranged in the display order (Weis et al. 2015/0032543 [0050 – displayed along with merchant recommendations through recommender app…][Claim 18 – display the list of recommended merchants in ascending order…]); and when a particular section of the first section and the second section is selected, sending, by the processor, instructions to the user device for collapsing a non-selected section (Weis et al. 2015/0032543 [0062 - instructions]), expanding the particular section, and replacing first detail related to the non-selected section with second detail related to a particular list of results corresponding to the particular section (Weis et al. 2015/0032543 [0025 - system sorts the merchant list in descending order…]).
Weis et al. 2015/0032543 may not expressly disclose the “accordion” features, however Dziuba et al. 2016/0092990 teaches (Dziuba et al. 2016/0092990 [0057 - the detail UI 700 displaying information for plan #1 with the “How The Plan Works” accordion area expanded and the remaining accordion areas collapsed. FIG. 9 shows the detail UI 700 as a result of the user selecting one of the navigation objects 805 of the detail UI 700 of FIG. 8. Specifically, FIG. 9 shows the detail UI 700 displaying information for plan #2, and having the same accordion area arrangement as was previously displayed to the user when viewing plan #1, i.e., with the “How The Plan Works” accordion area 740 expanded and the remaining accordion areas collapsed. This provides a technological advantage of permitting the user to drill down to a selected topic for a selected plan][0006; 0053; 0065]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Dziuba et al. 2016/0092990. One of ordinary skill in the art would have been motivated to do so to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
19/174,982 – Claim 4. The computer-implemented method of claim 1, further comprising: determining a display order among the first cognitive advisor and the second cognitive advisor, the summary of the first recommendation and the second recommendation being in an accordion format with a first section and a second section, respectively, for the first recommendation and the second recommendation arranged in the display order; and when a particular section of the first section and the second section is selected, sending, by the processor, instructions to the user device for collapsing a non-selected section, expanding the particular section, and replacing first detail related to the non-selected section with second detail related to a particular list of results corresponding to the particular section.
Claim 4, has similar limitations as of Claim 14, therefore it is REJECTED under the same rationale as Claim 14.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840.
19/174,982 – Claim 15. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, wherein the first set of input parameters comprises previous buyer online activities, including browsing product pages, selecting product data for review, adding products to shopping lists or carts, providing product or supplier reviews, obtaining requisitions or purchase orders, or placing product orders (Weis et al. 2015/0032543 [0004 - provide restaurant reviews or score… post a review based on a bad experience… ][0022 - system analyzes the cardholder's transaction history with brick and mortar merchants in certain merchant segments (e.g., dry cleaners and grocery stores)][0041 - during and/or after the clearing process, additional data, such as a time of purchase, a merchant name, a type of merchant, purchase information, cardholder account information, a type of transaction, itinerary information, information regarding the purchased item and/or service, and/or other suitable information, is associated with a transaction and transmitted between parties to the transaction as transaction data, and may be stored by any of the parties to the transaction]).
19/174,982 – Claim 5. The computer-implemented method of claim 1, wherein the first set of input parameters comprises previous buyer online activities, including browsing product pages, selecting product data for review, adding products to shopping lists or carts, providing product or supplier reviews, obtaining requisitions or purchase orders, or placing product orders.
Claim 5, has similar limitations as of Claim 15, therefore it is REJECTED under the same rationale as Claim 15.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840.
19/174,982 – Claim 16. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, wherein the buyer account is associated with a geographic location or a supplier preference (Weis et al. 2015/0032543 [0002 - recommending merchants to a potential consumer based at least in part on the a search location provided by the potential customer and historical payment transactions of cardholders local to the search location][0018 - generate a list of merchants based on a number of local and unique cardholders, and display a list of recommended merchants based on a search location input by a user][0029 - system 121 may designate a merchant as a "Local Favorite" when the merchant's local popularity score is above a certain threshold or when the merchant's local popularity score is in a top percentage of all local popularity scores for all merchants in a geographic area][0031; 0032; 0043; 0049; 0074; 0080; 0091; 0096 - preferences][claim 17 - receive a search location from the candidate consumer inputted using a recommender application stored on a user computing device, the search location including at least one of an address, a zip code, and a city; sort the merchant list in accordance with the candidate consumer search preference information; and display a list of recommended merchants to the candidate consumer in ascending order based on travel time from the search location]).
19/174,982 – Claim 6. The computer-implemented method of claim 1, wherein the buyer account is associated with a geographic location or a supplier preference.
Claim 6, has similar limitations as of Claim 16, therefore it is REJECTED under the same rationale as Claim 16.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840; in view of Buchner et al. 2009/0198644.
19/174,982 – Claim 18. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, the method further comprising: executing a query re-write cognitive advisor from the group of cognitive advisors, wherein the query re-write cognitive advisor generates a query rewrite suggestion based on a semantic model created from past queries and corresponding cart selections and purchases (Weis et al. 2015/0032543 [0022 - system identifies a cardholder's inferred residential zip code based on the cardholder's transaction history in the transaction information][0041 - purchase]).
Weis et al. 2015/0032543 may not expressly disclose the “query rewrite suggestion” features, however Buchner et al. 2009/0198644 teaches (Buchner et al. 2009/0198644 [0010 - a query rewrite suggestion tree] FIG. 2 is a query rewrite suggestion tree according to an embodiment of the invention [0019; 0044]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Buchner et al. 2009/0198644. One of ordinary skill in the art would have been motivated to do so to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
19/174,982 – Claim 8. The computer-implemented method of claim 1, further comprising: executing a query re-write cognitive advisor from the group of cognitive advisors, wherein the query re-write cognitive advisor generates a query rewrite suggestion based on a semantic model created from past queries and corresponding cart selections and purchases.
Claim 8, has similar limitations as of Claim 18, therefore it is REJECTED under the same rationale as Claim 18.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over: Weis et al. 2015/0032543; in view of Kaguma et al. 2018/0253814; in further view of Carson et al. 2007/0112840; in view of Mohamed 2018/0374138.
19/174,982 – Claim 20. Weis et al. 2015/0032543 further teaches The one or more non-transitory storage media of claim 11, wherein the first cognitive advisor comprises a learning engine configured to receive user behavior as input, wherein the user behavior comprises previous actions taken by users that received recommendations from the first cognitive advisor; wherein the previous actions comprise a click on an organization preference recommendation, adding to a cart, or purchasing (Weis et al. 2015/0032543 [0041 – purchases and transactions][0068 - activities][0102 - method and system of ranking merchants according to purchasing behaviors of local cardholders provide a cost-effective and reliable means for maintaining contact with a customer by merchants]).
Weis et al. 2015/0032543 may not expressly disclose the “learning” features, however Mohamed 2018/0374138 teaches (Mohamed 2018/0374138 [0029 - deep reinforcement learning system and the agent interact with the online environment by receiving one or more “observations.” Each observation fully or partially characterizes a user action performed in the online environment. For example, an observation can include certain characteristics of user's behavior (e.g., user feedback, browsing history, search history, user actions, etc.). In other embodiments, an observation can fully or partially characterize a user action performed in the online environment in response to at least one purchase recommendation][0043 - Deep reinforcement learning system 105 selects or determines actions … receives one or more observations characterizing a user action made in online environment … observations can refer to a user feedback in response to displaying a purchase recommendation. This feedback can include three typical industry standard actions: (1) a click action; (2) a save-for-later action (also known as “save-to-wish-list” action); and (3) an immediate purchase action. The observations can also, or in an alternative, include user online behavior, a user online browsing history, a user online searching history, a user action to review or watch a purchase recommendation, share a purchase recommendation via a social media]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Weis et al. 2015/0032543 to include the features as taught by Mohamed 2018/0374138. One of ordinary skill in the art would have been motivated to do so to better utilize well known tools and features for implementing intelligent, adaptive electronic procurement systems, which should prove to improve user experience, maximize profits, and optimize revenue.
19/174,982 – Claim 10. The computer-implemented method of claim 1, wherein the first cognitive advisor comprises a learning engine configured to receive user behavior as input, wherein the user behavior comprises previous actions taken by users that received recommendations from the first cognitive advisor; wherein the previous actions comprise a click on an organization preference recommendation, adding to a cart, or purchasing.
Claim 10, has similar limitations as of Claim 20, therefore it is REJECTED under the same rationale as Claim 20.
No Prior-art Rejection / Potentially Allowable
Claims 7, 9, 17, 19 cannot be rejected with prior-art. Individual claimed features are taught in the prior-art, however, the unique combination of features and elements are not taught by the prior-art without hindsight reasoning. These claims are further rejected to as being dependent upon a rejected base claim but might possibly be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
19/174,982 – Claim 17. The one or more non-transitory storage media of claim 11, wherein the second recommendation comprises a workflow recommendation based on previous approval patterns and times generated based on user identification, product category, cart size, and historical approval chain times.
19/174,982 – Claim 7. The computer-implemented method of claim 1, wherein the second recommendation comprises a workflow recommendation based on previous approval patterns and times generated based on user identification, product category, cart size, and historical approval chain times.
19/174,982 – Claim 19. The one or more non-transitory storage media of claim 11, the method further comprising: executing a result template cognitive advisor from the group of cognitive advisors, wherein the result template cognitive advisor receives the query and the context as input, and wherein the result template cognitive advisor generates a result template and a template parameter, the template parameter comprising sources of information and a layout for the sources of the information within the result template.
19/174,982 – Claim 9. The computer-implemented method of claim 1, further comprising: executing a result template cognitive advisor from the group of cognitive advisors, wherein the result template cognitive advisor receives the query and the context as input, and wherein the result template cognitive advisor generates a result template and a template parameter, the template parameter comprising sources of information and a layout for the sources of the information within the result template.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Melero et al. 2002/0111879 [0001] The invention relates generally to the location and selection of products, namely goods and/or services, for purchase, and the creation of purchase orders for electronic commerce and, more particularly, to an improved business automation method and system for selecting and ordering products through the Internet or an intranet.
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
S. Earley, "There Is No AI Without IA," in IT Professional, vol. 18, no. 3, pp. 58-64, May-June 2016, doi: 10.1109/MITP.2016.43.
W. E. Spangler, "The role of artificial intelligence in understanding the strategic decision-making process," in IEEE Transactions on Knowledge and Data Engineering, vol. 3, no. 2, pp. 149-159, June 1991, doi: 10.1109/69.87995.
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact Information
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b
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