DETAILED ACTION
Notice to Applicant
1. The following is a NON-FINAL Office action upon examination of application number 18/649,275 filed on 04/29/2024. Claims 1-5, 10-14, 17, and 21-26 are pending in the application and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
3. The preliminary amendment filed on 08/13/2024 has been entered, which amends claims 1-5, 10-14, and 17, cancels claims 6-9, 15, 16, and 18-20, and adds new claims 21-26.
Priority
4. Application 18/649,275 filed 04/29/2024 is a Continuation of application 17/743,252, filed 05/12/2022. Application 17/743,252 is a Continuation of application 16/594,408, filed 10/07/2019. Application 16/594,408 claims Priority from Provisional Application 62/742,622, filed 10/08/2018.
Information Disclosure Statement
5. The information disclosure statement (IDS) filed on 08/13/2024 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
6. Claim 26 is objected to because of the following informalities: typographical/grammatical errors:
Claim 26 recites “a one of the one or more transactions”. The phrase “a one” is grammatically incorrect. Examiner suggests amending the claim to replace “a one” with “one” to correct the grammatical error. Appropriate correction is required.
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 1-5, 10-14, 17, and 21-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
9. Claims 1 and 10 recite the limitation “the augmenting is responsive to the predicting”. The imitation “the predicting” is ambiguous. Claim 1 recites two separate “predicting” steps (i.e., predicting a future supply and predicting a future demand), but it is unclear which prediction step (s) the augmenting is responsive to, therefore rendering the claims indefinite. Appropriate correction/clarification is required.
10. Claim 10 recites the limitation “a processor-executable machine learning algorithm” and subsequently refers to “the machine learning algorithm” and “the processor-executable machine learning algorithm.” It is unclear whether these are the same algorithm or different algorithm, and it is unclear whether “the machine learning algorithm” is intended to refer to the previously recited “processor-executable machine learning algorithm”, therefore rendering the claim indefinite. Appropriate correction/clarification is required.
All claims dependent from above rejected claims are also rejected due to dependency.
Double Patenting
11. The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
12. Claim 1 is provisionally rejected on the ground of non-statutory double obviousness-type double patenting as being unpatentable over claims 1, 7, and 8 of copending Application No. 17/743,252 (hereinafter ‘252). Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of the instant application is obvious in view of claims 1, 7, and 8 in the listed application. Claim 1 of the instant application recites “A method of maintaining a machine-learning based system for sourcing materials; the method comprising: electronically receiving bid information from each of a plurality of candidate Sellers, the candidate Sellers having an inventory of materials; receiving demand data from a plurality of candidate Buyer Buyers each of the candidate Buyers having a respective need for materials in the inventory; predicting a future supply in accordance with historical data related to past transactions and market conditions via a machine learning algorithm executing on a processor; predicting a future demand in accordance with historical data related to past transactions and market conditions via the machine learning algorithm executing on the processor; comparing the bid information and the demand data in accordance with the predicted future supply and the predicted future demand; responsive to the comparing, matching one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers based in part upon the inventory, the respective need, and the predicted future supply and the predicted future demand to identify one or more transactions; automatically allocating the one or more portions portion of the inventory to respective ones of one or more of the plurality of candidate Buyers; and augmenting the historical data with a respective piece of information associated with the one or more identified transactions for subsequent use by the machine learning algorithm in subsequently training the machine-learning based system, wherein the augmenting includes augmenting the historical data with a value that represents the one or more portions of the inventory that was matched and allocated for the one or more identified transactions, wherein the predicting a future supply and the predicting a future demand are based on the historical data and the respective piece of information associated with the one or more identified transactions and the augmenting is responsive to the predicting.” Claim 1 of the ‘252 application recites: “A method of maintaining a machine-learning based system for sourcing materials; the method comprising: electronically receiving bid information from each of a plurality of candidate Sellers, the candidate Sellers having an inventory of materials; receiving demand data from a plurality of candidate Buyers each of the candidate Buyers having a respective need for materials in the inventory; predicting a future supply in accordance with historical data related to past transactions and market conditions via a machine learning algorithm executing on a processor; predicting a future demand in accordance with historical data related to past transactions and market conditions via the machine learning algorithm executing on the processor; comparing the bid information and the demand data in accordance with the predicted future supply and the predicted future demand; responsive to the comparing, matching one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers based in part upon the inventory, the respective need, and the predicted future supply and the predicted future demand to identify one or more transactions; automatically allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers; and augmenting the historical data with a respective piece of information associated with the one or more identified transactions for subsequent use by the machine learning algorithm in subsequently training the machine-learning based system, wherein the augmenting includes augmenting the historical data with a value that represents the one or more portions of the inventory that was matched and allocated for the one or more identified transactions.” Claim 7 of the ‘252 application recites: “The method of claim 1 wherein the predicting a future supply and the predicting a future demand are based on the historical data and the respective piece of information associated with the one or more identified transactions.” Claim 8 of the ‘252 application recites: “The method of claim 7 wherein the augmenting is responsive to the predicting.” Claim 1 of the instant application recites similar steps to those recited in claims 1, 7, and 8 of copending application ‘252. Therefore, the claims are not patentably distinct. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify claim 1 of the ‘252 application to include the additional limitations of claims 7 and 8 of that application, as those limitation merely specify the relationship between the predicting and the augmenting step already recited in claim 1 of the ‘252 application. Claim 1 of the instant application therefore represents and obvious variation of claims 1, 7, and 8 of the ‘252 application and is not patentably distinct.
This is a provisional non-statutory obviousness-type double patenting rejection because the patentably indistinct claims have not in fact been patented.
13. Claims 1-5, 10-14, 17, and 21-26 are provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-5, 7-8, 10-17, and 21-25 of copending Application No. 17/743,252.
Claims of instant Application
Claims of copending Application No. 17/743,252
1
1, 7, 8
2
2
3
3
4
4
5
5
10
10, 15, 16, 17
11
11
12
12
13
13
14
14
17
17
21
21
22
22
23
23
24
24
25
25
The chart above maps claims of the instant application to corresponding claims of copending Application No. 17/743,252 that are patentably indistinct, though not identical. One of ordinary skill in the art would have recognized the slight differences between the claim language/limitations of the corresponding claims as being directed towards intention, slight variations in terminology, or obvious variants of claim elements, and therefore these claims are not patentably distinct from one another despite these slight differences.
Claim Rejections - 35 USC § 101
14. 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.
15. Claims 1-5, 10-14, 17, and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
16. Claims 1-5, 10-14, 17, and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-5 and 21-26), and system (claims 10-14 and 17) are directed to at least one potentially eligible category of subject matter (i.e., process, and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-5, 10-14, 17, and 21-26 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), and managing commercial interactions (e.g., advertising, marketing or sales activities or behaviors; business relations), the claims also recite an abstract idea that falls into the “Mental Processes” or concepts performed in the human mind such as via observation, evaluation, and judgment; and (With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below:
electronically receiving bid information from each of a plurality of candidate Sellers, the candidate Sellers having an inventory of materials (This step is organizing human activity by managing interactions between people by following rules, or instructions, i.e., by collecting information about the bid information from each of a plurality of candidate Sellers, and also describes commercial activity such as sales activities.);
receiving demand data from a plurality of candidate Buyers each of the candidate Buyers having a respective need for materials in the inventory (This step is organizing human activity by managing interactions between people by following rules, or instructions, i.e., by collecting information about the demand information from a plurality of candidate Buyers, and also describes commercial activity such as sales activities.);
predicting a future supply in accordance with historical data related to past transactions and market conditions via a machine learning algorithm executing on a processor (The future supply predicting step sets forth commercial activities such as sales activities or business relations because the supply prediction directly pertains to activities in pursuit of sales.);
predicting a future demand in accordance with historical data related to past transactions and market conditions via the machine learning algorithm executing on the processor (The future demand predicting step sets forth commercial activities such as sales activities or business relations because the demand prediction directly pertains to sales outcomes and activities in pursuit thereof.);
comparing the bid information and the demand data in accordance with the predicted future supply and the predicted future demand (This step recites mental processes performed in the mind via observation, evaluation, and judgment.);
responsive to the comparing, matching one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers based in part upon the inventory, the respective need, and the predicted future supply and the predicted future demand to identify one or more transactions (This step is organizing human activity for similar reasons as provided for the “receiving” steps above, and also encompasses mental processes since the matching may be accomplished by a human judgment or evaluation, such as with pen and paper.);
automatically allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers (This step is organizing human activity by managing interactions between people by following rules, or instructions.); and
augmenting the historical data with a respective piece of information associated with the one or more identified transactions for subsequent use by the machine learning algorithm in subsequently training the machine-learning based system, wherein the augmenting includes augmenting the historical data with a value that represents the one or more portions of the inventory that was matched and allocated for the one or more identified transactions, wherein the predicting a future supply and the predicting a future demand are based on the historical data and the respective piece of information associated with the one or more identified transactions and the augmenting is responsive to the predicting (This step describes commercial activity such as sales/marketing activities or business relations because the historical data stored encompasses sales data obtained from historical data related to past transactions and market conditions.).
Considered together, these steps set forth an abstract idea of managing commercial interactions via rules or instructions that simply manage inventory allocation to candidate buyers, which falls under the realm of managing commercial interactions (e.g., marketing or sales activities or behaviors; business relations), thus falling under the “Certain methods of organizing human activity” grouping set forth in MPEP 2106, and also recites limitations that can be accomplished mentally (e.g., observation, evaluation, judgement, or opinion) and thus fit within the “Mental Processes” abstract idea grouping. Moreover, Applicant’s Specification supports the interpretation of the above-noted steps as implemented in the context of commercial interactions such as sales activities (See, e.g., paragraph [0002]: “Aspects of the disclosed subject matter relate generally to sourcing materials, and more particularly to a system and method of sourcing materials by connecting or matching sellers having present or expected inventory with buyers having present or expected necessity while minimizing or eliminating the use of a third party intermediary.”)
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claim 10 recites similar limitations as those discussed above and is therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: a machine learning algorithm executing on a processor and subsequently training the machine-learning based system (claim 1), a server platform, a plurality of remote devices operated by respective ones of the plurality of candidate Sellers, a plurality of remote devices operated by respective ones of the plurality of candidate Buyers, a processor, a nontransient computer readable storage medium, processor-executable order module instructions, a processor-executable application program, a processor-executable machine learning algorithm, and subsequently train the machine-learning based system (claim 10). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it”, and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “electronically receiving” and “receiving” steps are evaluated as additional elements, these steps amount at most to insignificant extra-solution data gathering activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: a machine learning algorithm executing on a processor and subsequently training the machine-learning based system (claim 1), a server platform, a plurality of remote devices operated by respective ones of the plurality of candidate Sellers, a plurality of remote devices operated by respective ones of the plurality of candidate Buyers, a processor, a nontransient computer readable storage medium, processor-executable order module instructions, a processor-executable application program, a processor-executable machine learning algorithm, and subsequently train the machine-learning based system (claim 10). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes that generic computer devices that may be used to implement the invention, which cover virtually any computing device under the sun (Specification at paragraph [0034]: e.g., “As noted above, remote device 120 may be embodied in or comprise a hand-held, mobile, portable, or other wireless device such as a wireless telephone, a laptop or tablet computer, or other portable apparatus; alternatively, in an embodiment in which mobility or wireless access may be foregone, remote device 120 may be implemented in the form of a desktop computer, workstation, hard-wired networked terminal device, or any other fixed or non-portable digital processing apparatus. The design, construction, and operation of the various functional blocks, hardware components, and operational characteristics of remote device 120 are conventional and generally well-known...”). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
With respect to the “electronically receiving” and “receiving” steps, these steps amount to insignificant extra-solution activity, which does not amount to a practical application (MPEP 2106.05(g)), nor add significantly more because such activity has been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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) (computer receives and sends information over a network).
Even if the machine-learning algorithm was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of machine learning is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 39: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art”). See also, Muchkaev, US 2010/0287011 (paragraph 47: “artificial intelligence algorithm such as a search algorithm, a learning algorithm, or any other artificial intelligence algorithm commonly known in the art”. See also, Anders et al., US 2020/0020015 (paragraph 101: “inferences may be performed by any combination of means known in the art, such as by pattern-matching, text analytics, semantic analytics, statistical methods, artificial intelligence, Bayesian analysis, machine learning, or keyword searching”).
With respect to the “subsequently training the machine-learning based system,” is noted that that the claimed training is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Liu et al., US 2018/0246966 (paragraph 55: “the process can be performed repeatedly on inputs selected from a set of training data as part of a conventional machine learning training technique to train the neural networks.”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-5, 11-14, 17, and 21-26 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-5, 11, 13, 17, and 21-26 recite: “wherein the electronically receiving bid information comprises electronically receiving data,” “wherein the electronically receiving bid information comprises electronically receiving data,” “wherein the electronically receiving bid information comprises electronically receiving data,” “wherein the electronically receiving bid information comprises electronically receiving data,” “receives demand data,” “receives the bid information,” “store the predicted future supply and the predicted future demand and the respective piece of information associated with the identified transaction for subsequent use,” “comparing material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids to identify fraudulent bids; and rejecting an identified fraudulent bid,” “further comprising: comparing material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids, to identify mistaken bids,” “automatically learns,” “wherein electronically receiving bid information from each of a plurality of candidate Sellers includes receiving images of at least some of the inventory from the candidate Sellers,” “further comprising: automatically routing the automatically allocated portion one or more portions of the inventory to one or more of the candidate Buyers via a transportation carrier,” “generating a purchase order memorializing a one of the one or more transactions”, however these limitations cover organizing human activity since they flow directly from the sellers/buyers activity, which encompasses activity for managing commercial interactions, which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. The dependent claims recite additional elements of: a software application executing on a remote device (claim 2), a software application executing on a wireless telephone (claim 3), a software application executing on a tablet computer (claim 4), a software application executing on a networked computing device (claim 5), the order module, a software application that executes on one of the remote devices operated by a respective one of at least one of the candidate Buyers (claim 11), wherein the remote device operated by at least one of the candidate Buyer is one of a wireless telephone, a tablet computer, or a networked computing device (claim 12), the application program and a software application executing on the respective remote device operated by at least one of the plurality of candidate Sellers (claim 13); wherein the respective remote device operated by at least one of the candidate Sellers is one of a wireless telephone, a tablet computer, or a networked computing device (claim 14), instructions, the processor, and the machine learning algorithm (claim 17), a machine learning algorithm (claims 21-22), the machine learning algorithm automatically learns (claim 23), a virtual purchase order (claim 26). Even if the “electronically receiving data” steps (claims 2-5) are evaluated as additional elements, these steps amount at most to insignificant extra-solution data gathering activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
17. 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.
18. 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.
19. 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.
20. 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.
21. Claims 1-5, 10-14, 17, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Psota et al., Pub. No.: US 2015/0073929 A1, [hereinafter Psota], in view of Belady et al., Pub. No.: US 2012/0109705 A1, [hereinafter Belady], in further view of Holmberg et al., Pub. No.: US 2018/0218449 A1, [hereinafter Holmberg].
As per claim 1, Psota teaches a method of maintaining a machine-learning based system for sourcing materials (paragraphs 0027, 0203, 0216, 0219); the method (paragraph 0027) comprising:
electronically receiving bid information from each of a plurality of candidate Sellers, the candidate Sellers having an inventory of materials (paragraph 0404, discussing that a supplier may use the bidding facility to provide information about its ability to deliver a particular item, type of item, or the like. The supplier may provide an item specification or description, an available quantity, location, speed with which it can be shipped, price, and the like. A plurality of buyers may bid to purchase from the supplier; paragraph 0417, discussing that suppliers [i.e., a plurality of candidate sellers] participating in the marketplace system may provide bids for supplying the products as specified by the buyers in their buyer inquiries. The buyer inquiry information, along with past supplier and/or buyer transaction data as well as data from one or more other data sources may be used for rating a supplier bid for a buyer inquiry. Therefore, the marketplace system may be included or be associated with to a bidding facility for allowing the suppliers and buyers to bid through the marketplace system);
receiving demand data from a plurality of candidate Buyers each of the candidate Buyers having a respective need for materials in the inventory (paragraph 0010, discussing methods and systems are for a platform by which buyers, sellers, and third parties can obtain information related to each other's transaction histories, such as a supplier's shipment history, the types of materials typically shipped, a supplier's customers, a supplier's expertise, what materials and how much a buyer purchases,…, similarity between buyers,…, and the like...; paragraph 0084, discussing that FIG. 14 depicts mapping variations of buyer names to a primary seller; paragraph 0182, discussing that a buyer marketing tool may provide information about what product materials, product techniques, and the like particular buyers require from their suppliers [i.e., candidate Buyers having a respective need for materials in the inventory]…; paragraph 0326, discussing a quote tool by which buyers may identify suppliers and then generate a request for a quote from selected suppliers; paragraph 0403, discussing that a plurality of buyers may bid for the services of a supplier; paragraph 0405, discussing that when a buyer wants to place an order for a particular product, he may post an inquiry regarding the particular type of product, such as a request for supply, a request for quotation, a request for inventory status, and the like; paragraph 0410, discussing that posting a buyer inquiry may result in the display of an inquiry form for a buyer to enter pre-determined, structured data for the inquiry process to use, such as, but not limited to, quantity, unit selection, buying time frame, uploading the specification for an order,…, specific parameters related to pricing, delivery, and the like; paragraph 0420, discussing that the input details provided by the buyer during submission of details about the buying inquiry are stored in the marketplace system and appear in the form of a buying inquiry display interface shown in FIG. 46. For example, the inquiry may include details about the product, its title, its specification, quantity of the product needed by the buyer, timelines for procurement and delivery of the product…; paragraphs 0132, 0183, 0416, 0289, 0308, 0401, 0419);
predicting a future supply in accordance with historical data (paragraph 0261, discussing a breakdown of supplier shipment history, where shipment history may be broken down by piece count, by month, by month to a certain country, and the like. FIG. 24 depicts a breakdown of shipment history as a piece-count chart. FIG. 25 breaks down shipment history into a monthly article chart and a monthly shipment count chart. In embodiments, the product may include shipment history graphs that show trends and volumes of shipments made over some period of time; paragraph 0362, discussing that by analyzing shipment data, the platform may predict an estimate of inventory; paragraph 0484, discussing that private data may include historical data that may be used to facilitate providing a prediction or projection of region-based shipping in the near term and longer term time frames; paragraph 0493, discussing that the methods and systems shipper's data to analyze trends in shipping for a particular supplier, buyer, etc. The shipper's data may provide details such as the buyer and the supplier locations and any specific changes in the transactions between these locations. For example, from shipper's data, it may be possible to track an increase in supply of "electronic components" from a supplier. This might be useful to understand the growth of the supplier…. Similarly, indications of business trends, such as rapid growth, may be used to automatically predict needs based on a detected trend and the methods and systems described may automatically attempt to fulfill those predicted needs. In an example of attempting to fulfill predicted needs based on business trend indications, if it is determined that a particular supplier has increased its manufacturing capacity threefold in the last three months (e.g. the private shipper data may include a new pricing structure for substantially increased shipments that are expected from the supplier), then it may be predicted that the supplier may welcome some potential buyers to order products based on the additional capacity. Likewise, buyers of the products that are similar to those that may be provided by the increased capacity may be notified of the potential increased availability of products; paragraph 0500, discussing that historical data and data over time may be useful in creating a model of capacity, such as capacity over time or for a particular time frame);
predicting a future demand in accordance with historical data (paragraph 0169, discussing that the prediction may be of an action of the buyer based on an analysis of customs data for the buyer transactions. The prediction may be related to a price, a change in price, a change in supplier, a quantity ordered by the buyer and the like; paragraph 0372, discussing forecasting demand of these products; paragraph 0493, discussing predicting needs based on a detected trend and the methods and systems described may automatically attempt to fulfill those predicted needs…; paragraph 0512, discussing that historical data and data over time may be useful in creating a model of buyer demand, such as demand over time or for a particular time frame);
comparing the bid information and the demand data (paragraph 0247, discussing that a user may select elements for comparison and a compare function may provide a tabular view of the comparison of suppliers that meet the selected comparison criteria; paragraph 0266, discussing that search ranking may be done using a ranking or relevance algorithm that functions more than merely matching buyers to suppliers who have had large quantities of similar shipments in the past. In embodiments, the ranking algorithm may include logarithmically weighting the sum of a number of different factors that may be relevant to a supplier's past and potential future performance, as well as matching a buyer's needs…Factors for the search ranking algorithm may include, among other things, the number of shipments made by the supplier that matches at least one aspect of a buyer's request (e.g. product type, such as sweaters). Additionally, the search ranking algorithm may account for the number of similar products a given supplier makes that match the buyer's request (e.g. quantity of sweaters across all types of sweaters offered by the supplier); paragraph 0310, discussing that the process depicted supports identifying a supplier of an item in a first transaction record by comparing the item in the first record to a second record. When a match is found, the supplier identified in the second record may be determined to supply the item; paragraph 0423, discussing that buyers may be interested in receiving bids from suppliers in a ranked order based on rating of the supplier, the bid, etc.…In looking at a request/offer/bid process from a buyers perspective, a buyer may want to compare bids from different suppliers as well as compare bids over time (e.g. compare today's bid from supplier x to last weeks bid from supplier x). With a comprehensive rating system coupled to the bidding facility, dynamic rating of buyers, suppliers, bids, offers, requests, and the like may be beneficially applied to ensure that all participants have access to quantitative assessments during the request/offer/bid process. Examples of bid facility rating may further include determining a rating of a request (bid) posted by a buyer based on the buyer's overall rating that may represent a composite rating based on an aggregation of a wide range of data sources. Likewise, a supplier may be rated based on his performance in the biding facility. A bid to supply a product at a particular price can also be rated by comparing the particular price to estimated and/or actual pricing of the product as reflected in the transaction records and other data sources accessible by the platform. If a supplier offers to sell a product at $100, but the going price for the product as produced by an Internet crawl is $87, the bid may be ranked low. Likewise, if the transactional records available to the platform indicate that recent transactions for this product priced out at $114, then the bid is likely to be ranked high; paragraphs 0129, 0133, 0141, 0247, 0309, 0386, 0392, 0412, 0416, 0452);
responsive to the comparing, matching one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers based in part upon the inventory and the respective need to identify one or more transactions (paragraph 0266, discussing that search ranking may be done using a ranking or relevance algorithm that functions more than merely matching buyers to suppliers who have had large quantities of similar shipments in the past. In embodiments, the ranking algorithm may include logarithmically weighting the sum of a number of different factors that may be relevant to a supplier's past and potential future performance, as well as matching a buyer's needs…Factors for the search ranking algorithm may include, among other things, the number of shipments made by the supplier that matches at least one aspect of a buyer's request; paragraph 0463, discussing that the user may create a project to compare different suppliers bidding for the user's product request fulfillment using the marketplace system; paragraph 0465, discussing that as shown in FIG. 53, three suppliers, such as Sinha Industries, Gazelle Enterprises, and Pan Taiwan Enterprises are associated with the respective selection boxes. The user may select any of the three suppliers for comparison. As illustrated in FIG. 53, the user may select the check boxes 5304 and 5308 to compare the profiles of suppliers Sinha industries and Gazelle enterprises respectively. Once the user has selected the suppliers for the project, the user may click on the add suppliers button to associate the suppliers with the project so that the user may be able to compare these suppliers for the project. Accordingly, the results of comparison may be displayed to the user using an interface as illustrated in FIG. 54; paragraph 0467, discussing that the user may conclude that for cotton fabric, this supplier may be better than the other supplier illustrated on the user interface and may select the bid sent by the supplier with company name Sinha Industries to fulfill to conclude the business transaction. Apart from selecting a supplier for fulfillment of the bidding request [i.e., responsive to the comparing, matching one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers based in part upon the inventory and the respective need to identify one or more transactions], the supplier comparison may be used for performing other transactions on the marketplace system; paragraph 0439);
allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers (paragraph 0439, discussing that supplier A, who wishes to fulfill Buyer X's request may choose to submit a bid in prose, such as "Will fulfill order of 50 pairs of Jeans to be delivered to Buyer X location FOB within 5 days at $2 per pair"; paragraph 0467, discussing that the plurality of result fields may be populated with corresponding information for each of the suppliers selected for comparison. Populating the result fields with relevant information that may be available in the marketplace system may facilitate the user to analyze the selected suppliers over various parameters. Such a presentation may enable a user to identify relevant information for considering a supplier, such as during selection of a supplier bid for a product. For example, a user may have posted a request for cotton fabric on the marketplace system and obtained bids from the two suppliers illustrated in FIG. 54. Based on the results of comparison illustrated on the user interface 5400, the user may identify that for the supplier with company name Sinha Industries, Levi Strauss company is one of the top buyers. The user may conclude that for cotton fabric, this supplier may be better than the other supplier illustrated on the user interface 5400 and may select the bid sent by the supplier with company name Sinha Industries to fulfill to conclude the business transaction); and
augmenting the historical data with a respective piece of information associated with the one or more identified transactions for subsequent use by the machine learning algorithm in subsequently training the machine-learning based system (paragraph 0056, discussing using the transactions as a training set to predict association of a particular transaction with an attribute; paragraph 0153, discussing that ratings may be customized to individual buyer preferences, such as by having buyer's rate suppliers with whom they have done business. Ratings may then be tuned to best match this empirical view of a buyer's preferences. Such an approach may use a machine learning technique such as a support vector machine. Over time, trends in ratings may then be captured and displayed to the buyer. Such trends may enable a graph-theory analysis on buyer-supplier networks to determine the relationships between groups of buyers and suppliers, which may lead to additional value-added services such as improving production allocation for buyers; paragraph 0217, discussing that because the vertical classifier may be a self-learning facility, each new record processed by the classifier can enhance the vertical classifier ability to classify new records; paragraph 0223, discussing that the data scraping technology may be configured to include one or more machine learning algorithms configured to identify information...Machine learning may include an initial seeding of data…, as well as feedback, such as from a manual or automated review, that indicates the extent to which initial rounds of searching have succeeded in finding relevant items. The success in each round of searching may be indicated to the learning system [i.e., augmenting the historical data with a respective piece of information associated with the one or more identified transactions for subsequent use by the machine learning algorithm in subsequently training the machine-learning based system], which may modify the searches iteratively in successive rounds until searches consistently produce better and better results…; paragraph 0412, discussing that administrators may enter in the information of a supplier that the administrator is aware of from factors such as past experience. Additionally, the interface may display statistics for administrators to view as well, such as, but not limited to, how many times buyers and suppliers are successfully matched, how many different suppliers are reviewed before a buyer selects a supplier, what the average rank of a selected supplier is when generated for the buyer, among others. Such dashboard capabilities may allow administrators to generate more effective algorithms by leveraging their work to better train the algorithms using machine learning or other algorithmic techniques and heuristics; paragraph 0512, discussing that historical data and data over time may be useful in creating a model of buyer demand, such as demand over time or for a particular time frame (e.g. seasonal demand, holiday buyer demand, etc.)),
wherein the augmenting includes augmenting the historical data with a value that represents the one or more portions of the inventory that was matched and allocated for the one or more identified transactions (paragraph 0025, discussing that the computer implemented methods and systems include using a computer implemented facility to collect and store a plurality of records of customs transactions among a plurality of buyers and a plurality of suppliers; aggregating the transactions; associating aggregated transactions...In the aspect the prediction is related to at least one of: price, a change in price, a change in supplier, and a quantity ordered by the buyer. In the aspect the prediction is of an action of a buyer based on analysis of customs data for transactions by a party other than the buyer. The prediction is related to a price, a change in price, a change in supplier, or a quantity ordered by a buyer. In the aspect, the prediction is of an action of a supplier based on analysis of customs data for transactions by the buyer. The prediction is related to a price, a change in price, a change in availability of an item, whether a supplier will work with a buyer of a given size, or whether a supplier will work with orders of a given size; paragraph 0030, discussing that the transaction is associated with a name of the entity, an order quantity; paragraph 0031, discussing using a computer implemented facility to collect and store a plurality of public records of transactions among a plurality of buyers and a plurality of suppliers; aggregating the transactions; associating the transactions with entities; and providing a computer-implemented tool for suggesting a marketing strategy for a supplier based on analysis of transactional data from the public records. In the aspect the transactional data is associated with a supplier, a buyer, region of interest, customs data, past shipment, country relevant experience, a number of shipments, a product category, a material, or a technique; paragraph 0141, discussing that the entity score may be based in part on transactional data related to the shipments by the entity such as delivery data, amount shipped,… and the like; paragraph 0142, discussing that a buyer who may be interested in knowing the quality of a product provided by a supplier may select feedback from previous customer groups on which to base an entity score for the supplier. This group may further include parameters such as timely delivery of goods, quality of goods, number of transactions and the like; paragraph 0153, discussing that ratings may then be tuned to best match this empirical view of a buyer's preferences. Such an approach may use a machine learning technique such as a support vector machine. Over time, trends in ratings may then be captured and displayed to the buyer. Such trends may enable a graph-theory analysis on buyer-supplier networks to determine the relationships between groups of buyers and suppliers, which may lead to additional value-added services such as improving production allocation for buyers; paragraph 0175, discussing that a buyer marketing tool may break down data for a particular supplier, such as raw customs records, records that show customer loyalty periods and switches to other suppliers, specific breakdowns of what the supplier has shipped (e.g. in terms of product category, material,.., and the like), breakdowns of the size of the shipments the supplier has made, breakdowns of the number of shipments that the supplier has made each month over some time period, as to determine the estimated capacity of a supplier, and the estimated minimum shipment that a supplier is willing to produce. In an example, a tool may show a breakdown of suppliers, where it is possible to see a history of which suppliers buyers have used…; paragraph 0190, discussing that an entity may be activated based on the transactions for the entity complying with criteria such as a shipment quantity threshold, and the like; paragraph 0205, discussing that when US customs training set data such as shipment quantity, shipper, and the like are applied to the China data, a supplier may be predicted for the rolled up China transaction data; paragraph 0261, discussing a breakdown of supplier shipment history, where shipment history may be broken down by piece count…and the like. FIG. 24 depicts a breakdown of shipment history as a piece-count chart...In embodiments, the product may include shipment history graphs that show trends and volumes of shipments made over some period of time. Embodiments may also show the number of articles of the shipped product shipped over time... Embodiments may also include a characterization of how large a supplier's shipments tend to be in terms of number of entities per shipping container. Such a characterization may allow a further characterization of whether a supplier may be able to fulfill small orders, if they will be willing to fulfill large orders, and the like; paragraph 0416, discussing that if there is a shortage of material X and suppliers have already committed to other shipments, an alert may be delivered to a user suggesting that they alter the inquiry to allow for lower quantity. Alerts may also be given to a user when a similar inquiry is fulfilled by a certain supplier or update a user of supplier's recent performances; paragraph 0497, discussing that using the number of shipments and other measures of shipment rates may be useful in characterizing a supplier as one of growing, shrinking or remaining stable...Machine learning is another technique that may be applied to shipment data that may help determine which components of the various data sources that provide data for a supplier are the most representative of leading indicators for the supplier; paragraphs 0159, 0217, 0227, 0241, 0266, 0293, 0387, 0411, 0443, 0459),
wherein the predicting a future supply and the predicting a future demand are based on the historical data and the respective piece of information associated with the one or more identified transactions (paragraph 0168, discussing that a prediction facility may predict an action of an entity. The action may be based on the analysis of the aggregated transactions. The prediction may relate to whether the supplier will work with the buyer of a given size. The prediction may also relate to whether the supplier will work with orders of a given size; paragraph 0169, discussing that the prediction may be of an action of the buyer based on an analysis of customs data for the buyer transactions. The prediction may be related to a price, a change in price, a change in supplier, a quantity ordered by the buyer and the like. In embodiments, the prediction may be of a buyer action of based on an analysis of customs data for transactions by a party other than the buyer. In embodiments, the prediction may be of a supplier action based on an analysis of customs data for transactions by the buyer…Those skilled in the art would appreciate that the prediction facility may provide the predictions to the plurality of buyers, plurality of suppliers or some other entities; paragraph 0311, discussing that the information from the aggregated transactions may also be utilized for supplier assessment...Cycle time calculations may also be used to evaluate a supplier's delivery performance. Significant increases in cycle time may indicate delay of shipment by the supplier. An assessment of supplier-buyer transaction status may also include factoring in buyer inventory. Buyer inventory may be factored in as a prediction or estimate of inventory; paragraph 0321, discussing that information regarding the public transaction records such as transaction receipts for a candle supplier selling a batch of factory-made candlesticks from a candle manufacturer may be aggregated and associated by the computer implemented facility. The analysis facility may perform detailed analysis of this information to generate various types of results. In an embodiment, the analysis facility may predict the minimum order requirement for an entity, based on the analysis of the transactions. As described in the above example, the analysis facility may predict the number of batches that the candle manufacturer may need to sell in order to cross the minimum profit mark. In another example, the analysis facility may predict the minimum number of candle-stick batches that may need to be supplied to a third party in order to fulfill the required terms laid down in a mutual contract. In yet another scenario, the analysis facility may also facilitate predicting the minimum order requirements that a subsidiary of a supplier may need to supply among the batch of suppliers; paragraph 0362, discussing that by analyzing shipment data, sales data, public financial records of entities, and the like, the platform may predict financial performance factors for an entity, such as an estimate of inventory that may be based on financial statement of the organization, past deliveries, and the like…Such predictions may also help estimate a company's potential change in earnings in the future; paragraph 0493, discussing that in an example of attempting to fulfill predicted needs based on business trend indications, if it is determined that a particular supplier has increased its manufacturing capacity threefold in the last three months (e.g. the private shipper data may include a new pricing structure for substantially increased shipments that are expected from the supplier), then it may be predicted that the supplier may welcome some potential buyers to order products based on the additional capacity. Likewise, buyers of the products that are similar to those that may be provided by the increased capacity may be notified of the potential increased availability of product; paragraph 0512, discussing that historical data and data over time may be useful in creating a model of buyer demand, such as demand over time or for a particular time frame (e.g. seasonal demand, holiday buyer demand, etc.); paragraph 0500, discussing that historical data and data over time may be useful in creating a model of capacity, such as capacity over time or for a particular time frame; paragraph 0261) and the augmenting is responsive to the predicting (paragraph 0223, discussing that the data scraping technology may be configured to include one or more machine learning algorithms configured to identify information such as phone numbers, emails, addresses, or any other information... Machine learning may include an initial seeding of data for which a web scraping technology is may search, as well as feedback, such as from a manual or automated review, that indicates the extent to which initial rounds of searching have succeeded in finding relevant items. The success in each round of searching may be indicated to the learning system, which may modify the searches iteratively in successive rounds until searches consistently produce better and better results. Further, the data scraping technology may be configured to include mapping related features that may include creation of maps to describe how data is laid out on business-to-business (B2B) pages. Accordingly, the web pages may be crawled to collect buyer and/or supplier information using the maps).
Psota does not explicitly teach predicting a future supply in accordance with historical data related to past transactions and market conditions via a machine learning algorithm executing on a processor; predicting a future demand in accordance with historical data related to past transactions and market conditions via the machine learning algorithm executing on the processor; and comparing the bid information and the demand data in accordance with the predicted future supply and the predicted future demand; and automatically allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers. Belady in the analogous art of bid evaluation systems teaches:
predicting a future supply in accordance with historical data related to past transactions and market conditions via a machine learning algorithm executing on a processor (paragraph 0074, discussing that the prediction functionality can also identify and apply factors which have a bearing on product availability and cost, such as time-of-day information, day-of-week information, seasonal information, special event information, weather information, and so on; paragraph 0077, discussing applying predictions regarding future supply and demand to determine whether it is appropriate to move a data component in the temporal dimension; paragraph 0113, discussing that the price determination module can assess supply and demand information by analyzing and extrapolating prior recorded instances of supply and demand; paragraph 0145, discussing that the supply determination module and the demand determination module can provide respective prediction modules to predict the supply and demand curves based on historical information; paragraph 0146, discussing that the supply determination module and the demand determination module can leverage this characteristic by approximating the current supply and demand curves based on historical supply and demand data, e.g., by using any type of machine learning techniques; paragraph 0147, discussing that the price determination module can use supervised learning and regression algorithms for learning the functions associated with the curves based on historical examples; paragraph 0069);
predicting a future demand in accordance with historical data related to past transactions and market conditions via the machine learning algorithm executing on the processor (paragraph 0074, discussing that the prediction functionality can also identify and apply factors which have a bearing on product availability and cost, such as time-of-day information, day-of-week information, seasonal information, special event information, weather information, and so on; paragraph 0143, discussing that a demand determination module determines the overall demand for a computational resource and expresses it as an aggregate demand curve. That is, the demand curve quantifies how many entities would utilize the computational resource as a function of the price. For example, FIG. 11 shows one such time-varying demand curve for a particular time t; paragraph 0145, discussing that the supply determination module and the demand determination module can provide respective prediction modules to predict the supply and demand curves based on historical information; paragraph 0146, discussing that the supply determination module and the demand determination module can leverage this characteristic by approximating the current supply and demand curves based on historical supply and demand data, e.g., by using any type of machine learning techniques); and
comparing the bid information and the demand data in accordance with the predicted future supply and the predicted future demand (paragraph 0011, discussing that the price determination module includes a supply determination module configured to determine a supply curve. The price determination module also includes a demand determination module. The price determination module also includes a price assessment module configured to determine a price at which to provide the resource at the particular time based on the supply curve and the demand curve. In one implementation, the supply determination module and the demand determination module include prediction modules for predicting supply information and demand information, respectively; paragraph 0052, discussing that the management system can be implemented by processing equipment; paragraph 0145, discussing that the supply determination module and the demand determination module can provide respective prediction modules to predict the supply and demand curves based on historical information. For the case of the supply determination module, the prediction module an mine information regarding past availabilities and costs of various types of resources. A supply-related prediction can be used to estimate the availability of resources, the cost of resources, etc. For the case of the demand determination module, the prediction module can mine information regarding past demand for various types of resources, and the prices paid for those resources…The price determination module leverages the predictions of the supply determination module and the demand determination module to determine the prices to be assigned to resources; paragraph 0146, discussing that the supply determination module and the demand determination module can leverage this characteristic by approximating the current supply and demand curves based on historical supply and demand data, e.g., by using any type of machine learning techniques; paragraph 0154, discussing that after collecting information, the bid assistance module suggests a price that the entity may bid in an attempt to secure the desired resources. To perform this task, the bid assistance module attempts to determine the value(s) of the resources requested by the entity. More specifically, the analysis module predicts a viable price based on historical demand information and supply information provided in a data store. The historical demand information and supply information encompass information regarding previous bidding activity; paragraph 0188).
Psota is directed towards a transaction facilitating marketplace platform. Belady is directed towards a method and system for evaluating collected bids from a group of entities. Therefore, they are deemed to be analogous as they both are directed towards bid evaluation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Psota to predict a future supply in accordance with historical data related to past transactions and market conditions via a machine learning algorithm executing on a processor; predict a future demand in accordance with historical data related to past transactions and market conditions via the machine learning algorithm executing on the processor; and compare the bid information and the demand data in accordance with the predicted future supply and the predicted future demand, as taught by Belady, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more robust system by leveraging the predictions of the supply determination module and the demand determination module to determine the prices to be assigned to resources [Belady, paragraph 0145].
While the Psota-Belady combination teaches allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers, it does not explicitly teach that the allocating is performed automatically. The Psota-Belady combination does not explicitly teach automatically allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers. However, Holmberg in the analogous art of procurement systems teaches this concept. Holmberg teaches:
automatically allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate Buyers (paragraph 0006, discussing that in sales auctions, the auctioneer sells one or more goods and bidders submit bids to buy…; paragraph 0184, discussing that the allocated items are automatically dispatched or prepared for dispatch; paragraph 0290, discussing that the determined allocation could be automatically dispatched or prepared for dispatch or delivery by the auction process, perhaps in cooperation with one or more automatic processes, dispatch clients. Similar automatic dispatch steps could be added in our other flow charts of an auction process at a point where the auction process has calculated an allocation for submitted bids. Examples of automatic dispatches in real-time are: automatic dispatch orders sent at the end of an auction to electricity consumers, electricity generators or network operators of an electric power network; automatic dispatch orders sent to network operators and clients in telecommunication systems; automatic dispatch orders sent to vehicles in a traffic network; automatic orders to prepare auctioned items for delivery and/or to deliver such items. Delivery and preparation for delivery could for example involve automatic collection of items from a storehouse, automatic packaging of items, automatic mail processing, including automatic printing of delivery addresses for packages and letters).
The Psota-Belady is directed towards methods and systems for connecting buyers and suppliers. Holmberg is directed towards a method and system for enabling a bidder to participate in an auction. Therefore, they are deemed to be analogous as they both are directed towards bid management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady combination to include automatically allocating the one or more portions of the inventory to respective ones of one or more of the plurality of candidate buyers, as taught by Holmberg, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more comprehensive system by allowing suppliers to obtain information about buyer’s needs, thereby increasing opportunities to consummate transactions in a manner that more efficiently meets the needs of buyers and sellers, and allowing suppliers to fully exploit available inventory and develop better approaches for supplying their products to their buyers. Furthermore, the combination offers the advantage of speed in the operation [Holmberg, paragraph 0015].
As per claim 2, the Psota-Belady-Holmberg combination teaches the method of claim 1. Psota further teaches wherein the electronically receiving bid information comprises electronically receiving data from a software application executing on a remote device (paragraph 0010, discussing a platform by which buyers, sellers, and third parties can obtain information related to each other's transaction histories, such as a supplier's shipment history, the types of materials typically shipped, a supplier's customers,…what materials and how much a buyer purchases, buyer and shipper reliability, similarity between buyers, similarity between suppliers, and the like. The platform may aggregate data from a variety of sources…; paragraph 0177, discussing that an application programming interface may be provided for the platform, whereby other computer programs may access the reports generated by the platform. Thus, other parties that engage in global trade, such as clients of the facilitator and partners, may obtain access to the platform [i.e., a software application executing on a remote device], allowing the ratings managed by the platform to become a standard measure by which suppliers are rated; paragraph 0349, discussing that a platform may include a transactional facility, such as for allowing buyers to transact with suppliers that have been identified by the search and ratings facilities. Such transactional facility may include modules related to ordering, pricing, payment, fulfillment, and the like; paragraph 0409, discussing that the inquiries posted by the buyers may generate and get converted into leads for the suppliers associated with the marketplace system; paragraph 0422, discussing that the marketplace system may provide a platform combining the data with communication information. The platform may enable both sides, such as a buyer or a supplier to find each other. Thus, by this way, the platform may allow connection of buyers to sellers, sellers to buyers, buyers to buyers, sellers to sellers for a variety of purposes, such as to facilitate a due diligence process involving search, introduction, and the like. Additionally, the marketplace system may require buyers or suppliers to be proactively looking for information about each other, in order to carry out a business transaction…; paragraph 0423, discussing that in addition to using supplier related data to rate a supplier bid, the platform may facilitate rating suppliers, buyers, bids, offers, requests, and platform-based communications among participants using a wide range of data sources and rating techniques; paragraph 0525).
As per claim 3, the Psota-Belady-Holmberg combination teaches the method of claim 1. Psota further teaches wherein the electronically receiving bid information comprises electronically receiving data from a software application executing on a wireless telephone (paragraph 0409, discussing that the inquiries posted by the buyers may generate and get converted into leads for the suppliers associated with the marketplace system. This may be automated within the marketplace system by proactively reaching out to the suppliers who may be capable of completing the inquiry based on associating information in the inquiry and in the various supplier profiles. The information collected by the marketplace system through the user interface may be shared with the suppliers through a separate interface of the marketplace system so that only the selected suppliers may receive the information. Examples of the separate interface may include automate telephone calls, email, private postings, SMS-like messages, and the like. While the inquiry may be posted on the marketplace, suppliers who may not be associated with the marketplace system may be contacted…; paragraph 0423, discussing that in addition to using supplier related data to rate a supplier bid, the platform may facilitate rating suppliers, buyers, bids, offers, requests, and platform-based communications among participants using a wide range of data sources and rating techniques; paragraph 0458, discussing that the marketplace system may provide an association between data and communication to provide an active, data-based communication marketplace. This may be achieved by allowing multi-media communication, such as though emails, SMS, chat, voice, video, or any other communication means. For example, the message section 5000 may also enable storing of chats in the chat messages section 5010. This stored chat information may be used to provide platform wide data enriched communications. For example, data stored in a previous chat or conversation may be determined to be relevant to a current chat session and therefor may be pulled into the active conversation or chat. An active conversation between parties in the marketplace may be parsed to produce a side bar that may be relevant to the communication…).
As per claim 4, the Psota-Belady-Holmberg combination teaches the method of claim 1. Although not explicitly taught by the Psota-Belady combination, Holmberg in the analogous art of procurement systems teaches: wherein the electronically receiving bid information comprises electronically receiving data from a software application executing on a tablet computer (paragraph 0338, discussing that computers could be of many different types. In FIG. 21 and FIG. 22, the auctioneer computer and bidder computer are thin clients… As shown in FIG. 16, the bidder client program 70001 can also be stored on a non-transitory, machine readable medium 70000; paragraph 0339, “with a computer we mean any general purpose device that can be instructed (programmed) to carry out a set of arithmetic or logical operations automatically. It could for example be a personal computer (portable or stationary), a work station, a desktop, a laptop, a mainframe computer, a supercomputer, a smart phone, a tablet, a terminal, a parallel computer, a quantum computer, a computerized wristwatch or other wearable computers. A computer could be embedded in a machine and it may not have a user interface. With terminal we mean an electronic or electromechanical hardware device that is used for entering data into, and outputting data from, a computing system).
The Psota-Belady is directed towards methods and systems for connecting buyers and suppliers. Holmberg is directed towards a method and system for enabling a bidder to participate in an auction. Therefore they are deemed to be analogous as they both are directed towards bid management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady combination to include wherein the electronically receiving bid information comprises electronically receiving data from a software application executing on a tablet computer, as taught by Holmberg, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more comprehensive system by allowing suppliers to obtain information about buyer’s needs, thereby increasing opportunities to consummate transactions in a manner that more efficiently meets the needs of buyers and sellers, and allowing suppliers to fully exploit available inventory and develop better approaches for supplying their products to their buyers. Furthermore, the combination offers the advantage of speed in the operation [Holmberg, paragraph 0015].
As per claim 5, the Psota-Belady-Holmberg combination teaches the method of claim 1. Psota further teaches wherein the electronically receiving bid information comprises electronically receiving data from a software application executing on a networked computing device (paragraph 0304, discussing that an interface may include a capability for buyers to network, chat or otherwise interact with each other with respect to suppliers. Such a network may include a capability of identifying other buyers as "friends" or the like, thereby allowing sharing of information only among trusted parties. In such a case, information about suppliers for particular buyers might be automatically populated, simplifying the sharing of information about experiences with particular suppliers used by a network of buyers; paragraph 0413, discussing that the inquiry process algorithms may comprise multiple components and take into account multiple factors. The inquiry process algorithms may comprise a structured data component, such as data that is captured in the inquiry form. Such an algorithm may comprise hierarchically structured data stored in a record. Initial information for organizing supplier structured data records may be derived when suppliers register with the marketplace system platform. When a supplier registers for the marketplace system platform, the supplier may be queried to enter data that can be used when searching for suppliers with one of the inquiry process algorithms. Such queries may facilitate receiving open text descriptions, may be taken from a drop down list of suitable response, or the like. For example, when a supplier first registers with the platform, during the registration phase, the supplier may be asked to enter in her location, number of employees, capabilities, business associations, previous reviews, and the like. Such information may then be stored in a structured record, which may then be searched by the inquiry algorithms; paragraph 0475, discussing that a user's web browser may transmit an IP address in standard electronic communication between a browser and a server that is hosting the on-line platform as described herein. The IP address may be analyzed and it may be determined that the computer that the user is using in the on-line interaction is within the range of IP addresses that have been determined to belong to buyer entity Panjiva. In this way, without the user taking any action to identify himself or his place of business, the platform may detect a relationship between the user's IP address and the Panjiva buyer).
Claim 10 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 10 the Psota-Belady-Holmberg combination teaches a machine-learning based system of sourcing materials; the machine-learning based system comprising: a server platform (Psota, paragraph 0050: “methods and systems, such as computer implemented methods and systems, include: taking a plurality of input data records from at least one data source of transactions; filtering the input data records to identify a set of filtered data records that are favorable candidates for automatic merging; classifying the filtered data records to produce a set of classified data records…classifying the data records is performed using at least one of canonical adaptation,…, vector generation, machine learning, and a decision tree; paragraph 0187: “The server or another computing device may convert the data…”; paragraph 0203: “Machine learning and other artificial intelligence techniques may be applied.”; paragraph 0225: “a server may store information”; paragraph 0223),
the server platform communicatively coupled with a plurality of remote devices operated by respective ones of the plurality of candidate Sellers and communicatively coupled with a plurality of remote devices operated by respective ones of the plurality of candidate Buyers (Psota, paragraph 0010, discussing a platform by which buyers, sellers, and third parties can obtain information related to each other's transaction histories, such as a supplier's shipment history, the types of materials typically shipped, a supplier's customers,…what materials and how much a buyer purchases, buyer and shipper reliability, similarity between buyers, similarity between suppliers, and the like. The platform may aggregate data from a variety of sources…; paragraph 0177, discussing that an application programming interface may be provided for the platform, whereby other computer programs may access the reports generated by the platform. Thus, other parties that engage in global trade, such as clients of the facilitator and partners, may obtain access to the platform, allowing the ratings managed by the platform to become a standard measure by which suppliers are rated; paragraph 0422, discussing that the marketplace system may provide a platform combining the data with communication information. The platform may enable both sides, such as a buyer or a supplier to find each other. Thus, by this way, the platform may allow connection of buyers to sellers, sellers to buyers, buyers to buyers, sellers to sellers for a variety of purposes, such as to facilitate a due diligence process involving search, introduction, and the like. Additionally, the marketplace system may require buyers or suppliers to be proactively looking for information about each other, in order to carry out a business transaction…; paragraph 0423, discussing that the platform may facilitate platform-based communications among participants using a wide range of data sources; paragraphs 0409, 0525);
wherein the server platform comprises a processor and a nontransient computer readable storage medium, the nontransient computer readable storage medium which stores: processor-executable order module instructions (Psota, paragraph 0225, discussing that a server may store information regarding scrap/web crawl status of websites. To make use of this information, when a website is encountered during a web crawl, communication with a server may be established to determine if the encountered website has already been accessed. If the website has already been accessed, the server may respond with a site and links associated therewith; paragraph 0343, discussing that buyers may supply data to the platform; paragraph 0527, discussing that the methods or processes described, and steps thereof, may be realized in hardware, software, or any combination of these suitable for a particular application. The hardware may include a general-purpose computer and/or dedicated computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as computer executable code created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software; paragraph 0528, discussing that each method described and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways; paragraphs 0049, 0349);
a processor-executable application program (Psota, paragraph 0177, discussing that an application programming interface may be provided for the platform described; paragraph 0306, discussing calls to an Application Programming Interface (API), or other methods; paragraph 0527);
a processor-executable machine learning algorithm (Psota, paragraph 0203: “Machine learning and other artificial intelligence techniques may be applied.”; paragraphs 0051, 0173);
the server platform including nontransient data and instructions causing the processor executing the instructions (paragraph 0527, discussing that the methods or processes described, and steps thereof, may be realized in hardware, software, or any combination of these suitable for a particular application. The hardware may include a general-purpose computer and/or dedicated computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as computer executable code created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software).
Furthermore, Holmberg teaches route the automatically allocated one or more portions of the inventory to the one or more of the plurality of candidate Buyers via a transportation carrier (paragraph 0006, discussing that in sales auctions, the auctioneer sells one or more goods and bidders submit bids to buy. Sometimes sales auctions are called forward auctions or buyer bidding auction. In procurement auctions, the auctioneer buys one or more goods and bidders submit bids to sell. Sometimes procurement auctions are called reverse auctions or supplier bidding auctions; paragraph 0290, discussing that as illustrated by step 406 in FIG. 32, the determined allocation could be automatically dispatched or prepared for dispatch or delivery by the auction process, perhaps in cooperation with one or more automatic processes, dispatch clients. Similar automatic dispatch steps could be added in our other flow charts of an auction process at a point where the auction process has calculated an allocation for submitted bids. Examples of automatic dispatches in real-time are: automatic dispatch orders sent at the end of an auction to electricity consumers, electricity generators or network operators of an electric power network; automatic dispatch orders sent to network operators and clients in telecommunication systems; automatic dispatch orders sent to vehicles [i.e., routing the automatically allocated one or more portions of the inventory to the one or more of the plurality of candidate Buyers via a transportation carrier], such as airplanes, cars, trucks, trains, boats, taxis etc., in a traffic network; automatic orders to prepare auctioned items for delivery and/or to deliver such items. Delivery and preparation for delivery could for example involve automatic collection of items from a storehouse, automatic packaging of items, automatic mail processing, including automatic printing of delivery addresses for packages and letters).
The Psota-Belady is directed towards methods and systems for connecting buyers and suppliers. Holmberg is directed towards a method and system for enabling a bidder to participate in an auction. Therefore, they are deemed to be analogous as they both are directed towards bid management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady combination to include routing the automatically allocated one or more portions of the inventory to the one or more of the plurality of candidate Buyers via a transportation carrier, as taught by Holmberg, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more comprehensive system by allowing suppliers to obtain information about buyer’s needs, thereby increasing opportunities to consummate transactions in a manner that more efficiently meets the needs of buyers and sellers, and allowing suppliers to fully exploit available inventory and develop better approaches for supplying their products to their buyers. Furthermore, the combination offers the advantage of speed in the operation [Holmberg, paragraph 0015].
As per claim 11, the Psota-Belady-Holmberg combination teaches the machine-learning based system of claim 10. Psota further teaches wherein the order module receives demand data from a software application that executes on one of the remote devices operated by a respective one of at least one of the candidate Buyers (paragraph 0343, discussing that buyers may supply data to the platform; paragraph 0349, discussing that a platform may include a transactional facility, such as for allowing buyers to transact with suppliers that have been identified by the search and ratings facilities described herein. Such transactional facility may include modules related to ordering, pricing, payment, fulfillment, and the like; paragraph 0475, discussing that a user's web browser may transmit an IP address in standard electronic communication between a browser and a server that is hosting the on-line platform as described herein. The IP address may be analyzed and it may be determined that the computer that the user is using in the on-line interaction is within the range of IP addresses that have been determined to belong to buyer entity Panjiva. In this way, without the user taking any action to identify himself or his place of business, the platform may detect a relationship between the user's IP address and the Panjiva buyer entity...).
As per claim 12, the Psota-Belady-Holmberg combination teaches the machine-learning based system of claim 11. Psota further teaches wherein the mote device operated by at least one of the candidate Buyer is one of a wireless telephone, a tablet computer, or a networked computing device (paragraph 0304, discussing that an interface may include a capability for buyers to network, chat or otherwise interact with each other with respect to suppliers. Such a network may include a capability of identifying other buyers as "friends" or the like, thereby allowing sharing of information only among trusted parties. In such a case, information about suppliers for particular buyers might be automatically populated, simplifying the sharing of information about experiences with particular suppliers used by a network of buyers; paragraph 0409, discussing that the inquiries posted by the buyers may generate and get converted into leads for the suppliers associated with the marketplace system...The information collected by the marketplace system through the user interface may be shared with the suppliers through a separate interface of the marketplace system so that only the selected suppliers may receive the information. Examples of the separate interface may include automate telephone calls, email, private postings, SMS-like messages, and the like. While the inquiry may be posted on the marketplace, suppliers who may not be associated with the marketplace system may be contacted…; paragraph 0413, discussing that the inquiry process algorithms may comprise multiple components and take into account multiple factors. The inquiry process algorithms may comprise a structured data component, such as data that is captured in the inquiry form. Such an algorithm may comprise hierarchically structured data stored in a record. Initial information for organizing supplier structured data records may be derived when suppliers register with the marketplace system platform. When a supplier registers for the marketplace system platform, the supplier may be queried to enter data that can be used when searching for suppliers with one of the inquiry process algorithms. Such queries may facilitate receiving open text descriptions, may be taken from a drop down list of suitable response, or the like. For example, when a supplier first registers with the platform, during the registration phase, the supplier may be asked to enter in her location, number of employees, capabilities, business associations, previous reviews, and the like. Such information may then be stored in a structured record, which may then be searched by the inquiry algorithms; paragraph 0458, discussing that the marketplace system may provide an association between data and communication to provide an active, data-based communication marketplace. This may be achieved by allowing multi-media communication, such as though emails, SMS, chat, voice, video, or any other communication means. For example, the message section 5000 may also enable storing of chats in the chat messages section 5010. This stored chat information may be used to provide platform wide data enriched communications. For example, data stored in a previous chat or conversation may be determined to be relevant to a current chat session and therefor may be pulled into the active conversation or chat. An active conversation between parties in the marketplace may be parsed to produce a side bar that may be relevant to the communication…; paragraphs 0423, 0475).
As per claim 13, the Psota-Belady-Holmberg combination teaches the machine-learning based system of claim 10. Psota further teaches wherein the application program receives the bid information from a software application executing on the respective remote device operated by at least one of the plurality of candidate Sellers (paragraph 0010, discussing a platform by which buyers, sellers, and third parties can obtain information related to each other's transaction histories, such as a supplier's shipment history, the types of materials typically shipped, a supplier's customers,…what materials and how much a buyer purchases, buyer and shipper reliability, similarity between buyers, similarity between suppliers, and the like. The platform may aggregate data from a variety of sources…; paragraph 0177, discussing that an application programming interface may be provided for the platform, whereby other computer programs may access the reports generated by the platform. Thus, other parties that engage in global trade, such as clients of the facilitator and partners, may obtain access to the platform [i.e., a software application executing on the respective remote device], allowing the ratings managed by the platform to become a standard measure by which suppliers are rated; paragraph 0349, discussing that a platform may include a transactional facility, such as for allowing buyers to transact with suppliers that have been identified by the search and ratings facilities. Such transactional facility may include modules related to ordering, pricing, payment, fulfillment, and the like; paragraph 0409, discussing that the inquiries posted by the buyers may generate and get converted into leads for the suppliers associated with the marketplace system; paragraph 0422, discussing that the marketplace system may provide a platform combining the data with communication information. The platform may enable both sides, such as a buyer or a supplier to find each other. Thus, by this way, the platform may allow connection of buyers to sellers, sellers to buyers, buyers to buyers, sellers to sellers for a variety of purposes, such as to facilitate a due diligence process involving search, introduction, and the like. Additionally, the marketplace system may require buyers or suppliers to be proactively looking for information about each other, in order to carry out a business transaction…; paragraph 0423, discussing that in addition to using supplier related data to rate a supplier bid, the platform may facilitate rating suppliers, buyers, bids, offers, requests, and platform-based communications among participants using a wide range of data sources and rating techniques).
As per claim 14, the Psota-Belady-Holmberg combination teaches the machine-learning based system of claim 13. Psota further teaches wherein the respective remote device operated by at least one of the candidate Sellers is one of a wireless telephone, a tablet computer, or a networked computing device (paragraph 0304, discussing that an interface may include a capability for buyers to network, chat or otherwise interact with each other with respect to suppliers. Such a network may include a capability of identifying other buyers as "friends" or the like, thereby allowing sharing of information only among trusted parties. In such a case, information about suppliers for particular buyers might be automatically populated, simplifying the sharing of information about experiences with particular suppliers used by a network of buyers; paragraph 0409, discussing that the inquiries posted by the buyers may generate and get converted into leads for the suppliers associated with the marketplace system...The information collected by the marketplace system through the user interface may be shared with the suppliers through a separate interface of the marketplace system so that only the selected suppliers may receive the information. Examples of the separate interface may include automate telephone calls, email, private postings, SMS-like messages, and the like. While the inquiry may be posted on the marketplace, suppliers who may not be associated with the marketplace system may be contacted…; paragraph 0413, discussing that the inquiry process algorithms may comprise multiple components and take into account multiple factors. The inquiry process algorithms may comprise a structured data component, such as data that is captured in the inquiry form. Such an algorithm may comprise hierarchically structured data stored in a record. Initial information for organizing supplier structured data records may be derived when suppliers register with the marketplace system platform. When a supplier registers for the marketplace system platform, the supplier may be queried to enter data that can be used when searching for suppliers with one of the inquiry process algorithms. Such queries may facilitate receiving open text descriptions, may be taken from a drop down list of suitable response, or the like. For example, when a supplier first registers with the platform, during the registration phase, the supplier may be asked to enter in her location, number of employees, capabilities, business associations, previous reviews, and the like. Such information may then be stored in a structured record, which may then be searched by the inquiry algorithms; paragraph 0458, discussing that the marketplace system may provide an association between data and communication to provide an active, data-based communication marketplace. This may be achieved by allowing multi-media communication, such as though emails, SMS, chat, voice, video, or any other communication means. For example, the message section 5000 may also enable storing of chats in the chat messages section 5010. This stored chat information may be used to provide platform wide data enriched communications. For example, data stored in a previous chat or conversation may be determined to be relevant to a current chat session and therefor may be pulled into the active conversation or chat. An active conversation between parties in the marketplace may be parsed to produce a side bar that may be relevant to the communication…; paragraphs 0423, 0475).
As per claim 17, the Psota-Belady-Holmberg combination teaches the machine-learning based system of claim 10. Psota further teaches wherein the nontransient data and instructions cause the processor executing the instructions to store the predicted future supply and the predicted future demand and the respective piece of information associated with the identified transaction for subsequent use by the machine learning algorithm (paragraph 0063, discussing that methods and systems, such as computer implemented methods and systems, include: using a computer implemented facility to collect and store a plurality of public records of transactions; associating the transactions with entities; and assessing whether a buyer has ceased doing business with a supplier based on the transactional data. In the aspect, the assessment is based on cycle time between shipments, departure of cycle time from a historical average, or based in part on a prediction as to inventory held by a buyer; paragraph 0185, discussing that the storage facility may store the plurality of public records of transactions among a plurality of buyers and a plurality of suppliers. The aggregation facility may aggregate the transactions. The association facility may associate the transactions with various entities that may include, but may not be limited to companies, buyers, sellers, suppliers, distributors, factories, subsidiaries of a supplier and the like. An analysis facility may analyze the aggregated transactions. The marketing tool may suggest a marketing strategy for the supplier based on analysis of transactional data from public records. In an example, the marketing tool may suggest to the supplier that it would be lucrative to sell 100 tons of silk fabric every week to the buyer located in the United States of America; paragraph 0217, discussing that because the vertical classifier may be a self-learning facility, each new record processed by the classifier can enhance the vertical classifier ability to classify new records; paragraph 0223, discussing that the data scraping technology may be configured to include one or more machine learning algorithms configured to identify information such as phone numbers, emails, addresses, or any other information...Machine learning may include an initial seeding of data for which a web scraping technology is may search, as well as feedback, such as from a manual or automated review, that indicates the extent to which initial rounds of searching have succeeded in finding relevant items. The success in each round of searching may be indicated to the learning system, which may modify the searches iteratively in successive rounds until searches consistently produce better and better results. Further, the data scraping technology may be configured to include mapping related features that may include creation of maps to describe how data is laid out on business-to-business (B2B) pages. Accordingly, the web pages may be crawled to collect buyer and/or supplier information using the maps; paragraph 0458).
As per claim 23, the Psota-Belady-Holmberg combination teaches the method of claim 1. Psota further teaches wherein the machine learning algorithm automatically learns (paragraph 0046, discussing that classification provides a vector that represents dimensions of similarity. The vector includes dimensions of similarity for at least two of canonical adaptation, text cleanup, multi-field classification, edit distance assessment, vector generation, machine learning, and decision tree processing; paragraph 0050, discussing that classifying the data records is performed using at least one of canonical adaptation, specific cleanups, multi-field comparison, an edit distance algorithm, vector generation, machine learning, and a decision tree; paragraph 0056, discussing using a computer implemented facility to collect and store a plurality of records of transactions among a plurality of buyers and a plurality of suppliers; aggregating the transactions; associating the transactions with entities; and using the transactions as a training set to predict association of a particular transaction with an attribute; paragraph 0158, discussing that based on the user's response, automatic merging of the two entity names may be learned by the platform; paragraph 0203, discussing that machine learning and other artificial intelligence techniques may be applied to determine if similarity vectors of pairs of records identify records that can be merged under a common entity. Through the use of training vectors, and decision tree logic, record mergability may be further assessed and a measure of such mergability may be made available to clustering techniques; paragraph 0204, discussing that a training set may also be useful for facilitating association of a shipment with an entity by enabling development of prediction parameters that may be used therefore. By identifying candidate relationships between shipments and attributes or entities, training sets of transaction records may reduce the computational load required for comprehensively filtering, classifying and clustering; paragraphs 0153, 0173, 0217, 0502, 0511).
As per claim 24, the Psota-Belady-Holmberg combination teaches the method of claim 1. Psota further teaches wherein electronically receiving bid information from each of a plurality of candidate Sellers includes receiving images of at least some of the inventory from the candidate Sellers (paragraph 0220, discussing that the data may include information such as contact information, product images, product information, third party ratings, and other meta-data such as company overviews, revenues or any other data corresponding to the supplier, so that the analytics facility 922 may utilize this information to establish ratings for the supplier; paragraph 0291, discussing that an option 3208 may facilitate the user to select any of the supplier profiles that may have contact information, photos associated with the products for selling by the supplier and shipments related information for the suppliers displayed in the search results for the supplier category 3002 [i.e., This shows that images of at least some of the inventory are received from the candidate Sellers]. Another option 3210 may allow the user to select only those profiles of the suppliers that may be active in the last predetermined number of days. The user interface 3200 may further include additional options 3212 for the user to selectively view the profiles as per the selected options as listed in the user interface 3200. In addition, each of the options such as the option 3204, the option 3208 and the option 3218 may include the number of profiles or the results that may be shown to the user on selection of a particular check-box listed under the corresponding options).
As per claim 25, the Psota-Belady-Holmberg combination teaches the method of claim 1. Although not explicitly taught by the Psota-Belady combination, Holmberg in the analogous art of procurement systems teaches: further comprising: automatically routing the automatically allocated one or more portions of the inventory to one or more of the candidate Buyers via a transportation carrier (paragraph 0006, discussing that in sales auctions, the auctioneer sells one or more goods and bidders submit bids to buy. Sometimes sales auctions are called forward auctions or buyer bidding auction. In procurement auctions, the auctioneer buys one or more goods and bidders submit bids to sell. Sometimes procurement auctions are called reverse auctions or supplier bidding auctions; paragraph 0290, discussing that as illustrated by step 406 in FIG. 32, the determined allocation could be automatically dispatched or prepared for dispatch or delivery by the auction process, perhaps in cooperation with one or more automatic processes, dispatch clients. Similar automatic dispatch steps could be added in our other flow charts of an auction process at a point where the auction process has calculated an allocation for submitted bids. Examples of automatic dispatches in real-time are: automatic dispatch orders sent at the end of an auction to electricity consumers, electricity generators or network operators of an electric power network; automatic dispatch orders sent to network operators and clients in telecommunication systems; automatic dispatch orders sent to vehicles (i.e., routing the one or more portions of the inventory to the candidate Buyer via a transportation carrier), such as airplanes, cars, trucks, trains, boats, taxis etc., in a traffic network; automatic orders to prepare auctioned items for delivery and/or to deliver such items. Delivery and preparation for delivery could for example involve automatic collection of items from a storehouse, automatic packaging of items, automatic mail processing, including automatic printing of delivery addresses for packages and letters).
The Psota-Belady is directed towards methods and systems for connecting buyers and suppliers. Holmberg is directed towards a method and system for enabling a bidder to participate in an auction. Therefore, they are deemed to be analogous as they both are directed towards bid management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady combination to include automatically routing the automatically allocated one or more portions of the inventory to one or more of the candidate Buyers via a transportation carrier, as taught by Holmberg, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more comprehensive system by allowing suppliers to obtain information about buyer’s needs, thereby increasing opportunities to consummate transactions in a manner that more efficiently meets the needs of buyers and sellers, and allowing suppliers to fully exploit available inventory and develop better approaches for supplying their products to their buyers. Furthermore, the combination offers the advantage of speed in the operation
22. Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Psota in view of Belady, in view of Holmberg, in further view of Hoover et al., Pub. No.: US 2016/0155069 A1, [hereinafter Hoover].
As per claim 21, the Psota-Belady-Holmberg combination teaches the method of claim 1, but it does not explicitly teach further comprising: comparing, via a machine learning algorithm, material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids to identify fraudulent bids; and rejecting an identified fraudulent bid. However, Hoover in the analogous art of bid evaluation systems teaches these concepts. Hoover teaches:
further comprising: comparing, via a machine learning algorithm, material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids to identify fraudulent bids (paragraph 0041, discussing that processes described with respect to FIG. 2A may be performed to generate training and validation sets for a classifier to classify data objects for items to be procured as high-risk or not. For example, the data from an initial data set from data source 101a is partitioned. Data objects for “general merchandise” may be removed since those items may be procured differently than other items, such as items prone to being high risk. Data objects may be further filtered and transformed, and factor analysis may be performed. Data objects indicative of a high-risk item are labeled for the training set. Examples of the variables for the data objects representing items to be procured may include number of instances an item was purchased over the past six years, an indicator of an item's criticality, and whether the item had a diminishing manufacturing source etc.; paragraph 0057, discussing that the system may be used to generate classifiers to classify data objects for a variety of different categories. The system is used to generate classifiers to classify different types of procurement data as high-risk or not. For example, an entity may acquire goods or services through a procurement process. The procurement process may encompass sending out a request for bids to supply goods or services, and receiving bids from vendors or other organizations to supply the goods or services. High-risk procurements may potentially represent a risk for fraud (e.g., substituting an unauthorized product, a counterfeit of a desired product, etc.), waste and abuse. For example, a high-risk procurement is a procurement having characteristics that meet certain criteria. The criteria may be related to identifying fraud, abuse, or general errors. A procurement is the acquisition of items, which may include one or more goods or services. A typical procurement process includes accepting bids to supply items from one or more suppliers and selecting one or more bids for the procurement of the items. The procurement process may include posting a request for bids or proposals that provides a description of the items being procured and any constraints on the procurement; paragraph 0059, discussing that the system includes a procurement system and data sources that provide data to a high-risk procurement analytics and scoring system, hereinafter referred to as system. The system may include the machine learning classifier system described above…The procurement system may provide historic procurement data, such as data objects from historic procurement data which may be used to generate training sets and validation sets. Also, the procurement system may provide the data objects for classification. The system develops one or more scoring models, including classifiers, and uses the scoring models to identify high-risk procurements from “live” data, such as data objects for classification. The feed of procurement data may include the live data that is sent to the system for scoring and to identify high-risk procurements…Procurement data includes any data that may be used for generating the models, including the classifiers, and evaluating procurement bids; paragraph 0067, discussing that the characteristics identifier module identifies characteristics of high-risk procurements. Machine learning, such as neural networks, logistic regression or other functions may be used to identify the characteristics. For example, the characteristics may include predictive variables for generating the models, including the classifiers 106. The predictive variables may be related to cost, quantity, industry-specific characteristics, etc.; paragraph 0072, discussing that a review of previously identified “high-risk” procurements produces a “domain” of potential rule based structures that can be utilized as rules for identifying high-risk procurements. In “machine learning”, these rule based structures may be applied in a decision tree based approach…The rules may be developed according to a model building data set or one of the training sets, which may be received from one or more of the data sources and then tested on one of the validation sets. The scoring model may be generated based on the rules, and the procurement risk analysis module uses the scoring model to score procurements and identify high-risk procurements [i.e., This shows that material terms and parameters of newly received bids are compared with historic data to identify fraudulent bids]; paragraph 0073, discussing that the model generator module generates the scoring models. Scoring models, including the classifiers, may be generated for different risk areas. The models may be generated using logistic regression, business rules or other ones of the machine learning functions based on variables; paragraph 0082, discussing that model-building data sets, such as training sets, and validation data sets, such as validation sets, are determined from historic procurement data including the identified high-risk procurements…Data mining techniques that can be used for creating the training sets, the validation sets, and scoring models may use both procurements that were problematic (high-risk), along with those that were not (non-high-risk, or low-risk, procurements); paragraph 0111, discussing that the supplier risk scoring model may be used to identify bids that are high-risk based on the supplier of the items being procured. For example, some suppliers may be considered “bad actors” based on previous procurement actions. For example, the supplier may have previously been found to provide counterfeit goods or was accused or indicted for fraud. Bids from these types of suppliers may be considered high-risk; paragraph 0116); and
rejecting an identified fraudulent bid (paragraph 0149, discussing that the alerts may be sent from the procurement system to mobile devices of supervisors or contracting officers for immediate review and action. The mobile application accompanying this capability allows supervisors to review procurement details, approve/reject contracting steps, and move procurements between automated processes and other processes, such as audit processes. For example, an automated workflow executed by the system includes performance of steps for solicitation, evaluation and contract awarding. In certain instances, such as due to item, supplier or price risk alerts, an interrupt is generated to halt the workflow until audit feedback is received concerning an alert. The alerts may be generated in response to the classifiers 106 identifying high risk suppliers, items or prices in bids. For example, the automated workflow is halted, and the alert is sent to the mobile application. The mobile application may send a command to trigger an audit and/or may directly cause the audit to be performed...If the audit operation indicates that an adverse action, such as rejecting a bid, should be taken, then the bid may not be accepted [i.e., This shows that the fraudulent bid is rejected]; paragraph 0151, discussing that FIG. 22 shows examples of automated safeguards that may be implemented in response to alerts for item, price and supplier risks. For example, if an item, price or supplier score exceeds a risk threshold, certain automated safeguards are executed by the system depending on the current step of the procurement process. For example, during purchase requisition, an audit may be required...During generation of the procurement request, as part of the bid solicitation, additional contracting safeguards may be automatically added by the contract writing system if a risk threshold is exceeded. The addition of automatic contracting clauses can provide automated risk mitigation, which allows the procurement organization to be sure they are protected when purchasing potentially risky items or dealing with potentially risky suppliers. Additional contracting clauses may also dissuade fraudulent suppliers from bidding on contracts when such provisions included. Finally, these additional safeguards may also provide the procurement agency with a legal means of recouping lost value when counterfeit or non-conforming parts are identified. Another example of an automated safeguard, such as during bid evaluation, may include automatic generation and sending of a request for additional information to the bidder if a threshold is exceeded, and during contact award, supervisor review may be initiated. These and other safeguards may be automatically executed in response to scores exceeding a threshold).
The Psota-Belady-Holmberg combination is directed towards bid management systems. Hoover is directed towards a method and system for evaluating bids. Therefore they are deemed to be analogous as they both are directed towards bid management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady-Holmberg combination to compare, via a machine learning algorithm, material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids to identify fraudulent bids, and reject an identified fraudulent bid, as taught by Hoover, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more comprehensive system by allowing users to implement safeguards in response to alerts for item, price and supplier risks [Hoover, paragraph 0023]; or in the pursuit of providing a more accurate and effective system for identifying high-risk procurements over time, thus allowing procuring entities to lower procurement costs while improving procurement quality and efficiency [Hoover, paragraph 0058].
As per claim 22, the Psota-Belady-Holmberg combination teaches the method of claim 1, but it does not explicitly teach further comprising: comparing via a machine learning algorithm material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids, to identify mistaken bids. However, Hoover in the analogous art of bid evaluation systems teaches these concepts. Hoover teaches:
further comprising: comparing via a machine learning algorithm material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids, to identify mistaken bids (paragraph 0057, discussing that the system may be used to generate classifiers to classify data objects for a variety of different categories. The system is used to generate classifiers to classify different types of procurement data as high-risk or not. For example, an entity may acquire goods or services through a procurement process. The procurement process may encompass sending out a request for bids to supply goods or services, and receiving bids from vendors or other organizations to supply the goods or services. High-risk procurements may potentially represent a risk for fraud (e.g., substituting an unauthorized product, a counterfeit of a desired product, etc.), waste and abuse. For example, a high-risk procurement is a procurement having characteristics that meet certain criteria. The criteria may be related to identifying fraud, abuse, or general errors [i.e., This shows that mistaken bids are identified]. A procurement is the acquisition of items, which may include one or more goods or services. A typical procurement process includes accepting bids to supply items from one or more suppliers and selecting one or more bids for the procurement of the items. The procurement process may include posting a request for bids or proposals that provides a description of the items being procured and any constraints on the procurement; paragraph 0059, discussing that the system includes a procurement system and data sources that provide data to a high-risk procurement analytics and scoring system, hereinafter referred to as system. The system may include the machine learning classifier system described above…The procurement system may provide historic procurement data, such as data objects from historic procurement data which may be used to generate training sets and validation sets. Also, the procurement system may provide the data objects for classification. The system develops one or more scoring models, including classifiers, and uses the scoring models to identify high-risk procurements from “live” data, such as data objects for classification. The feed of procurement data may include the live data that is sent to the system for scoring and to identify high-risk procurements…Procurement data includes any data that may be used for generating the models, including the classifiers, and evaluating procurement bids; paragraph 0067, discussing that the characteristics identifier module identifies characteristics of high-risk procurements. Machine learning, such as neural networks, logistic regression or other functions may be used to identify the characteristics. For example, the characteristics may include predictive variables for generating the models, including the classifiers. The predictive variables may be related to cost, quantity, industry-specific characteristics, etc.; paragraph 0073, discussing that the model generator module generates the scoring models. Scoring models, including the classifiers, may be generated for different risk areas. The models may be generated using logistic regression, business rules or other ones of the machine learning functions based on variables; paragraph 0082, discussing that model-building data sets, such as training sets 103, and validation data sets, such as validation sets, are determined from historic procurement data including the identified high-risk procurements…Data mining techniques that can be used for creating the training sets, the validation sets, and scoring models may use both procurements that were problematic (high-risk), along with those that were not (non-high-risk, or low-risk, procurements); paragraph 0111, discussing that the supplier risk scoring model may be used to identify bids that are high-risk based on the supplier of the items being procured. For example, some suppliers may be considered “bad actors” based on previous procurement actions. For example, the supplier may have previously been found to provide counterfeit goods or was accused or indicted for fraud. Bids from these types of suppliers may be considered high-risk; paragraph 0072).
The Psota-Belady-Holmberg combination is directed towards bid management systems. Hoover is directed towards a method and system for evaluating bids. Therefore, they are deemed to be analogous as they both are directed towards bid management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady-Holmberg combination to compare via a machine learning algorithm material terms and parameters of newly received bids with historic data maintained and associated with past or pending bids, to identify mistaken bids, as taught by Hoover, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides a more comprehensive system by allowing users to implement safeguards in response to alerts for item, price and supplier risks [Hoover, paragraph 0023]; or in the pursuit of providing a more accurate and effective system for identifying high-risk procurements over time, thus allowing procuring entities to lower procurement costs while improving procurement quality and efficiency [Hoover, paragraph 0058].
23. Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Psota in view of Belady, in view of Holmberg, in further view of Keresman, III et al., Pub. No.: US 2009/0119205 A1, [hereinafter Keresman].
As per claim 26, Psota-Belady-Holmberg teaches the method of claim 1. Although not explicitly taught by the Psota-Belady-Holmberg, Keresman in the analogous art of transaction management systems teaches further comprising: generating a virtual purchase order memorializing a one of the one or more transactions (paragraph 0037, discussing setting up virtual purchase orders and controlling or limiting the transaction authorization given by the coordinator; paragraph 0038, discussing setting up a virtual purchase order via the account privileges by pre-defining transaction boundaries for an identified seller; paragraph 0061, discussing that the corresponding transaction details are maintained in a transaction record).
The Psota-Belady-Holmberg combination is directed towards bid management systems.
Keresman is directed towards a transaction management system. Therefore, they are deemed to be analogous as they both are directed towards transaction management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Psota-Belady-Holmberg combination to include generating a virtual purchase order memorializing a one of the one or more transactions, as taught by Keresman, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same functions as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The combination provides and improved transaction processing system [Keresman, paragraph 0009].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li, Pub. No.: US 2015/0379596 A1 – describes a system and method for matching buyers and sellers.
Vandehey et al., Pub. No.: US 2012/0084119 A1 – describes a system for managing excess inventory.
Carbonneau, Real, Kevin Laframboise, and Rustam Vahidov. "Application of machine learning techniques for supply chain demand forecasting." European journal of operational research 184.3 (2008): 1140-1154 – investigates the applicability of advanced machine learning techniques, including neural networks, recurrent neural networks, and support vector machines, to forecasting distorted demand at the end of a supply chain.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625