DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
2. Applicant amended claim 1 and added new claims 2-20 on 03/26/2025. Claims 1-20 are pending.
Claim Interpretation
Intended Use
3. Intended use language is generally not given patentable weight. See MPEP 2114(II) ("A claim containing a 'recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus’ if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).”); see also MPEP 2103(C). Examples of claim limitations that are often found to precede intended use include “adapted to,” “capable of,” “sufficient to,” “whereby,” and “for.”
4. Claim 1 recites “interpreting … to determine a scope…”, “independently distributing and randomly combining … to produce a random distribution …”, “applying … to produce a plurality of market prices”, “producing … to improve a computational efficiency…”, and “reporting … to use said final bid price …”.
Claim 11 recites “interpret … to determine a scope…”, “independently distribute and randomly combine … to produce a random distribution …”, “apply … to produce a plurality of market prices”, “produce … to improve a computational efficiency…”, and “report … to use said final bid price …”.
The underlined limitations represent intended use.
Claim Rejections - 35 USC §101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
7. In the instant case, claims 1 and 11 are directed to a “method and system for producing a bid price for a service or a good”.
8. Claim 1 recites “determining market pricing”. Specifically, claim recites “receiving a request for proposal …; interpreting said request for proposal to determine a scope of and evaluation criteria for said request for proposal; breaking down said scope of said request for proposal into constituent parts by a first line item and a second line item including dynamic links therebetween; producing an offering to said request for proposal, comprising: determining market price of said offering, comprising: selecting a market pricing model, selecting a first range of prices for said first line item, selecting a second range of prices for said second line item, independently distributing and randomly combining said first range of prices with said second range of prices to produce a random distribution of prices, and applying said random distribution of prices to said market pricing model to produce a plurality of market prices, determining strategic pricing of said offering including market price adjustments of a competitor, determining a competitor evaluation score of said offering including a qualitative ranking value of said competitor based on said evaluation criteria, producing a plurality of bid prices for said offering within a confidence interval of a probability of winning a competition by independently distributing and randomly combining said plurality of market prices with said strategic pricing and said competitor evaluation score, calculation of said plurality of bid prices being bounded to said confidence interval to improve a computational efficiency and evaluation runtime …, and selecting of final bid price for said offering in accordance with said evaluation criteria from said plurality of bid prices at a confidence interval level within said confidence interval; and reporting said offering directing a decision-maker to use said final bid price for said competition …”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., commercial or legal interactions), “Mathematical concepts – mathematical calculations” and an abstract idea in prong one of step 2A (MPEP 2106.04(a)). Further, it has been held that “[a]dding one abstract idea (math) to another abstract idea … does not render the claim non-abstract") (RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017).
9. This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of claim 1 such as “a processor”, “memory”, and “an external system” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. With respect to “receiving a request for proposal from an external system” and “reporting said offering directing a decision-maker to use said final bid price for said competition to said external system” is simply transmitting data, “[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more, (MPEP 2106.05(f)(2)). The additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of determining market pricing.
10. When analyzed under step 2B (MPEP 2106.04 II), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of determining market pricing using computer technology. Therefore, as the use of these additional elements do no more than employ a computer as a tool to automate and/or implement the abstract idea, they cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
11. Hence, claim 1 is not patent eligible.
12. Claim 11 also recites “determining market pricing”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., commercial or legal interactions), “Mathematical concepts – mathematical calculations” and an abstract idea in prong one of step 2A (MPEP 2106.04(a)).
13. This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of claim 11 such as “a processor”, “memory”, and “an external system” represent the use of a computer as a tool to perform an abstract idea and/or do no more than generally link the abstract idea to a particular field of use. With respect to “receive a request for proposal from an external system” and “report said offering directing a decision-maker to use said final bid price for said competition to said external system” is simply transmitting data, “[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more, (MPEP 2106.05(f)(2)). The additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of determining market pricing.
14. When analyzed under step 2B (MPEP 2106.04 II), the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of determining market pricing using computer technology. Therefore, as the use of these additional elements do no more than employ a computer as a tool to automate and/or implement the abstract idea, they cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
15. Hence, claim 11 is not patent eligible.
16. Dependent claim 2 further describes the abstract idea of determining market pricing as recites “further comprising periodically receiving updates to said request for proposal”.
Dependent claims 3 and 13 further describe the abstract idea of determining market pricing as each recites “wherein said first line item represents a cost of a service of said offering and said second line item represents a cost of a good of said offering”.
Dependent claims 4 and 14 further describe the abstract idea of determining market pricing as each recites “wherein said strategic pricing includes market price adjustments of a plurality of competitors and said competitor evaluation score includes a qualitative ranking value of said plurality of competitors based on said evaluation criteria”.
Dependent claims 5 and 15 further describe the abstract idea of determining market pricing as each recites “wherein said producing said offering further comprises providing … a price-to-win curve of said probability of winning said competition for said plurality of bid prices within said confidence interval”. The additional element such as “a graphical representation of a price-to-win curve” represents the use of a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. And, therefore, do not improve the functioning of a computer, or to any other technology or technical field.
Dependent claims 6 and 16 further describe the abstract idea of determining market pricing as each recites “wherein said strategic pricing includes a magnitude and range of said market price adjustments based on likely actions by and discount tendencies of said competitor”.
Dependent claims 7 and 17 further describe the abstract idea of determining market pricing as each recites “wherein said likely actions by and discount tendencies of said competitor are based on competitive intelligence of said competitor”.
Dependent claims 8 and 18 further describe the abstract idea of determining market pricing as each recites “wherein said competitor evaluation score includes a range of competitor evaluation scores of said competitor”.
Dependent claims 9 and 19 further describe the abstract idea of determining market pricing as each recites “wherein said confidence interval level is set at 85 percent probability of winning said competition”.
Dependent claims 10 and 20 further describe the abstract idea of determining market pricing as each recites “wherein said final bid price for said offering is broken down and reported by said first line item and said second line item”.
Dependent claim 12 further describes the abstract idea of determining market pricing as recites “wherein … further configured to periodically receive updates to said request for proposal”. The additional elements such as “said processor” and “said memory” represent the use of a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. And, therefore, do not improve the functioning of a computer, or to any other technology or technical field.
Conclusion
17. The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
18. Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
19. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
20. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
21. 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.
22. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20070143171A1 to Boyd et al. in view of “Bid Pricing – Calculating the Possibility of Winning”, 1997 IEEE International Conference On Systems, Man, And Cybernetics; By Bussey, Cassaigne and Singh (further – Bussey et al.)
23. As per claims 1 and 11:
Boyd et al. discloses the following limitations:
a processor ([0086] “… the present inventive method is readily adaptable for use in an automated system, such as in software executing on a computer platform…”)
memory ([0086] “… the present inventive method is readily adaptable for use in an automated system, such as in software executing on a computer platform…”)
receiving a request for proposal from an external system ([0005] “…In making a bid for a contract or to provide a certain set of products or services, the goal is to make an exact bid…”, [0140] ““Bids”: a bid is a request for products over a specified time period for which a custom price wilt be generated by the target pricing method.”, [0146] “… A bid is a proposal to an account for delivery of products over a specified time period at a specified price…”)
breaking down said scope of said request for proposal into constituent parts by a first line item and a second line item including dynamic links therebetween ([0146] “…The bid contains at least one, and may contain more than one, product or service order…”, [0159] “… product orders are specific products which have been ordered in a bid. Product orders contain quantity, corresponding time period, and options…”, [0161] “…Options are sub-products that can be ordered for a specific product. An option can only be ordered after the corresponding product has been ordered…”, [0208] “Once bid optimization has been calculated, discounts are assigned for each product in the bid…”, [0209] “…Individual product incentives are aggregated to the bid level and are subject to any desired constraints. The incentives offered at the product level should aggregate to the bid level incentive determined by the bid optimization.”)
producing an offering to said request for proposal, comprising: ([0010] “…determining a target price for the value by selecting a price that maximizes the expected contribution…”, [0205] “The target pricing method generates minimum, target and maximum prices as its output. The values produced are unconstrained and constrained prices for the entire bid, and unconstrained and constrained prices for each product.”)
determining market price of said offering, comprising: selecting a market pricing model ([0089] “The present inventive method utilizes a market response model in calculating the target bid price. The market response model (MRM) calculates the win probability as a function of price through the examination of historical bid information at various prices…”, [0105] “The market response model (MRM) performs three key functions: updating the coefficients for market response predictors on the basis of historical data…”, [claim 40] “…processing of said auction item pricing, said auction item costing, and said equivalent competitor net price to calculate a probability of winning the auction item as a function of price using parameters from an electronically stored market response model…”)
selecting a first range of prices for said first line item, selecting a second range of prices for said second line item ([0203] “At each step, the method calculates a minimum price, target and maximum price…”, [0205] “… The values produced are unconstrained and constrained prices for the entire bid, and unconstrained and constrained prices for each product.”, [0208] “Once bid optimization has been calculated, discounts are assigned for each product in the bid…”, [claim 37] “…the method further comprises comprising the step of the calculating a target price range for the auction item.”)
determining strategic pricing of said offering including market price adjustments of a competitor ([0084] “Once the bid is costed, then an equivalent competitor net price for the bid is calculated. This is the price the competitor(s) would charge to this customer after any discounting has occurred…”, [0167] “The competitor net price (CNP) model used in the target pricing method estimates the prices competitors will offer to customers, including negotiated discounts…”, [0169] “… for each of the target pricing user's products that are intended to be competitively bid, there will be a competing product from each competitor in the system…”, [0170] “These competing products are maintained in the target pricing product model much like the target pricing user's products…”, [0171] “To compute a competing product's list price, the price model maintained in that product is utilized. Like all other products, the competitor's product price can be maintained as a n-dimensional model…”, [claim 35] “…determining an equivalent competitor net price for the auction item using an electronically stored competitor net price model…”)
determining a competitor evaluation score of said offering including a qualitative ranking value of said competitor based on said evaluation criteria ([0106] “…For every predictor specified by the user, the coefficient values that define the market response curve are estimated and stored. These coefficients are used in combination with account and bid characteristics to calculate win probabilities…”, [0110] “…Key competitor – For a pre-specified set of key competitors, define if any of the competitors exist for the given bid…”, [0133] “The price-independent predictors can be viewed as measures of customers' brand preferences…”, [0136] “…characteristics of individuals bids (such as volume or key competitor) can further influence customers' brand preference and willingness to pay…”
reporting said offering directing a decision-maker to use said final bid price for said competition to said external system ([0089] “…A further module that is alternately used in the present method is a reporting module that is used to produce reports on a regular or ad-hoc basis.”, [0090] “… This operation … is also used in reporting as it enables the user to analyze results in order to understand system and/or customer behavior…”, [0100] “Market segments are used for reporting purposes…”, [0205] “The target pricing method generates minimum, target and maximum prices as its output…”)
Boyd et al. does not disclose, however, Bussey et al., as shown, teaches the following limitations:
interpreting said request for proposal to determine a scope of and evaluation criteria for said request for proposal (page 3616, col.1 “…It is assumed in the model that tile client assesses a bid on the basis of a number of different factors including price In order to determine the total value of the bid…”, page 3618, col.1, “…In this scenario it is identified through expert knowledge that the client will consider three non-price factors in its bid selection, quality, implementation time and experience…”, page 3619, col.1 “…In the scenario it is identified that the client considers three non-price factors, experience, quality and time…”)
independently distributing and randomly combining said first range of prices with said second range of prices to produce a random distribution of prices (page 3616, col.2 “… It is assumed in the model that there is an underlying possibility distribution associated with the utility for the performance of a competitor on a factor which is distributed normally… Figure 2 – Possibility Density Function” - [shows normal distribution]”, page 3616, col.2 “…Similar distributions exist for a competitor for each of the bid factors considered in the bid such that for competitor l. l = 1,…,m, there exists a set of possibility density functions fi describing its possible bid performance on each factor i.”
applying said random distribution of prices to said market pricing model to produce a plurality of market prices (page 3616, col.2 “…From the set of possibility density functions for the performance of a competitor on each bid factor a possibility density function fl = (X, ml, sl) can be formed for the total value oi’ a competitor’s bid, in accordance with equation 1.”, page 3617, col.1 “…The distribution produced has a maximum point, the point of maximum expected profit…”, page 3619, col.2 “Table 10 – Distributions for Competitor Bids” [shows m and s parameters])
producing a plurality of bid prices for said offering within a confidence interval of a probability of winning a competition by independently distributing and randomly combining said plurality of market prices with said strategic pricing and said competitor evaluation score (page 3615, Title “Calculating the Possibility of Winning”, page 3616, col.1 “…determine the probability of the bidder winning a bid at different bid prices…”, page 3616, col.2, “From the set of possibility density functions for the performance of a competitor on each bid factor a possibility density function fl = (X, ml, sl) can be formed for the total value oi’ a competitor’s bid…”, page 3617, col.1 “…If it is assumed that the possible utility of a competitor’s bid is independent from the utility of any other competitor’s bid, then we can determine the probability of a bid of utility B, winning the bid as a product of it winning against each individual competitor. The expected profit from a bid, E(B), is the product of the probability of winning the bid and the actual profit, eb...”, page 3619, col.2, “Figure 5 – Expected Profits for Simulation” – [shows distribution across price range]”, page 3618, col.1 “…These intervals were specified with a confidence level of 75%...”, page 3618, col.2 “…The expert IS then asked to specify for each competitor two different intervals of performance on each factor at different levels of confidence…”, page 3619, col.1 and 2 – [Tables 7-9 show “75%” and “50%” confidence level].)
calculation of said plurality of bid prices being bounded to said confidence interval to improve a computational efficiency and evaluation runtime of said processor (page 3617, col.1 “…The distribution produced has a maximum point, the point of maximum expected profit, which can be approximately determined using a minimax search algorithm. One possible minimax search algorithm utilises the Fibonacci number sequence. If we can define a price interval, I, within which we believe the point of maximum expected profit lies, then we can subdivide the interval into Fn sub intervals…The point of maximum expected profit lies within one of the sub intervals calculated. This interval can be identified in at most n evaluations…”, col.1,2 “…The evaluations start at points aFn-1 and aFn-2 … The search ends when n = 0 where a*, the point of maximum expected profit is determined.”)
selecting of final bid price for said offering in accordance with said evaluation criteria from said plurality of bid prices at a confidence interval level within said confidence interval (page 3616, col.1 “… allowing the optimisation of the expected profits from the bid to be performed…”, page 3617, col.2 “…The search ends when n = 0 where a*, the point of maximum expected profit is determined.”, page 3618, col.2 “…Therefore for the bidder to have at least a 75% chance of winning the bid it should set its bid price at $88000…”, page 3620, col.1 “The resulting optimum bid price calculated for the bidder is determined to be $1,121,925, a profit of 6.85% on the estimated costs.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system with stochastic approach and functional elements used in model for determining the possibility of the bidder winning a bid of Bussey et al. (“Bid Pricing…”, abstract) with teaching of Boyd et al. for calculating the probability of winning as a function of price using the parameters from a market response model (‘171, [0010]) for using identified and determined evaluation criteria to assess bids, possibility theory with normal distributions, that similar stochastic distributions, combining factor distributions to produce bid value distribution and competitor evaluations, pricing, and win probabilities to produce potential bid price, and selecting the final bid price that related to confidence level (“Bid Pricing…” pages 3616-3618, 3620).
24. As per claims 2 and 12:
Boyd et al. discloses the following limitations:
further comprising periodically receiving updates to said request for proposal ([0036]-[0038] “’Bid Status’: Bid status specifies the current stage of negotiation for a given contract. Bid status currently supported by the Target Pricing system include: ‘Under Construction’: Account executive is in the process of putting the bid together. ‘Pending’: Account Executive is currently negotiating the bid.”, [0146] “… A bid is a proposal to an account for delivery of products over a specified time period at a specified price…”, [0157] “’Last modified date’—Date when the bid was last modified (either the product order was offered price was changed).”)
25. As per claims 3 and 13:
Boyd et al. discloses the following limitations:
wherein said first line item represents a cost of a service of said offering and said second line item represents a cost of a good of said offering ([0005] “…In making a bid for a contract or to provide a certain set of products or services…”, [0009] “… a bid pricing method that takes market and competitor response characteristics into account when generating bids for portfolios of products and services to be performed over extended contract periods…”, [0141] “’Products’: these are the products or services that the target pricing user produces and includes in a bid…”, [0146] “…The bid contains at least one, and may contain more than one, product or service order…”, [0158] “Products are the goods and services that a company provides to its customers at contracted or agreed terms…”)
26. As per claims 4 and 14:
Boyd et al. does not explicitly disclose, however, Bussey et al., as shown, teaches the following limitations:
wherein said strategic pricing includes market price adjustments of a plurality of competitors and said competitor evaluation score includes a qualitative ranking value of said plurality of competitors based on said evaluation criteria (page 3616, col.2 “… Similar distributions exist for a competitor for each of the bid factors considered in the bid such that for competitor l. l = 1,…,m, there exists a set of possibility density functions fi …”, page 3617, col.1 “…If it is assumed that the possible utility of a competitor’s bid is independent from the utility of any other competitor’s bid, then we can determine the probability of a bid of utility B, winning the bid as a product of it winning against each individual competitor…”, page 3618, col.1 “example consists of the client, the bidder and three competitors, A, B and C… the performances of the three competitors in the bid are as stated in Table 2
Factor
Competitor A
Competitor B
Competitor C
Quality
Med
High
Med
Time
Med
Med
High
Experience
Low
High
Med
Table 2 - Competitor Performance
The associated prices intervals of each competitors’ bid are determined to be as depicted in Table 3
Competitor A
Competitor B
Competitor C
$87000-$95000
$ I 00000-$1 10000
$95000-Sl05000
Table 3 - Competitor Prices”,
page 3618, col.2 “The utility of the client on a factor is described by a distribution. Bid experts are asked to specify what is required by the client to obtain each of five fixed levels of performance…”, page 3619, col.2 “… It is also estimated that the probable prices of competitors’ bids is as specified in Table 9.
Competitor
75%
50%
min
max
min
max
A
1090
1190
1120
1170
B
1060
1140
1090
1120
Table 9 - Competitor Prices
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system with stochastic approach and functional elements used in model for determining the possibility of the bidder winning a bid of Bussey et al. (“Bid Pricing…”, abstract) with teaching of Boyd et al. for calculating the probability of winning as a function of price using the parameters from a market response model (‘171, [0010]) for providing multiple competitors with quantitative ranking values across evaluation factors (“Bid Pricing…” pages 3617, 3619).
27. As per claims 5 and 15:
Boyd et al. discloses the following limitations:
wherein said producing said offering further comprises providing a graphical representation of a price-to-win curve of said probability of winning said competition for said plurality of bid prices within said confidence interval ([0019] “…the market response curve of the market response model that is generated for each bid reflects the likelihood of winning the bid as a function of bid price…”, [0023] “FIG. 1 is a graph illustrating the market response curve, the contribution and expected contribution curves for use in the market response model.”, [0024] “FIG. 2A is a bifurcated graph illustrating the win probability curves for a large and small volume customer for volume-based segmentation.”, [0025] “FIG. 2B is a bifurcated graph illustrating the win probability curves for a large and small volume customer for region-based segmentation.”, [0106] “The market response curve and win probabilities are illustrated in the graph of FIG. 1.”, [0133] “…Fig. illustrates the impact of the predictor coefficients on the market response curve.”)
28. As per claims 6 and 16:
Boyd et al. discloses the following limitations:
wherein said strategic pricing includes a magnitude and range of said market price adjustments based on likely actions by and discount tendencies of said competitor ([0012] “…calculating an equivalent competitor net price for the bid using a competitor net price model…”, [0091] “The dimensions allow competitor net price modeling which enables the user to model competitor discounting behavior once again using some form of market segmentation…”, [0167] “The competitor net price (CNP) model used in the target pricing method estimates the prices competitors will offer to customers, including negotiated discounts…”, [0194]-[0199] “As before, this is best illustrated by example: Honda: No segmentation used: Standard discount is 10%. Toyota: Product and market segments are used as follows:
Customer size
Product Market segment = Small Medium Large
Corolla 0% 5% 10%
Camry 0% 10% 15%
… To determine the net price for Toyota, we first need to determine what Customer size market segment the account falls into, and then apply the appropriate percentage against the product being priced… Because the competitor net price is a very important input for the target pricing method, precautions should be taken to ensure that the estimated competitor net price is reasonable. This is preferably accomplished by using an allowable range…The allowable range is used to determine values that fall outside the allowable range during the target bid price calculation…”)
29. As per claims 7 and 17:
Boyd et al. discloses the following limitations:
wherein said likely actions by and discount tendencies of said competitor are based on competitive intelligence of said competitor ([0020] “To isolate the correlation between specific drivers and the ultimate market response, a large database of historical bid information is collected. This database includes bid price, identification of competitors, and win/loss data for each bid…”, [0105] “The market response model (MRM) performs three key functions: updating the coefficients for market response predictors on the basis of historical data…”, [0204] “…Examples of additional parameters or factors are: products, options and quantity ordered; list price and quantity for all products in the bid; cost and quantity for all products in the bid; competitor's net price for all products in the bid.”, [0207] “In using the method, the MRM is used to analyze historical bid data and update the coefficients for the market response predictors with all account and bid characteristics…”)
30. As per claims 8 and 18:
Boyd et al. does not explicitly disclose, however, Bussey et al., as shown, teaches the following limitations:
wherein said competitor evaluation score includes a range of competitor evaluation scores of said competitor (page 3616, col.2 “…It is assumed in the model that there is an underlying possibility distribution associated with the utility for the performance of a competitor on a factor which is distributed normally…”, page 3618, col.2 “… The expert IS then asked to specify for each competitor two different intervals of performance on each factor at different levels of confidence…”, page 3619, col.1 “…Given the possible range of performance identified in the model of the client the non-price perfom1ance of competitor A is estimated to be as specified in Table 7, with the stated confidence levels.
Factor
75%
50%
min
max
min
max
Experience
75
85
77
82
Quality
68
76
70
73
Time
63
71
65
68
Table 7 – Performance of Competitor A
Similarly the performance of competitor B is determined to be as specified in Table 8.
Factor
75%
50%
min
max
min
max
Experience
51
59
54
57
Quality
58
66
61
64
Time
53
59
55
57
Table 8 – Performance of Competitor B
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system with stochastic approach and functional elements used in model for determining the possibility of the bidder winning a bid of Bussey et al. (“Bid Pricing…”, abstract) with teaching of Boyd et al. for calculating the probability of winning as a function of price using the parameters from a market response model (‘171, [0010]) for utilizing ranges and intervals for competitor performance on each evaluation factor, with different confident levels (“Bid Pricing…” pages 3618-3619).
31. As per claims 9 and 19:
Boyd et al. does not explicitly disclose, however, Bussey et al., as shown, teaches the following limitations:
wherein said confidence interval level is set at 85 percent probability of winning said competition (page3618, col.1 “These intervals were specified with a confidence level of 75%...”, col.2 “… Therefore for the bidder to have at least a 75% chance of winning the bid it should set its bid price at $88000. However if the bidder was to set the price of its proposal at $109000 then it would have at least a 75% chance of loosing the bid…”, See Tables 7 and 8: “75%” and “50%”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a system with stochastic approach and functional elements used in model for determining the possibility of the bidder winning a bid of Bussey et al. (“Bid Pricing…”, abstract) with teaching of Boyd et al. for calculating the probability of winning as a function of price using the parameters from a market response model (‘171, [0010]) for providing confidence levels for calculating win probabilities and price ranges (“Bid Pricing…” page 3618).
32. As per claims 10 and 20:
Boyd et al. discloses the following limitations:
wherein said final bid price for said offering is broken down and reported by said first line item and said second line item ([0203] “At each step, the method calculates a minimum price, target and maximum price…, [0205] “…The values produced are unconstrained and constrained prices for the entire bid, and unconstrained and constrained prices for each product.”, [0208] “Once bid optimization has been calculated, discounts are assigned for each product in the bid…”, [0209] “The method should maximize expected contribution (at the bid level) while allocating incentives for each of the products ordered in a given bid…”)
Conclusion
33. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US6963854B1 – Boyd et al. – Discloses a business process and computer system known as the “Target Pricing System” (TPS) that generates an optimum bid or value for a competitively bid good or service. The system is resident on one or more host processors in connection with one or more data stores, and includes a product model that defines list values for the bid using stored price data and costs the values using stored cost data, a competitor net price model that calculates an equivalent competitor net price for the value.
US20060136325A1 – Barry et al. – Discloses a method and system for automated proxy bidding in an auction. The invention includes defining a desired price position relative to competing bids based on qualitative ratings associated with competing bidders and submitting a bid related to a product.
US8209227B2 – Gindlesperger – Discloses an apparatus and method for selecting a lowest bidding vendor from a plurality of vendors of a customized good or service, including receiving a set of vendor's attributes from each of the plurality of vendors representing their respective capabilities, and receiving an invitation-for-bid data from the buyer defining a custom job.
US20210096520A1 – Steiger et al. – Discloses a computing device that includes memory storing a cost function of a plurality of variables. The computing device may further include a processor configured to, for a stochastic simulation algorithm, compute a control parameter upper bound.
Prepared by: Amanulla Abdullaev
/RYAN D DONLON/ Supervisory Patent Examiner, Art Unit 3692