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 filed the amendment on 05/26/2026. Claims 1 and 11 are amended. Claims 1-20 are pending.
Claim Rejections - 35 USC §101
3. 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.
4. 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.
5. 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”.
6. Claim 1 recites “determining a bid price for competition”. Specifically, claim recites ““receiving a request for proposal from an external system; interpreting said request for proposal and determining 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 producing a random distribution of prices, and applying said random distribution of prices to said market pricing model producing 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 improving a computational efficiency and evaluation runtime of said processor, and selecting a 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 as said final bid price for said competition to said external system for a decision maker”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., commercial or legal interactions) (emphasized in bold), “Mathematical concepts – mathematical calculations” (emphasized in underlined) 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).
7. 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. 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 a bid price for competition.
8. 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 a bid price for competition 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)).
9. Hence, claim 1 is not patent eligible.
10. Claim 11 also recites “determining a bid price for 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)).
11. 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. 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 a bid price for competition.
12. 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 a bid price for competition 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)).
13. Hence, claim 11 is not patent eligible.
14. The following dependent claims recent additional elements not addressed above:
claims 5 and 15 recite “a graphical representation of a price-to-win curve”.
When considered individually, and as a whole, each of these additional elements amount to merely "apply it", as they are merely applying the abstract idea to the technical environment of the graphical representation of the price-to-win curve.
Dependent claims 2-10 and 12-20 merely expand upon the abstract ideas of the independent claims and are therefore rejected under the same rationale as claims 1 and 11 respectively.
Conclusion of 35 USC §101
15. 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.
16. 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
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 (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.
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, 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. 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.)
21. As per claim 1:
Boyd et al. discloses the following limitations:
A method operating on a processor and memory, comprising: ([0086] “…the present inventive method is readily adaptable for use in an automated system, such as in software executing on a computer platform…”, [claim 35] “A computer-readable medium comprising computer executable instructions for executing a method for determining a target price for an auction item… determining an equivalent competitor net price for the auction item using an electronically stored competitor net price model…”)
receiving a request for proposal from an external system ([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.”, [0033] “‘Bid’: A bid is a clearly specified package of goods and services (called products in the Target Pricing context) for which the price will be negotiated (rather than automatically quoting list price). Also called a bid proposal.”, [0083] “…These costs may either have been gathered manually or obtained from a proprietary costing system from third parties as is known in the art or could be retrieved in real-tine from external systems or sources.”)
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…”, [0160] “…Products orders are the specific products and options that have been ordered in a bid…”, [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…”, [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: ([0146] “…A bid is a proposal to an account for delivery of products over a specified time period at a specified price…”, [0149] “‘Pending’ - The bid has been completed, target priced, and submitted to the customer, but no response has been obtained from the customer yet.”)
determining a market price of said offering, comprising: ([0082] “…Initially, the bid must be priced preferably using the list prices in a product model, as discussed below. These prices may be gathered directly from current data or obtained from a 3rd party or proprietary pricing system…”, [0089] “… The market response model (MRM) calculates the win probability as a function of price through the examination of historical bid information at various prices…”)
selecting a market pricing model [0068] “‘Price Model’: An object which estimates prices using a lookup table and an (optional) interpolation algorithm. Price models are used to provide list prices and competitor net prices, and may estimate prices using zero to n dimensions or through functional relationships or by retrieval from external systems.”, [0061] “… They also include various switches and values indicating preferred algorithms (where there are choices), an example being the choice of currency units…”, [0232] “…The user preferably selects from among three different pricing methods.”)
selecting a first range of prices for said first line item ([0062] “‘Price Range’: As well as the contribution-maximizing target price, target pricing computes a minimum price and a maximum price within which account executives can negotiate bids.”, [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.”)
selecting a second range of prices for said second line item ([0202] “The target pricing method computes prices. in a sequence of four steps: (1) Unconstrained bid-level prices. (2) Constrained bid-level prices. (3) Unconstrained product-level prices. (4) Constrained product-level prices.”, [0203] “At each step, the method calculates a minimum price, target and maximum price…”)
determining strategic pricing of said offering including market price adjustments of a competitor ([0075] “‘Strategic Objectives’: Business requirements established by senior management to promote long-term corporate growth, possibly at the expense of near-term profits…”, [0167] “The competitor net price (CNP) model used in the target pricing method estimates the prices competitors will offer to customers, including negotiated discounts…”, [0193] “After computing the competitor list price, the net price is computed by applying the appropriate discounting model…”, [0201] “…The multi-year optimization can model behavior like competitor response, changes in interest rates, changes in cost and price structures, and like 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, calculation of said plurality of bid prices being bounded to said confidence interval improving a computational efficiency and evaluation runtime of said processor ([0076] “‘Minimum Success Rate’: All affected bids will be priced to maintain the specified minimum win probability”, [0214] “… A feasible target range is calculated from the constraints determined by the strategic objectives. If the optimal target price is outside this feasible ranges the constrained target price that satisfies the constraints is found.”, [0114] “…Mathematical properties of the logic function offer efficient numerical computation and an intuitive interpretation of the fitted coefficients.”)
reporting said offering as said final bid price for said competition to said external system for a decision maker ([0149] “‘Pending’—The bid has been completed, target priced, and submitted to the customer, but no response has been obtained from the customer yet.”, [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.”, [0200] “…At a macro level, the target pricing method recommends a target price for each bid…”, [0069] “…target pricing computes a minimum price and a maximum price within which account executives can negotiate bids.”)
Boyd et al. does not disclose, however, Bussey et al., as shown, teaches the following limitations:
interpreting said request for proposal and determining a scope of and evaluation criteria for said request for proposal (page 3616, col.1 “…It is assumed in the model that the 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 and that it awards the contract to the bid with the lowest cost or value which meets its requirements …”, 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 3617, col.2 “… Initially the expert is asked to describe what is required by the client to obtain each level of performance on a factor…”)
independently distributing and randomly combining said first range of prices with said second range of prices producing 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 “…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 of a competitor’s bid, in accordance with equation 1.”, 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…”)
applying said random distribution of prices to said market pricing model producing a plurality of market prices (page 3615, col.1 “…The resulting models determine the possibility of the bidder winning a bid at different bid prices, allowing the bidder to optimise its bid price…”, page 3617, col.1 “…Since the bid utility, B, and the actual profit, eB, are both functions of the bid price, then the expected profit is also a function of the bid price, such that a distribution (Figure 3) can be produced for the variation in expected profit with price pB…”, page 3618, col.2 “Bidder Price = $88000 to $109000”)
determining a competitor evaluation score of said offering including a qualitative ranking value of said competitor based on said evaluation criteria (page 3618, col.1 “…Given the performance levels stipulated it is determined that the performances of the three competitors in the bid are as stated in Table 2”…”, page 3619, col.2 “…When this data is processed the model creates distributions for the possible value of the competitors’ bids for the client by translating the performance of their bid performances against the utility of the client.”…”, page 3618, col.1 “…Using the Zeng model the true value of the bids of each competitor in the eyes of the client are as specified in Table 4…”
selecting a 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 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 …The main advantage of the Bussey model against the Zeng model is that it enables the bidder to calculate a specific optimum bid price. It also enables the bidder to determine its chances of winning the bid at different prices and hence investigate alternative pricing strategies…”)
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 determining the evaluation criteria for the specific competition (price plus quality, implementation time, experience, identified per scenario) and what the client requires, distributing is formed over the specified monetary interval of each factor (independently distributing the first and second ranges), and the per-factor distributions are combined as independent random variables into a resulting distribution of total monetary value, applying the combined possibility distributions within the pricing model to produce a plurality of market-referenced prices, ranking a competitor evaluation score based on the evaluation criteria, and selecting the final bid price from the computed interval and plurality at a stated confidence level within that interval (“Bid Pricing…” pages 3615-3618, 3620).
Claim 11 is rejected using the same rationale that was used for the rejection of claim 1.
22. As per claim 2:
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).”)
Claim 12 is rejected using the same rationale that was used for the rejection of claim 2.
23. As per claim 3:
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…”)
Claim 13 is rejected using the same rationale that was used for the rejection of claim 3.
24. As per claim 4:
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).
Claim 14 is rejected using the same rationale that was used for the rejection of claim 4.
25. As per claim 5:
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.”)
Claim 15 is rejected using the same rationale that was used for the rejection of claim 5.
26. As per claim 6:
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…”)
Claim 16 is rejected using the same rationale that was used for the rejection of claim 6.
27. As per claim 7:
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…”)
Claim 17 is rejected using the same rationale that was used for the rejection of claim 7.
28. As per claim 8:
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).
Claim 18 is rejected using the same rationale that was used for the rejection of claim 8.
29. As per claim 9:
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).
Claim 19 is rejected using the same rationale that was used for the rejection of claim 9.
30. As per claim 10:
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…”)
Claim 20 is rejected using the same rationale that was used for the rejection of claim 10.
Response to Arguments
31. Claims 9-20 are rejected. After careful consideration of applicant arguments, the examiner finds them to be not persuasive.
Rejection under 35 USC § 101
32. Applicant’s arguments toward 35 U.S.C. § 101 rejection are not persuasive. Amended independent claims 1 and 11 do not have additional elements that could lead to an improvement in the functioning of a computer, or an improvement to other technology or technical field.
33. Applicant is of the opinion that “the independent claims independently distribute and randomly combine ranges of prices producing a random distribution of prices, apply the random distribution of prices to a market pricing model producing a plurality of market prices, select a final bid price for an offering in accordance with evaluation criteria from the plurality of bid prices at a confidence interval level within a confidence interval, and report the offering as the final bid price for a competition to an external system for a decision maker. This is significantly more than an abstract idea.”
Examiner respectfully disagrees.
Mentioned above the elements of the claims performed by using the computer components. The use of a processor/computer as a tool to implement the abstract idea does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field. An ordered combination of the limitations – “independently distributing and randomly combining said first range of prices…”, “applying said random distribution of prices to said market pricing model…”, “selecting a final bid price for said offering in accordance with said evaluation criteria…”, and “reporting said offering as said final bid price for said competition…” – merely implement an abstract idea (commercia/legal interaction) using the additional elements such as a processor, memory, and an external system. The claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims 1 and 11 are directed to the abstract idea.
34. Applicant also argues that the current application is analogous to app.#18/449,532 which was considered eligible under 35 U.S.C. § 101, and was granted the patent # 12,596,912.
Examiner respectfully disagrees.
The analysis of subject matter eligibility of claims is performed based on the Supreme Court’s decision in Alice v CLS Bank and MPEP 2106 guidance, but not comparing whether or not examining applications’ claims are analogous.
Moreover, Application 18/449,532 is directed to a system and method for predicting a characteristic of an object, and current application directed to a system and method for producing a bid price for a service or a good. However, there are no similarity and/or analogous limitations in the claims.
The claims are not patent eligible.
Rejections under 35 U.S.C. § 103
35. Applicant is of the opinion that the prior art reference Boyd et al. does not disclose claims 1 and 11 limitation “breaking down said scope … including dynamic links therebetween”.
Examiner respectfully disagrees.
In the revised claims mapping under broadest reasonable interpretation (BRI) Boyd et al. teaches limitation as “the bid scope is broken into plural product/service orders (first and second line items), and the line items are linked, wherein option orders depend on product orders, and product-level prices are dynamically interdependent because the optimizer allocates discounts across line items so they aggregate to the bid-level result (e.g., a change in one line item’s allocation affects the others).
36. Applicant is of the opinion that the prior art reference Boyd et al. does not disclose claims 1 and 11 limitation “selecting a first range of prices for said first line item” and “selecting a second range of prices for said second line item”.
Examiner respectfully disagrees.
Boyd et al. teaches limitation as “determining a minimum–maximum price range for each product (line item) (e.g., choosing an input for random combination) and “product-level pricing yields a minimum/target/maximum range for every product in the bid (e.g., a bid with a second product order therefore has a second price range determined for it)” respectively.
37. Applicant is of the opinion that the prior art reference Boyd et al. does not disclose claims 1 and 11 limitation “reporting said offering as said final bid price… for a decision maker”.
Examiner respectfully disagrees.
Boyd et al. teaches limitation as “the completed, target-priced bid (the offering at the final bid price) is submitted to the customer, wherein the external party whose request initiated the competition and whose decision maker accepts or rejects it and the reporting module and target-price recommendations additionally report the price to the bidder’s account executives”.
38. Applicant is of the opinion that the prior art reference Bussey et al. does not disclose claims 1 and 11 limitation “independently distributing and randomly combining … a random distribution of prices”.
Examiner respectfully disagrees.
Bussey et al. teaches limitation as “an independent, normally distributed possibility distribution is formed over the specified monetary interval of each factor (independently distributing the first and second ranges), and the per-factor distributions are combined as independent random variables into a resulting distribution of total monetary value (a random distribution of monetary values)”.
39. Applicant is of the opinion that the prior art reference Bussey et al. does not disclose claims 1 and 11 limitation “applying said random distribution of prices … a plurality of market prices”.
Examiner respectfully disagrees.
Bussey et al. teaches limitation as “the combined possibility distributions are applied within the pricing model to produce a plurality of market-referenced prices (e.g., the evaluated candidate bid prices across the price axis and the computed price interval endpoints)”.
40. Applicant is of the opinion that the prior art reference Bussey et al. does not disclose claims 1 and 11 limitation “producing a plurality of bid prices for said offering … improving a computational efficiency and evaluation runtime of said processor”.
Examiner respectfully disagrees.
In the revised claims’ mapping Boyd et al. teaches limitation as “producing plural candidate bid prices each carrying a win probability, bounded to a feasible range defined by win-probability constraints (minimum/maximum success rates) (e.g., reading on prices within a probability bounded interval) and notes the computational efficiency of the logistic form”.
Conclusion
41. 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.
42. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
43. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANULLA ABDULLAEV whose telephone number is (571)272-4367. The examiner can normally be reached Monday-Friday 9:30AM -4:30PM ET.
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/AMANULLA ABDULLAEV/ Examiner, Art Unit 3692
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692
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