DETAILED ACTION
This Non-Final Office Action is in response to the original filed claims and specification [October 10, 2025].
Claim 1-9 are currently pending and have been considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1-6 to a device (i.e., a machine), claims 7-9 is to a method (i.e., a process). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1, 4 and 7 are (claim 1 being representative):
A proposal generation device comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
convert, for each of one or more items defined as an item of a proposal target, each of all possible bids each of which is a bid being a combination of the item and a single proposal option among proposal options that are selectable proposal contents for the item, into a numerical vector capable of identifying the bids;
convert each bid included in a history of bids in a negotiation into a numerical vector capable of identifying the bid; and
determine, using the numerical vector into which each of all possible bids has been converted, and the numerical vector into which each bid included in the history of bids has been converted, a bid to be proposed to a negotiation opponent, from among all possible bids.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, converting, bidding and determining a possible bid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above relate to determining a possible bid, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by Page 6 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1, 4 and 7 recites the following additional elements: A proposal generation device, a memory, a processor, a numerical vector, a learning device and a computer.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [00144] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-3, 5-6, and 8-9 contain additional steps that further narrow the abstract idea above. This does not integrate the abstract idea into practical application and/or add significantly more. The claims are ineligible.
Accordingly, claims 1-9 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-4, 6-7 and 9 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Higa et al [US 2025/012,4386A1], hereafter Higa.
As per claim 1 and 7 (Similar scope);
Higa et al. teaches the conversion and identification of each bid.
A proposal generation device comprising: a
memory configured to store instructions; and
a processor configured to execute the instructions to:
{[0046] FIG. 2 is a diagram showing a configuration example of the learning device 100. In the configuration shown in FIG. 2 , the learning device 100 includes a first communication unit 110, a first display unit 120, a first operation input unit 130, a first storage unit 180, and a first control unit 190. The first control unit 190 includes a history information generation unit 191, an action determination unit 192, and a learning control unit 193.
[0051] The first control unit 190 controls each unit of the learning device 100 and executes various processes. Functions of the first control unit 190 are executed by a CPU (Central Processing Unit) included in the learning device 100 reading out a program from the first storage unit 180 and executing the program.}
Higa discloses;
convert, for each of one or more items defined as an item of a proposal target, each of all possible bids each of which is a bid being a combination of the item and a single proposal option among proposal options that are selectable proposal contents for the item, into a numerical vector capable of identifying the bids;
{[0060] However, the conversion performed by the history information generation unit 191 is not limited to a specific one, and various conversions may be used to convert history information into data in a format that can be easily processed in the own action model. The data format in which the history information generation unit 191 converts history information is not limited to the form of a vector of dummy variables as described above, but may also be various data formats represented by vectors. For example, elements of a vector and input nodes of an own action model configured using a neural network may be in one-to-one correspondence.}
Higa discloses conversion of the information into vectors;
convert each bid included in a history of bids in a negotiation into a numerical vector capable of identifying the bid; and
{[0060] The data format in which the history information generation unit 191 converts history information is not limited to the form of a vector of dummy variables as described above, but may also be various data formats represented by vectors. For example, elements of a vector and input nodes of an own action model configured using a neural network may be in one-to-one correspondence.}
Higa et al. teaches the conversion and identification of each bid;
determine, using the numerical vector into which each of all possible bids has been converted, and the numerical vector into which each bid included in the history of bids has been converted, a bid to be proposed to a negotiation opponent, from among all possible bids.
{[0144] Moreover, the history information generation unit 191 outputs the history information in vector format data. As an own action model, the action determination unit 192 includes a neural network that, upon receiving an input of history information in the vector format data, outputs its own proposal to the negotiation counterpart.}
As per claim 4;
Higa et al. teaches the conversion and identification of each bid.
A learning device comprising: a memory configured to store instructions;
and a processor configured to execute the instructions to:
{[0046] FIG. 2 is a diagram showing a configuration example of the learning device 100. In the configuration shown in FIG. 2 , the learning device 100 includes a first communication unit 110, a first display unit 120, a first operation input unit 130, a first storage unit 180, and a first control unit 190. The first control unit 190 includes a history information generation unit 191, an action determination unit 192, and a learning control unit 193.
[0051] The first control unit 190 controls each unit of the learning device 100 and executes various processes. Functions of the first control unit 190 are executed by a CPU (Central Processing Unit) included in the learning device 100 reading out a program from the first storage unit 180 and executing the program.}
Higa discloses conversion of each information into a vector variable;
convert, for each of one or more items defined as an item of a proposal target, each of all possible bids each of which is a bid being a combination of the item and a single proposal option among proposal options that are selectable proposal contents for the item, into a numerical vector capable of identifying the bids;
{[0060] However, the conversion performed by the history information generation unit 191 is not limited to a specific one, and various conversions may be used to convert history information into data in a format that can be easily processed in the own action model. The data format in which the history information generation unit 191 converts history information is not limited to the form of a vector of dummy variables as described above, but may also be various data formats represented by vectors. For example, elements of a vector and input nodes of an own action model configured using a neural network may be in one-to-one correspondence.}
Higa discloses;
convert each bid included in a history of bids in a negotiation into a numerical vector capable of identifying the bid;
{[0060] The data format in which the history information generation unit 191 converts history information is not limited to the form of a vector of dummy variables as described above, but may also be various data formats represented by vectors. For example, elements of a vector and input nodes of an own action model configured using a neural network may be in one-to-one correspondence.}
Higa et al. teaches the conversion and identification of each bid and the determination of the neural network;
determine, using the numerical vector into which each of all possible bids has been converted, and the numerical vector into which each bid included in the history of bids has been converted, a bid to be proposed to a negotiation opponent, from among all possible bids;
{[0144] Moreover, the history information generation unit 191 outputs the history information in vector format data. As an own action model, the action determination unit 192 includes a neural network that, upon receiving an input of history information in the vector format data, outputs its own proposal to the negotiation counterpart.}
Higa discloses method of learning a determination of a proposal;
and learn a method of determining the bid to be proposed to the negotiation opponent.
[0230] In the step of learning the proposal determination method (Step S613), the computer learns a determination method for an own proposal to the negotiation counterpart on the basis of the history information and the evaluation value.
As per claim 3, 6 and 9 (Similar scope);
Higa discloses;
The proposal generation device according to claim 1, wherein converting each bid included in the history of the bids in the negotiation comprises converting each bid included in the history of the bids, which includes both bids by the proposal generation device and bids by the negotiation opponent, into a numerical vector.
{[0060] However, the conversion performed by the history information generation unit 191 is not limited to a specific one, and various conversions may be used to convert history information into data in a format that can be easily processed in the own action model. The data format in which the history information generation unit 191 converts history information is not limited to the form of a vector of dummy variables as described above, but may also be various data formats represented by vectors. For example, elements of a vector and input nodes of an own action model configured using a neural network may be in one-to-one correspondence.}
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Higa et al, in view of Sunder et al [US 2020/002,0061 A1] hereafter Sunder.
As per claim 2, 5 and 8 (similar scope);
Higa discloses implementing the history of information into the negotiation and using a model to determine the proposal method. See Higa [0224- 0227]
The proposal generation device according to claim 1, wherein converting each of all possible bids comprises: converting, for each combination of a proposal target item and a proposal option included in a bid, each of the proposal target item and the proposal option into a numerical value as a result of being input to a function that converts both the proposal target item and the proposal option into an identifiable numerical value; and
[0224] With such a configuration, the history information acquisition unit 631 acquires history information about proposals which have been implemented in negotiations. The proposal output unit 632 inputs history information acquired by the history information acquisition unit 631 into the own action model that has been trained based on the history information of proposals made in negotiations and evaluation values of proposals from negotiation counterpart, and acquires and outputs an own proposal to the negotiation counterpart.
[0226] The proposal determination device 630 uses a learned own action model based on history information of proposals made in negotiations and evaluation values of proposals from the negotiation counterpart, whereby the negotiation counterpart's response to the own action is expected to be reflected in the proposal determination method. Since the counterpart's response to the own action is reflected in the proposal determination method, there is no need for the proposal determination device 630 to set or learn the utility and strategy of the negotiation counterpart.
[0227] Thus, according to the proposal determination device 630, automated negotiations can be performed even if the utility function or utility value, or strategy of the counterpart is unknown during operation.
Higa discloses;
wherein converting each bid included in the history of the bids in the negotiation comprises converting each bid included in the history of the bids into the numerical vector using a conversion method that is same as a method of converting each of all possible bids into the numerical vector.
{[0060] However, the conversion performed by the history information generation unit 191 is not limited to a specific one, and various conversions may be used to convert history information into data in a format that can be easily processed in the own action model. The data format in which the history information generation unit 191 converts history information is not limited to the form of a vector of dummy variables as described above, but may also be various data formats represented by vectors. For example, elements of a vector and input nodes of an own action model configured using a neural network may be in one-to-one correspondence.}
Sunder discloses;
taking a linear sum of the numerical value into which the proposal target item has been converted and the numerical value into which the proposal option has been converted, causing each of all possible bids to be converted into a numerical vector, and
{[0049] Considering there are 6 clauses in the contract agreement on which the negotiating agent and the opposition agent performs the negotiation task in the negotiation environment. The value that an agent attaches to the clauses is represented by a utility function which is a vector of 6 integers between −12 and 12 (excluding 0) such that their sum is zero. There is an additional constraint that there is at least one positive and one negative value in this vector and that the sum of positives is +12 and that of the negatives is −12. This vector is represented as U=Shuffle (P⊕N). Here, P=[p1, p2, p3 . . . pk] and N=[n1, n2, n3 . . . n6-k], where 0<k<6, ⊕ is the concatenation operator and shuffle (.) is a ‘random shuffling’ function. Also, pi∈{1, . . . ,12} and ni∈{−12, . . . , −1}, along the constraints that Σipi=12 and Σini=−12.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Higa’s conversion and identification of each bid to include Sunder et al’s conversion of bids into numerical value to enable identifying each bid during negotiations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Shmueli et al; [US 20040133526 A1], discloses A platform for supporting negotiation between parties to achieve an outcome, the platform comprising: a party goal program unit for: defining respective party's goal programs in respect of said outcome, said goal program comprising a plurality of objective functions and constraints associated with respective objective functions, for associating each of said objective functions with one of a plurality of levels of importance, and for assigning to objective functions within each level a respective importance weighting.
R. P. Sundarraj and W. W. H. Mok, "Models for Human Negotiation Elements: Validation and Implications for Electronic Procurement," in IEEE Transactions on Engineering Management, vol. 58, no. 3, pp. 412-430.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTOR CHIGOZIRIM ESONU whose telephone number is (571)272-4883. The examiner can normally be reached Monday - Friday 9:00 am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached on (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VICTOR CHIGOZIRIM ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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