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 .
DETAILED ACTION
This communication is a non-final action in response to application filed on 08/05/2025. Claims 1-10 are pending.
Information Disclosure Statement
The IDS filed on 08/05/2025, 01/13/2026, 02/18/2026 have been considered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-3, 8 includes limitation regarding “high-priced railway services”. The limitation is indefinite for at least the following reasons. The term “high-priced” is a relative/subjective term where each person may have different standard to define “high”. Specification provides the following explanation, but the explanation is still unclear. The application’s PG-pub has the following paragraph to discuss the relevant terms. In 0129, it describes “high-priced services” can be “reserved train seats or high-grade hotel rooms, or boosting store sales”. 0144 describes high-priced service as a high-value service. 0146 specifically describes high-priced railway services as “In particular, high-priced services in railway services include, in addition to regular reserved seats, reserved seats with good views, luxurious sleeper cars, and reserved seats on planned trains” (emphasis added). Examiner is unable to find any other context regarding what constitute “regular reserved seats” or any other use of the word “regular”.
The example given in 0146 is unclear because it provides no basis to differentiate the difference between a “regular reserved seats” and “reserved seats on planned trains”. Therefore, despite having 4 different types of seats, it is impossible to tell the difference between two types of seats apart. As of the discussion in 0144 and 0129, these doesn’t provide better context to explain 0146. In fact, 0129 actually makes it worse as 0129 suggest any reserved seats are high-priced railway service but that would contradict 0144 where a high-priced railway service requires 4 different types of seats. As a result, despite examples are provided, it’s still unclear to identify a standard to set the boundary for the term “high-priced railway services”, particularly the “high-priced” part.
For the purpose of examination, any reserved train service is treated as a high-price railway service to give applicant the broadest coverage.
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 8-10 are rejected under 35 U.S.C. 101 because they recite an abstract idea without significantly more.
Step 2A prong 1
Using claim 8 as representative example, the following limitation recites an abstract idea
estimating outcome variables by using supervised data including causal variables, process types, and the outcome variables for each of a plurality of processes and inputting the causal variables and the process types […]
outputting a result of the estimation; wherein,
the causal variables are at least one of attributes and purchase history of railway users, a factor affecting a sales value of railway services, action history of railway users, fare price increases, fare price reductions, and coupon amounts to promote railway use,
the process types are fare price increases, fare price reductions, campaigns to promote railway use, and distribution of coupons to promote railway use, and
the outcome variables are sales values of high-priced railway services.
The above limitations falls into mental process for at least the reason of being similar to Electric Power Group’s collecting information, analyzing it and displaying the result. Examiner particularly notes anything within the wherein clause is just describing the characteristics of the displayed result, which represent the outcome of analysis (estimating step). Therefore, claim 8 recites an abstract idea.
Step 2A prong 2
The additional element of claim 8 are:
a processor to execute a program; and
a memory to store the program which, when executed by the processor, performs processes of,
a learning model generated by learning the outcome variables from the causal variables and the process types for each of the processes;
As per processor and memory, they’re merely generic computer components discussed in high generality. Similarly, the learning model is already generated and 0188 shows the model is generated using “a known technique”. The learning model, therefore, at this level of breadth is still a generic computer component. Each of these generic computer components are just merely being used to implement the abstract idea onto them (e.g., merely being used to generate output). Even when viewed as an ordered combination, they’re still merely being instructions to implement them onto a computer. Therefore, they do not integrate the abstract idea into practical application.
Step 2B
As noted above in step 2A prong 2, of which the analysis is still applicable in step 2B, the additional elements, whether viewed individually or as an ordered combination, are nothing more of mere instructions to implement the abstract idea onto a generic computer. Such additional element would not provide significantly more to an abstract idea either. Therefore, claim 8 is not eligible.
Claim 9 can be similarly analyzed as claim 8. Examiner notes claim 9’s claim is written even more closely aligned to the claim of EPG and therefore would also fall into mental process. Claim 10 is merely further limiting the abstract idea in a manner similar to the wherein clause of claim 8 and would be similarly analyzed as claim 8-9 as well.
Examiner particularly notes the learning process of claim 8 is complexly outside the scope of claim 8 but is positively recited as part of claim 1. Therefore, claim 8’s learning model needs to be analyzed differently from claim 1. Applicant is recommended to rewrite claim 8-9 in a manner similar to claim 1.
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) 4-6, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20180025407) in view of Ali (Ali, Moez, “Supervised Machine Learning”, datacamp.com, https://www.datacamp.com/blog/supervised-machine-learning, 08/22/2022).
As per claim 4, Zhang discloses an information processing device comprising:
a processor to execute a program (Fig. 1 system 105 housing order processing engine 110); and
a memory to store the program which, when executed by the processor (Fig. 1 system 105 housing order processing engine 110), performs processes of,
generating, for each of the processes, Examiner notes a variable adjusted based on request or location would not be dependent on time); and
generating a learning model by using the supervised data to learn, for each of the processes, a change with time of the first causal variables at a second time from the first causal variables, the second causal variables, and the process types at a first time in accordance with the history information (0094-0095, comparison regarding similarity is made to historical information where historical information during different time intervals may have the same or different effects. Noting order types are also considered. See 0115 regarding training of model).
Zhang discloses using supervised learning technique to build its model (0079), which at least strongly suggest the dataset used for model generating are supervised data but doesn’t explicitly states so.
Ali teaches that supervised learning technique uses supervised data (page 3, section “supervised vs. Unsupervised learning”, subsection “type of data”, “supervised learning uses labeled data. Labeled data is a data that contains both the feature … and the target …”).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to apply supervised data as taught by Ali with supervised machine learning model for the purpose performing Zhang’s intended supervised learning.
As per claim 5, Zhang further discloses the information processing device according to claim 4, wherein,
the processor uses the learning model to estimate a change with time of the first causal variables obtained by a combination of two or more processes selected from the processes at a first period (0097, results of model output used to rank in order to make allocation. ).
As per claim 6, Zhang further discloses the information processing device according to claim 5, wherein the processor specifies an optimal combination of the two or more processes based on with the estimated change with time (0099, orders are allocated based on results output from earlier).
Claim 9 includes limitations substantially similar to claim 5 except for the outputting result. Zhang teaches outputting result (0192 regarding accepting order. See Fig. 1 for drivers having driver terminals 140-n).
Allowable Subject Matter
Claim 7 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 7 includes the following limitations:
generating, for each of the processes, supervised data including first causal variables that change with time, second causal variables that do not change with time, process types, and history information indicating a history of changes with time of the first causal variables; and
generating a learning model by using the supervised data to learn, for each of the processes, a change with time of the first causal variables at a second time from the first causal variables, the second causal variables, and the process types at a first time in accordance with the history information.
wherein
the first causal variables are fare price increases, fare price reductions, and amounts of coupons to promote railway use,
the second causal variables are at least one of attributes and purchase history of railway users, factors affecting the sales value of railway services, and action history of railway users,
the process types are fare price increases, fare price reductions, campaigns to promote railway use, and distribution of coupons to promote railway use, and
the history information is a history of fare price increases, a history of fare price reductions, and a history of distribution of coupons to promote railways use.
Best prior art Zhang discloses learning model that uses supervised learning to generate a prediction model for reserving transportation services that includes trains. The prediction model disclosed, however, is not generated for each of the processes, a change with time of the first causal variables at a second time from the first causal variables, the second causal variables, and the process types at a first time in accordance with the history information; wherein (1) the first causal variables are (i) fare price increases, (ii) fare price reductions, and (iii) amounts of coupons to promote railway use, (2) the second causal variables are at least one of (i) attributes and purchase history of railway users, (ii) factors affecting the sales value of railway services, and (iii) action history of railway users, (3) the process types are (i) fare price increases, (ii) fare price reductions, (iii) campaigns to promote railway use, and (iv) distribution of coupons to promote railway use, and (4) the history information is (i) a history of fare price increases, (ii) a history of fare price reductions, and (iii) a history of distribution of coupons to promote railways use.
Claims 1-3, 8, 10 includes similar limitations above and would be novel and non-obvious over the prior art of record. These claims would be allowable if they’re in independent form AND applicable 112 and/or 101 rejection are overcome.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE CHEN whose telephone number is (571)270-5499. The examiner can normally be reached Monday-Friday, 8:30 AM -5:00 PM Eastern.
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GEORGE CHEN
Primary Examiner
Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628
Prosecution Insights