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 is a Final Office Action on the merits. Claims 1, 2, and 4-15 are currently pending and are addressed below.
Response to Amendment
The amendment filed 08/11/2025 has been entered. Claims 1, 2, and 4-15 are currently pending. The previous 35 USC 112 rejection is overcome by Applicant’s amendments and comments. However, the amendment necessitated new grounds of rejection under 35 USC 112.
Response to Arguments
Applicant's arguments filed 08/11/2025 have been fully considered but they are not persuasive.
In response to Applicant's argument regarding the 35 USC 101 rejection in light of the newly amended claim limitations, the Examiner respectfully disagrees. The instant claim language regarding the “output” does not recite any specific form of the output. It is not recited as output on any particular device, such as a display. Broadly interpreted, any transfer of data between computer components is “outputted” to another component, for example, between a CPU and memory. Absent any tangible output format, the instant claims do not amount to significantly more than the recited abstract idea.
In response to Applicant's argument that Ni fails to teach the limitations of previous claim 3, the Examiner respectfully disagrees.
Applicant selectively quotes an irrelevant section from only one of the cited paragraphs, ignoring additionally cited paragraph ¶0006, which clearly teaches “Travel time and waiting time are associated as costs of the edges in the transportation graph.” Additional relevant sections of Ni are ¶0042, ¶0065-0068, among others. Applicant is reminded that the entire reference may be relied upon for a teaching, and specific mappings are for Applicant’s convenience, and do not necessarily contain all relevant sections for an understanding of the teachings, or rejections.
Therefore, the prior art meets the claim limitations, and Applicant’s arguments are not persuasive.
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, 2, and 4-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
[Claim 1] (Currently Amended) A planning assistance system comprising:
a memory storing instructions; and
one or more processors configured to execute the instructions to:
accept input of a cost function that calculates a cost incurred by an itinerary, the cost function being expressed as a linear sum of terms weighted for each feature that a traveler is expected to intend in the itinerary;
learn the cost function by inverse reinforcement learning using training data that includes scheduled information indicating travel planning of the traveler, attribute information indicating an attribute of the traveler, and actual information indicating an actual travel result of the traveler;
extract the training data whose specified attribute matches the attribute information, information; and
learn the cost function according to the attributes by inverse reinforcement learning using the extracted training data.
Step 1: Statutory Category – Yes
The claim recites a system. The claim falls within one of the four statutory categories. MPEP 2106.03.
Step 2A prong one evaluation: Judicial Exception – Yes
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” and “mathematical operations” because under its broadest reasonable interpretation in light of the specification, the claim covers performance using mental processes and math.
The claim recites ”learn the cost function by inverse reinforcement learning”. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind. For example, a person could mentally calculate a simple cost function based on training data either in their head or on paper, using basic algebra. This step is directed to a mental process and/or mathematical operation.
The limitation of “extract the training data whose specified attribute matches attribute information” is not precluded from being done in the mind. For example, a person could mentally identify a parameter in a data set that matches a particular attribute. This step is a mental process.
The limitation of “learn the cost function according to the attributes” is a mental process by the same logic as the learning step above.
The limitation of “generate the travel by seeking a combination of move or stay with the minimum total cost…” is not precluded from being done in the mind. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind. For example, a person could mentally calculate a lowest cost path using the calculated cost function in their head or on paper. This step is directed to a mental process.
The limitation of “output move information between each travel point included in the travel planning superimposed on a map” is not precluded from being done in the mind. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind and/or on paper or orally. For example, a person could mentally determine move information in their head or on paper and output this information orally and/or on a paper map, as was common prior to digital map technology. This step is directed to a mental process.
Alternately or in addition, this limitation may be considered extra-solution activity as detailed below.
Step 2A Prong Two evaluations – Practical Application – No
Claim 1 is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
Claim 1 recites additional elements of memory, a processor, and accepting input.
Claim 1 recites additional element of a system having a memory and processor. According to the specification, the processor is identified as a general purpose computer such that it represents no more than mere instructions to apply the judicial exceptions on a generic computer. The computer is recited at a high level of generality and merely automates the determining, identifying and send steps. The generically recited computer merely describes how to generally “apply” the otherwise mental processes and business transaction using a generic or general-purpose processor.
The underlined limitation of “accept input of a cost function…” is directed to the extra-solution activity of gathering data. This step amounts to mere data inputting which is a form of insignificant extra-solution activity, see MPEP2106.05(g).
The underlined limitation of “output move information…” is directed to the extra-solution activity of outputting data. This step amounts to mere data inputting which is a form of insignificant extra-solution activity, see MPEP2106.05(g).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limit on practicing the abstract idea. The claim is ineligible.
2B Evaluation: Inventive Concept – No
Claims 1 is evaluated as to whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
Per the evaluation in step 2A, general linking the use of the judicial exception to a particular technological environment or filed of use (vehicles) is not indicative of an inventive concept (significantly more).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B. Here, the step of “accept[ing] input of a cost function” were considered to be extra-solution activities in Step 2A, and thus they are reevaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification and background therein does not provide any indication that the processor and memory are anything other than possible generic, off the-shelf computer components, and the Symantec, TLI, and OIP Techs, court decisions cited in MPEP 2106.05(d)(ll) indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) MPEP2106.05 (g).
For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
Claims 9 and 11 are ineligible under similar analysis, with the exception of the differing statutory categories and the lack of computing structure.
Claims 2, 10, and 12 recite: accept input of a constraint condition for generating the travel planning; and
generate the travel planning with the minimum cost calculated by the cost function among the travel planning set up to travel to each candidate travel point to satisfy the constraint condition.
The underlined limitation of “accept input of a constraint condition…” is directed to the extra-solution activity of gathering data. This step amounts to mere data inputting which is a form of insignificant extra-solution activity, see MPEP2106.05(g).
The limitation of “generate the travel planning with the minimum cost calculated by the cost function” is not precluded from being done in the mind. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind. For example, a person could mentally calculate a lowest cost path using the calculated cost function in their head or on paper. This step is directed to a mental process.
Claim 5 recites:
output a feature included in the cost function and weight of the feature in correspondence with the weight of the feature.
The underlined limitation of “output a feature…” is directed to the extra-solution activity of outputting data. This step amounts to mere data inputting which is a form of insignificant extra-solution activity, see MPEP2106.05(g).
Claim 6 recites:
label to the learned cost function information that can identify contents of the learned cost function; and that indicates the contents of the feature with the highest weight.
The limitation of “label…information...” is directed towards a mental process. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind. For example, a person could mentally label of a feature based on costs in their head or on paper. This step is directed to a mental process.
Claim 7 recites:
extract training data of a person who has traveled or visited tourist spots more than a predetermined standard.
The limitation of “extract training data…” is directed towards a mental process. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind. For example, a person could mentally select data from among a set based on a characteristic of an expert. This step is directed to a mental process.
Claim 8 recites:
accept input for the cost function where the longer the moving time, the higher the cost is calculated and the higher the evaluation of the travel point, the lower the cost is calculated.
The underlined limitation of “accept input for the cost function…” is directed to the extra-solution activity of inputting data. This step amounts to mere data gathering which is a form of insignificant extra-solution activity, see MPEP2106.05(g).
New claims 13-14 merely further define data types, and are similarly ineligible as above.
Claim 15 recites “wherein the processor is configured to execute the instructions to extract training data of a person who satisfies a person who has more followers on a social networking services (SNS) than a predetermined standard.”
The limitation of “extract training data…” is directed towards a mental process. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation per the specification, covers performance of the limitation in the mind. For example, a person could mentally select data from among a set based on a characteristic of an expert. This step is directed to a mental process.
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.
Claim 4 is 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.
Claim 4 recites redundant claim language already present in claim 1 from which it depends, rendering the scope of intended claim language unclear.
In the art rejections below the claims have been treated as best understood by the examiner. Any claim not explicitly rejected under this heading is rejected as being dependent on an indefinite claim.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
As best understood by the Examiner, claims 1, 2, and 4-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lehoux-Lebacque et al. (US 2022/0057217) in view of Levine et al. (Feature Construction for Inverse Reinforcement Learning, 2010) and Ni et al. (US 2017/0059331).
Regarding claims 1, 13, and 14:
Lehoux teaches A planning assistance system comprising:
a memory storing instructions (see at least ¶0057, Fig. 1); and
one or more processors (see at least ¶0057, Fig. 1) configured to execute the instructions to:
accept input of a cost function that calculates a cost incurred by an itinerary the cost function being expressed as a linear sum of terms weighted for each feature that a traveler is expected to intend to include in the itinerary (cost function representing cost of an itinerary, see at least ¶0046-47);
wherein the processor is configured to execute the instructions to output move information between each travel point included in the travel planning superimposed on a map (see at least ¶0007-0016, Fig. 2).
Lehoux does not teach learning the cost function by inverse reinforcement learning.
Levine teaches learn parameters of a cost function by inverse reinforcement learning using training data;
extract the training data whose specified attribute matches attribute information, information; and
learn parameters of the input cost function according to the attributes by inverse reinforcement learning using the extracted training data (generating reward function using relevant component features based on expert policy, example, driver who likes to speed see at least p. 1-2).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to modify the system and method computing an itinerary from a cost function as taught by Lehoux with the well-known technique of learning a cost function as taught by Levine in order to generate an appropriate cost function from a data set when the cost function is unknown. The specific training data recited is an obvious consequence of the type of cost function being learned in the particular field (itinerary planning).
The combination of Lehoux and Levine teaches the limitations as above. The combination does not explicitly teach the cost function including a move and stay cost.
Ni teaches a system and method of route planning, including a cost function and generating a travel planning by seeking a combination of move or stay with the minimum total cost based on the set of the candidate travel points and the cost incurred in moving to or staying at the candidate travel points calculated by the cost function (see at least abstract, ¶0006, ¶0043) and execute the instructions to output move information between each travel point included in the travel planning superimposed on a map (see at least Figs. 6, 12-13).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the itinerary planning system and method as taught by Lehoux and Levine with the technique of utilizing move and stay costs when calculating a shortest path as taught by Ni in order to generate an optimal path in an itinerary network wherein the optimal path is not necessarily the shortest time.
Regarding claims 9 and 11, the combination of Lehoux and Levine teach a method and computer readable medium for performing a method as above.
Regarding claims 2, 10, and 12:
Lehoux further teaches accepting input of a constraint condition for generating the travel planning (see at least ¶0002); and
generate the travel planning with the minimum cost calculated by the cost function among the travel planning set up to travel to each candidate travel point to satisfy the constraint condition (see at least ¶0047).
Regarding claim 4:
Lehoux further teaches wherein the processor is configured to execute the instructions to output move information between each travel point included in the travel planning superimposed on a map (see at least ¶0007-0016).
Regarding claim 5:
The combination of Lehoux and Levine further teaches wherein the processor is configured to execute the instructions to output a feature included in the cost function and weight of the feature in correspondence with the weight of the feature (broadly interpreted, any operation by the processor operating on the cost function would “output” the features and weights of the cost function, at least to a cache, memory, storage, etc.).
Regarding claim 6:
Lehoux meets the claim limitations based on the broadest reasonable interpretation of the claims in light of the 35 USC 112(b) rejection above.
Regarding claim 7:
The combination of Lehoux and Levine further teaches The planning assistance system according to claim 1, wherein the processor is configured to execute the instructions to extract training data of a person who satisfies the predefined conditions of an expert (see at least Levine p. 1-2).
Regarding claim 8:
The combination of Lehoux and Levine teaches the limitations as in claim 1 above.
Levine is silent as to the specifics of the cost function.
It would have been obvious to one of ordinary skill in the art before the time of filing of the invention to modify the travel planning system and method as taught by Lehoux and Levine to utilize any known parameters for a cost function, including the well-known technique for travel cost of a higher cost based on time and lower cost based on desirability of a location as a matter of design choice in order to accurately reflect a desired travel itinerary utilizing common design goals of shorter travel times between destinations and more desirable waypoints.
Regarding claim 13:
Lehoux further teaches wherein the scheduled information indicates a travel itinerary.
Regarding claim 15:
Modified Lehoux teaches the limitations as in claim 7 above.
Lehoux is silent as to extracting training data based on SNS followers.
However, it would have been an obvious matter of design choice to one of ordinary skill in the art before the time of filing of the invention to modify the itinerary planner and cost function learning system and method as taught by Lehoux, Levine, and Ni by utilizing any known measure of expertise/trust including number of social media followers in order to utilize a readily available metric for determining trust and/or expertise with the expected result of enhancing reliability of training data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN J RINK whose telephone number is (571)272-4863. The examiner can normally be reached on M-F 8-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anna Momper can be reached on (571) 270-5788. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ryan Rink/Primary Examiner, Art Unit 3619