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
Response to Amendment
In the amendment dated 04 September 2025, the following has occurred: Claims 1, 2, 4, 5, and 7-11 have been amended; Claim 3 has been cancelled.
Claims 1, 2, and 4-11 are pending.
Priority
This application claims priority to PCT/JP2021/033836 dated 15 September 2021.
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 4-11 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.
Claims 1, 10, and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a system, method, and computer-readable medium (“CRM”) for meal recommendation, which are within a statutory category.
Step 2A1
The limitations of (Claim 10 being representative) receiving physical information or a health condition of a subject user and a request pertaining to a meal menu; generating response information including information pertaining to a meal menu which corresponds to the physical information or health condition of the subject user based on the request and a learned model which has learned pieces of physical information or health conditions of a plurality of second users and meal order histories of the plurality of second users; wherein the learned model is a second user graph that is a graph which includes: (i) a node indicating a second user, who is different from the subject user; (ii) nodes each indicating physical information, a health condition, or a meal menu related to the second user; and (iii) links indicating relationships between the nodes, and which has learned the relationships between the nodes, predicting a relationship between nodes which are not connected to each other by a link in a subject user graph including a plurality of nodes pertaining to the subject user, and one or more second user graphs; and controlling a display or an audio output apparatus to provide the subject user with the response information, wherein the generation process generates the response information based on the predicted relationship between nodes, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system implemented by at least one processor, a method implemented by a computer, or a computer-readable non-transitory storage medium and computer, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the noted computer components, this claim encompasses a person collecting meal and health information about a person and using past information to recommend a meal order in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of at least one processor (Claim 1), a computer (Claim 10), or a CRM/computer combination (Claim 11) that implements the identified abstract idea. The processor/computer/CRM is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using at least one processor (Claim 1), a computer (Claim 10), or a CRM/computer combination (Claim 11) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Claims 2 and 4-9 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 merely describe(s) generating and outputting additional information. Claim(s) 4, 7, 8 merely describe(s) the linking and generating of information. Claim(s) 5 merely describe(s) inputting and linking information. Claim(s) 6 merely describe(s) evaluating a recommendation level. Claim(s) 9 merely describe(s) receiving, calculating, and generating information.
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.
Claim(s) 1, 2, and 4-11 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Alkan et al. (U.S. Pre-Grant Patent Publication No. 2019/0073601) in view of Gutnik et al. (U.S. Pre-Grant Patent Publication No. 2019/0205942).
REGARDING CLAIM 1
Alkan teaches the claimed meal recommendation apparatus comprising at least one processor, the at least one processor carrying out:
a reception process of receiving physical information or a health condition of a subject user and a request pertaining to a meal menu; [Fig. 7, Para. 0091 teaches receiving health constrains for members (one of which is interpreted as a subject user) and other data which triggers the recommendation of a meal (interpreted as a request pertaining to a meal menu).]
a generation process of generating response information including information pertaining to a meal menu which corresponds to the physical information or health condition of the subject user based on the request and a learned model which has learned pieces of physical information or health conditions of a plurality of second users and meal order histories of the plurality of second users, […]; and [Para. 0091 teaches that a meal is recommended (information pertaining to a meal menu) taking into account the health constraints of the user (the subject user) and of other group members (a plurality of second users). Para. 0091 teaches that the data includes historical data of previously ordered meals. Para. 0061, 0062 teaches that the meal is recommended using a trained predictive machine learning model (a learned model).]
a control process of controlling a display or an audio output apparatus [Para. 0068, 0084, 0091 teaches that the recommendation is outputted via a GUI.] to provide the subject user with the response information, […]. [The Examiner notes that “to provide” is an intended use of the displayed/audio information; however, Para. 0068 teaches displaying the recommendation on a display (i.e., controlling the display) and/or outputting it audibly to a user. Para. 0084 alternately teaches that the recommendations is outputted and displayed on another device.]
Alkan may not explicitly teach
wherein the learned model is a second user graph that is a graph which includes
(i) a node indicating a second user, who is different from the subject user,
(ii) nodes each indicating physical information, a health condition, or a meal menu related to the second user, and
(iii) links indicating relationships between the nodes, and which has learned the relationships between the nodes.
Gutnik at Fig. 18, Para. 0027-0029, 0096 teaches that it was known in the art of information recommendation, at the time of filing, to provide a social network graph having nodes and edges that link user information such as meal preference information together and where links between nodes are provided
wherein the learned model is a second user graph that is a graph which includes
(i) a node indicating a second user, who is different from the subject user, [Gutnik at Fig. 18, Para. 0028, 0096 teaches a social network graph (the machine learning of Alkan) having multiple nodes representing users (interpreted to correspond to the members of Alkan, one of which is the member of Alkan).]
(ii) nodes each indicating physical information, a health condition, or a meal menu related to the second user, and [Gutnik at Para. 0027 teaches that a social network graph includes nodes describing a meal (a meal menu) for a particular user.]
(iii) links indicating relationships between the nodes, and which has learned the relationships between the nodes. [Gutnik at Fig. 18, Para. 0029, 0096 teaches that edge connections are created between nodes, which are learned relationships.]
a link prediction process of predicting a relationship between nodes which are not connected to each other by a link in a subject user graph including a plurality of nodes pertaining to the subject user, and one or more second user graphs; and [Gatnik at Para. 0028, 0029 teaches that nodes for each user are created and that edges between users and content are created. The edges are interpreted as a prediction that the information is linked. The edges also did not exist prior to being formulated (“nodes which are not connected”). Gatnik at Fig. 18, Para. 0029, 0101 teaches that edge connections between users are generated.]
[…],
wherein the generation process generates the response information based on the predicted relationship between nodes. [Gatnik at Para. 0031, 0035, 0039 teaches that the connections are used to recommend a meal (the recommendation of Alkan).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of information recommendation, at the time of filing, to modify the meal recommendations system of Alkan to provide a social network graph having nodes and edges that link user information such as meal preference information together and where links between nodes are provided as taught by Gutnik, with the motivation of improving the accuracy of information recommendation.
REGARDING CLAIM 2
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claim 1. Alkan/Gutnik further teaches
wherein:
the at least one processor further carries out a basis information generation process of generating basis information including information pertaining to a person whose physical information or health condition is similar to that of the subject user among the plurality of second users, and [Alkan at Para. 0073 teaches that “collaboration of data” for collaborative members in a group collected and that the members may include members of a family (interpreted as members that have “similar” health information to the member, there being no indication as to what “similar” entails). Para. 0094 teaches that “collaboration of data” information (information pertaining to “similar” members) is displayed along with the meal recommendation]
in the output process, the at least one processor further outputs the basis information. [Alkan at Para. 0094 teaches that “collaboration of data” information is displayed.]
REGARDING CLAIM 4
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claim 1. Alkan/Gutnik further teaches
wherein: in the link prediction process, the at least one processor fpredicts, by link prediction using the subject user graph and the second user graph, a node which links to a node included in the subject user graph from among nodes which are included in the second user graph and indicate meal menus related to the second user; and [Gatnik at Para. 0028, 0029 teaches that nodes for each user are created and that edges between users and content are created. The edges are interpreted as a prediction that the information is linked. The edges also did not exist prior to being formulated (“nodes which are not connected”). Gatnik at Para. 0029, 0101 teaches that edge connections between users are generated. Gatnik at Para. 0031, 0035, 0039 teaches that the connections are used to recommend a meal.]
the generation process, the at least one processor generates response information including information pertaining to a meal menu which corresponds to the node predicted in the link prediction process. [Gatnik at 0031, 0035, 0039 teaches that the connections are used to recommend a meal.]
REGARDING CLAIM 5
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claims 1 and 4. Alkan/Gutnik further teaches
wherein: in the reception process, the at least one processor receives input of a condition for the second user graph, and [Alkan at Fig. 7, Para. 0091 teaches receiving other data (interpreted as a condition, which is undefined) which triggers the recommendation of a meal (interpreted as a request pertaining to a meal menu). Gutnik at Para. 0035 teaches that the user specifies that they are trying to lose weight (a condition; see Spec. Para. 0072) and this information is used to traverse the social network graph.]
in the link prediction process, the at least one processor predicts, from among nodes that indicate meal menus and are included in a second user graph satisfying the condition, a node which links to a node included in the subject user graph. [Gutnik at Para. 0035, 0039, etc. teaches that the user-specified information is used to traverse the social network graph and arrive at a recommendation.]
REGARDING CLAIM 6
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claims 1 and 4. Alkan/Gutnik further teaches
wherein: the at least one processor further carries out an evaluation process of evaluating, based on another node included in the second user graph including the node predicted in the link prediction process, a recommendation level, for the subject user, of a meal menu indicated by the node. [Gatnik at Para. 0031, 0035, 0039 teaches that the node connections are used to recommend a meal (a recommend level of ‘yes, recommend’). Alternatively, Gatnik at Para. 0035 teaches that meal recommendations are ranked (a recommend level).]
REGARDING CLAIM 7
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claim 1. Alkan/Gutnik further teaches
wherein: in the link prediction process, the at least one processor identifies a second user who has a predetermined relationship with the subject user by link prediction using the subject user graph and a plurality of second user graphs the plurality of second user graphs having been respectively generated for a plurality of second users, and [Alkan at Para. 0073 teaches that “collaboration of data” for collaborative members in a group is collected and that the members may include members of a social network (a predetermined relationship). Gatnik at Para. 0028, 0029 teaches that nodes for each user are created and that edges between users and content are created. The edges are interpreted as a prediction that the information is linked. The edges also did not exist prior to being formulated (“nodes which are not connected”). Gatnik at Para. 0029 teaches that edge connections between users are generated. Gatnik at Para. 0031, 0035, 0039 teaches that the connections are used to recommend a meal.]
in the generation process, the at least one processor generates response information including information pertaining to a meal menu related to the second user who has been identified in the link prediction process. [Gatnik at 0031, 0035, 0039 teaches that the connections are used to recommend a meal.]
REGARDING CLAIM 8
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claims 1 and 7. Alkan/Gutnik further teaches
wherein: in the link prediction process, the at least one processor identifies a second user who is similar to the subject user, and [Gatnik at Para. 0029 teaches that edge connections between users are generated. The users are similar in that they are part of a social network (the social network of Alkan, see Alkan at Para. 0072). Alternately, Gatnik at Para. 0036, 0038 teaches determining lookalike users and use that information to create connections.]
in the generation process, the at least one processor generates response information that recommends, to the subject user, a meal menu indicated by a node included in a second user graph of the second user who has been identified in the link prediction process. [Gatnik at 0031, 0035, 0039 teaches that the connections are used to recommend a meal.]
REGARDING CLAIM 9
Alkan/Gutnik teaches the claimed meal recommendation apparatus comprising at least one processor of Claim 1. Alkan/Gutnik further teaches
wherein: the at least one processor further carries out a reception process of receiving input of a meal menu by the subject user, [Gutnick at Para. 0027 teaches that a user (interpreted as the subject user) inputs that the ate buffalo wings at a restaurant.]
in the link prediction process, the at least one processor further calculates, by link prediction using the second user graph and the subject user graph including a node indicating the meal menu which has been input, a probability that a predetermined node links to a node included in the subject user graph, and [Gutnick at Para. 0027 teaches that a node is created and that nodes corresponding to other users are connected via edges (interpreted as calculating a 100% prediction probability). The edges are interpreted as a prediction that the information is linked. The edges did not exist prior to being formulated (“nodes which are not connected”).]
in the generation process, the at least one processor generates response information based on the probability which has been calculated in the link prediction process. [Gatnik at 0031, 0035, 0039 teaches that the connections are used to recommend a meal.]
REGARDING CLAIM(S) 10 AND 11
Claim(s) 10 and 11 is/are analogous to Claim(s) 1, thus Claim(s) 10 and 11 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-11, the Applicant has cancelled Claim 3 rendering the rejection of that claim moot. Regarding the remaining claims, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
For instance, claim 1 now recites (in part) a control process of controlling a display or an audio output apparatus to provide the subject user with the response information. As explained in the present Specification, there is room for improvement in conventional devices in that it is difficult to recommend a category that the user himself/herself does not recognize but that conforms to preference of the user (see e.g., Specification, ¶0004). Accordingly, as described in the Specification, one or more example embodiments provide a technique that makes it possible to recommend a meal menu which is suitable for a user even if the user himself/herself does not recognize suitability.
The Examiner respectfully disagrees that a processor causing the outputting of data on a display of or via audio output on a general-purpose computer provides significantly more. Each of these features represent “apply it” on a computer. There is no improvement to another technology because no other technology is present in the claim. Further, outputting data on a computer is not an improvement to the computer.
Similarly, a technical problem is not present in the as-filed disclosure. Applicant cites to Specification Para. 0004 which states “it is difficult to recommend a category that the user himself/herself does not recognize but that conforms to preference of the user.” As such, any problem present was not a problem caused by the computer, but is a problem that is exists regardless of whether the computer is involved or not. Put another way, any improvements in the claim are improvements to the abstract idea. An abstract idea is still an abstract idea.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-11, the Applicant has cancelled Claim 3 rendering the rejection of that claim moot. Regarding the remaining claims, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
However, contrary to claim 1, Gutnik does not teach a social networking system that predicts a relationship between nodes which are not connected to each other in a subject user graph by using one or more second user graphs of one or more second users which are not the subject user.
Regarding (a), the Examiner respectfully disagrees. Gutnik teaches that edges are generated between nodes, meaning that the edges did not exist prior to generation. The generation of edges is a prediction that the nodes have a relationship between one another. As indicated by Fig. 18, nodes include people, one of which is interpreted to correspond to the member of Alkin and another of which is interpreted to be a second user.
Alkan does not teach the machine learning model is a graph.
Regarding (b), the Examiner respectfully submits that Alkan was not relied upon to teach this feature. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Kaleal et al. (U.S. Pre-Grant Patent Publication No. 2022/0384027) which discloses tracking and rewarding health and fitness activities of a user which includes providing food options.
Bennett et al. (U.S. Pre-Grant Patent Publication No. 2013/0216982) which discloses a nutritional analysis and recommendation system.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON S TIEDEMAN whose telephone number is (571)272-4594. The examiner can normally be reached 7:00am-4:00pm, off alternate Fridays.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683