DETAILED ACTION
This is a Non-Final Office Action in response to Claims on 09/27/2024. Claims 1-15 are pending. The effective filing date is 10/20/2023.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/27/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1-Claims 1-5 are directed to a device, which is a statutory category. Claims 6-10 are directed to a method, which is a statutory category. Claims 11-15 are directed to a non-transitory computer-readable recording medium, which is an article of manufacture, which is a statutory category. Claims 1-15 pass step 1.
Step 2A, Prong 1-The independent claim 1, and similarly claims 6 and 11, recite:
A meal suggestion device comprising:
at least one memory configured to store instructions (additional element to be analyzed in Step 2A, Prong 2); and
at least one processor configured to execute the instructions (additional element to be analyzed in Step 2A, Prong 2) to:
acquire target person related data related to a target person to whom a meal is to be suggested (acquiring information about a target person to suggest a meal is a way to use information for a commercial interaction, which can be grouped as a method of organizing human activity under MPEP 2106.04(a)(2)(II)(B) using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979); additionally, the acquiring of information can be a mental process of collecting data, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
determine a suggested meal to be suggested to the target person using the target person related data (making a determination about a target person to suggest a meal is a way to use information for a commercial interaction, which can be grouped as a method of organizing human activity under MPEP 2106.04(a)(2)(II)(B) using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979); additionally, making a determination can be a mental process of analyzing data, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
generate basis information indicating a basis for suggesting the suggested meal (generating a suggestion about a meal is a way to use information for a commercial interaction, which can be grouped as a method of organizing human activity under MPEP 2106.04(a)(2)(II)(B) using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979); additionally, generating a suggestion can be a mental process of analyzing data, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
present the suggested meal and the basis information (presenting information about the suggested a meal is a way to use information for a commercial interaction, which can be grouped as a method of organizing human activity under MPEP 2106.04(a)(2)(II)(B) using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979); additionally, the presentation of information can be a mental process of displaying results, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Step 2A, Prong 2- The additional elements of independent claim 1 are a memory and processor. This judicial exception is not integrated into a practical application because the elements are used as tools to perform the abstract idea. Under MPEP 2106.05(f)(2) when the claim invokes a computer merely as a tool to perform an existing process, it fails to integrate the abstract idea into a practical application. This is showcased by the memory storing instruction, and the instructions being the abstract ideas. The processors are used to execute the instruction, which additionally showcase its use as a tool to use a computer to perform instructions. Similarly claims 6 and 11 do not add additional elements that would integrate the abstract idea into a practical application, as a storage medium holding the instructions remains a tool to perform the abstract idea. Therefore, the claims fail step 2A, prong 2.
Step 2B-The independent claim 1, and similarly claims 6 and 11, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements describe a processor and memory, which are computers disclosed in generality. See MPEP 2106.05(f)(3) that when a claim has broad applicability across many fields, such as a memory storing information, and a processor executing instruction, they may not provide meaningful limitation that amounts to significantly more.
Dependent Claims
Claims 2-5, 7-10 and 12-15, add additional steps of making a determination and displaying information, both of which can be grouped as a mental process of analyzing and displacing results, under MPEP 2106.04(a)(2)(III). They do not provide additional elements beyond the previously presented memory and processor, and therefore do not integrate the abstract idea into a practical application, or provide significantly more under MPEP 2106.05(f).
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.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2020/0382454 A1 Gershony et al. (hereinafter Gershony).
Regarding claim 1, Gershony teaches a meal suggestion device (Gershony Abstract, suggestions based on input; [0005] the response may be to make a restaurant reservation or food order) comprising:
at least one memory configured to store instructions (Gershony [0020] processors and memory for storing and implementing instructions); and
at least one processor configured to execute the instructions (Gershony [0020] processors and memory for storing and implementing instructions) to:
acquire target person related data related to a target person to whom a meal is to be suggested (Gershony [0064] user can consent to profile information that can be used to aid in suggestions);
determine a suggested meal to be suggested to the target person using the target person related data (Gershony [0084] using a variety of factors to determine the suggestion, including type of food, time of food, etc.);
generate basis information indicating a basis for suggesting the suggested meal (Gershony [0087] learning from responses about the determination to generate a final suggestion); and
present the suggested meal and the basis information (Gershony [0100] the determined suggestion is sent to a display for a user; Fig. 3).
Regarding claim 2, Gershony teaches the meal suggestion device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: determine incentive information indicating an incentive for the target person to select the suggested meal (Gershony [0088] the system may provide a coupon as an incentive to go to a specific restaurant).
Regarding claim 3, Gershony teaches the meal suggestion device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: determine a meal provided at a place within a predetermined range from position information of the target person as the suggested meal (Gershony [0078] the location of the user may be used to determine the suggestion; Fig. 4A); and
present a map indicating position information of the target person and a place where the suggested meal is provided (Gershony [0100] the graphic representation may be a location of the suggested restaurant, Fig. 4A).
Regarding claim 4, Gershony teaches the meal suggestion device according to claim 3, wherein the at least one processor is further configured to execute the instructions to:
optimize a route from a location of the target person to the place where the suggested meal is provided using a constraint condition including at least part of the meal and the target person related data (Gershony [0063] requesting driving directions to the specific location; [0098] the application may provide the user with directions to the suggested restaurant location); and
display the route on the map (Gershony [0063] requesting driving directions to the specific location; [0098] the application may provide the user with directions to the suggested restaurant location; Fig. 4A).
Regarding claim 5, Gershony teaches the meal suggestion device according to claim 1, wherein the at least one processor is further configured to execute the instructions to: determine the suggested meal by inputting the target person related data to a model that machine learned a relationship between the target person related data and a meal related to the target person related data (Gershony [0010] the suggestion may be based on machine learning model, and can be trained using user information and history of suggestions).
Regarding claim 6, Gershony teaches a meal suggestion (Gershony Abstract, suggestions based on input; [0005] the response may be to make a restaurant reservation or food order) method comprising: acquiring target person related data related to a target person to whom a meal is to be suggested (Gershony [0064] user can consent to profile information that can be used to aid in suggestions); determining a suggested meal to be suggested to the target person using the target person related data (Gershony [0084] using a variety of factors to determine the suggestion, including type of food, time of food, etc.); generating basis information indicating a basis for suggesting the suggested meal (Gershony [0087] learning from responses about the determination to generate a final suggestion); and presenting the suggested meal and the basis information (Gershony [0100] the determined suggestion is sent to a display for a user; Fig. 3).
Regarding claim 7, Gershony teaches the meal suggestion method according to claim 6, further comprising: determining incentive information indicating an incentive for the target person to select the suggested meal (Gershony [0088] the system may provide a coupon as an incentive to go to a specific restaurant).
Regarding claim 8, Gershony teaches the meal suggestion method according to claim 6, further comprising: determining a meal provided at a place within a predetermined range from position information of the target person as the suggested meal (Gershony [0078] the location of the user may be used to determine the suggestion; Fig. 4A); and presenting a map indicating position information of the target person and a place where the suggested meal is provided (Gershony [0100] the graphic representation may be a location of the suggested restaurant, Fig. 4A).
Regarding claim 9, Gershony teaches the meal suggestion method according to claim 8, further comprising: optimizing a route from a location of the target person to the place where the suggested meal is provided using a constraint condition including at least part of the meal and the target person related data (Gershony [0063] requesting driving directions to the specific location; [0098] the application may provide the user with directions to the suggested restaurant location); and displaying the route on the map (Gershony [0063] requesting driving directions to the specific location; [0098] the application may provide the user with directions to the suggested restaurant location; Fig. 4A).
Regarding claim 10, Gershony teaches the meal suggestion method according to claim 6, further comprising: determining the suggested meal by inputting the target person related data to a model that machine learned a relationship between the target person related data and a meal related to the target person related data (Gershony [0010] the suggestion may be based on machine learning model, and can be trained using user information and history of suggestions).
Regarding claim 11, Gershony teaches a non-transitory computer-readable recording medium that records a program for causing a computer (Gershony Abstract, suggestions based on input; [0005] the response may be to make a restaurant reservation or food order; [0020] processors and memory for storing and implementing instructions) to execute: acquiring target person related data related to a target person to whom a meal is to be suggested (Gershony [0064] user can consent to profile information that can be used to aid in suggestions); determining a suggested meal to be suggested to the target person using the target person related data (Gershony [0084] using a variety of factors to determine the suggestion, including type of food, time of food, etc.); generating basis information indicating a basis for suggesting the suggested meal (Gershony [0087] learning from responses about the determination to generate a final suggestion); and presenting the suggested meal and the basis information (Gershony [0100] the determined suggestion is sent to a display for a user; Fig. 3).
Regarding claim 12, Gershony teaches the recording medium, according to claim 11, that records the program for causing the computer to further execute: determining incentive information indicating an incentive for the target person to select the suggested meal (Gershony [0088] the system may provide a coupon as an incentive to go to a specific restaurant).
Regarding claim 13, Gershony teaches the recording medium, according to claim 11, that records the program for causing the computer to further execute: determining a meal provided at a place within a predetermined range from position information of the target person as the suggested meal (Gershony [0078] the location of the user may be used to determine the suggestion; Fig. 4A); and presenting a map indicating position information of the target person and a place where the suggested meal is provided (Gershony [0100] the graphic representation may be a location of the suggested restaurant, Fig. 4A).
Regarding claim 14, Gershony teaches the recording medium, according to claim 13, that records the program for causing the computer to further execute: optimizing a route from a location of the target person to the place where the suggested meal is provided using a constraint condition including at least part of the meal and the target person related data (Gershony [0063] requesting driving directions to the specific location; [0098] the application may provide the user with directions to the suggested restaurant location); and displaying the route on the map (Gershony [0063] requesting driving directions to the specific location; [0098] the application may provide the user with directions to the suggested restaurant location; Fig. 4A).
Regarding claim 15, Gershony teaches the recording medium, according to claim 11, that records the program for causing the computer to further execute: determining the suggested meal by inputting the target person related data to a model that machine learned a relationship between the target person related data and a meal related to the target person related data (Gershony [0010] the suggestion may be based on machine learning model, and can be trained using user information and history of suggestions).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2021/0216920 A1 Mimassi teaches personalized advertisements (Abstract); US 2021/0097882 A1 Lyke et al. teaches machine learning meal prep suggestions; US 2020/0098466 A1 Murdoch et al. teaches machine learning model to create a menu (Abstract).
Conclusion
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/JESSICA E SULLIVAN/ Examiner, Art Unit 3627
/FAHD A OBEID/ Supervisory Patent Examiner, Art Unit 3627