DETAILED ACTION
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 .
Status of Claims
This action is in reply to the communications filed on 5/9/2023.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/5/2024 is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of U.S. Patent No. 11651413. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are anticipated by the claims of U.S. Patent No. 11651413.
Instant application and Patent No. 11,170,424 claim the same invention as follows:
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 1-10 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 pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation "wherein the advertisement machine-learning model." There is insufficient antecedent basis for this limitation in the claims. The limitation will be interpreted as referring to the descriptor machine-learning model.
Claims 2-10 inherit the deficiencies of claim 1.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A system for generating textual outputs based on descriptor classifications, the system comprising a computing device, the computing device designed and configured to:
receive an input from a first remote device of a plurality of remote devices, wherein the input comprises a specified location and a nourishment intake theme;
generate, as a function of a group machine-learning model, a user group theme for the first remote device, wherein the group machine-learning model receives the nourishment intake theme and the specified location as an input, and outputs the user group theme;
identify at least a food provider located within the specified location;
determine, as a function of a descriptor machine-learning model, a descriptor classification for the first remote device, wherein the advertisement machine-learning model receives the at least a food provider and the user group theme as input and outputs the descriptor classification; and
generate at least a user interface element at the first remote device as a function of the descriptor classification.
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that the input is from a remote device of a plurality of remote devices, the generating is a function of a group machine learning model, the determining is a function of a descriptor machine learning model, and the user element is a user interface element at the remote device, nothing in the claim element precludes the step from practically being performed by people. For example, “generating, receive, generate, receive, output, identify, determine, receive and generate” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction 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.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
A system for generating textual outputs based on descriptor classifications, the system comprising a computing device, the computing device designed and configured to:
receive an input from a first remote device of a plurality of remote devices, wherein the input comprises a specified location and a nourishment intake theme;
generate, as a function of a group machine-learning model, a user group theme for the first remote device, wherein the group machine-learning model receives the nourishment intake theme and the specified location as an input, and outputs the user group theme;
identify at least a food provider located within the specified location;
determine, as a function of a descriptor machine-learning model, a descriptor classification for the first remote device, wherein the advertisement machine-learning model receives the at least a food provider and the user group theme as input and outputs the descriptor classification; and
generate at least a user interface element at the first remote device as a function of the descriptor classification.
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of a computing device, a first remote device, a plurality of remote devices, a group machine-learning model, a descriptor machine-learning model, an advertisement machine-learning model, and a user interface element, are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the generating is performed as a function of a group machine-learning model, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of system claim 1, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 1 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 11 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-10, 12-20 are dependencies of claims 1, and 11. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
generating, by the computing device, the at least a user interface element at the first remote device as a function of the descriptor classification and the promotional datum. (further limiting the interface element not make the abstract idea less abstract)
receiving, by the computing device, interface feedback. (receiving data over a network)
transmitting, by the computing device, the at least a user interface element to other remote devices based on the interface feedback. (receiving and transmitting data over a network)
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
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.
Claims 1-7, 9-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2021/0183495 A1 to FLOE in view of U.S. Patent Application No. 2020/0342550 A1 to HALIMSAPUTERA.
Regarding Claim 1, FLOE discloses a system for generating textual outputs based on descriptor classifications, the system comprising a computing device, the computing device designed and configured to:
receive an input from a first remote device of a plurality of remote devices, wherein the input comprises a specified location and a nourishment intake theme; ([0003] FAM aims to address causes of chronic disease for prevention as well as provide support for patients through specific, personalized guidance addressing conditions that have already progressed. Hypertension, diabetes, heart disease, and other conditions are highly relevant for FAM. [0004] FAM therapy plans. For example, a patient who has been prescribed a specific FAM therapy that includes a low sodium diet (nourishment intake theme [0005] the disclosed technology can analyze patient records and diagnoses, and automatically identify relevant, clinically sound FAM therapies to provide to the patient that are specifically tailored to treat the patient's ailments.))
generate, as a function of a group machine-learning model, a user group theme for the first remote device, wherein the group machine-learning model receives the nourishment intake theme and the specified location as an input, and outputs the user group theme; ([0005] The disclosed technology can also translate FAM therapies into specific, actionable product and/or recipe recommendations at grocers and retailers, [0039] The user guidance system 308 can additionally provide guidance using the cross-system correlations 314, which can include mappings between FAM therapies and specific products, and using product taxonomies 318, which can provide hierarchical and logical groupings of products across a variety of characteristics, including food type, nutrition information, ingredients, and/or other information relevant to administering FAM therapies.)
identify at least a food provider located within the specified location; ([0039] The user guidance system 308 can additionally make guidance determinations based on product information 340 provided by retailers/grocers 326, such as information on specific products that are offered for sale by their establishments (e.g., product SKU information, product description, product details).)
determine, as a function of a descriptor machine-learning model, a descriptor classification for the first remote device, wherein the advertisement machine-learning model receives the at least a food provider and the user group theme as input and outputs the descriptor classification; and ([0009] The system can further include an insurer computer system configured to provide incentives for compliance with the one or more FAM therapies by the patient. The FAM recommendation information can further include one or more incentives for the patient to consume the one or more food items to adhere with the one or more FAM therapies. The functionalized FAM services can further include selecting the one or more incentives from among the incentives provided by the insurer computer system based on the one or more FAM therapies for the patient. The FAM therapy compliance information can be transmitted to the insurer computer system in association with the one or more incentives. The insurer computer system can be further configured to award the one or more incentives to the patient based, at least in part, on the FAM therapy compliance information indicating that the patient has adhered to the one or more FAM therapies for the one or more medical conditions diagnosed by the physician. [0032] The FAM-related incentives 212 can include, for example, discounts and/or reimbursements that are provided to patients who comply with particular FAM therapies to treat their medical conditions. [0044] The outcomes and results of the technique 400 can be used to update incentive models (424), such as through updating machine learning models that are used to identify, select, and provide incentives to users based on various criteria.) Examiner notes that this limitation is being interpreted as explained in the 112(b) rejection, above.
generate at least a user interface element at the first remote device as a function of the descriptor classification. ([0044] specific incentives (e.g., provided by insurers) for the FAM treatment products and/or recipes can be identified (408). Those incentives can be provided to and output by a user device (410), which may result in purchases of the incentives products and/or recipes by the user.)
But does not explicitly disclose generate, as a function of a group machine-learning model, a user group theme. FLOE does disclose [0063] Any of a variety of techniques can be used, such as image comparison operations using machine learning models trained on a variety of different dishes cooked by a variety of different users and photographed in a variety of different environments. The functionalized computer system 1002 can determine, based on the image analysis, whether to award the FAM incentive for the user action (following the recipe), as indicated by the verifiable evidence (picture of the dish) (step F, 1018).
HALIMSAPUTERA, on the other hand, teaches generate, as a function of a group machine-learning model, a user group theme. ([0028] the personalized recommendation logic 130 may collect contextual information from the user or group of users (including one or more of information from relevant user profiles 142, information from clock 118 indicating a time of day, transaction history (e.g., previous food orders placed via the server 104), information from location sensor 120 and/or other contextual sensors of the user device for each user … dietary restrictions/preferences of each user may be used to filter out restaurants from the first candidate pool that do not have (or have less than a threshold number of) menu items that meet the dietary restrictions/preferences. [0031] A hierarchy of the above factors may be dynamically inferred for each user based on the relative importance of each criterion to that user (e.g., determined via direct user input and/or via AI/machine learning). Based on the above filtering and ranking, higher/highest ranking restaurants may be selected that provide high ranking compliant menu items and are located near the user(s).)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE, the features as taught by HALIMSAPUTERA, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify FLOE, to include the teachings of HALIMSAPUTERA, in order to provide personalized recommendations (HALIMSAPUTERA, [0031]).
Regarding Claim 2, FLOE in view of HALIMSAPUTERA teaches the system of claim 1.
However FLOE does not explicitly teach wherein the computing device is further configured to: receive food interest data from the first remote device; and generate an interest group theme as a function of an interest machine-learning model, wherein the interest machine-learning model receives the food interest data and the specified location as input and outputs the interest group theme.
HALIMSAPUTERA, on the other hand, teaches wherein the computing device is further configured to: receive food interest data from the first remote device; and generate an interest group theme as a function of an interest machine-learning model, wherein the interest machine-learning model receives the food interest data and the specified location as input and outputs the interest group theme.. ([0028] the personalized recommendation logic 130 may collect contextual information from the user or group of users (including one or more of information from relevant user profiles 142, information from clock 118 indicating a time of day, transaction history (e.g., previous food orders placed via the server 104), information from location sensor 120 and/or other contextual sensors of the user device for each user … dietary restrictions/preferences of each user may be used to filter out restaurants from the first candidate pool that do not have (or have less than a threshold number of) menu items that meet the dietary restrictions/preferences. [0031] A hierarchy of the above factors may be dynamically inferred for each user based on the relative importance of each criterion to that user (e.g., determined via direct user input and/or via AI/machine learning). Based on the above filtering and ranking, higher/highest ranking restaurants may be selected that provide high ranking compliant menu items and are located near the user(s).)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE, the features as taught by HALIMSAPUTERA, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify FLOE, to include the teachings of HALIMSAPUTERA, in order to provide personalized recommendations (HALIMSAPUTERA, [0031]).
Regarding Claim 3, FLOE in view of HALIMSAPUTERA teaches the system of claim 2.
However FLOE does not explicitly teach wherein the computing device is further configured to: determine a targeted theme using a targeted machine-learning model, wherein the targeted machine-learning model receives the at least a food provider and the interest group theme as inputs and outputs the targeted theme; and generate the at least a user interface element at the first remote device as a function of the targeted theme.
HALIMSAPUTERA, on the other hand, teaches wherein the computing device is further configured to: determine a targeted theme using a targeted machine-learning model, wherein the targeted machine-learning model receives the at least a food provider and the interest group theme as inputs and outputs the targeted theme; and generate the at least a user interface element at the first remote device as a function of the targeted theme. ([0055] If a selection of a restaurant is received (e.g., “YES” at 428), the method proceeds to output a menu of the selected restaurant, as indicated at 434 in FIG. 4B. For example, the menu may include the menu of the selected restaurant, as generated and maintained using a mechanism such as method 300 of FIG. 3. [0028] For example, time of day information may be used to target menu items that match a particular meal time (e.g., menu items tagged under the category “breakfast” may be preferentially recommended during a pre-determined window of time, such as from 6 AM to 11 AM). [0030] Personalized recommendation logic 130 may produce the ranked list by applying a mathematical formula that weights a plurality of scoring factors for each restaurant in the second candidate pool, and uses the weighted average score to determine a ranking of each restaurant, wherein, in one example, a restaurant with a greater weighted average score may be ranked higher than a restaurant with a lower weighted average score. In some examples, the weighting of each of the scoring factors may be determined via a machine learning algorithm, which may include one or more machine learning algorithms, including gradient descent.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE, the features as taught by HALIMSAPUTERA, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify FLOE, to include the teachings of HALIMSAPUTERA, in order to provide personalized recommendations (HALIMSAPUTERA, [0031]).
Regarding Claim 4, FLOE in view of HALIMSAPUTERA teaches the system of claim 1.
However FLOE does not explicitly teach wherein computing device is configured to receive an availability datum.
HALIMSAPUTERA, on the other hand, teaches wherein computing device is configured to receive an availability datum. ([0022] for each restaurant in the restaurant menu database 136, a list of available menu items for that restaurant may be stored alongside tags or other notation protocols to indicate the associated details for the menu items (determined using the information gathering mechanisms described above). For example, each menu item for a restaurant may be tagged with one or more food category descriptors (e.g., a type of food/cuisine, a meal time associated with the menu item), one or more dietary descriptors (e.g., whether the food adheres to a dietary restriction, such as vegetarian, vegan, dairy-free, gluten-free, etc.), one or more ranking descriptors (e.g., whether the menu item is a specialty of the restaurant and/or a highest-rated/most often recommended menu item or menu item of a given food category for the restaurant), and/or other detailed information.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE, the features as taught by HALIMSAPUTERA, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify FLOE, to include the teachings of HALIMSAPUTERA, in order to provide details regarding specific menu items (HALIMSAPUTERA, [0022]).
Regarding Claim 5, FLOE in view of HALIMSAPUTERA teaches the system of claim 4.
However FLOE does not explicitly teach wherein the computing device is further configured to determine the at least an advertising material for the first remote device as a function of the advertisement theme and the availability datum.
HALIMSAPUTERA, on the other hand, teaches wherein the computing device is further configured to determine the at least an advertising material for the first remote device as a function of the advertisement theme and the availability datum. ([0055] If a selection of a restaurant is received (e.g., “YES” at 428), the method proceeds to output a menu of the selected restaurant, as indicated at 434 in FIG. 4B. For example, the menu may include the menu of the selected restaurant, as generated and maintained using a mechanism such as method 300 of FIG. 3. [0028] For example, time of day information may be used to target menu items that match a particular meal time (e.g., menu items tagged under the category “breakfast” may be preferentially recommended during a pre-determined window of time, such as from 6 AM to 11 AM). [0030] Personalized recommendation logic 130 may produce the ranked list by applying a mathematical formula that weights a plurality of scoring factors for each restaurant in the second candidate pool, and uses the weighted average score to determine a ranking of each restaurant, wherein, in one example, a restaurant with a greater weighted average score may be ranked higher than a restaurant with a lower weighted average score. In some examples, the weighting of each of the scoring factors may be determined via a machine learning algorithm, which may include one or more machine learning algorithms, including gradient descent.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE, the features as taught by HALIMSAPUTERA, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify FLOE, to include the teachings of HALIMSAPUTERA, in order to provide personalized recommendations (HALIMSAPUTERA, [0031]).
Regarding Claim 6, FLOE in view of HALIMSAPUTERA teaches the method of claim 1.
FLOE discloses wherein computing device is configured to receive a promotional datum.. ([0052] the retailers/grocers 706 may have some level of autonomy to select among and to prioritize among multiple different products, some of which may have promotions or other offers being provided by the retailers/grocers 706, to provide as the FAM product recommendations 718.)
Regarding Claim 7, FLOE in view of HALIMSAPUTERA teaches the method of claim 6.
FLOE discloses wherein the computing device is further configured to generate the at least a user interface element at the first remote device as a function of the descriptor classification and the promotional datum. ([0052] the retailers/grocers 706 may have some level of autonomy to select among and to prioritize among multiple different products, some of which may have promotions or other offers being provided by the retailers/grocers 706, to provide as the FAM product recommendations 718. [0009] The system can further include an insurer computer system configured to provide incentives for compliance with the one or more FAM therapies by the patient. The FAM recommendation information can further include one or more incentives for the patient to consume the one or more food items to adhere with the one or more FAM therapies. The functionalized FAM services can further include selecting the one or more incentives from among the incentives provided by the insurer computer system based on the one or more FAM therapies for the patient. The FAM therapy compliance information can be transmitted to the insurer computer system in association with the one or more incentives. The insurer computer system can be further configured to award the one or more incentives to the patient based, at least in part, on the FAM therapy compliance information indicating that the patient has adhered to the one or more FAM therapies for the one or more medical conditions diagnosed by the physician. [0032] The FAM-related incentives 212 can include, for example, discounts and/or reimbursements that are provided to patients who comply with particular FAM therapies to treat their medical conditions. [0044] The outcomes and results of the technique 400 can be used to update incentive models (424), such as through updating machine learning models that are used to identify, select, and provide incentives to users based on various criteria.)
Regarding Claim 9, FLOE in view of HALIMSAPUTERA teaches the method of claim 1.
FLOE discloses wherein the computing device is further configured to receive interface feedback. ([0085] Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.)
Regarding Claim 10, FLOE in view of HALIMSAPUTERA teaches the system of claim 9.
However FLOE does not explicitly teach wherein the computing device is further configured to transmit the at least a user interface element to other remote devices based on the interface feedback..
HALIMSAPUTERA, on the other hand, teaches wherein the computing device is further configured to transmit the at least a user interface element to other remote devices based on the interface feedback.. ([0034] the restaurant interaction coordinator 134 may request feedback from the user(s) regarding the restaurant experience and update the restaurant menu database (e.g., with feedback regarding the particular menu items that were ordered) and/or add a review for the selected restaurant in the reviews database 140 based on the user feedback. [0040] the server 204 sends the restaurant recommendation (including information from the reviews of the associated restaurants) to the user device 206 for presentation to the user.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE, the features as taught by HALIMSAPUTERA, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify FLOE, to include the teachings of HALIMSAPUTERA, in order to provide personalized recommendations (HALIMSAPUTERA, [0031]).
Claim 11 recites a method comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Claim 12 recites a method comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 13 recites a method comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 14 recites a method comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 15 recites a method comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 16 recites a method comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Claim 17 recites a method comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 19 recites a method comprising substantially similar limitations as claim 9. The claim is rejected under substantially similar grounds as claim 9.
Claim 20 recites a method comprising substantially similar limitations as claim 10. The claim is rejected under substantially similar grounds as claim 10.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2021/0183495 A1 to FLOE in view of U.S. Patent Application No. 2020/0342550 A1 to HALIMSAPUTERA in view of U.S. Patent Application No. 2017/0201779 A1 to PUBLICOVER.
Regarding Claim 8, FLOE in view of HALIMSAPUTERA teaches the method of claim 1.
However the combination of FLOE and HALIMSAPUTERA does not explicitly teach wherein the computing device is further configured to identify the at least a food provider as a function of a bid ranking.
PUBLICOVER, on the other hand, teaches wherein the computing device is further configured to identify the at least a food provider as a function of a bid ranking.. ([0044] an exemplary bidding scenario wherein the highest bidding advertiser wins the right to deliver advertising to a public profile and the public profile shares in the proceeds. It is also an exemplary bidding scenario of a single advertiser bidding on several Profiles at different rates and winning a subset of these bids.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by FLOE and HALIMSAPUTERA, the features, as taught by PUBLICOVER, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of PUBLICOVER, in order to reach a targeted audience (PUBLICOVER, [0365]).
Claim 18 recites a method comprising substantially similar limitations as claim 8. The claim is rejected under substantially similar grounds as claim 8.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 9am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kelly Campen can be reached on (571)272-6740. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3688