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 Applicant’s communication filed on July 31, 2025.
Claim 1 has been amended and is hereby entered.
Claim 1 is currently pending and has been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 31, 2025 has been entered.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 recites “to display the generated at least one of the generated at least one personalized recommendation and the generated prediction of the level of effectiveness”. Examiner notes that the grammar of this sentence is unclear and suggests amending to recite “to display at least one of the generated at least one personalized recommendation and the generated prediction of the level of effectiveness”. Appropriate correction is required.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
Claim 1 is directed to a method and therefore falls into one of the four statutory categories. (Step 1: Yes, the claim falls into one of the four statutory categories).
Step 2A analysis - Prong one:
The independent method claim 1 recites the following: A method for generating, by an application executing on a computing device and in communication with at least one machine learning engine, at least one personalized recommendation and a prediction of a level of effectiveness of the personalized recommendation for a user and modifying a user interface within the application to display the generated at least one of the generated at least one personalized recommendation and the generated prediction of the level of effectiveness, may comprise: receiving, by an application executing on a computing device, via a user interface, user input identifying a portion of a quadrant displayed within the user interface descriptive of an emotional state of a user and of an energetic state of a user; accessing, by the application, from at least one data source, data associated with the user; analyzing, by the application, the accessed data and the received user input; identifying, by the application, a health pattern of the user based on the analyzing, wherein identifying further comprises: generating a vector based upon the received identified portion of the quadrant, and characterizing a state of a chronic condition of the user based upon the generated vector; determining, by the application, whether the health pattern of the user is aligned with a goal of the user, wherein determining further comprises: executing, by at least one machine learning engine in communication with the application, to identify at least one behavior impacting the identified health pattern, and determining, by the at least one machine learning engine, whether the at least one behavior is aligned with the goal of the user; generating, by the application, a recommendation to the user to align at least one user behavior pattern with the goal of the user, the recommendations personalized to a preference of the user and having a level of effectiveness satisfying a threshold level of effectiveness, wherein generating further comprises: generating, by a recommendation engine, a prediction of a state of the chronic condition of the user at a time subsequent to a time of receiving the user input, and selecting, by the recommendation engine, the recommendation from a category of recommendations having the level of effectiveness satisfying the threshold level of effectiveness for improving the state of the chronic condition of the user at the time subsequent to the time of receiving the user input; and modifying, by the application, the user interface to display the recommendation.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations above, 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 method implemented by a computing device (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the additional elements identified/bolded above, this claim encompasses a person collecting and analyzing data regarding a user, and predicting a future state of the users chronic condition in order to ultimately provide a personalized recommendation for a user in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). 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 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claim 1 recites the following additional elements beyond the abstract idea: an application executing on a computing device and in communication with at least one machine learning engine, modifying a user interface within the application, an application executing on a computing device, at least one machine learning engine in communication with the application, and a recommendation engine. The application appears to be purely software and is being interpreted as such.
This judicial exception is not integrated into a practical application. In particular, the claim recites an application, a computing device, a machine learning engine, modifying a user interface, a computing device and a recommendation engine which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification indicates that the computing device simply executes an application, receives data and user inputs, analyzes inputs, etc. (See Applicant’s specification para 31). The identified additional elements equate to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claim 1 is directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of well-understood, routine, and conventional activities previously known to the industry. Further, the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention. See MPEP 2106.05(d).
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of computing devices, computer programs, processors, memories, input/output devices, networks, buses, operating systems, etc. (See Applicant’s specification paras 2, 17, 55, 70-73). The invention, may use any computer via any transmission medium (a communication network or broadcast waves) capable of transmitting the program.
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization.
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 elements of an application, a computing device, a machine learning engine, modifying a user interface, a computing device and a recommendation engine to perform all of the steps discussed above amount to no more than mere instructions to apply the exceptions using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims do not provide an inventive concept significantly more than the abstract idea. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2B: No, the claim does not provide significantly more).
Thus, Claim 1 is rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
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, 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 1 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (WO2016130232A1) in view of Makhija et al. (Makhija, S., & Wadhwa, B. (2019). Mood Board: An IOT based group Mood Evaluation Tool. 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 41 4, 1–4. https://doi.org/10.1109/iot-siu.2019.8777677)
Regarding claim 1, Liu discloses the following:
A method for generating, by an application executing on a computing device and in communication with at least one machine learning engine, at least one personalized recommendation and a prediction of a level of effectiveness of the personalized recommendation for a user (Liu discloses methods, systems, and media for personalizing computerized services based on mood and/or behavior information from multiple data sources. The recommended action (generating at least one personalized recommendation) is determined to have a likelihood of impacting the physical or emotional state of the user (a prediction of a level of effectiveness of the personalized recommendation). The recommendation system can use machine learning techniques (in communication with at least one machine learning engine) and the user device executes the program (an application executing on a computing device). – abstract and paras 14, 80-81, 107)
and modifying a user interface within the application to display the generated at least one of the generated at least one personalized recommendation and the generated prediction of the level of effectiveness, (Liu discloses that the system uses the application programming interface to cause (modifying a user interface) the recommended output (at least one personalized recommendation) which includes a likelihood of impacting the physical or emotional state of the user (prediction of the level of effectiveness) to be executed on one or more user devices (modifying a user interface to display). – paras 15, 36, 72,79)
may comprise: receiving, by an application executing on a computing device, via a user interface, user input…descriptive of an emotional state of a user and of an energetic state of a user; (Liu discloses that the mechanisms can receive user feedback and, based on the received user feedback, determine goals for the user. For example, the user may indicate a lack of energy (an energetic state of a user) on weekdays via a user interface on the user device (an application executing on a computing device, via a user interface) and the mechanisms can interpret such an indication and determine various goals for the user, such as increasing the amount of exercise or related activities. In another example, the user can be provided with an interface that requests the user provides feedback as to the user's general mood, emotional state, and/or behavioral disposition (an emotional state of a user) and the mechanisms can determine goals for the user based on the provided feedback. FIG 11 item 1110 shows an example of the user feedback relating to the users mood or emotional state and the user may select an option according to their mood or emotional state (a quadrant displayed within the user interface)– paras 30, 33 and FIG 11 item 1110)
accessing, by the application, from at least one data source, data associated with the user; (Liu discloses FIG. 2 which shows a more particular example of a server of FIG. 1 that can receive various types of data from multiple data sources (at least one data source) where the data is data relating to the user (data associated with the user) – paras 21 and 32)
analyzing, by the application, the accessed data and the received user input; (Liu discloses analyzing the data relating to the user from multiple data sources. In response to analyzing the data relating to the user from multiple data sources at 430, the recommendation system can select particular categories of data from particular data sources and select particular time portions of data that are indicative or representative of the physical or emotional state of the user. The data source 110 can include a computing device, such as a desktop, a laptop, a mobile phone, a tablet computer, a wearable computing device, etc. Examples of data provided by such a computing device can include user generated data (the received user input) (e.g., text inputs, photos, touch inputs, etc.) – paras 21, 34, 37, 44, 101 and FIG. 4)
identifying, by the application, a health pattern of the user based on the analyzing, (Liu discloses generating a baseline profile for the user (identifying a health pattern of the user) of the computing device based on the portion of information from each of the data sources (based on the analyzing). Wherein the portion of information from each data sources is analyzed by the recommendation system. FIG. 4 further shows that the current profile is generated for the user based on updated data from the one or more data sources (e.g., data indicative of a user’s current mood and/or behavior). – abstract; paras 39, 66-67, 101 and FIG. 4)
determining, by the application, whether the health pattern of the user is aligned with a goal of the user, (Liu discloses comparing (determining) the baseline profile (the health pattern of the user) with the target profile (a goal of the user), where the target profile is generated based on the goals or objectives of the user. – abstract; paras 39, 66-67)
wherein determining further comprises: executing, by at least one machine learning engine in communication with the application, to identify at least one behavior impacting the identified health pattern, (Liu discloses that the baseline profile (the identified health pattern) is generated based on (impacting) the received data (at least one behavior), wherein the received data may include, for example, the user inputting information about one or more user behaviors or habits (e.g., commuting, lunch break, weekly meetings, exercise groups, etc.) (identify at least one behavior). – paras 50, 66-67, 104)
and determining, by the at least one machine learning engine, whether the at least one behavior is aligned with the goal of the user; (Liu discloses taking the generated profile that is indicative of the user's current mood, emotional state, and/or behavioral disposition (the at least one behavior) and comparing it with a target profile (determining whether the at least one behavior is aligned with the goal of the user) – paras 39, 104)
generating, by the application, a recommendation to the user to align at least one user behavior pattern with the goal of the user, the recommendations personalized to a preference of the user (Liu discloses that the mechanisms can generate one or more profiles associated with a user device. For example, in some implementations, the mechanisms can generate various profiles that can be used to determine recommended actions suitable for the user of the user device. For example, the mechanisms can generate a profile that is indicative of the user's current mood, emotional state, and/or behavioral disposition, as well as establish baseline patterns for emotional state, and compare the generated profile (behavior pattern) with a target profile (the goal of the user) to determine a recommended action (generating, by the application, a recommendation to the user) that, if performed, may move the user towards an objective or goal (user to align at least one user behavior pattern with the goal of the user). – paras 32, 36, 39, 104 and FIG. 4 item 490)
the recommendations having a level of effectiveness satisfying a threshold level of effectiveness, (Liu discloses that the recommended action is determined to have a likelihood of impacting the physical or emotional state of the user (a level of effectiveness). In another more particular example, the recommendation system can rank the recommendations based on any suitable criterion and can then select a predetermined number of actions (e.g., top five) and designate them as group actions. In particular, the common actions can be ranked based on a deviation between a current profile associated with the user and a target profile so that the recommendation system can determine which actions have a higher likelihood of affecting the aggregated emotional state of the group of users (satisfying a threshold level of effectiveness). – abstract and paras 64, 73, 79, 83, 111 and 129)
wherein generating further comprises: generating, by a recommendation engine, a prediction of a state of the chronic condition of the user at a time subsequent to a time of receiving the user input, (Liu discloses that, by using data relating to the user from multiple sources (at a time subsequent to a time of receiving the user input), a user's physical or emotional state can be predicted (generating a prediction of a state of the chronic condition of the user), which can be used to determine whether to recommend a particular action at a given time. Moreover, changes to a user's physical or emotional state can be predicted based on new or updated data relating to the user from multiple sources. The system may use any suitable machine learning technique (a recommendation engine). – paras 47, 87, 64, 107, 126)
and selecting, by the recommendation engine, the recommendation from a category of recommendations having the level of effectiveness satisfying the threshold level of effectiveness for improving the state of the chronic condition of the user at the time subsequent to the time of receiving the user input; (Liu discloses the recommendation system can cause an output device to execute a particular action (selecting the recommendation) based on the predicted impact of the particular action on the current physical or emotional state of the user (at the time subsequent to the time of receiving the user input). For example, prior to executing a particular action using the output device, the recommendation system can determine the predicted impact of the action on the physical or emotional state of the user and, upon determining that the predicted impact is not within a particular range ( e.g., the emotional state correlated with the user data remains unchanged) (a category of recommendations having the level of effectiveness satisfying the threshold level of effectiveness for improving the state of the chronic condition), can inhibit the action from being executed on the output device. – paras 64, 107, 126)
and modifying, by the application, the user interface to display the recommendation. (Liu discloses that the recommended action includes generating content to be presented on the computing device that prompts the user to engage in an activity and causing the content to be presented on the computing device (display the recommendation). – paras 15, 36 and 79).
Liu does not disclose the following limitations met by Makhija:
receiving, via a user interface, user input identifying a portion of a quadrant displayed within the user interface (Makhija teaches an app called Mood Board adopted from the Mood Meter tool used for social and emotional learning. Mood Meter is based on two psychometric parameters, namely Energy and Pleasantness to determine the emotional status (mood) of an individual in the emotional space into hundred definite values. The values are distributed in four quadrants (a quadrant displayed) each emulating four broad emotional patterns namely Angry, Happy, Sad and Calm, thus harboring twenty-five sub-emotions in them. Data entries are input via the Mood Board app (receiving, via a user interface). – See sections I, II, III-A, III-D; Fig. 1)
wherein identifying further comprises: generating a vector based upon the received identified portion of the quadrant, and characterizing a state of a chronic condition of the user based upon the generated vector; (Makhija teaches that the four quadrants are based on two axis of core values, which are: valence (Pleasantness, represented by the X axis) and arousal (Energy, represented by the Y axis). Thus, for example one is feeling Joyful, this implies x = 2 and y = 2 and similarly, if we consider Enraged x = −5 and y = 5 (characterizing a state of a chronic condition of the user). The group mood value is calculated using the vector decomposition (generating a vector) of the data entries existing on the Mood Board (based upon the received identified portion of the quadrant). – see sections I, II, III-B, III-E; Table 1)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified receiving user input indicating the user’s energy level and mood as disclosed by Liu to incorporate using quadrants to receive user inputs indicative of the user’s energy and pleasantness and correlating the inputs to coordinate values as taught by Makhija in order to quantify and map individual and group mood. (see Makhija section IV).
Relevant Prior Art of Record Not Currently Being Applied
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Mirabile (US 20160055760) discloses a system and method is disclosed for generating health and lifestyle related observations and recommendations for a user, based on information collected from the user. Invention references aggregated information across users and from additional sources, and generates relevant recommendations and observations, based on the system's computational methods applied to the user's inputs. The system and method considers the user's goals and system default goals in the generation of recommendations. The system and method allows for the data and generated observations and recommendations to be outputted within the system's user experience, or to a third party.
Response to Arguments
Regarding the drawing objections, the Applicant has submitted an amended specification to overcome the drawing objections.
Regarding rejections under 35 USC § 112(b) to Claim 1, the previous rejections are now moot in light of Applicant’s amendments.
Regarding rejections under 35 USC § 101 to Claim 1, Applicant’s arguments have been fully considered, and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(a) Applicant respectfully submits that examining the claim as a whole, the specification, and the focus of the technological advancement makes clear that the claims are not directed to mathematical concepts. (p. 8).
Regarding (a), Examiner has removed the mathematical concepts grouping from the updated Step 2A Prong 1 analysis above, therefore this argument is moot.
(b) Applicant maintains that the MPEP at 2106.04(a)(2) and the October 2019 Update explicitly states that not all methods of organizing human activity are abstract ideas and that this grouping is limited to activity that falls within enumerated sub- groupings of fundamental economic principles or practices, commercial or legal interactions, or management of behavior or relationships or interactions between people and that the claim relates to allowable subject matter. The claims relate to steps executed by a computing device and resulting in a modification to a user interface and lack any recitation of an activity occurring between humans. The claims cannot therefore fall within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, or management of behavior or relationships or interactions between people as set forth in the October 2019 Update to the Subject Matter Eligibility Guidance. Therefore, the claims are not directed to an abstract idea under Prong One. (p. 9 and 11).
Regarding (b), Examiner respectfully disagrees. MPEP 2106.04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to provide a personalized recommendation for a user. The Examiner also notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)).
Further, multiple CAFC decisions that the Office has characterized as Certain Methods of Organizing Human Activity did not actively recite a person or persons performing the steps of the claims (see e.g., EPG, TLI communications, Ultramercial). Because whether a human is required to perform the steps of the claim is not a requirement for claims to encompass certain methods of organizing human activity, this argument is not persuasive.
(c) Applicant further submits that the claims recite limitations that improve a technological field. The claim recites a method executing specific innovative technologies to generate data using functionality to predict a state of a chronic condition not available to a human simply following a rule. Conventional tools - technological or otherwise - fail to provide functionality for analyzing data from a plurality of sources and generating a prediction of a state of the user's chronic condition in the future and generating an effective and personalized recommendation for the user. Simply telling a user to follow a rule will not address those failures - and is an inappropriate oversimplification of the functionality. Therefore, Applicant maintains that the execution of the technological elements recited in the claim result in an improvement to the technological field for receiving both user-reported data and data from other technological sources (such as electronic health records) and using both types of data in generating effective and personalized recommendations for maintaining or improving health. (p. 9-11).
Regarding (c), Examiner respectfully disagrees. MPEP 2106.04(d)(1) states "the word 'improvements' in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B." Here there is no improvement to the computer itself nor is there an improvement to another technology. For example, there is no indication that the computer is made to reduce computing resources or network load. In fact, the computer may be caused to operate less efficiently through the implementation of Applicant’s claimed invention; we do not know. Further, the stated problems of generating effective and personalized recommendations are interpreted as not being rooted in technology. The problems are not caused by nor related to computer technology and the claims do not provide any limitations that may be interpreted as technical improvements to computer technology. The claimed invention is using a computer as a tool to gather and analyze data and any improvement present is an improvement to the abstract idea of, to paraphrase, providing personalized recommendations.
Applicant further points to the unconventionality of the invention. The conventionality does not dictate whether there is an abstract idea or not. At best, determining whether or not there is an inventive step is a consideration for the additional elements in Step 2B. But it is not a consideration in Step 2A Prong 1.
Examiner notes that Applicant continually refers to subject matter disclosed in the specification but not in the claims. Applicant is reminded that it’s the claimed invention that is being evaluated and rejected, not the disclosure itself.
(d) Applicant argues that any abstraction of the limitations in the pending claim as hereby amended is integrated into a practical application due to the execution of the application and the at least one machine learning engine that execute the steps of identifying health patterns based on data analysis and generate recommendations accompanied by levels of effectiveness satisfying certain thresholds. The recitation of novel, non-obvious, artificial intelligence-based components executing to executing the recited steps provides a meaningful limit on any abstract idea that maybe present in the pending claims. One of ordinary skill in the art would understand that simply instructing a human to follow a general guideline for good health - especially a human managing a chronic condition - neither ensures compliance with the guideline, nor effectiveness of the guideline for the user. (p. 10-12).
Regarding (d), Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem, or improves the functioning of a computer or any other technology/technical field. As noted in response to argument (c) above, the claims do not recite an improvement to any technology/technical field and further do not provide a technical solution to a technical problem. Further, Examiner notes that the additional elements of an application, a computing device, a machine learning engine, modifying a user interface, a computing device and a recommendation engine are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) and therefore equate to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application.
Examiner agrees with Applicants’ argument that “One of ordinary skill in the art would understand that simply instructing a human to follow a general guideline for good health - especially a human managing a chronic condition - neither ensures compliance with the guideline, nor effectiveness of the guideline for the user.” (p. 10 of remarks). The end result of the claimed invention is displaying the recommendation and thus essentially instructing a human to follow a guideline; which, as pointed out by the Applicant, does not ensure compliance nor effectiveness of the recommendation for the user.
(e) Applicant respectfully submits that the claims amount to "significantly more" than an abstract idea. Additional limitations include, by way of example, novel and non-obvious machine learning engine executing unconventional functionality in communication with the unconventional application to generate data and modify user interfaces with the generated data. As will be understood by those of skill in the art, and as explained above, conventional computing devices do not provide the functionality made available by executing the methods recited in the pending claims. (p. 12-13).
Regarding (e), Examiner respectfully disagrees. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements used 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”).
Further, Examiner notes that there is no particularity to the claimed machine learning engine and therefore there is no indication that it is “executing unconventional functionality”. The claim is simply using the machine learning engine as a tool to gather and analyze data. MPEP 2106.04(d)(2) indicates that a practical application may be present where the judicial exception is implemented using or in conjunction with a particular machine or manufacture. MPEP 2106.05(b)(I) indicates that applying the judicial exception “by use of conventional computer functions does not qualify as a particular machine.” Because there is no particularity with respect to the computer that implements the abstract idea, thus requiring the Examiner to conclude that the abstract idea is implemented by a general-purpose computer, a practical application is not present.
Regarding rejections under 35 USC § 103 to Claim 1, Applicant’s arguments have been fully considered and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(f) Therefore, the combination of Liu and Makhija fails to teach or suggest at least the following: (1) receiving user input identifying a portion of a quadrant displayed within the user interface descriptive of an emotional state of a user and of an energetic state of a user, (2) generating vectors that characterize a state of a chronic condition of the user, (3) predicting a state of the chronic condition of the user in the future, and (4) generating recommendations that will improve the predicted state. For at least these reasons, the Applicant respectfully submits that the combination of Liu and Makhija does not teach or suggest each and every limitation of the pending claim as hereby amended. Accordingly, Applicant respectfully requests the withdrawal of the rejection of claim 1 and that a Notice of Allowance be issued.
Regarding (f), Examiner respectfully disagrees and will take these limitations in turn:
(1) The claim recites: receiving, by an application executing on a computing device, via a user interface, user input identifying a portion of a quadrant displayed within the user interface descriptive of an emotional state of a user and of an energetic state of a user. Examiner relies upon Liu to teach the limitations regarding the user input descriptive of an emotional state of a user and an energetic state of a user (non-bolded portion above). While Makhija is relied upon to teach the limitations regarding the quadrant displayed within the user interface (bolded portion above).
Liu discloses that the user may indicate a lack of energy (an energetic state of a user) on weekdays via a user interface on the user device (an application executing on a computing device, via a user interface). In another example, the user can be provided with an interface that requests feedback as to the user's general mood, emotional state, and/or behavioral disposition (an emotional state of a user) and the mechanisms can determine goals for the user based on the provided feedback. Further, Makhija teaches a mood board application that includes four quadrants (a quadrant displayed within the user interface) (red, yellow, blue and green) as shown in figure 1. Where the y-axis indicates energy and the x-axis indicates pleasantness.
(2) The claim recites: generating a vector based upon the received identified portion of the quadrant, and characterizing a state of a chronic condition of the user based upon the generated vector. Examiner relies upon Makhija to teach that the four quadrants are based on two axis of core values, which are: valence (Pleasantness, represented by the X axis) and arousal (Energy, represented by the Y axis). Thus, for example one is feeling Joyful, this implies x = 2 and y = 2 and similarly, if we consider Enraged x = −5 and y = 5 (characterizing a state of a chronic condition of the user). The group mood value is calculated using the vector decomposition (generating a vector) of the data entries existing on the Mood Board (based upon the received identified portion of the quadrant).
(3) The claim recites: generating, by a recommendation engine, a prediction of a state of the chronic condition of the user at a time subsequent to a time of receiving the user input. Examiner relies upon Liu which discloses using the collected data to determine whether to recommend a particular action at a given time based on a predicted physical or emotional state of the user (a prediction of a state of the chronic condition of the user at a time subsequent to a time of receiving the user input). The system may use any suitable machine learning technique (a recommendation engine).
(4) The claim recites: selecting, by the recommendation engine, the recommendation from a category of recommendations having the level of effectiveness satisfying the threshold level of effectiveness for improving the state of the chronic condition of the user at the time subsequent to the time of receiving the user input. Examiner relies upon Liu which discloses that the recommendation system can cause an output device to execute a particular action (selecting the recommendation) based on the predicted impact of the particular action on the current physical or emotional state of the user (at the time subsequent to the time of receiving the user input). For example, prior to executing a particular action using the output device, the recommendation system can determine the predicted impact of the action on the physical or emotional state of the user and, upon determining that the predicted impact is not within a particular range ( e.g., the emotional state correlated with the user data remains unchanged) (a category of recommendations having the level of effectiveness satisfying the threshold level of effectiveness for improving the state of the chronic condition), can inhibit the action from being executed on the output device.
Therefore, Liu and Makhija in combination teach each and every limitation of claim 1. See updated rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY VANDER WOUDE whose telephone number is (703)756-4684. The examiner can normally be reached M-F 9 AM-5 PM.
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, PETER H CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681