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
In the amendment filed 09/22/2025 the following occurred: Claims 1-13 were canceled; and Claims 14-19 were added as new. Claims 14-19 are presented for examination.
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 14-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 14-19 are drawn to a method, which is/are statutory categories of invention (Step 1: YES).
Independent claim 14 recites determine an impact that an environmental condition may have on allergy symptoms of an individual and to determine a therapeutic option for the individual, comprising: tracking said individual's behavior data over time, wherein said individual's behavior data includes said individual's allergy symptoms; behavior; environmental conditions; medications; and quality of life; measuring an outcome for each of said individual's behavior data at multiple time points; wherein said individual's behavior data includes said individual's treatment regime (including medication response (no response) data), adherence to use of one or more of said medications and symptom perception, wherein measuring an outcome of said individual's behavior data includes: • calculating, for the individual the change in symptom and allergen outcome relationship over time (SnPn/dt) (or the modeling of nested/clustered data, wherein there are multiple individuals in each cluster) using a multivariate regression or probabilistic approach; • generating and including an analysis engine for analyzing potential treatment options based on the individual's behavior data and data regarding the individual's response (or no response) to medication in combination with the calculated change in symptom and allergen outcome relationship over time (SnPn/dt) data by applying at least a portion of the crowdsourced information acquired from the SnPn/dt relationship between user symptoms (Sn) and allergens (P) over time (dt) and the individual's data tracked over time; and • comparing said individual's behavior data to determine one or more of the individual's treatment recommendation(s) for increasing the individual's pollen and/or dander threshold.
The respective dependent claims 15-19, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
Said recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states the invention is to “determining the potential impact of environmental conditions on an individual's allergy symptoms, and more particularly,…predicting the impact that local environmental conditions may have on an individual's allergy symptoms in order to proactively treat the individual” (see: specification page 1). In this way, the invention “monitors environmental conditions of an individual and assists the individual with managing their allergy treatment in response to these environmental conditions and allergic symptoms” (see: specification page 7). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The presents claim cover certain methods of organizing human activity because they address the “need for improved methods to treat allergy symptoms” (see: specification page 6) and “can be used by doctors to recommend OTC and/or prescribe Rx allergy medications” (see: specification page 18). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “using a computerized processing device to…on the computerized processing device…on the computerized processing device…employing the analysis engine to train machine learning models for increasing the individual's pollen and/or dander threshold, wherein such machine learning models are selected from support vector machines, k-nearest neighbors, random forests, k-means, neural network or mixtures thereof…to said machine learning models, using the computerized processing device…” (claim 14), which are additional elements that are recited at a high level of generality (e.g., the “computerized processing device” is configured through no more than a statement than that functions are performed “using” or “on” said device; and similarly, the “machine learning models…selected from support vector machines, k-nearest neighbors, random forests, k-means, neural network or mixtures thereof” are configured through no more than a statement than that said machine learning models “train[ed]” to perform a desired function, and where said machine learning models are interchangeably selectable from the group including “support vector machines, k-nearest neighbors, random forests or mixtures thereof” without altering or affecting said function) such that they amount to no more than mere instruction to apply the exception using generic computer elements. See: MPEP 2106.05(f).
The claims recite the additional elements of “at least one sensor is employed to determine at least one of said environmental conditions” (claim 18), which may additionally or alternately be considered nominal or tangential additions to the abstract idea(s) such as to amount to extra-solution activity concerning mere data gathering. The addition of an insignificant extra-solution activity limitation does not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, these claimed additional elements are incidental to the performance of the recited abstract idea(s) as outlined in the recitations above. See: MPEP 2106.05(g).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer elements and limitations directed toward extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
The claims do 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(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea(s). The originally filed specification supports this conclusion at Fig. 3 and:
Page 8, where “"Machine learning models" are computer algorithms that improve automatically through experience. Examples include support vector models, k-nearest neighbors and random forests. • "Support-vector models" are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. • The "k-nearest neighbors" (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. • "Random forests" are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees.”
Page 9, where “A "neural network" as used herein is an artificial neural network used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information.”
Page 12, where “…The results of these analyses can be incorporated into rules operating on rules engine, which is included in software operating on a processing device, such as a mainframe computer, a desktop computer, a laptop computer, or a hand-held computational device such as a personal digital assistant or a cell phone…”
Page 12-13, where “Figure 3 shows the device and network methodology employed to predict an allergy impact profile in accordance with the invention. According to aspects of the present disclosure, the system described herein preferably includes: a) base stations 2a and 2b in communication with a network 3, b) one or more sensors 4a and 4b in communication with the base stations 2a and 2b that are configured to monitor environmental conditions in proximity to the user, c) a communication device 5 in communication with the network 3; and d) a remote server 6 and associated data store 7 in communication with the network 3. The remote server 6 is operative to: I) access information from the data store 7 indicative of information specific to the user, 2) receive information from the sensors 4a and 4b via the base stations 2a and 2b indicative of one or more measures of environmental conditions, 3) receive information from the communication device 5 indicative of user's location, 4) recommend at least one action to be taken as a function of the various inputs; and 5) transmit the recommended action to the communication device 5 for execution by the user. According to another aspect of the present disclosure, the remote server 6 may thereafter be preferably operative to: a) confirm that the recommended action was applied, b) receive updated information from the sensors 4a and 4b indicative of environmental conditions, c) receive updated information from the communication device 5 indicative of location of the user, d) receive an updated user symptom; and e) evaluate the effectiveness of the recommended action in improving the user symptom.”
Page 15, where “Individual level allergy symptom level predictions can be made using personal, individual data (as shown previously). This data can include environmental factors (e.g., pollen, weather, pollution), as well as factors specific to an individual's behavior (e.g., treatment regimes, compliance/adherence to a product, symptom perception).”
Page 15-16, where “A model can then be trained on the population level data. This has significant advantages due to larger volume of data. For example, a zip code may have between 50-100 AllergyCast® users who have recently submitted data, and this larger volume of data enables the use of more classical machine learning models (e.g., support vector machines, k-nearest neighbors, random forests) with less risk of bias due to small datasets. Population level models can be created using known geographical data (e.g., zip code, township, city, state or other regional basis) or using a clustering algorithm to create clustering constructs. For example, the data could be clustered using an unsupervised machine learning algorithm, e.g., k-means, which can propose an optimum number of clusters and grouping of individuals. This approach allows the testing of less arbitrary grouping schemas (e.g., zip codes, which have no inherent meaning to the data).”
Page 18, where “Figure 13 shows how modeling through neural nets (in combination with "My Allergy Impact" profile) can be employed to determine the best treatment for given environmental condition/symptom. Specifically, Figure 13 depicts an analysis engine for analyzing data described in reference to Figure 11 and Figure 12 and provides actions for changing behaviors or treatments. The engine is preferably implemented as a neural network, which applies at least a portion of the large-scale data set crowdsourced information acquired according to the data assimilation hierarchy in Figure 7 and Figure 8. This portion of the data is used as training data for building best-fit models for minimizing specific allergy symptoms based on behavior or treatment options.”
The claims recite the additional elements directed to pre-solution and post-solution activity, as recited and indicated above, each of which amount to extra-solution activity. The specification (e.g., as excerpted above) does not indicate that the additional element(s) provide anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are presently). Further, the concept of performing clinical tests on individuals to obtain input for an equation has been identified by the courts as insignificant extra-solution activity. See: MPEP 2106.05(g).
Further, the concepts of receiving or transmitting data over a network, such as using the Internet to gather data, and storing and retrieving information in memory have been identified by the courts as well-understood, routine, and conventional activities. See: MPEP 2106.05(d)(II).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 15-19, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
Applicant’s arguments from the response filed on 06/16/2025 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because “Applicant has cancelled claims 1-13 and added new claims 14-19 so that they recite, in more particularity, the steps and elements required in the method steps of the present invention - namely use of a computerized processing device and the including the language for specifically increasing an individual's pollen and dander threshold (as described, in the case of pollen, in the specification at Figure 15 - at top of page 19 and drawings) and the associated treatment recommendation(s).”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
The functions argued of “increasing an individual's pollen and dander threshold…and the associated treatment recommendation(s)” are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 102/103 rejections should be withdrawn in view of the amendments “Tamraz nowhere teaches tracking and comparing an individual's behavior, which includes the individual's treatment regime, adherence to use of one or more medications and symptom perception, to behavioral information generated in machine learning models trained using a large-scale data set... Neither Tamraz (described above) nor Holmes discloses or suggests the invention as claimed in new claims 14-19. The subject-matter of the claims differs from the disclosure of Tamraz and Holmes in that the individual's behavior, which includes the individual's treatment regime, adherence to use of one or more medications and symptom perception, are compared to behavioral information generated in machine learning models trained using a large-scale data set. The specification discloses that the use of such machine learning models to minimize specific allergy symptoms based on behavior or treatment option. See, e.g., pages 13 and 18, Figs. 4 and 13…”
The rejections are withdrawn, though perhaps for reasons slightly different than argued. The rejections are withdrawn in view of the amendments. The closest prior art of record, U.S. Patent Application Publication 2018/0266933 to Tamraz and U.S. Patent Application Publication 2014/0114677 to Holmes, do not teach the invention in the particular combination as claimed in the amended independent claims as a whole including, in combination with the previously claimed limitations, the amendment of “analyzing potential treatment options based on the individual's behavior data and data regarding the individual's response (or no response) to medication in combination with the calculated change in symptom and allergen outcome relationship over time (SnPn/dt) data by applying at least a portion of the crowdsourced information acquired from the SnPn/dt relationship between user symptoms (Sn) and allergens (P) over time (dt) and the individual's data tracked over time”; there; therefore, the closest prior art of record does not anticipate or otherwise render the claimed invention obvious.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT A SOREY whose telephone number is (571)270-3606. The examiner can normally be reached Monday through Friday, 8am to 5pm.
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/ROBERT A SOREY/Primary Examiner, Art Unit 3682