DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed February 10, 2026.
Claims 1-4, 7-13, and 16-17 are pending.
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 .
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/10/2026 has been entered.
Claim Objection(s)
Claims 8 & 9 are objected to because of the following informalities: Claims 8 recites “The computer program product of claim 1…[…]…” and Claim 9 recites “The computer program product of claim 8…[…]…” but claim 1 is directed to a method. 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.
Claims 1-4, 7-13, and 16-17 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 10, and 17:
Eligibility Step 1 (does the subject matter fall within a statutory category?): Independent Claim 1 falls within the statutory category of method. Independent Claim 10 falls within the statutory category of machine. Independent Claim 17 falls within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 10, and 17 (claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims (claim 1 being representative) is:
A method of determining medication events, the method comprising:
capturing an image of a medicine…[…]…;
determining the type of medicine based on the captured image;
wherein the determining of the type of medicine is performed by a training set of medicine related images;
is configured to reduce medication data to include a transferred identification of medication and medicament devices
…[…]…accept a dosage input of the determined medicine;
and recording a dosage input of the determined medicine.
This abstract idea is “mental process” as it is merely an evaluation, observation, or judgement of data to make a determination on a medication and recording a dosage of it (see MPEP § 2106.04(a)(2), subsection III)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claim 1 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
an image sensor on a mobile computing device
an input interface on a display
a machine learning engine trained, wherein the machine learning engine includes a last layer of a complex machine learning model
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
No additional element is executing the abstract idea
The additional element, an image sensor on a mobile computing device, is recited as a tool to gather data as “apply-it” to apply the abstract idea
The additional element, an input interface on a display, is recited as a tool to display data as “apply-it” to apply the abstract idea
The additional element, a machine learning engine trained, wherein the machine learning engine includes a last layer of a complex machine learning model, is merely generally linking the abstract idea to the technological environment of machine learning
Independent claim 10 recites the additional claim elements below not already recited in claim 1:
A memory device coupled to a processor
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, A memory device coupled to a processor, is executing the abstract idea and recited as a general computer element or tool as “apply-it” to apply the abstract idea
Independent claim 17 recites the additional claim elements below not already recited in claim 1 and 10:
A data collection system with an image sensor, storage device, a transceiver, a display and a processor
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, A data collection system with an image sensor, storage device, a transceiver, a display and a processor, is executing the abstract idea and recited as a general computer element or tool as “apply-it” to apply the abstract idea
Accordingly, independent claims 1, 10, and 17 as a whole does not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element as analyzed above in step 2A prong 2, is merely applying the abstract idea and therefore, does not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2-4, 7-9, 11-13, and 16:
Eligibility Step 1 (does the subject matter fall within a statutory category?): The dependent claims 2-4 and 7-9 fall within the statutory category of method. Dependent claims 11-13 and 16 fall within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-4, 7-9, 11-13, and 16 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims abstract idea by (1) further limiting the medication details and (2) further limiting determination of the medication. Therefore, the dependent claims inherit the same abstract idea which is “mental process” as it is merely an evaluation, observation, or judgement of data to make a determination on a medication and dosage and recording it (see MPEP § 2106.04(a)(2), subsection III)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-4, 7-9, 11-13, and 16 this judicial exception is not integrated into a practical application.
The dependent claims recite the additional elements below not already recited in the independent claims.
a medicament device such as an inhaler
a transceiver
a server
trained machine learning engine
a complex machine learning model
computer program product is a non-transitory computer readable medium executed by a computer
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a medicament device such as an inhaler, is generally linking the abstract idea to the technological environment of handheld delivery medication devices
The additional element, a transceiver, is a computer element used as “apply-it” to apply the abstract idea of communicating data
The additional element, a server, is a computer element used as “apply-it” to apply the abstract idea of communicating data
The additional elements, trained machine learning engine and a complex machine learning model, is generally linking the abstract idea to the technological environment artificial intelligence
The additional element, computer program product is a non-transitory computer readable medium executed by a computer, is a computer element used as “apply-it” to apply the abstract idea of data gathering and analyzing
Accordingly, the dependent claims as a whole does not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, is merely applying or generally linking the abstract idea and therefore, do not amount to significantly more. The claims are patent ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 16, and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Naumov et. al (hereinafter Naumov) (US20220148730A1) in view of AMANO et. al (hereinafter AMANO) (US20230394785Al) and in further view of Anushiravani et. al (hereinafter Anushiravani) (US10706329B2)
As per claim 1, Naumov teaches:
A method of determining medication events, the method comprising: capturing an image of a medicine from an image sensor on a mobile computing device; ([0013] discloses, “The DHP is configured to receive data relating to usage events and a time of each of the usage events ( e.g. , a time stamp for the usage event ) for a plurality of inhalers , where the inhalers are associated with a plurality of different users . Each inhaler is associated with at least one user and one of a rescue medicament type or a maintenance medicament type.” And see [0306] discloses, “The QR code 42 may provide a facile way of pairing the respective inhaler 100 with the processing module 34 , in examples in which the user device 40 comprises a suitable optical reader , such as a camera , for reading the QR code” / examiner notes that an optical reader is interpreted by plain definition of having a sensor)
determining the type of medicine based on the captured image; ([0122] discloses, “The inhalers 401a - d may pair with a user device using, for example, an identifier, such as a barcode or a QR code, printed on the inhaler or its packaging. As further described herein, the inhaler's identifier may indicate certain information associated with the inhaler, such as the respective medicament delivered by the inhaler and the dose strength of the medicament” / examiner notes the respective medicament is interpreted as synonymous with type of medicine)
…[…]…displaying an input interface on a display to accept a dosage input of the determined medicine; ([0101] discloses, “The system 10 further comprises a user interface 38. The processing module 34 is configured to control the user interface 38 to communicate the first , second , and / or third usage information. The arrow pointing from the block representing the processing module 34 to the block representing the user interface 38 is intended to represent the control signal(s) which causes or cause the user interface to communicate, for example display , the respective usage information . In this respect , the user interface 38 may comprise any suitable display , screen , for example touch screen , etc. which is capable of displaying the respective usage information . Alternatively or additionally , the respective usage information may be provided by the user interface 38 via an audio message…[…]…” and see [0102] discloses, “The system 10 thus enables the subject to be informed of their usage of the respective medicaments , which may be administered according to a treatment regimen and / or an administration protocol specific to the respective medicament , as previously described.” And see [0068] discloses, “The use determination system 12 may include one or more of a switch configured to detect usage of inhaler 100 , one or more sensors configured to detect use of inhaler 100 , one or more buttons configured to be depressed upon use of inhaler 100 , and / or the like.” And see [ 0069 ] For example , the use determination system 12 may , for instance , comprise a mechanical switch configured to be actuated prior to , during , or after use of the respective inhaler . The mechanical switch may indicate that a dose of medicament has been primed and is ready for inhalation ( e.g. , such as by metering a dose from a hopper , advancing and / or opening a blister pack , breaking open a pill comprising medicament , etc. ) . In a non - limiting example , the inhaler 100 comprises a medicament reservoir ( not visible in FIG . 1 ) , and a dose metering assembly ( not visible in FIG . 1 ) configured to meter a dose of the rescue medicament from the reservoir . The use determination system 12 may be configured to register the metering of the dose by the dose metering assembly , each metering being thereby indicative of the inhalation performed by the subject using the inhaler 100. Accordingly , the inhaler 100 may be configured to monitor the number of inhalations of the medicament , since the dose should be metered via the dose metering assembly before being inhaled by the subject . One non - limiting example of the dose metering assembly will be explained in greater detail with reference to FIGS . 12-16” / examiner notes that by the disclosure stating the determination system allowing actuation of an e.g. switch to confirm a dose/usage Is completed by the inhaler which is then registered in the system and a user interface is present displaying usage information the interface is necessarily accepting a dosage input of the determined medicine based on the respective protocol or usage regimen predetermined)
and recording a dosage input of the determined medicine. ([0185] discloses, “In addition , a user profile may include the inhalers that are associated with the user ( e.g. , prescribed to the user ) . For example , the user profile may indicate each of the inhalers prescribed to the user , the medicament delivered by each of the respective inhalers , the dosage regiment associated with each of the respective inhalers , the associated user devices ( e.g. , mobile applications ) , an individualized compliance score for the user , an individualized future compliance score for the user , an individualized risk score for the user , their personal information ( e.g. , date of birth , gender , etc. ) , their HCP information , etc. A respective user profile may also include the relevant data ( e.g. , usage data , self - assessment responses , etc. ) for a respective user . For example , the user profile may include a list of all of the usage events ( e.g. , inhalations ) , as well as the respective inhaler data associated with a given usage event ( e.g. , inhalation parameter ( s ) , classification of inhalation event , time , location , etc. ) . Further , in some examples , the user profile may be generated using enriched usage events ( e.g. , including enriched usage parameters ).” And see [0015] discloses, “The compliance score may indicate how compliant the user has been during usage events in a last predetermined number of days . For instance , in some examples , the compliance score may further indicates how adherent the user has been with respect to a dosing schedule associated with a maintenance medicament .” / examiner notes the usage data and compliance score is saved which includes dosages input of the determined medicine)
However, Naumov does not explicitly teach:
wherein the determining of the type of medicine is performed by a machine learning engine trained with a training set of medicine related images, wherein the machine learning engine includes a last layer of a complex machine learning model,
such that the last layer is configured to reduce medication data to include a transferred identification of medication and medicament devices;
However, AMANO does teach:
wherein the determining of the type of medicine is performed by a machine learning engine trained with a training set of medicine related images, wherein the machine learning engine includes a last layer of a complex machine learning model, …[…]..(See Fig. 8 and see [0112] discloses, “The SS model is a model based on a convolutional neural network (CNN), and has an encoder-decoder structure to generate an output image having the same size as an input image. For example, an encoder structure is constructed by a first neural network having an input layer, a hidden layer, and an output layer, and a decoder structure is constructed by a second neural network having an inverted structure of the encoder structure. That is, each structure of the input layer, the hidden layer, and the output layer of the decoder structure is the same as each structure of the output layer, the hidden layer, and the input layer of the encoder structure.” And see [0010] discloses, “in accordance with an input of medical information to an input layer of the trained model, validity of a discrimination result output from an output layer of the trained model is determined, based on information on a calculation result stored in a storage unit and a calculation result” and see [0127] discloses, “The discrimination unit 65 outputs the discrimination result of the type of the target drug to the sorting control unit 62. For example, when the type of the target drug can be specified as one type, or when the number of candidates is narrowed to a predetermined number or less, the drug data related to the drug is output as the discrimination result. In this case, the discrimination unit 65 associates the drug data related to the drug with the captured image 82 of the drug, and stores the associated data in the storage unit 80.” And see [0134] and see [0143] discloses, “A plurality of the teacher data 181 is prepared by preparing a plurality of the captured images 182 and associating the correct data 183 with each of the captured images 182 as the teacher data 181.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Naumov’s teachings of recording the medication type of the inhaler as previously cited with AMANO’s explicit teaching of machine learning such as neural networks taking medication images and classifying them as previously cited, the motivation being Naumov discloses utilizing trained machine learning to determine attributes for example a user should improve in relation to a usage event which includes dosage information and type of medication (see e.g. abstract and [0022]) therefore it would be obvious to someone of ordinary skill in the art to combine with AMANO to increase the accuracy and efficiency of determining the type of medicine and dosing to reduce errors in compliance scoring in Naumov and increase success of treatment successful outcomes by utilizing a deep more complex learning model for increased higher accuracy results.
However, AMANO does not explicitly teach:
such that the last layer is configured to reduce medication data to include a transferred identification of medication and medicament devices;
However, Anushiravani does teach:
such that the last layer is configured to reduce medication data to include a transferred identification of medication and medicament devices; (see Col. 11 lines 33-67 and Col. 12 lines 1-3 and see Col. 16 lines 38-46 discloses, transfer learning within machine learning models that is configured o reduce the amount of data needed to learn identification of e.g. such as medication and medication devices as well as dosages. / see instant specification [0024] giving definition of this transfer learning examiner also notes the claim construction is configured to reduce …[…]… to include which is interpreted as intended use)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Naumov’s teachings of recording the medication type of the inhaler as previously cited and AMANO’s explicit teaching of machine learning such as neural networks taking medication images and classifying them as previously cited with Anushiravani teaching of transfer learning, the motivation being Naumov discloses utilizing trained machine learning to determine attributes for example a user should improve in relation to a usage event which includes dosage information and type of medication (see e.g. abstract and [0022]) therefore it would be obvious to someone of ordinary skill in the art to combine with AMANO and Anushiravani as substitution of one type of machine learning for another to increase the accuracy and efficiency of determining the type of medicine and dosing to reduce errors in compliance scoring in Naumov and increase success of treatment successful outcomes by utilizing a deep more complex learning model for increased higher accuracy results.
As per claim 2, Naumov teaches:
The method of claim 1, wherein the medicine is one of an oral administered medication or a medication administered by a medicament device. ([0013] discloses, “Each inhaler is associated with at least one user and one of a rescue medicament type or a maintenance medicament type.” And see [0008] discloses, “Systems may be designed to monitor a patient's adherence to a prescribed dosing regimen . For example , systems may be designed to monitor a patient's adherence by detecting administrations of a medicament by the patient and confirm that the patient is conforming to the prescribed dosing regimen . In order to detect administrations of the medicament , certain components may be added to or included in the drug delivery device used to administer the medicament . For example , components may be added to detect events or actuations at the drug delivery device that are indicative of an administration of the medicament by the drug delivery device . In addition , communication components may be added to the drug delivery device so that certain information associated with a respective administration of medicament by the drug delivery device ( e.g. , time of administration , number of remaining doses , etc. ) can be transmitted to and subsequently stored at and / or analyzed by one or more external devices . Often , these components are add - on components , such as after - market processing and / or communication components that can be attached to a drug delivery device.”)
As per claim 3, Naumov teaches:
The method of claim 2, wherein the medicament device is an inhaler. ([0044] discloses, “However , the described solutions are equally applicable to other drug delivery devices , such as an injector ,a metered - dose inhaler , a nebulizer , a transdermal patch , or an implantable.”)
As per claim 4, Naumov teaches:
The method of claim1, further comprising: assigning a time stamp to the recorded dosage input; ([011] discloses, “The inhaler may assign a time to each usage event”)
and transmitting the recorded dosage input, the time stamp and the associated identified medication via a transceiver to a server. ([0012] discloses, “The DHP may include or be associated with a transceiver , memory , and a processor. For instance , the DHP may reside on one or more servers. And see [0013] discloses, “The DHP is configured to receive data relating to usage events and a time of each of the usage events ( e.g. , a time stamp for the usage event ) for a plurality of inhalers , where the inhalers are associated with a plurality of different users . Each inhaler is associated with at least one user and one of a rescue medicament type or a maintenance medicament type.”and see [0139] discloses, “Whilst the respective transmission modules of the inhalers 401a - d are configured to transmit the encrypted data , in some non - limiting examples the respective trans mission modules may be further configured to receive data , for example from the End - User or Patient facing processing module to which the encrypted data is sent . In such examples , the respective transmission modules of the inhal ers 401a - d may be regarded as a transceiver , in other words as a transmitting and receiving module.” And see [ [0144] discloses, “In an example , the End - User or Patient facing processing module receives a first identifier assigned to the first medicament ( e.g. , user device 402a receives a first identifier from inhaler 401a assigned to the medicament delivered by inhaler 401a ) . In such an example , a second identifier is assigned to the medicament delivered by another inhaler , such as inhaler 401b . In such an example , the first identifier is not associated with or assigned to the second medicament and the second identifier is not associated with or assigned to the first medicament.” And see [0145] discloses, “The respective identifier may , in certain examples , also denote further information , such as the dose strength of each dose delivered by the respective inhaler , for example via a suitable dose metering assembly included in the inhaler” / examiner notes that the encrypted data such as recorded dosage and time stamp is transmitted utilizing the transceiver to server of the DHP)
As per claim 7, Naumov teaches:
The method of claim 1, further comprising: storing the determined medicine in a memory device; and generating an interface showing the determined medicine in subsequent recording of dosage inputs of the determined medicine. ([0226] discloses, “If , however , the DHP determines not to tag the inhaler data for an enrichment procedure , the DHP may flatten the raw inhaler data at 510 and store the flattened data at 512. For example , flattening and storing the raw inhaler data may allow the inhaler data to be stored at single location ( e.g. , single database or table ) , which may increase the efficiency or speed at which the inhaler data may be retrieved or otherwise manipulated and / or the efficient or speed at which analytics may be performed on the inhaler data.” And see [0008] discloses, “Systems may be designed to monitor a patient's adherence to a prescribed dosing regimen . For example , systems may be designed to monitor a patient's adherence by detecting administrations of a medicament by the patient and confirm that the patient is conforming to the prescribed dosing regimen . In order to detect administrations of the medicament , certain components may be added to or included in the drug delivery device used to administer the medicament . For example , components may be added to detect events or actuations at the drug delivery device that are indicative of an administration of the medicament by the drug delivery device . In addition , communication components may be added to the drug delivery device so that certain information associated with a respective administration of medicament by the drug delivery device ( e.g. , time of administration , number of remaining doses , etc. ) can be transmitted to and subsequently stored at and / or analyzed by one or more external devices . Often , these components are add - on components , such as after - market processing and / or communication components that can be attached to a drug delivery device.” And see [0101] discloses, “The system 10 further comprises a user interface 38. The processing module 34 is configured to control the user interface 38 to communicate the first , second , and / or third usage information. The arrow pointing from the block representing the processing module 34 to the block representing the user interface 38 is intended to represent the control signal(s) which causes or cause the user interface to communicate, for example display , the respective usage information . In this respect , the user interface 38 may comprise any suitable display , screen , for example touch screen , etc. which is capable of displaying the respective usage information . Alternatively or additionally , the respective usage information may be provided by the user interface 38 via an audio message…[…]…” and see [0102] discloses, “The system 10 thus enables the subject to be informed of their usage of the respective medicaments , which may be administered according to a treatment regimen and / or an administration protocol specific to the respective medicament , as previously described.” / examiner notes that usage information including medication type is disclosed as being stored and shown on a user interface for total usage of the device)
As per claim 8, Naumov teaches:
A computer program product of claim 1, comprising instructions which, when executed by a computer, cause the computer to carry out the method of. ([0066] discloses, “The use determination system 12 may comprise a suitable processor and memory configured to perform the functions described herein for the processing module . For example , the processor may be a general purpose processor programmed with computer executable instructions for implementing the functions of the use determination system”)
As per claim 9, Naumov teaches:
The computer program product of claim 8, wherein the computer program product is a non-transitory computer readable medium. ([0066] discloses, “The use determination system 12 may comprise a suitable processor and memory configured to perform the functions described herein for the processing module . For example , the processor may be a general purpose processor programmed with computer executable instructions for implementing the functions of the use determination system” and see [0326] discloses, “The controller may access information from , and store data in the memory . The memory may include any type of suitable memory , such as non - removable memory and / or removable memory . The non - removable memory may include random - access memory ( RAM ) , read - only memory ( ROM ) , a hard disk , or any other type of memory storage device . The removable memory may include a subscriber identity module ( SIM ) card , a memory stick , a secure digital ( SD ) memory card , and the like . The memory may be internal to the controller . The controller may also access data from , and store data in , memory that is not physically located within the electronics module 120 , such as on a server or a smart phone)”)
As per claims 10-13, 16, and 17, they are system claims which repeat the same limitations of claims 1-4 and 7 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Naumov disclose the underlying process steps that constitute the methods of claims 1-4 and 7 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well as cited below. As such, the limitations of claims 10-13 and 16 and 17 are rejected for the same reasons given above for claims 1-4 and 7. The new feature of classification in claim 17 is also cited below as taught in Naumov.
As per claim 16, Naumov teaches:
A mobile device allowing collection of medication use data, the mobile device comprising: ([0109] discloses, “Provided herein is a system for capturing data from a plurality of drug delivery devices ( e.g. , via a mobile device in communication with the drug delivery device) , such as the inhaler 100”)
,an image sensor; (And see [0306] discloses, “The QR code 42 may provide a facile way of pairing the respective inhaler 100 with the processing module 34 , in examples in which the user device 40 comprises a suitable optical reader , such as a camera , for reading the QR code” / examiner notes that an optical reader is interpreted by plain definition of having a sensor)
a memory device; ([0012] discloses, “The DHP may include or be associated with a transceiver , memory , and a processor.”)
a display; ([0286] discloses, “In one example , the DHP 406 causes the computer or server 408 associated with the health care provider to provide the inhalation data via a GUI that is presented on a display device associated with the health care provider's computer.”)
and a processor coupled to the image sensor, the display, and the memory device, the processor operable to: ([0012] discloses, “The DHP may include or be associated with a transceiver , memory , and a processor.”)….[….]…
As per claim 17, Naumov teaches:
A data collection system, the system comprising: a mobile computing device having an image sensor operable to capture an image of a medicine, a storage device, a transceiver, a display and a processor operable to: classify the medicine; display an input for dosage of the classified medicine on the display; record an input of the dosage of the classified medicine; send the collected dosage and classified medicine data via the transceiver; and an analysis server receiving the collected data and recording the dosage and the classified medicine data in relation to a patient. ([0196] discloses, “The analytical subsystem 406b may also employ machine learning and / or predictive modeling techniques . The analytical subsystem 406b may comprise one or more machine learning algorithms , such as but not limited to , an instance - based algorithm ( e.g. , k - Nearest Neighbor ( kNN ) , Learning Vector Quantization ( LVQ ) , Self - Organizing Map ( SOM ) , Locally Weighted Learning ( LWL ) , Support Vector Machines ( SVM ) , etc. ) , a regression algorithm ( e.g. , a linear regression algorithm , a logistic regression algorithm , etc. ) , a decision tree algorithm , a Bayesian algorithm ( e.g. , a Naive Bayes classifier ) , an ensemble algorithm ( e.g. , a weighted average algorithm ) , etc.” and see [0128] discloses, “As described herein , the End - User or Patient Fac ing processing module may classify a given usage event based on the respective inhalation parameters associated with the inhalation event ( e.g. , peak inhalation airflow parameters ).” And see [0016] discloses, “Each usage event may be associated with an inhaler , a medicament type , and a user of the plurality of different users .”)
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on page 1-2 of remarks that the rejection of claims 1-17 under 35 U.S.C § 101 should be withdrawn in light of the arguments below: Furthermore, assuming, arguendo, the claims contain an abstract idea (which Applicant does not concede), Prong Two of the Revised Step 2A requires the additional claim elements integrate the alleged abstract idea into a practical application, which renders the claims-as a whole-patent eligible. Similar to the Holding in Ex parte Desjardin, there may be sufficient evidence of patentable subject matter when the technical improvement is in the manner the model operates or learns, not merely in what the model predicts. See Ex parte Desjardins Appeal No. 2024-000567 (ARP Sept. 26, 2025) (precedential) For example, "A transfer learning routine reduces the need for gathering large amounts of medication data to train a medication identification machine learning model for the identification of the medication or the medicament device. In this example the core machine learning framework allows for out of the box image classification of images of medication and medicament device. A pre-trained complex computer vision model has a last layer with the most meaningful information. The last layer of the pre-trained complex computer vision model is transferred to the new task of identification of medication and medicament devices." See paragraph [0024]. Here, the Applicant discloses a specific manner in which machine learning model is applied to allow for out of the box image classification of images of medication and medicament device on the last layer allowing medication and medicament labels to be classified accurately without a large data set. Consequently, under Prong Two of the Revised Step 2A6 procedure, the claims are not directed to an abstract idea. With respect to the claim amendments, reconsideration and withdrawal of these rejections are respectfully requested.
The other claims currently under consideration in the application are dependent from their respective independent claims discussed above and therefore are believed to be allowable over the applied references for at least similar reasons to at least some of the explanations described above. Because each dependent claim is deemed to define an additional aspect of the invention, the individual consideration of each on its own merits is respectfully requested.
Therefore, Applicants respectfully request that the Examiner withdraw the rejection of claims 1-17 under 35 U.S.C. § 101.
Examiner appreciates applicant’s arguments but does not find them persuasive. The MPEP § 2106.05(a) states, “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691…..It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer)….. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
The Examiner notes that although the claims are reviewed in light of the specification, examiner must not read the specification into the claims and reviews the limitations which are positively recited in the claims when determining integration into a practical application. It is also noted that the abstract idea itself cannot set forth integration into a practical application but rather the additional elements. Therefore, the claims do not purport to improve the functioning of computer or computer technology itself and there is no nexus with Ex Parte Desjardins, as the claims do not reflect or recite an improvement to any additional element such as a machine learning model or the technological process of training a machine learning model. Rather the claims are confined to general purpose hardware (see e.g. instant application spec. [0071]) and do not solve a problem rooted in computer technology, but instead apply transfer learning and computer vision machine learning technology within a general computer environment to gather and analyze data and are constructed to claim an improvement to the abstract idea of determining a dosage input and a medication type. Further the claims do not recite or reflect the technology of the machine learning improvement but rather the broad recitation of the abstract elements and data processed to determine a real world output of mediation dosage.
Examiner maintains the 35 U.S.C § 101 rejection.
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant argues on page 2-3 of the remarks dated 6/10/2025 that claims 1-17 rejected under 35 U.S.C § 102/103 should be withdrawn. Applicant’s arguments with respect to claims 1-4, 7-13, and 16-17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Prior Art Cited But Not Relied Upon
Manice et. al - US11335447B2
An inhaler tracker module is secured to an inhaler and has an activation sensor for sensing use of the inhaler . The tracker module includes a memory for storing inhaler use data , and a communications component for wirelessly trans mitting the stored inhaler use data . The tracker module is wrapped around the inhaler body and includes a pressure switch located at the top of the canister to detect a user pressing the canister into the body for inhaler use . The tracking module has a standby mode in which it remains until inhaler use or until inhaler use data is stored and it is transmitted . A pairing function is provided .
Hanina et. al (hereinafter Hanina) (US9679113B2)
medication management system is described that is operable to determine whether a user is actually following a protocol, provide additional assistance to a user, starting with instructions, video instructions, and the like, and moving up to contact from a medication administrator if it is determined that the user would need such assistance in any medical adherence situation, including clinical trial settings, home care settings, healthcare administration locations. Such as nursing homes, clinics, hospitals and the like. Suspicious activity on the part of a patient or other user of the system is identified and can be noted to a healthcare provider or other service provider where appropriate.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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