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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/20/2026 has been entered.
Applicant's amendments and remarks, filed, 05/20/2026, are acknowledged. Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 21-30 are under examination. Claims 1-20 have been cancelled.
Priority
Applicant’s claim for the benefit of priority under 35 U.S.C. 119(a)-(d) is acknowledged. This application is the National Stage filing under 35 USC 371 of PCT/EP2019/083724, filed on 12/04/2019. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Withdrawn Rejection
The rejected of claims 21-30 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a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more is withdrawn in view of applicant’s amendments. In particular, the claims have been amended and now recite an integration of the judicial exception into a practical application.
Claim rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
This is a written description rejection.
Claims 21-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
The written description requirement is separate and distinct from the enablement requirement. The specification must: (1) describe the claimed invention in a manner understandable to a person of ordinary skill in the art, and (2) show that the inventor actually invented the claimed subject matter. In this case, the specification fails to provide written description support for the following steps:
obtaining, via one or more sensors associated with a device, successive values for a person corresponding to repeated measurements over time of at least one parameter relating to a lifestyle or activity of the person;
predicting, by at least one processor, an evolution over time of a vision- related parameter of the person from said obtained successive values by using a machine learning model comprising a neural network trained on data from a group of individuals by assigning weights to node connections of the neural network, wherein the machine learning model receives as inputs (i) an aggregation of said successive values associated with a same one of the at least one parameter, formed over a predetermined period of time, as a first prediction input, and (ii) a plurality of individual ones of said successive values associated with the same one of the at least one parameter as additional prediction inputs, and wherein the machine learning model applies respective weights to said plurality of individual ones of said successive values that depend on a respective time of measurement of each of said individual ones of said successive values, such that the predicted evolution depends differentially on each of said plurality of individual ones of said successive values (claims 21, 27, 30).
Regarding claims 21, 27, 30, the claimed “obtaining” step results in obtaining “successive values” corresponding to repeated measurements of a “parameter relating to a lifestyle or activity”. The claims are not limited to any particular “device” or any specific type of lifestyle or activity, i.e. they encompass all ways of performing the claimed function and literally any type of data that can be measured over time. In this case, however, a review of the specification does not provide sufficient disclosure of methods and/or devices that achieve the above function for the full scope of what is being claimed. At best, the specification provides a limited discussion different vision-related parameters, e.g. myopia levels of the person, hypermetropia, astigmatism, refractive error, etc. [page 7] as well as sensors and motion units for measuring particular types of data [page 8]. However, these limitations are much narrower in scope than what is being claimed and it is improper to impart narrowing limitations into the claims. See MPEP 2111.01. Notably, the specification also does not provide any evidence of particular lifestyles or activities associated with risk or progression of myopia. Accordingly, there is insufficient evidence of possession of measurement data required for achieving the claimed method for the full scope of parameters encompassed by the claims.
Regarding claims 21, 27, 30, the claimed “predicting” step as best understood purports to reduce a risk of myopia onset or progression by predicting the evolution of a vision-related parameter over time using neural network (running on a computer), wherein the neural network has been trained on successive time-series data from a group of individuals and data associated with lifestyle or activity of a person. Notably, the claim is not limited to any particular type of “successive values”, “parameters”, “weights”, or thresholds. A review of the specification fails to provide any technical details with regards to the claimed ‘neural network’, how it was trained, and how it is implemented to achieve the claimed functionality. The specification also fails to provide any evidence that applicant had knowledge of specific parameters associated with myopia onset or risk and threshold values associated with predicting the evolution of vision-related parameters given the scope of what is claimed. The specification [page 12, Figures 4, 5, 6] provides generic “profiles” that purport to be associated with myopia evolution risk. However, this description is not sufficient as the profiles are nominally recited and are not associated with any specific “lifestyle” or “activity” data or neural networks. Notably, the term “neural networks” only appears twice in the entire specification. In particular, the specification generically discloses that “In a particular implementation, the processor used at step 16 may implement a machine learning algorithm. Namely, one or more neural networks may be trained by inputting series of successive values for numerous individuals and building a correlation table or any other database means containing lots of data, for better accuracy of the predicting method. In such an implementation, the associating of step 16 may be implemented by assigning weights to node connections in the neural network [page 11]. Accordingly, the cited sections of the specification provide nothing more than results-based language of the claims and there is no evidence that applicant has actually disclosed the requisite “neural network” for predicting the evolution of a vision-related parameter over time based on generic lifestyle or activity data, i.e. the invention is essentially using a “black box” to achieve the claimed function.
Moreover, one of ordinary skill in the art would recognize that developing, training, and validating a machine learning model (neural network) for predicting disease risk is not trivial. In this case, it requires knowledge of specific predictor data associated with myopia risk, a trained model capable of comparative analysis between test and reference datasets (associated with myopia), and knowledge of specific “trigger” threshold values such that meaningful recommendations can be made (e.g. for reducing myopia risk). This position is further supported by the following prior art and/or post-filing art.
Brennan et al. (AU2018/202725) teaches methods and a system for determining myopia progression in an individual and recommending a myopia control treatment option for controlling refractive progression based on the predicted axial elongation. Unlike the claimed method, Brennan predicts changes in the axial length of the individual's eye based on that individual's past refractive rate of change, and provides quantitative data associated with normal and myopic conditions [0001-0007]. Unlike the claimed invention, Brennan teaches specific algorithms and/or models for predicting an individual's eye growth, i.e., the axial elongation of an individual's eye, based on that individual's past myopia progression rate, and particularly, as a function of refractive change values detected for that individual over a past predetermined time period, i.e., a past progression rate (e.g., over a past year), as well as model validation [0011-0014, 0016, 0043, 0052]. Unlike the claimed invention, Brennan teaches that historical refractive progression is a valid predictor of future myopic progression [0050]. Brennan does not teach using using general lifestyle or activity data for developing neural networks to predict the evolution of vision-related parameters to reduce myopia onset risk.
Lu et al. (COMMUNICATIONS BIOLOGY, 2021, 4:1225, pp.1-8) teaches methods and deep learning-based detecting systems for pathologic myopia. In particular, Lu teaches that application of deep learning technology in myopia screening is still a challenge, due to the complexity of myopia classification [page 2], which is typically done using image data (and not lifestyle or activity data). Lu provides sufficient tests datasets and also provides evidence of model performance [pages 2-4 and Figure 1].
Patil (Automatic Pathological Myopia Detection Using Ensemble Model, Chapter 13, Proceedings of International Conference on Computational Intelligence ICCI 2021, pp.176-188). In particular, Patil provides a well-defined methodology that includes labelling and normal and pathological data; detecting and classifying pathological myopia images using deep learning; data collection (ocular disease datasets) and applying the needed preprocessing to improve and enhance the images; designing a predictive deep learning model and training it on the collected images. Patil teaches testing the trained model on the testing data (images) to find the performance of the model if the model performs better than the existing models stop training else repeat from the designing step [Section 3 and 4].
Burlina et al. (PLOS ONE, 2017, 12(8); e0184059, pp.1-15) teaches automated diagnosis of myositis using machine learning and deep learning methods. In particular, Burlina teaches that such methods require large data sets and expert knowledge to label the data according to whether they correspond to a phenotype associated with a healthy or diseased patient; and building a model from training data (e.g., statistical model) which then allows one to perform inference on new data, for example by doing classification or regression. In addition, deep learning makes use of neural networks consisting of a multi-layered cascade of mathematical functions through which input data is processed to infer class labels. The mathematical functions involve millions of parameters that are automatically learned using training data with known class labels. See entire.
In this case, however, a review of the specification does not provide any evidence that applicant has obtained sufficient data associated with the conditions that are encompassed by the claims and needed to train a neural network to predict an evolution over time of a vision-related parameter associated with (treating or reducing risk of) myopia, e.g. profiles associated with healthy and diseased subjects, activity or lifestyle data associated with normal or high risk subject, etc. There is no discussion or evidence of specific weights or optimization functions that are necessary and sufficient for achieving the claimed functions. There is no discussion or evidence of how to select specific trigger threshold values that are sufficient for performing the claimed function of treating or reducing myopia onset risk or progression, as claimed. The specification discloses specific threshold values associated with specific activities and diopter values [page 16 and 18]. However, such features are not commensurate in scope with what is claimed and it is improper to import narrowing limitations into the claims. MPEP 2111.01. Moreover, there is no objective evidence to suggest that applicant has knowledge that the claimed activities actually result in treating or reducing a risk of myopia onset or progression. While the reader can certainly appreciate the goal of “treating or reducing a risk of myopia onset”, establishing goals does not make a patent. As the Court of Appeals for the Federal Circuit stated in a case involving similar issues, an inadequate patent description that merely identifies a plan to accomplish an intended result “is an attempt to preempt the future before it has arrived.” Fiers v. Revel, 984 F.2d 1164, 1171 (Fed. Cir.1993). Accordingly, the specification fails to provide evidence that applicant has actually determined a sufficient correlation between structure (i.e. a trained neural network) and function for the full scope of parameters and thresholds encompassed by the claim.
Regarding claim(s) 21, 27, 30, these claims additionally recite “the prompt being provided via at least one of a vibration of the device, an audio reminder, or a visual prompt on a display of the device.” In this case, a review of the specification fails to teach any combination of hardware and/or software that would serve to achieve the function of causing the “vibration of a device” or an “audio reminder”. The specification does teach various kinds of sensors including light sensors, which may be included in smart eyewear equipment or in a smartphone; an inertial motion unit (IMU) located for instance in a head accessory may be used to detect posture; a GPS may be used to detect an outdoor activity or whether the individual is in a rural or in an urban environment; a camera or a frame sensor may be used to detect the frequency and/or time duration of wearing eyeglasses [page 8]; an audio interface [page 11]; a display unit and/or a smartphone or smart tablet or smart eyewear [page 14]; and alert message to provide reminders [page 15]. However, none of these elements serve to perform the claimed functions with regards to providing vibration or audio. As such, it is unclear how the claimed method/device/CRM are actually performing the functions of “vibration of a device” or an “audio reminder”. For the reasons discussed above, the disclosure fails to link specific structures or instructions to the specialized functions and therefore the claims lack sufficient written description given the scope of what is being claimed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Regarding claim(s) 29, this claim limits the device to smart eyewear, a smartphone, or a smart tablet. However, a review of the specification does not provide any evidence or significant details to indicate applicant had possession of smart eyewear, smartphone, or tablet capable of running a neural network for predicting an evolution over time of a vision-related parameter as claimed. For the reasons discussed above, the disclosure fails to link specific algorithms, structures or step-by-step instructions to the specialized functions and therefore the claims lack sufficient written description given the scope of what is being claimed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Claim rejections - 35 USC § 112b
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims that depend directly or indirectly from claim(s) 21, 27, 30 is/are also rejected due to said dependency.
Claims 21, 27, 27 are now directed to a method, system, and non-transitory computer-readable medium storing instructions for “treating or reducing a risk of myopia onset or progression in a person”. As such, the claims now lends itself to one or more implausible interpretations. In particular, the artisan would understand what is meant by “treating” a disease as well as “reducing” a risk of a disease. However, because the claim as written encompasses persons who do not actually have myopia, it is unclear what is meant by treating a “risk” of myopia in a person, i.e. in what way does one treat a risk or probability of a disease in a person with no disease. Clarification is requested via amendment. The examiner suggests amending the claim to remove the word “treating” unclear it is associated with treating an actual disease.
Claims 21, 27, 30 recite “wherein the machine learning model receives as inputs (i) an aggregation of said successive values associated with a same one of the at least one parameter”. In each case, it is unclear what limiting effect is intended by the phrase “values associated with a same one of the at least one parameter”. Stated differently, in what way are the successive values “associated with a same one” of the at least one parameters, i.e. are they equal, similar, averaged, or otherwise. A review of the specification does not provide any limiting definitions or examples that would serve to clarify the scope.
Claim 27 is directed to a “device of treating or reducing a risk of myopia onset or progression in a person, the device comprising: at least one sensor…and at least one processor configured to…trigger a prompt to the person…, the prompt being provided via at least one of a vibration of the device, an audio reminder, or a visual prompt on a display of the device.” Firstly, it is unclear what is meant by a “device of treating or reducing”. The examiner believes this to be a grammatical error, in which case the claim should be amended to recite “a device for treating…”. Secondly, with regards to the phrase “thereby administering a vision therapy regimen”, it is unclear what structural element of the claimed device is responsible for performing this function. Claim scope is not limited by claim language (e.g. ‘wherein’, ‘thereby’) that suggests but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. MPEP 2111.04. In this case, the device as claimed only comprises a sensor and processor and the ‘administering’ function does not flow from either of these elements. The processor is additionally configured to provide a prompt that is “being provided via at least one of a vibration of the device, an audio reminder, or a visual prompt on a display of the device”. Clearly, the artisan would appreciate that administering and displaying are entirely different processes. As such, this does not provide functionality for “administering” anything to a user. Furthermore, it is unclear (i) what structural element(s) of the claimed device perform these functions (i.e. in what way does the claimed sensor or processor create a vibration or audible noise), and (ii) whether or not applicant intends for a “display” to be a structural element of the system as claimed. If so, the claim should explicitly recite that the claimed device comprises a display. In each case, clarification is requested via amendment.
Claim 27 recites “…and in response to the prompt, the person performing the vision-protective activity, thereby administering a vision therapy regimen that treats or reduces the risk of myopia onset or progression, wherein performing the vision-protective activity comprises the person at least one of (i) increasing the distance between the person's eves and the text being read, and (ii) increasing the time duration the person spends outdoors, to treat or reduce the risk of myopia onset or progression of the person.”
The above limitation is problematic for the following reasons. (1) The above “wherein” and “thereby” clauses recite functional limitations directed to how a person is using the ‘activity’ information. Therefore, it is unclear what limiting effect of the claimed device is intended by the above “wherein” and “thereby” clauses because they are directed to intended use recitations that do not focus the capabilities of the device and thus have no patentable weight on the device as claimed. Applicant is reminded that claim scope is not limited by claim language (e.g. ‘wherein’, ‘thereby’) that suggests but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. MPEP 2111.04.
(2) A single claim which claims both an apparatus/device and the method steps of using the apparatus is indefinite. See MPEP 2173.05(p). In contrast, when a claim recites a product and additional limitations which focus on the capabilities of the system, not the specific actions or functions performed by the user, the claim may be definite under 35 U.S.C. 112(b). Clarification is requested via amendment.
Claim 30 is directed to a non-transitory computer-readable medium storing instructions that cause a device to perform a method comprising…”triggering the device to provide a prompt to the person…, the prompt instructing the person to perform a vision-protective activity comprising at least one of: (i) increasing the distance between the person's eyes and the text being read; and (ii) increasing a time duration the person spends outdoors, the prompt being provided via at least one of a vibration of the device, an audio reminder, or a visual prompt on a display of the device; and in response to the prompt, the person performing the vision-protective activity, wherein performing the vision-protective activity comprises the person at least one of (i) increasing the distance between the person's eves and the text being read, and (ii) increasing the time duration the person spends outdoors, to treat or reduce the risk of myopia onset or progression of the person.
The above italicized limitations are problematic for the following reasons. (1) The phrase “the prompt being provided via at least one of a vibration of the device, an audio reminder, or a visual prompt on a display of the device” describes how the device provides the prompt to a user and has nothing do with the instructions stored in the computer-readable medium. A review of the specification also does not provide any evidence of hardware or software that would cause a device to vibrate [see at least page 11]. Accordingly, it is unclear in what way this phrase further limits the instructions performed by the claimed computer-readable medium.
(2) With regards to the phrase “and in response to the prompt, the person performing the vision-protective activity, wherein performing the vision-protective activity comprises the person at least one of (i) increasing the distance between the person's eves and the text being read, and (ii) increasing the time duration the person spends outdoors, to treat or reduce the risk of myopia onset or progression of the person”, the above “wherein” clause recites functional limitations directed to how a person is using the ‘activity’ information. Therefore, it is unclear what limiting effect of the claimed computer-readable instructions is/are intended by the above “wherein” clause because it is directed to intended use recitations that do not focus the capabilities of the device and thus have no patentable weight on the instructions as claimed. Applicant is reminded that claim scope is not limited by claim language (e.g. ‘wherein’, ‘thereby’) that suggests but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. MPEP 2111.04.
Claim rejections - 35 USC § 112d
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 22 and 23 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. In particular, claims 22 and 23 limit the vision-related parameter to (i) a time duration spent outdoors by the person, and (ii) a distance between the person's eyes and a text being read, respectively. However, parent claim 21 already recites these limitations. Accordingly, claims 22 and 23 do not further limit the subject matter of the parent claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PABLO S WHALEY whose telephone number is (571)272-4425. The examiner can normally be reached between 1pm-9pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Anita Coope can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PABLO S WHALEY/Primary Examiner, Art Unit 3619
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