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 .
Response to Arguments
Applicant's arguments filed 07/07/2026 have been fully considered but they are not persuasive. With regards to the claim interpretation under 35 U.S.C. 112(f), sixth paragraph Applicant argues that there is no recitation of the terms “means” or “step” in claims 1-10 and 12-19, nor are there any terms that are used that may be considered a substitute for “means” that is a generic placeholder. Additionally, Applicants argue that the claims do not modify the terms “means” or “step” by functional language. Examiner respectfully disagrees as the claims clearly recite a plurality of “units”, for example a “common sense model training unit, a preference model training unit, gaze tracking unit, etc.”. MPEP § 2181 discloses “The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): “mechanism for,” “module for,” “device for,” “unit for,” “component for,” “element for,” “member for,” “apparatus for,” “machine for,” or “system for.” Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998)”. Further the claimed units are clearly modified by functional language, for example “a gaze tracking unit configured to, a determination synchronization unit configured to, a focus matching determination unit configured to, etc.”. Therefore the claim interpretation under 35 U.S.C. 112(f), sixth paragraph is maintained.
With regards to the rejection of claims 1-10 and 12-19 under 35 U.S.C. § 112(a) and 112(b) Applicant argues that the plurality of “units” claimed are implemented by sufficient structure in the specification. Applicant specifically points to paragraphs 0054 and 0057 which disclose the plurality of units implemented in either the lens control device 100 or the controller 140 and therefore there is sufficient structure cited to implement the claimed units. Examiner respectfully disagrees as there is nowhere in the specification that specifically defines the “units” themselves as being some sort of structure or hardware such as for example some type of circuitry. Therefore the claims are indefinite because the specification fails to disclose the corresponding structure for the units in order perform the claimed functions. The rejection of claims 1-10 and 12-19 under 35 U.S.C. § 112(a) and 112(b) are sustained.
Applicant’s arguments with respect to the rejection of claims 1 and 12 under 35 U.S.C. § 102(a)(1) 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.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a common sense model training unit, a preference model training unit, a gaze tracking unit, a determination synchronization unit, a focus matching determination unit, and a feedback collection unit.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(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 the first paragraph of pre-AIA 35 U.S.C. 112:
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.
Claims 1-11 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1-11 and 13 all invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph by reciting a plurality of “units”, however after a review of the specification it has not been determined what structure the claimed “units” correspond to. Claim 1 also now claims a “gaze determination processor”, however after review of the specification Examiner can find no support for a gaze determination processor. The specification only discloses a gaze determination “unit” and there does not appear to be any disclosure that defines the unit as a processor. Therefore the claims lack an adequate written description.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “a common sense model training unit, a preference model training unit, a gaze tracking unit, a determination synchronization unit, a focus matching determination unit, and a feedback collection unit” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 6, 9, 10, 12, 13, 16, 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murray et al (pub # 20250054423) in view of Attia et al (WO 2025/166241 A1).
Consider claim 1. Murray et al teaches A lens control device (Fig. 1A). comprising:
a gaze determination processor; (paragraph 0079, The respective mono (AI) processing modules 120 are configured to continually monitor the user's eye during display of information. A video microdisplay in the display module receives image data to be displayed to the user's respective eyes. However, instead of directly displaying the raw video, an image processing module 122 adjusts the video/image based on gaze/location, pupil and/or iris size and/or change, blink count, gaze duration, and eye openness. Fig. 16 and paragraph 0116, Data collection occurs and provides the raw data (e.g., from the sensor systems described above). Low Level feature extraction from the data collection provides information such as fixation, saccades, gaze, etc. High Level features can then be extracted from the low level features also using area of interest (AOI) input. This can be then visualized in supervised learning and the AOI can be adjusted to correct for the feature extraction. The trained model that results can then provide a user behavior characteristic such as fight or flight, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level.).
and a controller configured to control a focus of a lens based on a result value derived by the preference model. (Fig. 1B and paragraph 0079, The respective mono (AI) processing modules 120 are configured to continually monitor the user's eye during display of information. A video microdisplay in the display module receives image data to be displayed to the user's respective eyes. However, instead of directly displaying the raw video, an image processing module 122 adjusts the video/image based on gaze/location, pupil and/or iris size and/or change, blink count, gaze duration, and eye openness. Then the dual-eye processing module 124 compares the two processed images/videos. Meanwhile, the mono processing modules 120 perform brightness adjustment, may add an overlay to the image, adjust the focus, and output the video to the microdisplay, and display to the user through the optics.).
Murray et al does not specifically disclose configured to train a common sense model based on a common sense dataset and train a preference model device based on user preference data, wherein the preference model is a device that is trained using datasets collected from a user environment and having a size that is smaller than a dataset used for training the common sense model.
In an analogous art Attia et al teaches configured to train a common sense model based on a common sense dataset (paragraph 00201, The general process of creating an LLM, includes pretraining and finetuning, which is further discussed below with reference to FIG. 20. The term "pre" in "pretraining" refers to the initial phase where a model like an LLM is trained on a large, diverse dataset to develop a broad understanding of language. This pretrained model then serves as a foundational resource that can be further refined through finetuning, thus a common sense model).
and train a preference model device based on user preference data, (paragraph 0071, a preference model and a policy optimization algorithm. The preference model is a neural network that learns to predict the probability of a human preferring one output over another, given a context and a query. The preference model is trained on pairwise comparisons of LLM outputs collected from human raters.).
wherein the preference model is a device that is trained using datasets collected from a user environment and having a size that is smaller than a dataset used for training the common sense model. (paragraph 0070, Direct Preference Optimization (DPO) is a technique for reinforcement learning in LLMs that aims to overcome some of the limitations of fine-tuning and distillation. DPO is based on the idea of learning from human preferences, rather than from task-specific labels or rewards. DPO collects human feedback on the outputs of an LLM and uses it to optimize the LLM's parameters towards producing more preferred outputs. Paragraph 00201, The general process of creating an LLM, includes pretraining and finetuning, which is further discussed below with reference to FIG. 20. The term "pre" in "pretraining" refers to the initial phase where a model like an LLM is trained on a large, diverse dataset to develop a broad understanding of language. This pretrained model then serves as a foundational resource that can be further refined through finetuning, a process where the model is specifically trained on a narrower dataset that is more specific to particular tasks or domains.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Attia et al with the system and method of Murray et al in order to provide an improved system and method of lens control by providing a more accurate model for learning user preferences.
Consider claim 12. Murray et al teaches A lens control method (paragraph 0079). comprising:
a model training step of training, by a gaze determination processor; (paragraph 0079, The respective mono (AI) processing modules 120 are configured to continually monitor the user's eye during display of information. A video microdisplay in the display module receives image data to be displayed to the user's respective eyes. However, instead of directly displaying the raw video, an image processing module 122 adjusts the video/image based on gaze/location, pupil and/or iris size and/or change, blink count, gaze duration, and eye openness. Fig. 16 and paragraph 0116, Data collection occurs and provides the raw data (e.g., from the sensor systems described above). Low Level feature extraction from the data collection provides information such as fixation, saccades, gaze, etc. High Level features can then be extracted from the low level features also using area of interest (AOI) input. This can be then visualized in supervised learning and the AOI can be adjusted to correct for the feature extraction. The trained model that results can then provide a user behavior characteristic such as fight or flight, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level.).
and a focus signal generation step of controlling, by a controller, a focus of a lens based on a result value derived by the preference model. (Fig. 1B and paragraph 0079, The respective mono (AI) processing modules 120 are configured to continually monitor the user's eye during display of information. A video microdisplay in the display module receives image data to be displayed to the user's respective eyes. However, instead of directly displaying the raw video, an image processing module 122 adjusts the video/image based on gaze/location, pupil and/or iris size and/or change, blink count, gaze duration, and eye openness. Then the dual-eye processing module 124 compares the two processed images/videos. Meanwhile, the mono processing modules 120 perform brightness adjustment, may add an overlay to the image, adjust the focus, and output the video to the microdisplay, and display to the user through the optics.).
Murray et al does not specifically disclose a common sense model based on a common sense dataset and train a preference model device based on user preference data, wherein the preference model a device that is trained using datasets collected from a user environment and having a size that is smaller than a dataset used for training the common sense model.
In an analogous art Attia et al teaches a common sense model based on a common sense dataset (paragraph 00201, The general process of creating an LLM, includes pretraining and finetuning, which is further discussed below with reference to FIG. 20. The term "pre" in "pretraining" refers to the initial phase where a model like an LLM is trained on a large, diverse dataset to develop a broad understanding of language. This pretrained model then serves as a foundational resource that can be further refined through finetuning, thus a common sense model).
and train a preference model device based on user preference data, (paragraph 0071, a preference model and a policy optimization algorithm. The preference model is a neural network that learns to predict the probability of a human preferring one output over another, given a context and a query. The preference model is trained on pairwise comparisons of LLM outputs collected from human raters.).
wherein the preference model a device that is trained using datasets collected from a user environment and having a size that is smaller than a dataset used for training the common sense model. (paragraph 0070, Direct Preference Optimization (DPO) is a technique for reinforcement learning in LLMs that aims to overcome some of the limitations of fine-tuning and distillation. DPO is based on the idea of learning from human preferences, rather than from task-specific labels or rewards. DPO collects human feedback on the outputs of an LLM and uses it to optimize the LLM's parameters towards producing more preferred outputs. Paragraph 00201, The general process of creating an LLM, includes pretraining and finetuning, which is further discussed below with reference to FIG. 20. The term "pre" in "pretraining" refers to the initial phase where a model like an LLM is trained on a large, diverse dataset to develop a broad understanding of language. This pretrained model then serves as a foundational resource that can be further refined through finetuning, a process where the model is specifically trained on a narrower dataset that is more specific to particular tasks or domains.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Attia et al with the system and method of Murray et al in order to provide an improved system and method of lens control by providing a more accurate model for learning user preferences.
Consider claims 3 and 19. Murray et al further teaches The lens control device of claim 1, wherein the controller comprises:
a gaze tracking unit configured to track a gaze fixing point of a user; (paragraph 0103, employing user eye focus and gaze data to create a feedback loop to the AI processor 1002 for correct target identification.).
a determination synchronization unit configured to identify and synchronize whether the gaze fixing point of the user and a gaze determined by the preference model are identical with each other; (paragraph 0103, the system initially incorrectly identified two threats, whereas the user correctly identified the single threat. The AI model receives the user's eye data, as well as the video feed, and determines only one of the targets is a threat.).
a focus matching determination unit configured to determine an object by using an algorithm; (paragraph 0103, employing user eye focus and gaze data to create a feedback loop to the AI processor 1002 for correct target identification.).
and a feedback collection unit configured to collect feedback of a user with respect to whether focuses determined by the algorithm are identical with each other. (paragraph 0103, employing user eye focus and gaze data to create a feedback loop to the AI processor 1002 for correct target identification. In this diagram, the system initially incorrectly identified two threats, whereas the user correctly identified the single threat. The AI model receives the user's eye data, as well as the video feed, and determines only one of the targets is a threat.).
Consider claim 6. Murray et al further teaches A smart variable-focus lens comprising:
a data collection unit configured to collect data to train the common sense model; (paragraph 0083, data collection occurs).
an additional data collection unit configured to collect small-scale data based on a feedback signal; (paragraph 0083, Low level feature extraction provides information such as fixation, saccades, gaze, etc.).
the lens control device of claim 1;
and a variable-focus lens having a changeable focus. (Fig. 1B and paragraph 0079, Meanwhile, the mono processing modules 120 perform brightness adjustment, may add an overlay to the image, adjust the focus, and output the video to the microdisplay, and display to the user through the optics.).
Consider claim 9. Murray et al further teaches The smart variable-focus lens of claim 6, wherein the data collection unit is further configured to generate a dataset by collecting data comprising one or more of pieces of information on an image, a natural language tag, a saliency ranking label, and a salient object boundary label. (paragraph 0084, The predictive biologic insight AI model is trained on a large variety of existing and custom labeled data sets. Custom labeled data sets can be derived from simulated conditions such as first-person shooter (FPS) video game inputs and flight simulators. These custom data sets can be used on separate models, or to finetune the predictive biologic insight AI model. Supervised learning can be adjusted to correct for the feature extraction. This provides a trained model that provides information such as: fight or flight response, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level, and/or applications,).
Consider claim 10. Murray et al further teaches The smart variable-focus lens of claim 6, wherein the data collection unit is further configured to generate a dataset by collecting data a size of which is increased, by repeating a process of auto-labeling a new dataset that is not labeled, re-training a model a dataset generated through auto-labeling, and auto-labeling new data by using the re-trained model. (paragraph 0009, AI training and retraining can be employed. Paragraph 0084, The predictive biologic insight AI model is trained on a large variety of existing and custom labeled data sets. Custom labeled data sets can be derived from simulated conditions such as first-person shooter (FPS) video game inputs and flight simulators. These custom data sets can be used on separate models, or to finetune the predictive biologic insight AI model. Supervised learning can be adjusted to correct for the feature extraction. This provides a trained model that provides information such as: fight or flight response, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level, and/or applications. Paragraph 0104, the feedback can be provided to a separate automated targeting system to help it improve (e.g., continuous training via use of the system).
Consider claim 13. Murray et al further teaches The lens control method of claim 12, further comprising: a data collection step of using a data collection unit prior to the model training step; (paragraph 0083, data collection occurs).
an additional data collection step of collecting feedback of a user with respect to whether the focus of the lens matches the user’s focus are identical with each other; (paragraph 0104, the feedback can be provided to a separate automated targeting system to help it improve (e.g., continuous training via use of the system). The updated model continues to optimize display operation for an interesting or threatening object (e.g. higher frame rate, higher resolution, brighter, etc.).).
and a model re-training step of re-training a model based on the feedback of the user. (paragraph 0009, AI training and retraining can be employed. Paragraph 0084, The predictive biologic insight AI model is trained on a large variety of existing and custom labeled data sets. Custom labeled data sets can be derived from simulated conditions such as first-person shooter (FPS) video game inputs and flight simulators. These custom data sets can be used on separate models, or to finetune the predictive biologic insight AI model. Supervised learning can be adjusted to correct for the feature extraction. This provides a trained model that provides information such as: fight or flight response, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level, and/or applications. Paragraph 0104, the feedback can be provided to a separate automated targeting system to help it improve (e.g., continuous training via use of the system).
Consider claim 16. Murray et al further teaches The lens control method of claim 12, further comprising a data collection step of generating a dataset comprising pieces of information on an image, a natural language tag, a saliency ranking label, and a salient object boundary label. (paragraph 0084, The predictive biologic insight AI model is trained on a large variety of existing and custom labeled data sets. Custom labeled data sets can be derived from simulated conditions such as first-person shooter (FPS) video game inputs and flight simulators. These custom data sets can be used on separate models, or to finetune the predictive biologic insight AI model. Supervised learning can be adjusted to correct for the feature extraction. This provides a trained model that provides information such as: fight or flight response, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level, and/or applications,).
Consider claim 17. Murray et al further teaches The lens control method of claim 12, further comprising a data collection step of generating a dataset a size of which is increased, by repeating a process of auto-labeling a new dataset that is not labeled, re-training a model a dataset generated through auto-labeling, and auto-labeling new data by using the re-trained model. (paragraph 0009, AI training and retraining can be employed. Paragraph 0084, The predictive biologic insight AI model is trained on a large variety of existing and custom labeled data sets. Custom labeled data sets can be derived from simulated conditions such as first-person shooter (FPS) video game inputs and flight simulators. These custom data sets can be used on separate models, or to finetune the predictive biologic insight AI model. Supervised learning can be adjusted to correct for the feature extraction. This provides a trained model that provides information such as: fight or flight response, emotion, tiredness, ocular fatigue/eye strain, cognitive load, attention, stress level, and/or applications. Paragraph 0104, the feedback can be provided to a separate automated targeting system to help it improve (e.g., continuous training via use of the system).
Claim(s) 2, 4, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murray et al (pub # 20250054423) in view of Attia et al (WO 2025/166241 A1) in view of Bharaj et al (U.S. Pat # 12,277,753) and further in view of Madruga et al (U.S. Pat # 11,670,060).
Consider claims 2 and 18. Murray et al in in view of Attia et al does not specifically disclose The lens control device of claim 1, wherein the gaze determination processor comprises: a common sense model training unit configured to train the common sense model which determines a priority of a gaze based on common sense; and a preference model training unit configured to train the preference model that aligns results determined by the common sense model by incorporating user preference.
In an analogous art Bharaj et al teaches a common sense model training unit configured to train a common sense model that determines priority of a gaze on the basis of common sense. (col. 6 lines 42-45, Embodiments of the present disclosure include techniques for discovering and reducing bias in training datasets for training machine-learning models, including a common-sense bias discovery method).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Bharaj et al with the system and method of Murray et al in view of Attia et al so as to improve the functioning of the computer system, result in more efficient training of machine-learning models with reduced bias, and facilitate identification/retraining of biased machine-learning models (Bharaj et al col. 3 lines 13-16).
Murray et al in view of Attia et al and further in view of Bharaj et al does not specifically disclose and a preference model training unit configured to train a preference model that aligns results determined by the common sense model by incorporating user preference.
In an analogous art Madruga et al teaches and a preference model training unit configured to train a preference model that aligns results determined by the common sense model by incorporating user preference. (col. 11, lines 19-22, Model training module 526 may be configured to train a machine learning (ML) model to determine the user preferences. The ML model may be used to analyze user media and/or other information to determine the user preferences.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Madruga et al with the system and method of Murray et al in view of Attia et al and further in view of Bharaj et al so as to provide a more personal and individualized user experience by adjusting the focus of the lens based on the user’s own personal preferences.
Consider claim 4. Bharaj et al further teaches The lens control device of claim 2, wherein the common sense model training unit comprises: an image encoder configured to encode an image; and a text encoder configured to encode text. (col. 7 lines 45-50, In some embodiments, the machine-learning model can be an encoder model such as a pretrained Universal Sentence Encoder (USE) and Contrastive Language-Image Pretraining (CLIP) model, but it should be appreciated that any model that can encode text into an embedding can be used.).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murray et al (pub # 20250054423) in view of Attia et al (WO 2025/166241 A1) and further in view of Ajagbusi et al (pub # 20240377882).
Consider claim 5. Murray et al in view of Attia et al does not specifically disclose The lens control device of claim 1, wherein the controller comprises a focus matching determination unit having an algorithm that determines whether a result value, which is determined by using a point at which an eye tracker tracks a gaze as a reference point and setting 50 pixels around the reference point as a threshold, is identical with a gaze tracking result value of the eye tracker.
In an analogous art Ajagbusi et al teaches wherein the controller comprises a focus matching determination unit having an algorithm that determines whether a result value, which is determined by using a point at which an eye tracker tracks a gaze as a reference point and setting 50 pixels around the reference point as a threshold, is identical with a gaze tracking result value of the eye tracker. (paragraph 0086, For pixel-basis comparison, the comparison can consider the number of matched pixels and mismatched pixels. The comparison can output all pixel coordinates between the images that differ. In embodiments, for pixel-based comparison, images can be formatted into a same/similar resolution with a same/similar image center point. A match percentage threshold can be implemented for determining whether there is a match between the current gaze image and the reference gaze image. For example, a pixel match percentage threshold can be implemented indicating that if 50% or more pixels match between the current gaze image and the reference gaze image, a match is determined.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Ajagbusi et al with the system and method of Murray et al in view of Attia et al in order to improve the technical field of gaze tracking and device management by improving the accuracy of gaze identification, providing automated manners for control, improving usability of gaze identification systems, and improving the speed at which gaze identification can be completed (Ajagbusi et al paragraph 0021).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murray et al (pub # 20250054423) in view of Attia et al (WO 2025/166241 A1) and further in view of Keyvanara et al (pub # 20250124892).
Consider claims 7 and 14. Murray et al in view of Attia et al does not specifically disclose The smart variable-focus lens of claim 6, wherein the data collection unit is configured to collect data in various environments, label importance by using an eye tracking camera, label a natural language comprising ranking information and category information, label saliency ranking, and auto-label new dataset that is not labeled.
In an analogous art Keyvanara et al teaches wherein the data collection unit is configured to collect data in various environments, label importance by using an eye tracking camera, label a natural language comprising ranking information and category information, label saliency ranking, and auto-label new dataset that is not labeled. (paragraph 0074, In examples where the point of focus is a predicted salient region of an image to be outputted as visual output, a trained neural network may be used to implement a saliency detection module (e.g., the saliency detection module 320 of FIG. 4). The saliency detection module may detect one or more highest saliency regions of the visual output. The saliency detection module may output the bounding box (or other location indicator) of each detected salient region, and optionally may include a saliency score or saliency ranking with each detected salient region. In some examples only one most salient region (e.g., the salient region with highest saliency score or highest saliency ranking) may be determined to be the point of focus. In other examples, there may be multiple points of focus corresponding to multiple highly salient regions (e.g., top five salient regions).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Keyvanara et al with the system and method of Murray et al in view of Attia et al in order to provide an accurate method of determining a point of gaze of the user.
Claim(s) 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murray et al (pub # 20250054423) in view of Attia et al (WO 2025/166241 A1) and further in view of Bharaj et al (U.S. Pat # 12,277,753).
Consider claims 8 and 15. Murray et al in view of Attia et al does not specifically disclose The smart variable-focus lens of claim 6, wherein the data collection unit is further configured to collect the data so that a bias does not occur by differentiating one or more of a race, gender, an age, and culture of a test subject.
In an analogous art Bharaj et al teaches wherein the data collection unit collects the data so that a bias does not occur by differentiating one or more of a race, gender, an age, and culture of a test subject. (col. 9 lines 29-45, At block 210, the system receives a user input identifying at least one biased correlation from the one or more correlations. For example, the system can display the one or more correlations between the plurality of visual features in the training image dataset (e.g., text descriptions of each correlation), and the user can indicate whether each of the one or more correlations are benign or not. Benign correlation refers to a correlation between two features that are naturally connected, such as “teeth visible” with “a smile”, and most facial features with “the face”. These correlations are usually robust and generalizable, and the user can identify them as benign. In some embodiments, instead of presenting all the correlations identified in block 208, the system displays only a subset of the identified correlations, such as sensitive correlations that may affect the target task (e.g., race/gender/disability/sexuality). Thus, the human-in-the-loop component allows flexibility and transparency for bias mitigation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and method of Bharaj et al with the system and method of Murray et al in order to reduce bias in a training image dataset for training a machine-learning model (Bharaj et al col. 2 lines 15-17).
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.
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/CHAYCE R BIBBEE/Examiner, Art Unit 2624
