DETAILED ACTION
This Office action is responsive to the claims filed on June 29, 2023. Claims 1-13 are pending.
Claims 1-13 are rejected under 35 USC 101 as ineligible.
Claims 1-13 are rejected under 35 USC 103 as unpatentable over El in view of Hoyle.
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 .
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Specifically, the Applicant’s claims receive data as input, determine other data based on the input, and then output the other determined data. The Applicant’s claims are analogous to the claims in Electric Power Group, which were found to be ineligible. With respect to the broad independent claim 1, the determinations of parameter values for glass frame fitting based on data, as expressed in claim 1, is a longstanding practice as demonstrated in these longstanding references: FR-2663528-A3; US-20010026351-A1; US-6535223-B1; US-20040189935-A1; WO-2006079540-A1. Further, the FR-2663528-A3 reference from the 90’s demonstrates an example of the methods being conducted without the aid of a computer. As determined in Recentive, automating an abstract idea with generic computing equipment to improve speed or efficiency does not confer eligibility. Further still, the use of image data to determine parameters such as pupillary distance (PD) and fitting height (FH), as well as the determination of any parameters from 3D-image data was well-understood, routine, and conventional (WURC) activity at the time of filing, as is demonstrated in the following deluge of references of record: US-1981439-A; US-3740857-A; US-9091867-B1; US-9395562-B1; US-11642018-B1; US-11768378-B1; US-6535223-B1; FR-2663528-A3; WO-2006079540-A1; WO-2013045531-A1; EP-2772795-A1; CA-3068948-A1; KR-20210100102-A; DE-102020131580-B3; EP-4006628-A1; WO-2021122826-A1; US-20040189935-A1; US-20110267578-A; US-20160054594-A1; US-20150293382-A1; US-20160299360-A1; US-20170168323-A1; US-20170336654-A1; US-20180024385-A1; US-20180164609-A1; US-20180164610-A1; US-20190011731-A1; US-20190033624-A1; US-20210012525-A1; US-20220163822-A1; US-20220397389-A1; US-20230046950-A1; US-20240069366-A1; US-20240061278-A1; US-20240045229-A1; US-20230221585-A1; US-20230020160-A1; US-20220035182-A1; US-20200292307-A1; US-20200333635-A1; US-20200211218-A1; US-20200233239-A1; US-20170269384-A1; US-20010026351-A1; US-20190339546-A1; US-20230093044-A1; US-20220113563-A1; US-20170371178-A1. Accordingly, claims 1-13 are ineligible.
Independent Claim
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process, which is an abstract idea.
Claim 1 recites (Claim language in bold italic):
A […] method […] for determining lens fitting parameters used for manufacturing a customized visual defect correction device for a customer, the customized visual defect correction device comprising a selected frame and customized lenses inserted in the selected frame, and the lens fitting parameters for the customer comprising a pupillary distance (PD) of the customer and a fitting height (FH) for the customer, the method comprising:
[…]
performing a FH determination method for determining the FH for the customer, the FH determination method depending on and using the retrieved historical data; and
Determinations of data based on other data are practically performable in the mind or with the aid of pen and paper, so they are evaluations, mental processes, abstract ideas.
Claim 1 recites abstract ideas.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
The Additional limitations:
[…] computer implemented […]
[…] using a computer system […]
These elements recite generic computing components/code at a high level and, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application at Step 2A, Prong 2
[…]
retrieving, from a provisioning entity, the PD of the customer;
retrieving, from a database, historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, each historical data item being assigned to a previous customer for a previously customized visual defect correction device and forming a tuple comprising at least the PD, the FH, and at least one frame specific feature;
This is mere data gathering and, under MPEP 2106.05(g), is insignificant extra-solution activity. Under MPEP 2106.05(g), these limitations fail to integrate the abstract idea into practical at Step 2A, Prong 2.
providing and/or transmitting the FH for the customer and the PD of the customer as the lens fitting parameters to a manufacturing facility for manufacturing the customized visual defect correction device for the customer.
This is post-solution insignificant extra-solution activity similar to the 2106.05(g) examples: “e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display” “ii. Printing or downloading generated menus.” This, under MPEP 2106.05(g), is insignificant extra-solution activity. Under MPEP 2106.05(g), these limitations fail to integrate the abstract idea into practical at Step 2A, Prong 2.
Any specific details about the parameters the data recited represent, the parameters merely limit the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to integrate the abstract idea into practical at Step 2A, Prong 2.
Claim 1 fails to provide any additional limitations that integrate the abstract idea into a practical application.
Claim 1 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
The Additional limitations:
[…] computer implemented […]
[…] using a computer system […]
These elements recite generic computing components/code at a high level of generality and, under MPEP 2106.05(f), fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
retrieving, from a provisioning entity, the PD of the customer;
retrieving, from a database, historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, each historical data item being assigned to a previous customer for a previously customized visual defect correction device and forming a tuple comprising at least the PD, the FH, and at least one frame specific feature;
providing and/or transmitting the FH for the customer and the PD of the customer as the lens fitting parameters to a manufacturing facility for manufacturing the customized visual defect correction device for the customer.
These are well-understood, routine, and conventional activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network,” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “i. Determining the level of a biomarker in blood by any means” (sensors) “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because these are WURC and, as previously demonstrated, insignificant extra-solution activity, under MPEP 2106.05(d) and MPEP 2106.05(g), the steps fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
Any specific details about the parameters the data recited represent, the parameters merely limit the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
The additional limitations of claim 1 fails to combine with the other elements of their respective claims to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Claim 1 is ineligible.
Dependent Claims
The dependent claims fail to provide any additional limitations that would confer eligibility at Step 2A, Prong 2 and Step 2B.
NOTE: For all of the dependent claims, the parameters the data represents merely limit the abstract idea to a particular technological field and fail to confer eligibility under MPEP 2106.05(g). Also, all recited computing elements or the use thereof are recited at a high level of generality and represent generic computing processes, so, under MPEP 2106.05(f), these fail to confer eligibility.
Claim 2
further comprising compiling and providing the database with the historical data.
This is mere data gathering, so it is insignificant extra-solution activity and WURC for the same reasons as the retrieving steps pf claim 1.
Claim 2 fails to provide any additional limitations that confer eligibility.
Claim 2 is ineligible.
Claim 3
wherein at least one frame specific feature is chosen from a group comprising at least: frame ID, gender, frame type, frame color, frame size, frame width, frame height, frame depth, lens form, lens type, bridge attribute, bridge height, pantoscopic angle, wrap angle, nose pad, frame geometry, product images, or technical information of the frame.
This merely describes the data gathering, so they it is an element of the retrieving of claim 1 and fails to confer eligibility for at least the same reasons.
Also, the quantities the data represents merely limits the abstract idea to a particular field and fails to confer eligibility under MPEP 2106.05(h).
Claim 3 fails to provide any additional limitations that confer eligibility.
Claim 3 is ineligible.
Claim 4
further comprising clustering the historical data, the clustering comprising a grouping and a classification and/or any combination and order of grouping and classification of the historical data.
Grouping data by classification is insignificant, extra-solution activity (e.g., similar to MPEP 2106.05(g) examples: “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display,” “i. Limiting a database index to XML tags,) and are WURC (e.g., similar to MPEP 2106.05(d) examples: “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”
Also, the quantities the data represents merely limits the abstract idea to a particular field and fails to confer eligibility under MPEP 2106.05(h).
Claim 4 fails to provide any additional limitations that confer eligibility.
Claim 4 is ineligible.
Claim 5
wherein the FH determination method is performed on a selected cluster of the historical data, the cluster of historical data being selected based on one or more frame features of the selected frame.
This is mere data gathering for the data used in the determination, so it fails to confer eligibility for at least the same reasons as the retrieving steps.
This is also an element of the determination, so it is part of the abstract idea itself.
Also, the quantities the data represents merely limits the abstract idea to a particular field and fails to confer eligibility under MPEP 2106.05(h).
Claim 5 fails to provide any additional limitations that confer eligibility.
Claim 5 is ineligible.
Claim 6
further comprising binning the historical data into a plurality of bins, each bin being assigned to a PD value, the PD value being a discrete value.
Grouping data by classification is insignificant, extra-solution activity (e.g., similar to MPEP 2106.05(g) examples: “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display,” “i. Limiting a database index to XML tags,) and are WURC (e.g., similar to MPEP 2106.05(d) examples: “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”
Also, the quantities the data represents merely limits the abstract idea to a particular field and fails to confer eligibility under MPEP 2106.05(h).
Claim 6 fails to provide any additional limitations that confer eligibility.
Claim 6 is ineligible.
Claim 7
further comprising matching frame features of the selected frame and/or patient features of the customer to one or more of the historical data items.
Matching data is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.
Claim 7 fails to provide any additional limitations that confer eligibility.
Claim 7 is ineligible.
Claim 8
wherein performing the FH determination method comprises using the FH and at least one frame feature from the retrieved historical data, and utilizing a frequency distribution of the FH in the retrieved historical data to determine the FH for the customer from the at least one frame feature of the selected frame.
Making a determination based on data/frames, determining a frequency distribution of data, and making a determination based on a determined frequency distribution of data are all practically performable in the mind or with the aid of pen and paper.
Claim 8 fails to provide any additional limitations that confer eligibility.
Claim 8 is ineligible.
Claim 9
wherein performing the FH determination method comprises using a relationship between the PD and the FH, and at least one frame feature from the retrieved historical data, and utilizing a frequency distribution of the FH in the retrieved historical data to determine the FH for the customer from at least one frame feature of the selected frame and the PD of the customer.
Making a determination based on data/frames, determining a frequency distribution of data, and making a determination based on a determined frequency distribution of data, evaluating relationships, and making determinations based on these relationships are all practically performable in the mind or with the aid of pen and paper.
Claim 9 fails to provide any additional limitations that confer eligibility.
Claim 9 is ineligible.
Claim 10
wherein performing the FH determination method comprises using a relationship between the PD and the FH, and at least one frame feature from the retrieved historical data, and […] determine the FH for the customer from at least one frame feature of the selected frame and the PD of the customer.
Making determinations, evaluating relationships, determining distributions, and making determinations based on these elements are practically performable in the mind or with the aid of pen and paper.
[…] using a trained machine learning model to […]
The machine learning model is a generic computing element recited at a high level, so it fails to confer eligibility under MPEP 2106.05(f).
Claim 10 fails to provide any additional limitations that confer eligibility.
Claim 10 is ineligible.
Claim 11
wherein performing the FH determination method comprises using at least one further feature of the customer, the at least one further feature including at least one of a video of the customer, a 3D scan of the customer, an age of the customer, a gender of the customer and/or a nose form of the customer.
This is mere data gathering for the data used in the determination, so it fails to confer eligibility for at least the same reasons as the retrieving steps.
This is also an element of the determination, so it is part of the abstract idea itself.
Also, the quantities the data represents merely limits the abstract idea to a particular field and fails to confer eligibility under MPEP 2106.05(h).
Claim 11 fails to provide any additional limitations that confer eligibility.
Claim 11 is ineligible.
Claim 12
A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed by one or more controllers, facilitate carrying out.
This merely recites a generic computer implementation recited at a high level, so it fails to confer eligibility under MPEP 2106.05(f)
a method according to claim 1
See the eligibility analysis of claim 1, which demonstrates claim 1 lacks an additional limitation that confers eligibility.
Claim 12 fails to provide any additional limitations that confer eligibility.
Claim 12 is ineligible.
Claim 13
A system comprising a database […] and at least one computing unit being connected to the database and being configured for carrying out
This merely recites a generic computer implementation recited at a high level, so it fails to confer eligibility under MPEP 2106.05(f)
with historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, each historical data item being assigned to a previous customer for a previously customized visual defect correction device and forming a tuple comprising at least a PD, a FH, and at least one frame specific feature,
This is mere data gathering for the data used in the determination, so it fails to confer eligibility for at least the same reasons as the retrieving steps.
This is also an element of the determination, so it is part of the abstract idea itself.
Also, the quantities the data represents merely limits the abstract idea to a particular field and fails to confer eligibility under MPEP 2106.05(h).
a method according to claim 1.
See the eligibility analysis of claim 1, which demonstrates claim 1 lacks an additional limitation that confers eligibility.
Claim 13 fails to provide any additional limitations that confer eligibility.
Claim 13 is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13 : El and Hoyle
Claim(s) 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0339546 A1 to El-Hajal et al. (El) in view of US 2023/0093044 A1 to Hoyle et al. (Hoyle).
Regarding claim 1, El teaches:
A computer implemented method, using a computer system, (El [0019] “The diagnostic information obtained from the eye exam is used to generate prescription data 16. The prescription data 16 is typically stored in the computer system 18 of the optician. This information may be stored on site at the optician's office.” [0032] “See Block 59. The goods of the online retailer 58 are viewed using a computing device 60, such as a laptop or smart phone. The new frames 56 are viewed via data transmission through a data network 24. Accordingly, the user 10 cannot physically touch and try on the selected new frames 56. The user 10 selects the new frames 56 using the computing device 60, See Block 62. If the new frames 56 are to be a part of customized prescription eyewear, then the online retailer 58 needs to fabricate the eyewear to the requirements of the user 10. In order to create a proper fabrication, the online retailer 58 needs information, both about the new frames 56 selected and about the user 10 who selected the frames. Once a user 10 selects the new frames 56 online, the user 10 will be prompted to input information that the online retailer 58 will need to reference relevant data for the user 10. See Block 64.” – Computers and databases, including the online retailer, which is a computer-based entity.)
for determining lens fitting parameters used for manufacturing a customized visual defect correction device for a customer, the customized visual defect correction device comprising a selected frame and customized lenses inserted in the selected frame, (El [0032] “The goods of the online retailer 58 are viewed using a computing device 60, such as a laptop or smart phone. The new frames 56 are viewed via data transmission through a data network 24. Accordingly, the user 10 cannot physically touch and try on the selected new frames 56. The user 10 selects the new frames 56 using the computing device 60, See Block 62. If the new frames 56 are to be a part of customized prescription eyewear, then the online retailer 58 needs to fabricate the eyewear to the requirements of the user 10. In order to create a proper fabrication, the online retailer 58 needs information, both about the new frames 56 selected and about the user 10 who selected the frames. Once a user 10 selects the new frames 56 online, the user 10 will be prompted to input information that the online retailer 58 will need to reference relevant data for the user 10. See Block 64.” [0036] “The online retailer 58 knows the dimensions of the selected new frames 56. What the online retailer 58 does not know is how the new frames 56 will sit on the user's face and how the resting position of the new frames 56 may require modifications to the prescription data 16. In order to find this information, the online retailer 58 references the images 44 from the cloud database 22. See Block 72. The images 44 are used to obtain the needed measurements. See Block 74. From the images 44, the online retailer 58 can measure the distance between the nose pads 52 on the properly fitted eyewear. The online retailer 58 sets the nose pads on the selected new frames 56 to match.“ – Lens fitting parameters used for manufacturing are determined for a customer’s customized glasses, including a user-selected frame and prescription lenses.)
and the lens fitting parameters for the customer comprising a pupillary distance (PD) of the customer and a fitting height (FH) for the customer, the method comprising: (El [0030] “Other measurements that depend upon the anatomy of the person wearing the fitted frames 45 include pupil height “PH”, pupil distance “PD”, and rear vertex distance “RVD”. The pupil height “PH” is the measured height of the pupils above the bottom of the lens. The pupil distance “PD” is the distance between pupils in the horizontal plane.” – FH and PD.)
retrieving, from a provisioning entity, the PD of the customer; (El [0028] “Referring to Table 1 in conjunction with FIG. 3 and FIG. 4, it will be understood that each model and style of frames has variables that need to be known in order to customize lenses for the frames.” [0030] “Other measurements that depend upon the anatomy of the person wearing the fitted frames 45 include pupil height “PH”, pupil distance “PD”, and rear vertex distance “RVD”. The pupil height “PH” is the measured height of the pupils above the bottom of the lens. The pupil distance “PD” is the distance between pupils in the horizontal plane.” [0034] “Using the same data network 24, the online retailer 58 references the user's records from the cloud accessible database 22. See Block 68. The records contain the prescription data 16 of the user 10 and images 44 of the user wearing properly fitted eyewear.” – PD data of the customer are retrieved.)
retrieving, from a database, historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, and forming a tuple comprising at least the PD, the FH, and at least one frame specific feature; (El [0034] “Since the selected new frames 56 are part of the online user's inventory, the online retailer 58 may have the physical dimensions of the new frames 56. If not, those dimensions can be downloaded from the frame detail database 30 via the data network 24. Using the same data network 24, the online retailer 58 references the user's records from the cloud accessible database 22. See Block 68. The records contain the prescription data 16 of the user 10 and images 44 of the user wearing properly fitted eyewear.”
performing a FH determination method for determining the FH for the customer, ; and (El [0036] ‘If the distance is the same, the new frames 56 will hold the prescription lenses at the same position in front of the eyes. If there is a difference in the distances, it can be determined that the new frames 56 will hold the lenses either higher on the face or lower on the face, relative to the user's eyes. This differential in position is then used to alter the prescription data 16 so that the pupil height in the prescription data 16 is correct for the new frames 56.”
providing and/or transmitting the FH for the customer and the PD of the customer as the lens fitting parameters to a manufacturing facility for manufacturing the customized visual defect correction device for the customer. (El [0038] “Once all of the variables listed in Table A are calculated for the selected new frames 56, the prescription lenses for those new frames 56 are fabricated. See Block 82. The proper adjustments are made to the nose pads and to the temples. The result is a custom fit set of prescription eyewear that fits as well as the original frames fitted by the optician.” – The parameters, including FH and PD, are provided to the manufacturer, and the glasses are fabricated.)
El teaches using fitting data to determine the specifications of the frames and lenses, but does not appear to explicitly teach, but El in view of Hoyle et al. teaches:
retrieving, from a database, historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, each historical data item being assigned to a previous customer for a previously customized visual defect correction device and forming a tuple comprising at least the PD, the FH, and at least one frame specific feature; (Hoyle [0024] “A head data cluster, correspondingly, refers to a group where head data is grouped together based on a similarity criterion, such that similar-looking heads are grouped together in the same cluster. In case of the compressed head data, a head cluster refers to a group of head data grouped together based on similarity criteria provided by the respective compression method.”
performing a FH determination method for determining the FH for the customer, the FH determination method depending on and using the retrieved historical data; (Hoyle [0025] “A mapping links head data clusters to frame data clusters. The mapping may indicate a probability that a frame represented by frame data of the frame data cluster is suitable for a head represented by head data of the head data cluster. Through mapping of the head and frame clusters, the present disclosure enables frame recommendation and selection to be made without the need to provide, predict, or confirm detailed information about the person or the frames.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generic frame fitting determinations of El by the specific frame fitting methods of Hoyle because the person of ordinary skill in the art would be motivated, based on the aim of El to fit frames and lenses to a customer online without visiting the retailer, to look to Hoyle, which recommends frames that have a good probability of anatomically fitting the person. (El [0007] “A need therefore exists for a system and method that can be used to determine the measurements needed to accurately fabricate prescription eyewear selected by a user online, wherein the user is not directly being fitted for the eyewear selected. This need is met by the present invention as described and claimed below.” [0018] “The present invention is a system and method that is used to purchase custom fit eyewear online or in some other manner where the purchaser is unavailable for a fitting.”; Hoyle [0014] “No prior knowledge about the person, for example previous frames used, should be required, such that the methods and devices are also applicable if a frame is bought for the first time by the respective person. The recommended frames should have a good probability of anatomically fitting the person.”)
Claim 2
Regarding claim 2, El in view of Hoyle teaches the features of claim 1 and further teaches:
further comprising compiling and providing the database with the historical data. (Hoyle [0024] “Head data refers to data characterizing a head of a person, in particular the face thereof. […] A head data cluster, correspondingly, refers to a group where head data is grouped together based on a similarity criterion, such that similar-looking heads are grouped together in the same cluster. In case of the compressed head data, a head cluster refers to a group of head data grouped together based on similarity criteria provided by the respective compression method.” [0045]-[0049] “Similarly, providing a plurality of head data clusters typically comprises: providing head data for a plurality of heads, compressing the head data, and clustering the compressed head data based on a similarity criterion to provide the head data clusters. The providing, compressing and clustering for the head data may be performed as explained above for the frame data. As head data, 2D images are typical, as in later use 2D images of a head of a person can be easily taken and associated with a cluster, as will be described below when methods of using such a frame selection device will be discussed.” – Historical data is provided to a database and compiled.)
Claim 3
Regarding claim 3, El in view of Hoyle teaches the features of claim 1 and further teaches:
wherein at least one frame specific feature is chosen from a group comprising at least: frame ID, gender, frame type, frame color, frame size, frame width, frame height, frame depth, lens form, lens type, bridge attribute, bridge height, pantoscopic angle, wrap angle, nose pad, frame geometry, product images, or technical information of the frame. (El [0021] “Once the user 10 selects frames, the selected frames are checked to ensure that they are properly sized for the user's head and that the frames can hold the proper prescription lenses. See Block 28. Once the frames are selected and verified, the frame's physical specifications are obtained. See Block 29. These specifications are needed to properly grind and fit the prescription lenses to the selected frames. The frame's physical specifications can be measured from the frame or can be downloaded from a frame detail database 30 provided online by the frame's manufacturer or distributor.” – Frame-specific features.)
Claim 4
Regarding claim 4, El in view of Hoyle teaches the features of claim 1 and further teaches:
further comprising clustering the historical data, the clustering comprising a grouping and a classification and/or any combination and order of grouping and classification of the historical data. (Hoyle [0024] “Head data refers to data characterizing a head of a person, in particular the face thereof. […] A head data cluster, correspondingly, refers to a group where head data is grouped together based on a similarity criterion, such that similar-looking heads are grouped together in the same cluster. In case of the compressed head data, a head cluster refers to a group of head data grouped together based on similarity criteria provided by the respective compression method.” [0045]-[0049] “Similarly, providing a plurality of head data clusters typically comprises: providing head data for a plurality of heads, compressing the head data, and clustering the compressed head data based on a similarity criterion to provide the head data clusters. The providing, compressing and clustering for the head data may be performed as explained above for the frame data. As head data, 2D images are typical, as in later use 2D images of a head of a person can be easily taken and associated with a cluster, as will be described below when methods of using such a frame selection device will be discussed.” – Historical data is clustered, grouped, and classified.)
Claim 5
Regarding claim 5, El in view of Hoyle teaches the features of claim 4, and further teaches:
wherein the FH determination method is performed on a selected cluster of the historical data, the cluster of historical data being selected based on one or more frame features of the selected frame. (Hoyle [0025] “A mapping links head data clusters to frame data clusters. The mapping may indicate a probability that a frame represented by frame data of the frame data cluster is suitable for a head represented by head data of the head data cluster. Through mapping of the head and frame clusters, the present disclosure enables frame recommendation and selection to be made without the need to provide, predict, or confirm detailed information about the person or the frames. Possibilities for establishing such a mapping will be discussed further below.” – Relevant head cluster data is selected for the frame fitting determinations based on the frame.)
Claim 6
Regarding claim 6, El in view of Hoyle teaches the features of claim 1 and further teaches:
a PD value, the PD value being a discrete value. (El [0030] “Other measurements that depend upon the anatomy of the person wearing the fitted frames 45 include pupil height “PH”, pupil distance “PD”, and rear vertex distance “RVD”. The pupil height “PH” is the measured height of the pupils above the bottom of the lens. The pupil distance “PD” is the distance between pupils in the horizontal plane.” – PD is a value used to characterize fitting data and based on which fitting determinations are made.)
further comprising binning the historical data into a plurality of bins, each bin being assigned to a PD value, the PD value being a discrete value. (Hoyle [0023] A cluster, as used herein, refers to a group of items grouped together based on a similarity criterion. For example, a frame data cluster refers to a group of frame data from different frames grouped together based on the similarity of the frames to each other. Examples of such a similarity criterion will be discussed below. In case of the compressed head data, a cluster refers to a group of head data grouped together based on similarity criteria provided by the respective compression method. Application of clustering enables recommendation and selection of the frames which fit to the person's head to be done without extensive measurement of or prediction of detailed information about the head or frames.” – Head data is clustered based on relevant characteristics, for example, the PD of the El reference.)
Claim 7
Regarding claim 7, El in view of Hoyle teaches the features of claim 1 and further teaches:
further comprising matching frame features of the selected frame and/or patient features of the customer to one or more of the historical data items. (Hoyle [0025] “A mapping links head data clusters to frame data clusters. The mapping may indicate a probability that a frame represented by frame data of the frame data cluster is suitable for a head represented by head data of the head data cluster. Through mapping of the head and frame clusters, the present disclosure enables frame recommendation and selection to be made without the need to provide, predict, or confirm detailed information about the person or the frames. Possibilities for establishing such a mapping will be discussed further below.” – Frame features and patient features are matched to the historical data.)
Claim 8
Regarding claim 8, El in view of Hoyle teaches the features of claim 1 and further teaches:
wherein performing the FH determination method comprises using the FH and at least one frame feature from the retrieved historical data, and utilizing a frequency distribution of the FH in the retrieved historical data to determine the FH for the customer from the at least one frame feature of the selected frame. (Hoyle [0050] “Providing the mapping between the head data clusters to the frame data clusters may comprise assigning estimated probabilities or probability distributions for pairs of head data cluster and frame data cluster. “Estimated” indicates that these usually are not based on exact measurements. The probabilities or probability distributions indicate a likelihood that a frame from a respective frame data cluster is suitable for a head of a respective head data cluster. For example, if head data clusters A, B, C and frame data clusters 1, 2, 3 are given, p(A,1) would indicate a probability or probability distribution that a frame from frame data cluster 1 is suitable for a head of head data cluster A. Similar probabilities or probability distributions may be assigned to other pairs of head data clusters and frame data clusters, i.e., p(A,2), p(A,3), p(B,1) etc. It should be noted that using three head data clusters and three frame data clusters in the example above is only for illustration purposes, and depending on the amount of head data and frame data available different numbers of clusters may be provided.” – In an embodiment, a frequency distribution of the data for a feature (including the FH from El) is used to determine the value of the feature for the customer’s selected frame.)
Claim 9
Regarding claim 9, El in view of Hoyle teaches the features of claim 1 and further teaches:
wherein performing the FH determination method comprises using a relationship between the PD and the FH, and at least one frame feature from the retrieved historical data, and utilizing a frequency distribution of the FH in the retrieved historical data to determine the FH for the customer from at least one frame feature of the selected frame and the PD of the customer. (Hoyle [0050] “Providing the mapping between the head data clusters to the frame data clusters may comprise assigning estimated probabilities or probability distributions for pairs of head data cluster and frame data cluster. “Estimated” indicates that these usually are not based on exact measurements. The probabilities or probability distributions indicate a likelihood that a frame from a respective frame data cluster is suitable for a head of a respective head data cluster. For example, if head data clusters A, B, C and frame data clusters 1, 2, 3 are given, p(A,1) would indicate a probability or probability distribution that a frame from frame data cluster 1 is suitable for a head of head data cluster A. Similar probabilities or probability distributions may be assigned to other pairs of head data clusters and frame data clusters, i.e., p(A,2), p(A,3), p(B,1) etc. It should be noted that using three head data clusters and three frame data clusters in the example above is only for illustration purposes, and depending on the amount of head data and frame data available different numbers of clusters may be provided.” – In an embodiment, a frequency distribution of the data for a feature (including the FH from El) is used to determine the value of the feature for the customer’s selected frame, for example, by a conditional probability with another feature (such as PD from El).)
Claim 10
Regarding claim 10, El in view of Hoyle teaches the features of claim 1 and further teaches:
wherein performing the FH determination method comprises using a relationship between the PD and the FH, and at least one frame feature from the retrieved historical data, and using a trained machine learning model to determine the FH for the customer from at least one frame feature of the selected frame and the PD of the customer. (Hoyle [0050] “Providing the mapping between the head data clusters to the frame data clusters may comprise assigning estimated probabilities or probability distributions for pairs of head data cluster and frame data cluster. “Estimated” indicates that these usually are not based on exact measurements. The probabilities or probability distributions indicate a likelihood that a frame from a respective frame data cluster is suitable for a head of a respective head data cluster. For example, if head data clusters A, B, C and frame data clusters 1, 2, 3 are given, p(A,1) would indicate a probability or probability distribution that a frame from frame data cluster 1 is suitable for a head of head data cluster A. Similar probabilities or probability distributions may be assigned to other pairs of head data clusters and frame data clusters, i.e., p(A,2), p(A,3), p(B,1) etc. It should be noted that using three head data clusters and three frame data clusters in the example above is only for illustration purposes, and depending on the amount of head data and frame data available different numbers of clusters may be provided.” – In an embodiment, a frequency distribution of the data for a feature (including the FH from El) is used to determine the value of the feature for the customer’s selected frame, for example, by a conditional probability with another feature (such as PD from El). [0105]-[0106] “Frame data or head data 40 is provided to a convolutional neural network including layers 41-45. It should be noted that layers 41-45 serve merely as an example, and more layers may be provided. Layer 41 serves as an input layer. From layers 41-43, a spatial compression of the data is performed, i.e., the data is represented by less and less numerical values from layer to layer. Layer 43, where a maximum compression is present, is also sometimes referred to as bottleneck. From layer 43 to an output layer 45, spatial expansion occurs, and layer 45 outputs reference frame/head data 46.” – The determination is made using a trained machine learning model.)
Claim 11
Regarding claim 11, El in view of Hoyle teaches the features of claim 1 and further teaches:
wherein performing the FH determination method comprises using at least one further feature of the customer, the at least one further feature including at least one of a video of the customer, a 3D scan of the customer, an age of the customer, a gender of the customer and/or a nose form of the customer. (El [0028] Table 1 “[…] DND – Distance between nose pad and contact and Datum […] NCP – Nose Contact Points“ [0028] “Once all of the variables listed in Table [1] are calculated for the selected new frames 56, the prescription lenses for those new frames 56 are fabricated. See Block 82. The proper adjustments are made to the nose pads and to the temples. The result is a custom fit set of prescription eyewear that fits as well as the original frames fitted by the optician.” – The nose form of the customer is considered in the determination.)
Claim 12
Regarding claim 12, El in view of Hoyle teaches the features of claim 1 and further teaches:
A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed by one or more controllers, facilitate carrying out a method according to claim 1. (Hoyle Claim 16 “ A device having a processor and instructions stored on a non-transitory storage medium, which, when carried out by the processor, cause execution of a method for selecting a frame for a person, the method comprising: providing head data of a person; identifying a head data cluster from a plurality of head data clusters based on the head data of the person; selecting a frame data cluster from a plurality of frame data clusters based on the identified head data cluster and a mapping between the plurality of head data clusters and the plurality of frame data clusters; and providing at least one selected frame based on the selected frame data cluster.“ – This teaches a CRM for the fitting determinations. See the rejection of claim 1 for the claim 12 features of claim 1.)
Claim 13
Regarding claim 12, El in view of Hoyle teaches the features of claim 1 and further teaches:
A system comprising a database with historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, each historical data item being assigned to a previous customer for a previously customized visual defect correction device and forming a tuple comprising [relevant features] , and at least one frame specific feature, and at least one computing unit being connected to the database and being configured for carrying out a method according to claim 1. (Hoyle [0081] “A system comprising a database with historical data, the historical data being gathered from one or more different data sources and consolidated in one or more historical data items, each historical data item being assigned to a previous customer for a previously customized visual defect correction device and forming a tuple comprising at least a PD, a FH, and at least one frame specific feature, and at least one computing unit being connected to the database and being configured for carrying out a method according to claim 1.” – A database for storing all of the data types and connected to all determining computers. [0023] A cluster, as used herein, refers to a group of items grouped together based on a similarity criterion. For example, a frame data cluster refers to a group of frame data from different frames grouped together based on the similarity of the frames to each other. Examples of such a similarity criterion will be discussed below. In case of the compressed head data, a cluster refers to a group of head data grouped together based on similarity criteria provided by the respective compression method. Application of clustering enables recommendation and selection of the frames which fit to the person's head to be done without extensive measurement of or prediction of detailed information about the head or frames.” – Head data is clustered based on relevant characteristics, for example, the PD of the El reference.
At least a PD, a FH (El [0030] “Other measurements that depend upon the anatomy of the person wearing the fitted frames 45 include pupil height “PH”, pupil distance “PD”, and rear vertex distance “RVD”. The pupil height “PH” is the measured height of the pupils above the bottom of the lens. The pupil distance “PD” is the distance between pupils in the horizontal plane.” – FH and PD as relevant parameters.)
For the elements of claim 1, see the rejection of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0113563 A1 to Baranton et al. (Teaches determining customized glasses for a patient)
US 2017/371178 A1 to Crespo et al. (Teaches determining customized glasses based on historical data)
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/J.M.W./Examiner, Art Unit 2188 /RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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