DETAILED ACTION
Response to Amendment
The Amendment filed 7/21/2025 has been entered. Claims 1-13, 15 and 17-20 remain pending in the application. Claim 14 and 16 were cancelled.
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, 15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Good (US 20220001556) in view of Ioannidis (US 20210216891).
Regarding claim 1, Good teaches a sensor-based method of analyzing shaving performance ("Sensor-based shaving systems and methods of analyzing a user's shave event"; Abstract):
collecting, by one or more processors, sensor data from one or more sensors of a shaving device having a blade, the sensor data collected during one or more shaving strokes of a user shaving with the shaving device (“The communication device may transmit shaving data and/or datasets from the sensor to a processor-based computing device (which may be on the handle and/or remote from the grooming device). The shaving data and/or dataset(s) may be analyzed by the processor-based computing device to determine relevant shave events, e.g. whole shaves or individual strokes”; Para. [007]);
determining, based on the sensor data, shave stroke data defining the one or more shaving strokes (“Generally, in various embodiments, unique, specific, and/or personalized threshold values, as implemented by a grooming device as described herein, may be generated to provide corresponding specific users with unique, specific, and/or personalized indications of stroke count, stroke direction or stroke pressure (load) for the purpose of reducing skin irritation”; Para. [007]);
inputting, into a model executing on the one or more processors (paragraph 0043), the shave stroke data and a threshold value to output from the model a user-specific shave score, wherein generation of the user-specific shave score comprises comparing the shave stroke data to the threshold value to determine a deviation from the threshold value (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores), data output (e.g., either raw data or processed data), and/or graphs of the data (e.g., either raw data or processed data)”; Para. [0035]); and
generating an output based on the user-specific shave score ("In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores), data output (e.g., either raw data or processed data), and/or graphs of the data (e.g., either raw data or processed data)”; Para. [0035]).
Good fails to teach wherein the output comprises an indication identifying an expected blade life of the blade based on the user-specific shave score, and wherein the output comprises initiating, based on the indication, a replacement blade for shipment to the user.
Ioannidis teaches a sensor-based method wherein the output comprises an indication identifying an expected blade life of the blade based on the user-specific shave score (“The contextual data collected from the one or more data sources may include shaving behaviors 110, shaving performance scores 120…”; Para. [0022] and “It should be noted that other machine learning algorithms may also be used to predict durability or usable life of shaving devices based on user data”; Para [0029]), a sensor-based method wherein the output comprises initiating, based on the indication, a replacement blade for shipment to the user (“Furthermore, before or during the time period associated with the most probable durability cluster, shaver monitoring application 400 may automatically connect to an electronic commerce (e-Commerce) server and place an order for a replacement blade/cartridge”; Para. [0038]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide the user with a predicted blade life, as taught in Ioannidis, to the sensor-based method taught by Good in order to improve the shaving experience of the user (Para. [0020] of Ioannidis).
Therefore, it would also have been obvious to one of ordinary skill in the art, prior to the effective filing date, to include an output that initiates a replacement blade for shipment, as taught by Ioannidis, to the sensor-based method taught by Good in order to “improve the user's shaving experience” (Para. [0038] of Ioannidis).
Regarding claim 2, modified Good further teaches the shave stroke data comprises: a stroke pressure, a count of the one or more shaving strokes taken with the blade, and a frequency of the one or more shaving strokes taken with the blade ("Generally, in various embodiments, unique, specific, and/or personalized threshold values, as implemented by a grooming device as described herein, may be generated to provide corresponding specific users with unique, specific, and/or personalized indications of stroke count, stroke direction or stroke pressure (load) for the purpose of reducing skin irritation”; Para. [007] and “The sensor-based shaving method of any one of aspects 1-4, wherein the unique threshold value is a load value, a shave count, a stroke count, a stroke direction, a stroke speed, a stroke frequency, a stroke distance, a stroke duration, a shave duration, a stroke location, a shave location, a temperature value, a device parameter, a hair parameter, or a skin parameter”; Para. [0098]).
Regarding claim 3, modified Good further teaches the shave stroke data comprises one or more of: a stroke pressure (paragraph 0007), a count of the one or more shaving strokes taken with the blade, a frequency of the one or more shaving strokes taken with the blade (paragraph 0098), a speed of the one or more shaving strokes taken with the blade (paragraph 0098), a number of shaving sessions during a blade life of the blade, an acceleration of the one or more shaving strokes taken with the blade (paragraph 0098), one or more stroke directions taken with the blade, a stroke length, a blade pivot angle, a rinse count, a rinse duration, a water temperature, and a razor temperature (“stroke direction”; Para. [0098]), a stroke length (“stroke distance”; Para. [0098]) and a temperature (“temperature value”; Para. [0098]).
Regarding claim 4, modified Good further teaches the shave stroke data comprises one or more of: stroke duration, stroke position, shave duration, duration between shaves, and duration between strokes (“stroke location”; Para. [0098]), shave duration (“shave duration”; Para. [0098]), and duration between strokes (“stroke frequency”; Para. [0098]).
Regarding claim 5, modified Good further teaches the threshold value for the user comprises one or more of: a universal threshold value, a user selected threshold value, and a unique threshold value for the user (“The present disclosure generally relates to sensor based shaving systems and methods, and more particularly to, sensor-based shaving systems and methods of analyzing a user's shave event for determining a unique threshold value of the user”; Para [001]).
Regarding claim 6, modified Good further teaches the threshold value is determined for the user by analyzing user input or device data comprising one or more of: a user rating of a shave event as reported by the user, a blade type, a cartridge type, a shaving preparation type, a style type, a style match result, a hair type, and a duration between shaves (“The sensor-based shaving method of any one of aspects 1-4, wherein the unique threshold value is a load value, a shave count, a stroke count, a stroke direction, a stroke speed, a stroke frequency, a stroke distance, a stroke duration, a shave duration, a stroke location, a shave location, a temperature value, a device parameter , a hair parameter, or a skin parameter”; Para [0098]).
Regarding claim 7, modified Good further teaches the user-specific shave score is based on one or more of: a quantity of the one or more user shaving strokes detected as having a value above or below the threshold value for the user, a magnitude of the one or more user shaving strokes detected above or below a threshold deviation from the threshold value for the user, and a time duration of the one or more user shaving strokes detected as having a value above or below the threshold value for the user (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data” and “The instructions may further cause processor 156 to provide, based on the comparison data, an indication 152 to indicate a deviation from the threshold value and to influence the user behavior”; Para [0035]).
Regarding claim 8, modified Good further teaches the output comprises one or more visual indicia (“While the embodiment of FIG. 1 illustrates one type of indication, an indication may comprise any one or more of a visual indicator, a light emitting diode (LED), a vibrator, or an audio indicator”; Para. [0035]).
Regarding claim 9, modified Good further teaches the output comprises rendering the user-specific shave score via a graphic user interface (GUI) on a display screen (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores), data output (e.g., either raw data or processed data), and/or graphs of the data (e.g., either raw data or processed data)”; Para. [0035]).
Regarding claim 10, modified Good further teaches collecting user-specific data comprising at least one of: (a) one or more user inputs, or (b) an initial dataset defining one or more initial shaving strokes of the user (“The sensor-based shaving method of any one of aspects 1-3, wherein the first dataset comprises data defining one or more shaving strokes, one or more shaving sessions, or one or more user inputs”; Para. [0097]); and configuring the model by adjusting or updating the model with the user-specific data of the user (“the sensor-based learning model, executing on the server, is able to accurately identify, based on shave data and/or datasets of a specific user, a unique threshold value designed for implementation on a grooming device to provide an indication to indicate a deviation from the threshold value and to influence the user behavior”; Para [0010]), wherein the output of the user-specific shave score is adjusted based on the user-specific data of the user (“the sensor-based learning model, executing on the server, is able to accurately identify, based on shave data and/or datasets of a specific user, a unique threshold value designed for implementation on a grooming device to provide an indication to indicate a deviation from the threshold value and to influence the user behavior”; Para. [0010]).
Regarding claim 11, modified Good further teaches generating, by the one or more processors, a user-specific electronic recommendation based on the user-specific shave score ("Live feedback and/or indicators may be provided the user via an indication, e.g., green light-emitting diode (LED) feedback when the user is applying pressure within or below a unique threshold value, or a red LED feedback when the user is applying pressure above the unique threshold value of the user"; Para. [005]).
Regarding claim 12, modified Good further teaches all elements of the current invention as set forth in claim 1 above.
Modified Good fails to teach wherein the user-specific electronic recommendation comprises a product recommendation for a manufactured product.
Ioannidis teaches a sensor-based method further comprising: wherein the user-specific electronic recommendation comprises a product recommendation for a manufactured product (“Such a consumer mode may only request basic user characteristics and/or provide guidance on how to determine certain user characteristics ( e.g., instructions on how to determine a skin type, hair thickness, user type, barrier function, etc.), and provide shaving device replacement recommendations and/or shaving tips or suggestions to the consumer based on the received user characteristics”; Para. [0032]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide shaving device replacement recommendations, as taught by Ioannidis, to the sensor-based shaving method in order to improve the user’s shaving experience (Para. [0020] of Ioannidis).
Regarding claim 13, modified Good further teaches wherein the shaving device is communicatively coupled to a user computing device having a display screen (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores), data output (e.g., either raw data or processed data), and/or graphs of the data (e.g., either raw data or processed data). Such display(s), GUI(s), or otherwise visualization(s) may be rendered or implemented via the app configured to execute on a user computer device (e.g., user computing device 111c1 as described herein)”; Para. [0035]), and wherein the sensor-based method further comprises rendering, by the one or more processors, the user-specific shave score on the display screen of the user computing device (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores), data output (e.g., either raw data or processed data), and/or graphs of the data (e.g., either raw data or processed data). Such display(s), GUI(s), or otherwise visualization(s) may be rendered or implemented via the app configured to execute on a user computer device (e.g., user computing device 111c1 as described herein)”; Para. [0035]).
Regarding claim 15, modified Good further teaches all elements of the current invention as set forth in claim 1 above.
Modified Good fails to teach the indication comprises an indication to update or replace the blade based on the expected blade life.
Ioannidis teaches a sensor-based method wherein the indication comprises an indication to update or replace the blade based on the expected blade life (“For example, upon receiving user data from a user, a computing device may use the trained machine learning model to determine the durability cluster of the user based on the user data. Based on the determined durability cluster, the computing device may provide a recommendation to the user regarding whether to keep or replace the razor blade or cartridge, and/or provide other useful information that improves the shaving experience” Para. [0020]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to include an indication to update or replace the blade based on the expected blade life, as taught by Ioannidis, to the sensor-based method taught by Good in order to improve the shaving experience (Para. [0020] of Ioannidis).
Regarding claim 17, modified Good further teaches activating one or more of: a motor, a visual indicator, a tactile indicator, or an audible indicator of the shaving device, when a high-load shaving stroke of the one or more shaving strokes is detected ("Live feedback and/or indicators may be provided the user via an indication, e.g., green light-emitting diode (LED) feedback when the user is applying pressure within or below a unique threshold value, or a red LED feedback when the user is applying pressure above the unique threshold value of the user"; Para. [005]), wherein the high-load shaving stroke comprises a pressure value above or below a threshold deviation from the threshold value (“Live feedback and/or indicators may be provided the user via an indication, e.g., green light-emitting diode (LED) feedback when the user is applying pressure within or below a unique threshold value, or a red LED feedback when the user is applying pressure above the unique threshold value of the user"; Para. [005]).
Regarding claim 18, modified Good further teaches the model is an artificial intelligence model (“The memorie(s) 106 may also store a sensor-based learning model 108, which may be an artificial intelligence based model, such as a machine learning model, trained on shave data or datasets, as described herein”; Para. [0043]), and wherein the sensor-based method further comprises training, by the one or more processors, the model with respective user-specific pressure data and respective shave stroke data determined during one or more training shaving strokes of a plurality of respective users as the respective users shave with a respective shaving device(“in some embodiments, a grooming device and/or a server to which the grooming device is communicatively connected, is improved where the intelligence or predictive ability of the server or grooming device is enhanced by a trained (e.g., machine learning trained) sensor-based learning model. In such embodiments, the sensor-based learning model, executing on the server, is able to accurately identify, based on shave data and/or datasets of a specific user, a unique threshold value designed for implementation on a grooming device to provide an indication to indicate a deviation from the threshold value and to influence the user behavior”; Para. [0010]), wherein the model is trained to output the user-specific shave score (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores)”; Para [0035]).
Regarding claim 19, Good teaches a sensor-based system configured to analyze shaving performance, the sensor-bases based system ("Sensor-based shaving systems and methods of analyzing a user's shave event"; Abstract) comprising:
a shaving device having a blade and comprising one or more sensors (“The grooming device can include a handle and a connecting structure for connecting a hair cutting implement (e.g., a razor blade). The grooming device can also comprise, or be associated with, a shave event sensor”; Para. [005]);
one or more processors communicatively coupled to the shaving device (“The sensor-based shaving system may further comprise a processor, configured onboard or offboard the grooming device”; Para [009]);
a memory communicatively coupled to the one or more processors (“the processor may further be configured to execute computing instructions stored on a memory communicatively coupled to the processor”; Para. [009]);
a model configured to execute on the one or more processors (“In such embodiments, the sensor-based learning model, executing on the server…”; Para. [0010] and (paragraph 0043)); and
computing instructions stored on the memory and that, when executed by the one or more processors, cause the one or more processors to (“the processor may further be configured to execute computing instructions stored on a memory communicatively coupled to the processor”; Para. [009]):
collect sensor data from the one or more sensors of the shaving device, the sensor data collected during one or more shaving strokes of a user shaving with the shaving device (“The instructions may cause the processor to collect a first dataset from the shave event sensor. The first dataset may comprise shave data defining the shave event”; Para. [009]);
determine, based on the sensor data, shave stroke data defining the one or more shaving strokes (“The instructions may cause the processor to collect a first dataset from the shave event sensor. The first dataset may comprise shave data defining the shave event”; Para. [009]);
input, into the model, the shave stroke data and a threshold value to output from the model a user-specific shave score, wherein generation of the user-specific shave score comprises comparing the shave stroke data to the threshold value to determine a deviation from the threshold value (“In such embodiments, the sensor-based learning model, executing on the server, is able to accurately identify, based on shave data and/or datasets of a specific user, a unique threshold value designed for implementation on a grooming device to provide an indication to indicate a deviation from the threshold value and to influence the user behavior”; Para. [0010]); and
generate an output based on the user-specific shave score (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores)”; Para [0035]).
Good fails to teach the output comprises an indication identifying an expected blade life of the blade based on the user-specific shave score, and wherein the output comprises initiating, based on the indication, a replacement blade for shipment to the user.
Ioannidis teaches a sensor-based method wherein the output comprises an indication identifying an expected blade life of the blade based on the user-specific shave score (“The contextual data collected from the one or more data sources may include shaving behaviors 110, shaving performance scores 120…”; Para. [0022] and “It should be noted that other machine learning algorithms may also be used to predict durability or usable life of shaving devices based on user data”; Para [0029]), a sensor-based method wherein the output comprises initiating, based on the indication, a replacement blade for shipment to the user (“Furthermore, before or during the time period associated with the most probable durability cluster, shaver monitoring application 400 may automatically connect to an electronic commerce (e-Commerce) server and place an order for a replacement blade/cartridge”; Para. [0038]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide the user with a predicted blade life, as taught in Ioannidis, to the sensor-based method taught by Good in order to improve the shaving experience of the user (Para. [0020] of Ioannidis).
Therefore, it would also have been obvious to one of ordinary skill in the art, prior to the effective filing date, to include an output that initiates a replacement blade for shipment, as taught by Ioannidis, to the sensor-based method taught by Good in order to “improve the user's shaving experience” (Para. [0038] of Ioannidis).
Regarding claim 20, Good teaches a non-transitory computer-readable medium storing computing instructions that when executed by one or more processors, cause the one or more processors to (“In general, a computer program or computer based product, application, or code (e.g., the model(s), such as AI models, or other computing instructions described herein) may be stored on a computer usable storage medium, or tangible, non-transitory computer-readable medium; Para. [0054]):
collect sensor data from one or more sensors of a shaving device having a blade, the sensor data collected during one or more shaving strokes of a user shaving with the shaving device (“The grooming device can include a handle and a connecting structure for connecting a hair cutting implement (e.g., a razor blade). The grooming device can also comprise, or be associated with, a shave event sensor”; Para. [005] and “the sensor-based shaving system may further comprise a shave event sensor configured to measure a user behavior associated with a shave event of a user”; [Para. 009]);
determine, based on the sensor data, shave stroke data defining the one or more shaving strokes (“the sensor-based shaving system may further comprise a shave event sensor configured to measure a user behavior associated with a shave event of a user”; [Para. 009]);
input, into a model executing on the one or more processors (paragraph 0043), the shave stroke data and a threshold value to output a user-specific shave score, wherein generation of the user-specific shave score comprises comparing the shave stroke data to the threshold value to determine a deviation from the threshold value (“in some embodiments, a grooming device and/or a server to which the grooming device is communicatively connected, is improved where the intelligence or predictive ability of the server or grooming device is enhanced by a trained (e.g., machine learning trained) sensor-based learning model. In such embodiments, the sensor-based learning model, executing on the server, is able to accurately identify, based on shave data and/or datasets of a specific user, a unique threshold value designed for implementation on a grooming device to provide an indication to indicate a deviation from the threshold value and to influence the user behavior”; Para. [0010]); and
generate an output based on the user-specific shave score (“In various embodiments, a display or GUI indication may include one or more visualizations of post-shave data, score(s) based on the shave data (e.g. load or pressure scores)”; Para [0035]).
Good fails to teach the output comprises an indication identifying an expected blade life of the blade based on the user-specific shave score, and wherein the output comprises initiating, based on the indication, a replacement blade for shipment to the user.
Ioannidis teaches a sensor-based method wherein the output comprises an indication identifying an expected blade life of the blade based on the user-specific shave score (“The contextual data collected from the one or more data sources may include shaving behaviors 110, shaving performance scores 120…”; Para. [0022] and “It should be noted that other machine learning algorithms may also be used to predict durability or usable life of shaving devices based on user data”; Para [0029]), a sensor-based method wherein the output comprises initiating, based on the indication, a replacement blade for shipment to the user (“Furthermore, before or during the time period associated with the most probable durability cluster, shaver monitoring application 400 may automatically connect to an electronic commerce (e-Commerce) server and place an order for a replacement blade/cartridge”; Para. [0038]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide the user with a predicted blade life, as taught in Ioannidis, to the sensor-based method taught by Good in order to improve the shaving experience of the user (Para. [0020] of Ioannidis).
Therefore, it would also have been obvious to one of ordinary skill in the art, prior to the effective filing date, to include an output that initiates a replacement blade for shipment, as taught by Ioannidis, to the sensor-based method taught by Good in order to “improve the user's shaving experience” (Para. [0038] of Ioannidis).
Response to Arguments
Applicant's arguments filed 7/21/2025 have been fully considered but they are not persuasive.
In response to applicant's argument that Good fails to teach a model. The examiner disagree and notes that Good teaches a model to collect user data (paragraph 0043).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG DONG whose telephone number is (571)270-0479. The examiner can normally be reached Monday - Thursday 8 AM-6 PM.
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/LIANG DONG/Examiner, Art Unit 3724 8/20/2026
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