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 .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/30/2026 has been entered.
Status of Claims
Claims 1, 3-8, 10, 12-15, 17-18, 20-21, and 24 remain pending, and are rejected/
Claims 2, 9, 11, 16, 19, and 22-23 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 6/30/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 6/30/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 7-8 of the Applicant’s Remarks, arguments are made that the claims have been amended to recite a concrete and computer-implemented operation of a sensor used to determine a volume and weight of a supply fluid and a frequency of use of the appliance, and a machine learning parts algorithm trained on information associated with manufacturing material of the parts and usage patterns, and is configured to process determinations from the sensor to identify parts due for replacement. It is argued that the claim requires a specific arrangement of components since the sensor comprises a usage timer, a machine learning tool, and a trilateration sensor. The Applicant cites specification paragraphs [0002-0004] as support for a technical problem of uncertainty surrounding the ideal time for replacing the consumable components, lack of proactive monitoring of operational conditions, and consumable usage and inability to efficiently identify nearby locations having replacement parts. The solution utilizes sensors measuring parameters associated with the appliance, a machine learning tool processing the measured parameters, and identifying parts requiring replacement, replacement timing, and resetting the usage timer, and a trilateration sensor tracks the location of the user device and determine proximity with inventory locations.
Examiner respectfully disagrees. The recitation of the sensors and machine learning are merely recited with a high level of generalization to gather data and automate the analyzing of the gathered data. The claims do not recite any particular improvements or changes to any sensors or the machine learning, and merely recites what information is gathered and what data of the abstract idea is used to determine further information of the abstract idea (parts due for replacement). The components to do offer a specific combination of items, but merely recites various components that perform individually to provide information for the abstract idea. A usage timer is not recited in the claims. Even if a particular type of sensor was recited in the claims, the claims merely recite gathering of data, and do not recite any changes or improvements to how the sensor functions at a technical level. Additionally, replacing the consumable components, lack of proactive monitoring of operational conditions, and consumable usage and inability to efficiently identify nearby locations having replacement parts do not represent any technical problems, but are directed to the abstract idea. Replenishment of consumable items is part of the abstract idea, and is a sales and marketing activity. As discussed above, the claims do not recite any particular technical process of monitoring operational conditions, but merely recites the gathering of data using generic sensors to process. Identifying inventory of parts nearby also is a commercial activity, and does not represent any technical endeavor. The trilateration sensor is not recited in any technical level, and merely gathers data of user location for the abstract idea. The claims merely recite generic components without any technical detail that merely gather data and process data for the abstract idea of determining items to replace and nearby availability of those items, and merely implements the abstract idea within a computing environment.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 6/30/2026 with respect to the rejection under 35 U.S.C. 103 have been fully considered, but are moot in light of new grounds of rejection. Applicant’s amendments necessitated new grounds of rejection.
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, 3-8, 10, 12-15, 17-18, 20-21, and 24 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1, 3-7, 21 and 24 are directed to a system, which is an apparatus. Claims 8, 10, and 12-14 are directed to a method, which is an apparatus. Claims 15, 17-18, and 20 are directed to a computer-readable media, which includes signals, but will be analyzed in steps 2A and 2B for the sake of compact prosecution. Therefore, claims 1, 3-8, 10, 12-15, 17-18, 20-21, and 24 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of determining parts to order and providing notifications when the user is close to a location with the part in stock:
detect usage of the appliance;
identify a part for replacement to determine the part needed for the appliance;
process determinations;
based on the processing of determinations, identify the one or more parts that are due for replacement;
stores inventory information at a plurality of locations for the one or more parts for the appliance;
a routing algorithm to determine distance between the location of the user and the plurality of locations;
based on information from the location and the routing algorithm, an alerting module that triggers when the location of the user is within a determined distance from the plurality of locations having the one or more parts in stock.
The recited limitations above set forth the process for determining parts to order and providing notifications when the user is close to a location with the part in stock. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to parts to purchase, determining merchants with the part in stock, and providing a notification when the user is nearby one of the locations (see specification [0002-0003] disclosing the challenge of finding the ideal time to order and replace components), which is an advertising and marketing activity. Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
a sensor at a first location, the sensor used to determine:
a volume of a supply fluid for the appliance;
a weight of the supply fluid;
a frequency of use of the appliance;
a machine learning parts algorithm within the appliance;
an inventory database;
a communications interface for the appliance to transmit data between the first location and the second location;
a trilateration sensor to track a location of a user device;
within the user device;
Taken individually and as a whole, representative claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a sensor and trilateration sensor, these elements are recited with a very high level of generality as merely gathering data. As disclosed in specification paragraph [0021], the sensor is any sensor to measure the usage of the appliance. Paragraph [0038] further discloses examples of the sensor, such as a general weight sensor or a flow meter, motion or optical sensors, and any sensor that can record information when the appliance is in use. Specification paragraph discloses the trilateration sensor can use any of GPS, fixed beacons, Russian or Chinese navigation sensors, and use any algorithm including least square method, iterative trilateration, and non-linear optimization techniques. It is evident that the claims merely apply any generic sensor to the abstract idea for data to use in the abstract idea, and the claims are not directed to any technology of the sensors. The inventory database merely represents the storage of information, and does not recite any new technology for the ability of a computer to store or retrieve data. The communications interface is also not disclosed with any particularity, the claims and specification not providing any further detail other than transmitting data between a first and second location. Additionally, the location of the algorithms do not represent any particular technical design, and the claims, nor the specification discusses any technical significance of the location of the algorithms. As such, it is evident that the communications interface is also any generic computing component to provide a general link to a computing environment. The machine learning is also not disclosed in the specification with any detail except that the parts algorithm can also be a machine learning tool to process the data without any further detail. As such, it is evident that the machine learning is any generic machine learning that is merely applied to the abstract idea to provide an output of data for the abstract idea.
In view of the above, under Step 2A (Prong 2), representative claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment. The sensors are any generic sensor that represents extra-solution activity for mere data gathering for use in the abstract idea.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 8 (method): Claim 8 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 8 is rejected under at least similar rationale as provided above regarding claim 1.
Regarding Claim 15 (computer-readable media): Although Claim 15 is not directed to one of the four statutory categories of invention, claim 15 is addressed for the sake of compact prosecution. Claim 15 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 15 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 3-7, 10, 12-14, 17-18, 20-21, and 24 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of determining parts to order and providing notifications when the user is close to a location with the part in stock, and do not recite any further additional elements. Thus, each of claims 3-7, 10, 12-14, 17-18, 20-21, and 24 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 3-7, 10, 12-14, 17-18, 20-21, and 24 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 3-7, 10, 12-14, 17-18, 20-21, and 24 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged (e.g. a GPS (claim 7); signal strength or propagation (claim 24)); however, the additional elements of claims 3-7, 10, 12-14, 17-18, 20-21, and 24 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 3-7, 10, 12-14, 17-18, 20-21, and 24 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 3-7, 10, 12-14, 17-18, 20-21, and 24 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 3-7, 10, 12-14, 17-18, 20-21, and 24 do not add “significantly more” to the abstract idea.
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, 3, 5, 8, 10, 12, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable by Chen (US 20150170439 A1) in view of MacLaurin (US 20150134488 A1), and in further view of Reuther (US 20160283898 A1), and in even further view of Gronsbell (US 20200234515 A1).
Regarding Claim 1: Chen discloses a system comprising:
a sensor at a first location to detect usage of the appliance; (Chen: [0039] – “The data retrieving tool 12 may also be embodied in a dongle which may be plugged into an onboard diagnostic port of the vehicle 26 to retrieve real time information from the vehicle”; Chen: [0007] – “Many vehicles are equipped with systems and sensors which help the vehicle owner anticipate and identify needed repairs. In this regard, most modern vehicles are equipped with an onboard electronic unit (ECU) which is in communication with several systems and sensors located on the vehicle”).
based on the processing of the determinations from the sensor, identify the one or more parts that are due for replacement; (Chen: [0045] – “the tool 12 receives data and information from a vehicle 26 for analysis. The analysis may be conducted locally on the tool 12, or uploaded to the diagnostic server 14 either directly from the tool 12 or via the computing device 20, such as a mobile device, computer, etc. When the diagnostic database 14 determines that a repair is needed”; Chen: [0013] – “The referral signal may include information regarding a replacement part associated with the diagnostic assessment”).
an inventory database at a second location that stores inventory information at a plurality of locations for the one or more parts for the appliance; (Chen: [0056] – “an electronically searchable parts catalog or database 21 to determine if the selling retailer 18 carries the specific repair part needed (e.g., the repair part associated with the specific part number), if the repair part is in stock, as well as determining the price of the repair part. The search of the parts database 21 may be completed automatically without any input from the user. The repair server 19 may be in communication with the repair parts identification database 35 and an electronic catalogue 21 for effectuating the sale”).
a communications interface for the appliance to transmit data between the first location and the second location; (Chen: [0035] – “An external communication module 30 may be in communication with the storage module 28 to upload the retrieved vehicle operational data and information to a remote location. The external communication module 30 may be capable of communicating with a remote device (e.g., a smartphone 20, remote server, etc.) via wired or wireless communication. For wired communication, the external communication module 30 may be plug connectable to the remote device, such as to a USB port on a computer. For wireless communication, the external communications module 30 may be capable of short range wireless communication (e.g., Bluetooth.TM. or the like) to upload the vehicle operational data to a nearby electronic device, such as a smartphone 20, which may ultimately upload the vehicle operational data to a remote server”; Chen: [0056] – “The repair server 19 may be in communication with the repair parts identification database 35 and an electronic catalogue 21 for effectuating the sale”).
Chen does not explicitly teach a system comprising:
the sensor used to determine:
a volume of a supply fluid for the appliance;
a weight of the supply fluid;
a frequency of use of the appliance;
a machine learning parts algorithm within the appliance, wherein the machine learning parts algorithm:
is trained on information associated with manufacturing material of one or more parts and usage patterns of the appliance;
is configured to process determinations from the sensor;
a trilateration sensor to track a location of a user device;
a routing algorithm to determine distance between the location of the user device and the plurality of locations;
based on information from the trilateration sensor and the routing algorithm within the user device, an alerting module of the user device that triggers when the location of the user device is within a determined distance from the plurality of locations having the part in stock.
Notably, however, Chen does disclose identifying retailers that carry the specific repair part and have it in stock (Chen: [0056]).
To that accord, MacLaurin does teach a system comprising:
a trilateration sensor to track location of a user device; (MacLaurin: [0034] – “The location module 306 monitors a user's location via their client device 106. In example embodiments a location device (e.g., global positioning system (GPS) device) in the client device 106 provides location information to the location module”).
a routing algorithm to determine distance between the location of the user device and the plurality of locations; MacLaurin: [0047] – “transmit map information to a user interface of the client device 106. In a local shopping embodiment, the user searches for items in a particular neighborhood or area. When an item of interest is found, the map module 412 places the item (or merchant location of the item) on a map that is transmitted to the user interface of the client device 106. The user may add other items from other merchants, or the consumer module 410 may recommend items from other merchant locations based on proximity and user affinities. The map module 412 may indicate a path to all the items along with distances between the merchant locations and the user's location”).
based on information from the location and the routing algorithm, an alerting module of the user device that triggers when the location of the user device is within a determined distance from the plurality of locations having the part in stock. (MacLaurin: [0078] – “if the user was searching for a particular item online, but did not purchase the item, and is now in a location within a predetermined distance to a nearby store that sells the same item, the shopping module 404 or consumer module 410 can transmit (or cause to be transmitted) a notification of the shopping opportunity (e.g., item information and an offer to purchase) to the client device”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Chen disclosing the system of sensing operational data to determine repair parts and retailers carrying the repair parts with the determining of distances to locations with the trilateration sensor within the user device, item available and providing notifications when the user is within a predetermined distance near to the locations as taught by MacLaurin. One of ordinary skill in the art would have been motivated to do so in order to identify where the user can obtain the item based on various distances (walking, driving, delivery, etc.) (MacLaurin: [0034]).
Chen in view of MacLaurin does not explicitly teach a system comprising:
the sensor used to determine:
a volume of a supply fluid for the appliance;
a weight of the supply fluid;
a frequency of use of the appliance;
a machine learning parts algorithm within the appliance, wherein the machine learning parts algorithm:
is trained on information associated with manufacturing material of one or more parts and usage patterns of the appliance;
is configured to process determinations from the sensor;
Notably, however, Chen does teach using the sensor data to determine that repair is needed (Chen: [0045]).
To that accord, Reuther does teach the sensor used to determine:
a volume of a supply fluid for the appliance; (Reuther: [0015] – “the detector device may also draw a final conclusion from the measurement data, for example by means of processing the measurement data, for example the determining of a quantity of merchandise elements (for example the determining of a number (for example number of screws in a carton) or an amount (or quantity) (for example volume of a liquid in a container) of merchandise elements)”).
a weight of the supply fluid; (Reuther: [0022] – “A weight measuring device may be provided, according to an embodiment, for example at the bottom of a container for bulk goods, in order to determine the weight of the bulk goods and to determine the number of the merchandise elements, which are currently present in the respective filling device, with knowledge of the weight respectively the average weight of a bulk goods element. Alternatively, the weight measuring device may also be a weighing device which determines at individual compartments respectively receiving spaces of a filling device in a space-resolved way, whether a respective compartment is occupied with a merchandise element or not. Even the occupation with a correct, i.e. expected, merchandise element with a known weight can thus be determined in a space-resolved way. For example, such a weight measurement may be realized by means of bending beam sensors, which may be attached at individual locations respectively compartments of a filling device, for example in a matrix-shaped way. A pressure measuring device of the detector device may measure for example piezoelectrically or by means of an electrical capacity measurement, whether a merchandise element is present at a specific location or not, for example in a specific compartment, of a filling device”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Chen disclosing the system of sensing operational data to determine repair parts and retailers carrying the repair parts with the determining of distances to locations with the sensors to determine a volume and weight of the supply fluid as taught by Reuther. One of ordinary skill in the art would have been motivated to do so in order to check the amounts of items and re-order the items as required (Reuther: [0004]).
Chen in view of MacLaurin and Reuther does not explicitly teach a system comprising:
a frequency of use of the appliance;
a machine learning parts algorithm within the appliance, wherein the machine learning parts algorithm:
is trained on information associated with manufacturing material of one or more parts and usage patterns of the appliance;
is configured to process determinations from the sensor;
Notably, however, Chen does disclose identifying retailers that carry the specific repair part and have it in stock (Chen: [0056]), and MacLaurin does disclose identifying the location of the user with GPS (trilateration) (MacLaurin: [0034]).
To that accord, Gronsbell does teach a system comprising:
a frequency of use of the appliance; (Gronsbell: [0294] – “the aggregated vehicle data sets may include repair data (e.g., relating to cost to fix), vehicle reliability history (e.g., frequency and/or severity of various types of malfunctions including information relating to when such malfunctions occur), maintenance details about vehicles, and/or driving behavior data”).
a machine learning parts algorithm within the appliance, wherein the machine learning parts algorithm: (Gronsbell: [0144] – “the repair circuitry 314 may include AI including supervised learning models or other machine learning algorithms to process the diagnostic data objects (e.g., maintenance and/or malfunction data objects) in combination with the vehicle owner's vehicle protection policies, and other data, such as the vehicle location and customer preferences, to optimize and recommend one or more remedial actions. In some embodiments, diagnostic modeling and/or predictive analytics may be used to inform any one or more of the functions described herein”; Gronsbell: [0124] – “the vehicle apparatus may be a part of the vehicle (e.g., software, firmware, and/or hardware residing on an onboard computing system of the vehicle capable of facilitating the functionalities described herein, which may include an API or other means to retrieve the relevant data)”).
is trained on information associated with manufacturing material of one or more parts and usage patterns of the appliance; (Gronsbell: [0216] – “a “trained model” may be trained based on the algorithms and processes described herein, and trained models described herein may be generated using the processes and methods described herein and known in the art. The trained model may use an aggregated data set comprising information associated with one or more vehicles, subscribers, and/or other sources of information (e.g., third-parties)”; Gronsbell: [0217] – “a classifier representing a particular categorization problem may be developed using supervised learning based on using a training set of patterns and their respective known categorizations. Each training pattern is input to the classifier, and the difference between the output categorization generated by the classifier and the known categorization is used to adjust the classifier coefficients to more accurately represent the problem”; Gronsbell: [0205] – “while a user with a higher valued vehicle may want a complete repair using OEM parts and high rated mechanics. In some embodiments, the user may be able to indicate the preferred level of repairs. Alternatively, the system 300 may be configured to provide a remedial measure with a certain level of repairs regardless of user input (e.g., a repair must use OEM replacement parts)”).
is configured to process determinations from the sensor; (Gronsbell: [0213] – “The tracking of vehicles using the sensor apparatus may begin as early as during the manufacturing of the vehicle (e.g., a factory may use the tracking information for work flow purposes or otherwise). In some embodiments, the vehicle apparatus may be an integral portion of the vehicle (e.g., embedded technology, such as GPS, cellular communication, or otherwise) and therefore may not require any specific installation before being operational”).
Examiner Note:
Although the reference of Gornsbell was cited to disclose the machine learning parts algorithm being within the appliance and routing algorithm being within the user device, such modification would have been an obvious matter of design choice in light of the system already disclosed in the combination.
Such modification would not have otherwise affected the invention of the combination of Chen, MacLaurin, and Reuther, and would have merely represented one of numerous locations of the algorithm, whether within the device or in a remote server, the skilled artisan would have found obvious for the purposes already discloses in the invention. Notably, Applicant has also failed to persuasively demonstrate the criticality of the locations of the algorithms.
Regarding Claim 3: Chen in view of MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
Chen further discloses wherein the sensor contacts the inventory database when the appliance is getting faulty and/or the appliance needs the one or more parts for replacement. (Chen: [0037] – “The diagnostic processing may include interpretation of diagnostic trouble codes (DTCs). The processing module 34 may be able to generate a "repair-needed" signal, which may be relayed to a selling retailer”).
Regarding Claim 5: Chen in view of MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
Chen does not explicitly teach wherein the logistics system is integrated with a plurality of online vendors that suggest an alternative purchase path to a user. Notably, however, Chen does disclose identifying retailers that carry the specific repair part and have it in stock (Chen: [0056]).
To that accord, MacLaurin does teach wherein the logistics system is integrated with a plurality of online vendors that suggest an alternative purchase path to the user. (MacLaurin: [0086] – “The shopping trip planning map may indicate a path to view or pick up all the items along with distances between the merchant locations and the user's location. In example embodiments, the map module 412 determines an optimal route or path for the user (e.g., based on distance, store hours, limited inventory), which the user can rearrange”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Chen disclosing the system of sensing operational data to determine repair parts and retailers carrying the repair parts with the determining of distances to locations with the alternative purchase path as taught by MacLaurin. One of ordinary skill in the art would have been motivated to do so in order to have options for the available items (MacLaurin: [0051]).
Regarding Claim 8 and 15: Claims 8 and 15 recite substantially similar limitations as claim 1. Therefore, claims 8 and 15 are rejected under the same rationale as claim 1 above.
Regarding Claim 10 and 17: Claims 10 and 17 recite substantially similar limitations as claim 3. Therefore, claims 10 and 17 are rejected under the same rationale as claim 3 above.
Regarding Claim 12: Claim 12 recites substantially similar limitations as claim 5. Therefore, claim 12 is rejected under the same rationale as claim 5 above.
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Chen (US 20150170439 A1), MacLaurin (US 20150134488 A1), Reuther (US 20160283898 A1), and Gronsbell (US 20200234515 A1), in view of Balan (US 20210304522 A1).
Regarding Claim 4: The combination of Chen, MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the logistics system further provides recommendations to a user for purchasing the one or more parts for the appliance from an original equipment manufacturer instead of getting a replica. Notably, however, Chen does disclose matching the repair part with a universal part identification number (Chen: [0051]).
To that accord, Balan does teach wherein the logistics system further provides recommendations to a user for purchasing the part for the appliance from an original equipment manufacturer instead of getting a replica. (Balan: [0020] – “The documentation 230 may also include a report of compliance with original equipment manufacturer (OEM) requirements or specifications, For example, an OEM compliance report may include a list of which vehicle sensors or other vehicle components are recommended to be replaced with OEM replacement parts, and may include a list of which of those vehicle sensors or other vehicle components were replaced using OEM parts”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Chen, MacLaurin, Reuther, and Gronsbell disclosing system of sensing operational data to determine repair parts and retailers carrying the repair parts with the recommendation to purchase OEM parts as taught by Balan. One of ordinary skill in the art would have been motivated to do so in order to ensure correct repair and calibration of parts (Balan: [0040]).
Regarding Claim 18: Claims 18 recites substantially similar limitations as claim 4. Therefore, claim 18 is rejected under the same rationale as claim 4 above.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Chen (US 20150170439 A1), MacLaurin (US 20150134488 A1), Reuther (US 20160283898 A1), and Gronsbell (US 20200234515 A1), in view of Gulati (US 20210350437 A1).
Regarding Claim 6: The combination of Chen, MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the user sets a frequency and a time for the notification on the application of the user device. Notably, however, MacLaurin does disclose transmitting a notification when the user location is within a predetermined distance to a nearby store that sells the item (MacLaurin: [0078]).
To that accord, Gulati does teach wherein the user sets a frequency and a time for the notification on the application of the user device. (Gulati: [0064] – “Monitoring by the best deal service may be turned ON/OFF via the sales notification application for specific products or a frequency or threshold of alerts may be set (e.g., alert provided monthly or daily or 10 total alerts within a time frame (e.g., day, month or week)”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Chen, MacLaurin, Gronsbell, and Isaacs disclosing system of sensing operational data to determine repair parts and retailers carrying the repair parts with the setting of a frequency and time for notifications as taught by Gulati. One of ordinary skill in the art would have been motivated to do so in order to allow the user to opt-in to the service as they want (Gulati: [0064]).
Regarding Claim 13: Claim 13 recites substantially similar limitations as claim 6. Therefore, claim 13 is rejected under the same rationale as claim 6 above.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Chen (US 20150170439 A1), MacLaurin (US 20150134488 A1), Reuther (US 20160283898 A1), and Gronsbell (US 20200234515 A1), in view of Eramian (US 20150317708 A1).
Regarding Claim 7: The combination of Chen, MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the application displays on the user device a floor plan of a location of the plurality of the locations having the one or more parts, indicating a plurality of aisles and shelves, using a global positioning system (GPS). Notably, however, Chen does disclose identifying retailers that carry the specific repair part and have it in stock (Chen: [0056]), and MacLaurin does disclose identifying the location of the user with GPS (MacLaurin: [0034]).
To that accord, Eramian does teach wherein the application displays on the user device a floor plan of a location of the plurality of the locations having the one or more parts, indicating a plurality of aisles and shelves, using a global positioning system (GPS). (Eramian: [0036] – “service application 120 may display information for service location 130, such as an alert associated with a nearby item on a shopping list, a map, merchant store offerings, group-specific product or service locations, information desk locations, sales/coupons/rebates, or other general information. Additionally, service application 120 may use a location device and/or application of user device 110, such as a GPS device and application in addition to or in place of beacon communications, to locate users”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Chen, MacLaurin, Reuter, and Gronsbell disclosing system of sensing operational data to determine repair parts and retailers carrying the repair parts with the floor plan of the location indicating the aisles and shelves and locations as taught by Eramian. One of ordinary skill in the art would have been motivated to do so in order to alert the user to the location of a specific item (Eramian: [0078]).
Regarding Claim 14 and 20: Claims 14 and 20 recite substantially similar limitations as claim 7. Therefore, claims 14 and 20 are rejected under the same rationale as claim 7 above.
Claims 21 and 24 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Chen (US 20150170439 A1), MacLaurin (US 20150134488 A1), Reuther (US 20160283898 A1), and Gronsbell (US 20200234515 A1), in view of Isaacs (US 20240330983 A1).
Regarding Claim 21: The combination of Chen in view of MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the routing algorithm queries the inventory database to provide the plurality of locations having the one or more parts to the user device, wherein the user device receives a selection of a location of the plurality of locations from the user. Notably, however, Chen does disclose identifying retailers that carry the specific repair part and have it in stock (Chen: [0056]).
To that accord, Isaacs does teach wherein the routing algorithm queries the inventory database to provide the plurality of locations having the one or more parts to the user device, wherein the user device receives a selection of a location of the plurality of locations from the user. (Isaacs: [0096] – “The types of the establishments are matched to the types of establishments providing the item of interest as specified in the item listing data structure (step 530). For each matching establishment, i.e., an establishment having a type matching one of the types associated with the item of interest, additional notification criteria are evaluated (step 540). The evaluation of the notification criteria specifies whether or not a notification should be output to the user indicating that an item of interest on their item listing data structure is available from an establishment within the specified proximity (step 550). If a notification is to be generated, then the notification is generated and sent to the user's computing device for presentation to the user”; Isaacs: [0091] – “user selectable buttons 460-490 are provided to allow the user to indicate how the user wishes to proceed based on the item availability notification. For example, if the user wishes to accept the deviation of the user's current travel so that the user will go to the identified location to pick up the item of interest, then the user may select the Accept virtual button 460. If the user wishes to reject the notification and not deviate, then the user may select the Reject virtual button 470. If the user wishes to instead remove the item from the user's item listing, then the user may select the Remove virtual button”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Chen, MacLaurin, Reuther, and Gronsbell disclosing the system of sensing operational data to determine repair parts and retailers carrying the repair parts with the determining of distances to locations with the providing of the locations and a user selection as taught by Isaacs. One of ordinary skill in the art would have been motivated to do so in order to indicate how the user wishes to proceed and deviate from the current travel (Isaacs: [0091]).
Regarding Claim 24: Chen in view of MacLaurin, Reuther, and Gronsbell discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the trilateration sensor tracks the location of the user device based on signal strength and/or signal propagation. Notably, however, MacLaurin does disclose identifying the location of the user with GPS (trilateration) (MacLaurin: [0034]).
To that accord, Isaacs does teach wherein the trilateration sensor tracks the location of the user device based on signal strength and/or signal propagation. Examiner notes that Applicant recites and/or in the claim. (Isaacs: [0059] – “The location reporting system 163 may comprise any location identification system, such as a global positioning system (GPS), cellular triangulation system, or the like, to identify the user's current location”). Cellular triangulation determines location by measuring signals from at least three cell towers.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Chen, MacLaurin, Reuther, and Gronsbell disclosing the system of sensing operational data to determine repair parts and retailers carrying the repair parts with the trilateration sensor tracking the user based on signal strength or signal propagation as taught by Isaacs. One of ordinary skill in the art would have been motivated to do so in order to indicate how the user wishes to proceed and deviate from the current travel (Isaacs: [0091]).
Conclusion
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 8/26/2026
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