DETAILED ACTION
Status of Claims
The following is a non-final, first office action in response to the communication filed 5/2/2025.
Claims 1-20 are currently pending and have been examined.
Priority
Applicant’s claim for the benefit of prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
The applicant’s claim for benefit of Provisional Patent Application Serial No. 63/421,820 filed 11/2/2022 has been received and acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 8/5/2025 has been reviewed and considered.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-20 are directed to a method (process), a system (machine or manufacture), and a non-transitory medium (manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including those identified in bold text below:
1. A method comprising: receiving, by a server, data characterizing a characteristic property of a first target object; generating, by the server, a recommendation for a user of the first target object based on the received data and data characterizing a result associated with an implementation of a previous recommendation on the first target object, wherein the generating includes application of recommendation rules associated with one or more of the first target object and a target object group that includes the first target object; and transmitting the generated recommendation.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, other than reciting “by a server” nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of a generic computer does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
Independent claims 14 and 20 recite abstract limitations analogous to the ones presented above with respect to claim 1.
If the claim recites a judicial exception in step 2A Prong One , the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 1 recites additional elements, including those underlined below:
1. A method comprising: receiving, by a server, data characterizing a characteristic property of a first target object; generating, by the server, a recommendation for a user of the first target object based on the received data and data characterizing a result associated with an implementation of a previous recommendation on the first target object, wherein the generating includes application of recommendation rules associated with one or more of the first target object and a target object group that includes the first target object; and transmitting the generated recommendation.
Claim 14 recites further additional elements, including those underlined below:
14. A system comprising: at least one data processor; memory storing instructions which, when executed by the at least one data processor, causes the at least one data processor to perform operations
Claim 20 recites further additional elements, including those underlined below:
20. A non-transitory computer program product storing instructions, which when executed by at least one data processor of at least one computing system, implement a method
The functions of the server, system (and its respective components), and non-transitory computer-readable medium is/are recited at a high level of generality, and, as applied, are tools used in their ordinary capacity to perform the abstract idea, and therefore amount to “apply it.”
The transmitting step, when recited at this level of breadth, amounts to extra-solution activity (see MPEP 2106.05(g)).
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the server, system (and its respective components), and non-transitory computer-readable medium amount to mere instructions to apply the exception. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As discussed above, the transmitting step amounts to extra-solution activity. The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). In addition, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
Claims 2-3 and 15-16 further recite the abstract concept of generating rules based on received information (i.e., a mental process), and further recites an object machine learning algorithm executed by the server, which at this level of breadth merely amounts to applying the abstract idea using a generic computing device. The receipt of information provided by the user, at this level of breadth could be characterized as abstract (i.e. mental process) or an additional element (i.e. receipt of information via an interface) that is well-understood, routine and conventional in the art - The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here)). Thus, even when viewed as an ordered combination, nothing in the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Claims 4, 7, 12, and 17 recite limitations that merely narrow the previously recited abstract idea limitations (e.g., variables considered when modifying rules, variables considered when generating the recommendation, further characterizing the recommendation, etc.) without introducing any further additional elements. For the reasons described above with respect to its parent claims above, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claims 5 and 18 further recite the abstract concept of determining properties associated with a transmission based on inputs and rules (i.e., a mental process), and further recites a transmission machine learning algorithm executed by the server, which at this level of breadth merely amounts to applying the abstract idea using a generic computing device. Thus, even when viewed as an ordered combination, nothing in the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Claims 6 and 19 further recite the abstract concept of generating new recommendations based on received information (i.e., a mental process), and further recites (i) the receipt of information used in generating said recommendation, which when recited at this level of breadth could be characterized as abstract idea (i.e., mental process) or an additional element (i.e. receipt of information via an interface) that is well-understood, routine and conventional in the art - The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here)), and (ii) application of the abstract idea by the server (see analysis with respect to claims above). Thus, even when viewed as an ordered combination, nothing in the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Claims 8-9 further characterize the additional elements (i.e., the server further includes an application on a computing device, the receipt of data by the server being via the application, and establishing to which device the recommendation is transmitted), which (i) act to characterize a field of use or technological environment in which to apply the judicial exception, and (ii) act to further characterize the collection/receipt of information via a network, which amounts to extra-solution activity (The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).) Thus, even when viewed as an ordered combination, nothing in the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Claims 10-11 further recite the abstract concept of generating answers to queries using received information (i.e., a mental process), and further recites (i) the receipt of said query via the application on the computing device, and the receipt/transmission of information to/from a second user, which amounts to extra-solution activity (i.e. receipt of information via an interface) that is well-understood, routine and conventional in the art - The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here)), and (ii) the application of a support engine supported by the server, which merely acts to apply the previously identified abstract idea. Thus, even when viewed as an ordered combination, nothing in the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Claims 13 further recite the abstract concept of registering the target object (i.e., a mental process), and further recites that the registering occurs at the server via an application of the computing device, wherein (i) the sending/receiving of information between devices amounts to extra-solution activity (i.e. receipt of information via an interface) that is well-understood, routine and conventional in the art - The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here)), and (ii) the presumed storage of information at a server amounts to extra-solution activity (i.e. storing information) that is well-understood, routine and convention in the art (The Versata and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that storing and retrieving data in memory is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).). Thus, even when viewed as an ordered combination, nothing in the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Audi (WO 2019213547 A1).
Regarding claim 1, Audi discloses:
A method comprising:
Audi [0041] FIG. 1 is a flow chart of an exemplary method for providing a recommendation to a target object by an object monitoring system
receiving, by a server, data characterizing a characteristic property of a first target object;
Audi [0007] The method can include receiving data characterizing a second result associated with the implementation of the generated recommendation; receiving new data characterizing a measurement of the characteristic property of the first target object by the sensor;
Audi [0041] At 102, data characterizing a measurement of a characteristic property of a first target object (e.g., which can be detected by a sensor operatively coupled to the first target object) is received. The data can be received, for example, by a platform (or a server) of the object monitoring system.
Audi [0042] Various components of the object monitoring system can be distributed over a cloud, operating devices of users of multiple target objects, locations of the target objects (e.g., sensors coupled to target objects and the like). For example, FIG. 2 illustrates an exemplary object monitoring system 200 that includes a platform 202; applications 204a and 204b; sensors 205a and 205b; and a supplier interface 208.
Audi [0042] The object monitoring system 200 can monitor and provide recommendations to the target objects 206a and 206b. The sensor 205a (or 205b) can detect a characteristic property of the target object 206a (or 206b) and transmit the detected characteristic property to the application 204a (or 204b). The application 204a (or 204b) can be installed on a computing device (e.g., laptop, mobile device, and the like) of the user of the target object 206a (or 206b). The application can curate the received sensor data and/or transmit the sensor data to the platform 202.
Audi [0043] The platform 202 can receive the data from the applications 204a (or 204b)(e.g., data characterizing a measurement of the characteristic property of the target object) and/or sensor data directly from the sensor 205a (or 205b). The supplier interface 208 can allow the supplier to access information in the object monitoring system 200 (e.g., information about the product / object, end users, and the like).
generating, by the server, a recommendation for a user of the first target object based on the received data and data characterizing a result associated with an implementation of a previous recommendation on the first target object, wherein the generating includes application of recommendation rules associated with one or more of the first target object and a target object group that includes the first target object; and
Audi [0042] The object monitoring system 200 can monitor and provide recommendations to the target objects 206a and 206b.
Audi [0045] Referring again to FIG. 1, at 104, the platform 202 generates a
recommendation for the target object 206a (or 206b) based on the received data. As described below, the generation of the recommendation can also be based on various data (e.g., result associated with the implementation of a previous recommendation on the target object 206a, sensor data from multiple target objects, expert data, and the like). Furthermore, the recommendation can be generated by application of various rules (e.g., predetermined rules, rules provided by experts, and the like) on the various data.
transmitting the generated recommendation.
Audi [0008] The generated recommendation can be transmitted to the computing device
[See also claim 1 of WO 2019213547]
Regarding Claim 2, Audi discloses the limitations of claim 1 and further discloses further comprising: generating, by an object machine learning algorithm executed by the server, a first set of object rules associated with the first target object based on one or more of information associated with the first target object provided by the user, data characterizing the result associated with an implementation of previous recommendations by the server associated with a plurality of target objects of the target object group, wherein the recommendation rules includes the first set of object rules.
Audi [0005] One or more of the following features can be included in any feasible combination. For example, the method can include generating, by an object machine learning algorithm executed by the server, a first set of object rules associated with the first target object based on one or more of information associated with the first target object provided by the user, previous measurement of the characteristic property by the sensor, data characterizing the result associated with an implementation of previous recommendations by the server and sensor measurements associated with a plurality of target objects of the target object group. The recommendation rules can include the first set of object rules.
[See also claim 2 of WO 2019213547]
Regarding Claim 3, Audi discloses the limitations of claim 2 and further discloses further comprising: generating, by a group machine learning algorithm executed by the server, a second set of object rules associated with the target object group based on one or more of the information associated with the first target object provided by the user, the data characterizing the result associated with the implementation of previous recommendations by the server associated with the plurality of target objects of the target object group, wherein the recommendation rules includes the second set of object rules.
Audi [0006] The method can include generating, by a group machine learning algorithm executed by the server, a second set of object rules associated with the target object group based on one or more of the information associated with the first target object provided by the user, the previous measurement of the characteristic property by the sensor, the data characterizing the result associated with the implementation of previous recommendations by the server and the sensor measurements associated with the plurality of target objects of the target object group. The recommendation rules can include the second set of object rules.
Audi [0056] FIG. 5 illustrates an exemplary rules engine 304. The rules engine 304 can include a personal machine learning algorithm 502, a group machine learning algorithm 504 and analytical models 506.
Audi [0057] The group machine learning algorithm 504 can generate a second set of object rules based on information associated with a group (e.g., predefined group) associated with the target object. For example, the information can include macro trend information associated with the group of target objects. The target object information can include sensor measurements associated with a plurality of target objects in the group, group data from the data storage 310. In some implementations, the information can include personalized information.
[See also claim 3 of WO 2019213547]
Regarding Claim 4, Audi discloses the limitations of claim 3 and further discloses further comprising modifying one or more of the first set of object rules and the second set of object rules based on input rules provided by a product subject matter expert.
Audi [0006] The method can include modifying one or more of the first set of object rules and the second set of object rules based on input rules provided by a product subject matter expert.
[See also claim 4 of WO 2019213547]
Regarding Claim 5, Audi discloses the limitations of claim 3 and further discloses further comprising determining, by a transmission machine learning algorithm executed by the server, one or more properties associated with the transmission of the generated recommendation based on input rules provided by a digital subject matter expert.
Audi [0006] The method can include determining, by a transmission machine learning algorithm executed by the server, one or more properties associated with the transmission of the generated recommendation based on input rules provided by a digital subject matter expert.
[See also claim 5 of WO 2019213547]
Regarding Claim 6, Audi discloses the limitations of claim 3 and further discloses further comprising: receiving data characterizing a second result associated with the implementation of the generated recommendation;
Audi [0007] The method can include receiving data characterizing a second result associated with the implementation of the generated recommendation;
receiving new data characterizing the characteristic property of the first target object;
Audi [0007] receiving new data characterizing a measurement of the characteristic property of the first target object by the sensor;
updating the first and the second set of object rules based on the received data characterizing the second resuIt and the new data characterizing the characteristic property; and
Audi [0007] updating the first and the second set of object rules based on the received data characterizing the second result and the new data characterizing the measurement of the characteristic property;
generating, by the server, a new recommendation for the first target object based on application of the updated first and the updated second set of object rules on the received new data.
Audi [0007] generating, by the server, a new recommendation for the first target object based on application of the updated first and the updated second set of object rules on the received new data.
[See also claim 6 of WO 2019213547]
Regarding Claim 7, Audi discloses the limitations of claim 1 and further discloses wherein generating the recommendation for the first target object is further based on one or more of environmental data associated with the first target object, usage of the first target object, location of the first target object, an expertise level associated with the user, a type associated with the target object, a time associated with the generation of the recommendation, previous user or similar user actions or behavior, user interests, geographic data, proximal objects, and other objects.
Audi [0008] Generating the recommendation for the first target object can be further based on one or more of environmental data associated with the first target object, usage of the first target object, location of the first target object, an expertise level associated with the user, a type associated with the target object, a time associated with the generation of the recommendation, previous user or similar user actions or behavior, user interests, geographic data, proximal objects, and other similar objects.
[See also claim 7 of WO 2019213547]
Regarding Claim 8, Audi discloses the limitations of claim 1 and further discloses wherein the server further includes an application on a computing device associated with the user of the first target object, and the receiving of the data by the server is via the application.
Audi [0008] The object monitoring system can include an application on a computing device associated with the user of the first target object, and the receiving of the data by the server is via the application.
[See also claim 8 of WO 2019213547]
Regarding Claim 9, Audi discloses the limitations of claim 8 and further discloses wherein the generated recommendation is transmitted to the computing device.
Audi [0008] The generated recommendation can be transmitted to the computing device.
[See also claim 9 of WO 2019213547]
Regarding Claim 10, Audi discloses the limitations of claim 8 and further discloses further comprising: receiving a user query associated with the first target object by the application on the computing device associated with the user of the first target object; and generating, by a support engine supported by the server, an answer to the user query based on one or more of historical data associated with the first target object and an input from a second user.
Audi [0008] The method can include receiving a user query associated with the first target object by the application on the computing device associated with the user of the first target object; and generating, by a support engine supported by the server, an answer to the user query based on one or more of historical data associated with the first target object and an input from a second user of the object monitoring system.
[See also claim 10 of WO 2019213547]
Regarding Claim 11, Audi discloses the limitations of claim 10 and further discloses further comprising: generating, by the support engine, a support engine query indicative of the user query; transmitting the support engine query to the second user; receiving a response from the second user; and generating the answer to the user query based on the received response from the second user.
Audi [0008] The method can include generating, by the support engine, a support engine query indicative of the user query; transmitting the support engine query to the second user; receiving a response from the second user; and generating the answer to the user query based on the received response from the second user.
[See also claim 11 of WO 2019213547]
Regarding Claim 12, Audi discloses the limitations of claim 1 and further discloses wherein the generated recommendation includes information and/or instructions associated with care of the first target object.
Audi [0008] The generated recommendation can include information and/or instructions associated with care of the first target object.
[See also claim 12 of WO 2019213547]
Regarding Claim 13, Audi discloses the limitations of claim 1 and further discloses further comprising registering the target object with the server via the application on the computing device.
Audi [0008] The method can include registering the target object with the server via the application on the computing device.
[See also claim 13 of WO 2019213547]
Regarding Claim 14, Audi discloses A system comprising: at least one data processor; memory storing instructions which, when executed by the at least one data processor, causes the at least one data processor to perform operations (Audi Fig. 2, [0003], [0009], [0088]-[0089])
In addition, see the above rejection of claim 1.
[See also claim 14 of WO 2019213547]
Regarding Claim 15, see the above rejection of claim 2, which recites analogous limitations.
Regarding Claim 16, see the above rejection of claim 3, which recites analogous limitations.
Regarding Claim 17, see the above rejection of claim 4, which recites analogous limitations.
Regarding Claim 18, see the above rejection of claim 5, which recites analogous limitations.
Regarding Claim 19, see the above rejection of claim 6, which recites analogous limitations.
Regarding Claim 20, Audi discloses A non-transitory computer program product storing instructions, which when executed by at least one data processor of at least one computing system, implement a method (Audi Fig. 2, [0003], [0009], [0088]-[0089])
In addition, see the above rejection of claim 1.
[See also claim 15 of WO 2019213547]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella et al. (US 20190339687), disclosing methods and systems for data collection, learning and streaming of machine signals for analytics and maintenance using the industrial internet of things.
Rudrappa et al. (US 20150178787), disclosing a method and system for interaction between users, vendors, brands, and stakeholders for products and services in real time during usage or consumption life cycle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABBY J FLYNN whose telephone number is (571)272-9855. The examiner can normally be reached Monday - Friday 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at 571-272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABBY J FLYNN/Examiner, Art Unit 3663
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