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 Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
Claim Rejections - 35 USC § 102
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) 1-9, 11-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2019/0096217 A1 to Pourmohammad et al (hereinafter Pourmohammand).
With regards to claim 1, Pourmohammand discloses:
1. A method, comprising:
generating, by one or more processors using at least one machine learning model (see, detailed description, including, the analytics system provides a set of algorithms for scalable risk analytics pipeline including the threat data ingestion, enrichments, analytics, and machine learning models, risk modeling, reports, and presentation, para. 0210), an output responsive to a prompt received via a user interface of a client device, the user interface generated according to an identifier of a user, the prompt indicating a request to retrieve data regarding an item of equipment, the at least one machine learning model configured using training data comprising at least one of structured data or unstructured data regarding one or more items of equipment (see, detailed description, including, the analytics system can present information to a user, e.g., a security officer, via user interface systems. The user interface system can facilitate alarm handling by providing contextual information together with risk scores for particular threats. Using the risk asset score for an alarm event, security personnel can filter and/or sort alarm events to show or highlight the highest risk alarms, para. 0213);
presenting, by the one or more processors using the user interface, the output (see, detailed description, including, the risk applications 126 can be configured to generate various risk interfaces and present the interfaces to a user via the user devices 108 via network 104, para. 0233);
receiving, by the one or more processors via the user interface, see, detailed description, including, the RAP 120, and the risk applications 126) are shown to include processor(s) 112 and memories 114, para. 0227; an input indicating a selection of the output (see, detailed description, including, receiving a selection of the particular asset from the list indicating the identifiers of each of the assets, and updating the vulnerabilities of the data structure in response to receiving the selection of the particular asset, para. 0081); and
assigning, by the one or more processors responsive to receiving the input, an association between the output and the identifier of the user in a data structure assigned to the identifier of the user.
With regards to claim 2, Pourmohammand discloses:
2. The method of claim 1, wherein the input is a first input, the method further comprising:
receiving, by the one or more processors via the user interface, a second input to present a user interface element representing one or more selections of outputs including the selection of the output indicated by the first input (see, detailed description, including, the instructions cause the one or more processors to generate a suggested subset of the set of predetermined labels from which the one or more users can select the label to be applies to the historical threat event para. 0012);
causing, by the one or more processors, the client device to present the user interface element, wherein the user interface element comprises the output (see, detailed description, including, In some embodiments, the instructions cause the one or more processors to generate the suggested subset of predetermined labels by performing a similarity analysis between the description of the historical threat event and the labels of the set of predetermined labels and including one or more predetermined labels from the set of predetermined labels having a highest similarity with the description of the historical threat event in the suggested subset, para. 0012).
With regards to claim 3, Pourmohammand discloses:
3. The method of claim 2, wherein the one or more selections of outputs comprise a plurality of outputs, causing the client device to present the user interface element comprises arranging, by the one or more processors, the plurality of outputs according to an order based on at least one of a recency score, a relevance score, or a category assigned to each output of the plurality of outputs (see, detailed description, including, In some embodiments, the instructions cause the one or more processors to generate the suggested subset of predetermined labels by performing a similarity analysis between the description of the historical threat event and the labels of the set of predetermined labels and including one or more predetermined labels from the set of predetermined labels having a highest similarity with the description of the historical threat event in the suggested subset, para. 0012).
With regards to claim 4, Pourmohammand discloses:
4. The method of claim 1, wherein the selection of the output indicates a portion of the output and not a remainder of the output, and assigning the association between the output and the identifier of the user comprises storing, in the data structure, the portion and not the remainder (see, detailed description, including, one of the predefined threat categories includes determining whether a stored data identifies a direct mapping of at least a portion of the description to one of the predefined threat categories, para. 0015).
With regards to claim 5, Pourmohammand discloses:
5. The method of claim 1, wherein assigning the association between the output and the identifier of the user comprises:
retrieving, from the at least one machine learning model, at least one source data used by the at least one machine learning model to generate the output (see, detailed description, including, the method includes generating a classifier for the natural language processing engine using historical threat data including multiple historical threat events each having a description. In some embodiments, processing the description includes using the classifier of the natural language processing engine, para. 0017); and
storing, in the data structure, an identifier of the at least one source data and an association between the identifier of the at least one source data and the output (see, detailed description, including, the method includes pre-processing the historical threat data, wherein pre-processing the historical threat data includes filtering historical threat events from the set having a description longer than a first threshold length or filtering historical threat events from the set having a description shorter than a second threshold length, para. 0018).
With regards to claim 6, Pourmohammand discloses:
6. The method of claim 6, further comprising:
detecting, by the one or more processors, a modification to the at least one source data (see, detailed description, including, the method includes pre-processing the historical threat data, wherein pre-processing the historical threat data includes filtering historical threat events from the set having a description longer than a first threshold length or filtering historical threat events from the set having a description shorter than a second threshold length, para. 0018); and
updating, by the or more processors, the output responsive to detecting the modification (see, detailed description, including, the method further includes generating vector representations from the historical threat data subsequent to applying the labels, para. 0019).
With regards to claim 7, Pourmohammand discloses:
7. The method of claim 6, wherein updating the output comprises at least one of (1) modifying the association to indicate the modification to the at least one source data or (2) causing the at least one machine learning model to generate an updated representation of the output based at least on the modified at least one source data (see, detailed description, including, the method includes pre-processing the historical threat data, wherein pre-processing the historical threat data includes filtering historical threat events from the set having a description longer than a first threshold length or filtering historical threat events from the set having a description shorter than a second threshold length, para. 0018).
With regards to claim 8, Pourmohammand discloses:
8. The method of claim 5, wherein presenting the output includes presenting one of the at least one source data or a link configured to allow the user to access the at least one source data (see, detailed description, including, The system further includes the one or more processors configured to execute the instructions to receive threat events from one or more data sources indicating a potential threat to at least one of buildings, building equipment, people, or spaces within a building, each threat event including a description, wherein the threat events are received from multiple data sources having multiple different data formats., para. 0023.
With regards to claim 9, Pourmohammand discloses:
9. The method of claim 1, wherein the at least one machine learning model comprises at least one neural network comprising a transformer (see, detailed description, including, The weather threat analyzer 1836 can be configured to generate the normal weather rules 1840 based on the historical threat events and/or the raw environmental data stored by the historical weather database 1838. The weather threat analyzer 1836 can be configured to implement various forms of machine learning, e.g., neural networks, decision trees, regressions, Bayesian models, etc. para. 0349).
With regard to claim 11, claim 11 (a system claim) recites substantially similar limitations to claim 1 (a method claim) (with the addition of a one or more processing circuits having one or more processors and one or more memories having instructions stored thereon, see detailed description, including, the RAP 120, and/or the risk applications 126 could each be implemented on their own respective memories 114 and/or processors 112 or alternatively multiple of the components could be implemented on particular memories and/or processors (e.g., two of or more of the components could be stored on the same memory device and executed on the same processor) see, Pourmohammand para. 0227), and is therefore rejected using the same art and rationale set forth above.
With regards to claim 12, claim 12 (a system claim) recites substantially similar limitations to claim 2 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 13, claim 13 (a system claim) recites substantially similar limitations to claim 3 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 14, claim 14 (a system claim) recites substantially similar limitations to claim 4 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 15, claim 15 (a system claim) recites substantially similar limitations to claim 5 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 16, claim 16 (a system claim) recites substantially similar limitations to claim 8 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 17, claim 17 (a system claim) recites substantially similar limitations to claim 9 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 18, claim 18 (a non-transitory storge media string instructions claim) recites substantially similar limitations to claim 1 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 19, claim 19 (a non-transitory storge media string instructions claim) recites substantially similar limitations to claim 4 (a method claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 20, claim 20 (a non-transitory storge media string instructions claim) recites substantially similar limitations to claim 5 and 8 (both a method claim) and is therefore rejected using the same art and rationale set forth above.
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 should not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Pourmohammand in view of U.S. Patent Application Publication No. 20210103972 A1 to Baal et al. (hereinafter Baal).
With regards to claim 10, Pourmohammand fails to explicitly disclose:
10. The method of claim 1, further comprising:
configuring, by one or more processors, the at least one machine learning model using a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment, the plurality of first unstructured service reports comprising unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats;
receiving, by the one or more processors, a second service request for servicing building equipment;
generating, by the one or more processors using the at least one machine learning model, the user interface to prompt the user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats; and
automatically initiating, by the one or more processors, one or more actions to address the problem based on the information provided via the user interface.
Baal discloses:
configuring, by one or more processors, the at least one machine learning model using a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment, the plurality of first unstructured service reports comprising unstructured data not conforming to a predetermined format (see, detailed description, including, o provide one or more ad hoc online marketplaces for the acquisition of new equipment and/or new component parts and/or one or more ad hoc business to business forums for the disposal of old equipment and/or component parts, para. 0020) or conforming to a plurality of different predetermined formats (see, detailed description, including, the generated and/or created report data, together with artificial intelligence components with appropriate training sets and/or adaptive learning with continuous feedback, can be used to provide training for personnel to beneficially use and/or repair of equipment, para. 0020);
receiving, by the one or more processors, a second service request for servicing building equipment (see, detailed description, including, cost benefit analyses can be performed wherein costs associated with pursuing a selected or identified course of action can be weighed against the benefits of not pursuing the selected or identified course of action. Similarly, cost benefit analyses can be performed to determine and attain Pareto optimal solutions and/or Pareto optimal efficiencies in regard to the aforementioned goals, para. 0020);
generating, by the one or more processors using the at least one machine learning model, the user interface to prompt the user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats (see, detailed description, including, artificial intelligence components, and the like, can standardize the part information data into an appropriate data structure, such as a list, and thereafter provide mapping between the standardized part information data and the naming conventions of the customer. It should be noted in this regard since customer naming conventions can be fluid over time and since the standardized names of parts can change periodically, parts component 202 can over defined or definable time periods update the mapping between the standardized part information and the naming conventions supplied by the customer, para. 0029); and
automatically initiating, by the one or more processors, one or more actions to address the problem based on the information provided via the user interface (see, detailed description, including, as above, and It should be noted in this regard since customer naming conventions can be fluid over time and since the standardized names of parts can change periodically, parts component 202 can over defined or definable time periods update the mapping between the standardized part information and the naming conventions supplied by the customer, para. 0029).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Pourmohammand with Ball before her, to be motivated to combine the features from Baal, with Pourmohammand, including, configuring, by one or more processors, the at least one machine learning model using a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment, the plurality of first unstructured service reports comprising unstructured data not conforming to a predetermined format (see, detailed description, including, o provide one or more ad hoc online marketplaces for the acquisition of new equipment and/or new component parts and/or one or more ad hoc business to business forums for the disposal of old equipment and/or component parts, para. 0020) or conforming to a plurality of different predetermined formats (see, detailed description, including, the generated and/or created report data, together with artificial intelligence components with appropriate training sets and/or adaptive learning with continuous feedback, can be used to provide training for personnel to beneficially use and/or repair of equipment, para. 0020);
receiving, by the one or more processors, a second service request for servicing building equipment (see, detailed description, including, cost benefit analyses can be performed wherein costs associated with pursuing a selected or identified course of action can be weighed against the benefits of not pursuing the selected or identified course of action. Similarly, cost benefit analyses can be performed to determine and attain Pareto optimal solutions and/or Pareto optimal efficiencies in regard to the aforementioned goals, para. 0020);
generating, by the one or more processors using the at least one machine learning model, the user interface to prompt the user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats (see, detailed description, including, artificial intelligence components, and the like, can standardize the part information data into an appropriate data structure, such as a list, and thereafter provide mapping between the standardized part information data and the naming conventions of the customer. It should be noted in this regard since customer naming conventions can be fluid over time and since the standardized names of parts can change periodically, parts component 202 can over defined or definable time periods update the mapping between the standardized part information and the naming conventions supplied by the customer, para. 0029); and
automatically initiating, by the one or more processors, one or more actions to address the problem based on the information provided via the user interface (see, detailed description, including, as above, and It should be noted in this regard since customer naming conventions can be fluid over time and since the standardized names of parts can change periodically, parts component 202 can over defined or definable time periods update the mapping between the standardized part information and the naming conventions supplied by the customer, para. 0029).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art1.
A sampling of the prior art made of record and not relied upon and considered
pertinent to Applicants’ disclosure includes: U.S. Patent Application Publication No. 2024/0345560A1 to Brown et al. that discusses: A method includes receiving, by one or more processors, unstructured service data corresponding to one or more service requests handled by technicians for servicing building equipment of a building. The method may include detecting, by the one or more processors, an identifier of the building equipment, a space of the building, or a customer associated with the building using the unstructured service data. The method may include retrieving, by the one or more processors based on the identifier of the building equipment, the space, or the customer, additional data associated with the building equipment, the space, or the customer from one or more additional data sources separate from the unstructured service data. The method may include training, by the one or more processors, a generative AI model using training data including the unstructured service data and the additional data.
Conclusion
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/Primary Examiner, Art Unit 2178 7-16-2026
1 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
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