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 .
Response to Amendment
Applicant’s response filed 02 January 2026 has been considered and entered. Accordingly, claims 1-20 are pending in this application. Claims 1, 18, and 20 are currently amended; claims 2-17 and 19 are original.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 15 January 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claims 1, 18, and 20, there is no support found in the specification for “selecting, using the context, a machine learning model to execute to answer the user question” and “transmit a request to a machine learning service including the data to cause the machine learning service to instantiate the machine learning model to execute on the data” as currently added to the claims. Applicant broadly refers to paragraphs [0013], [0102]-[0111], and [0122-[0128] of the specification for support of the amendments. However, none of these passages recite the claimed material. At best, the specification has support for selecting an analytic algorithm, but this is not the same as selecting a machine learning model, let alone selecting the model based on the context as claimed. Similarly, while there is generic support for instantiating an analytic engine, this is not the same as the claimed instantiation of the same learning model selected.
Accordingly, Applicant’s specification in the instant application fails to provide adequate support for the newly added claim language. As set forth in the priority section above, the parent applications from which the instant application claim priority also fail to disclose these features. As such, the claimed features are new matter, and the claims are rejected accordingly under 35 USC §112(a).
As to claims 2-17 and 19, the claims depend from claims 1 and 18 above, and inherit the deficiencies of those claims under 35 USC §112(a) without curing them. Accordingly, claims 2-17 and 19 are rejected under 35 USC §112(a) for the same reasons as claims 1 and 18 above.
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 and 3-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes reasonably performed in the human mind or on pen and paper without significantly more.
As to claim 1, the claim recites the mental processes of
receive a string comprising unstructured data in a natural language from a user device of a user, the string representing a user question (A person can mentally receive natural language text by reading it from a user device.);
interpret the user question by querying a knowledgebase with the string for context associated with the user question from contextual information of a building stored by the knowledgebase (A person can use the question to look up information in a general knowledgebase to mentally identify related context therein.) and selecting, using the context, a machine learning model to execute to answer the user question (A person can mentally make a selection, using context they’ve mentally acquired, of machine learning model to use.);
query the knowledgebase or a datastore based at least in part on the context to identify data for the machine learning model to execute on (A person can look up information in a general knowledgebase or datastore based on context they’ve mentally acquired and mentally identify matching data therein.);
and
responsive to receiving the result from the machine learning service, compose a response based on the result data (A person can compose a response in their mind based on what they’ve received and mentally analyzed.).
This judicial exception is not integrated into a practical application because the features of a “building system comprising: one or more processors; and one or more storage devices having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to” perform the mental processes merely attempts to implement the abstract idea on a general purpose computer. See MPEP §2106.05(f). The features of “transmit a request to a machine learning service including the data to cause the machine learning service to instantiate the machine learning model to execute on the data to produce a result” recite insignificant extra-solution activity of necessary gathering and output. The claim only actively outputs data to effect a result, and then receives a generic output therefrom which is necessary to perform the mental process operations of the abstract idea above. As such, the features are not indicative of integration into a practical application. See MPEP §2106.05(g). Furthermore, the machine learning model is recited at a high level of generality of merely generically executing on the data and producing a generic result. Any broad ‘instantiation’ is required to enable a process to execute. As such, the machine learning model is nothing more than a generic computer component performing its routine function, and the feature is, at best, merely using a computer as a tool to perform an abstract idea. See MPEP §2106.05(f). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements beyond those discussed regarding integration into a practical application to possibly amount to significantly more. Again, those elements merely attempt to apply the abstract idea on a general purpose computer.
As to claim 3, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the additional elements of a data ingestion service configured to collect data from an edge device of the building and ingest the data into the knowledgebase;
the knowledgebase configured to store the data; and
a dynamic user experience service configured to receive the string, query the knowledgebase, and compose the response.
This judicial exception is not integrated into a practical application or amounts to significantly more because “a data ingestion service configured to collect data from an edge device of the building and ingest the data into the knowledgebase” recites insignificant extra-solution activity of necessary data gathering to collect the data needed to implement the abstract idea (See MPEP §2106.05(g). The feature of “the knowledgebase configured to store the data” merely recites a generic computer component performing is generic function of storing data, and thus merely further implements the abstract idea on a computer (See MPEP §2106.05(f)). The feature “a dynamic user experience service configured to receive the string, query the knowledgebase, and compose the response” is also insignificant extra-solution activity of data gathering an outputting necessary to implement the abstract idea on a computer (See MPEP §2106.05(g)). Furthermore, the steps are recited a high level of generality such that they are well-understood, routine, and conventional for any computerized query system which generically receives a query, searches a data source, and returns results.
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claims 4 and 19, the claims are rejected for the same reasons as claims 1 and 18 above. In addition, the claims recite the mental processes of decompose the string to determine a requested information context and a presentation context based on the string (A person can read or hear a string and mentally break it down into what’s needed to understand contextual information recited therein.);
query the knowledgebase for the context of the building based on the requested information context (A person can look up information in a general knowledgebase or datastore based on context they’ve mentally acquired and mentally identify matching data therein.); and
compose the response based on the presentation context by determining a format of the response based on the presentation context (A person can mentally formulate a response in their mind by mentally determining a format for the response based on the mentally obtained presentation context.).
Accordingly, the claim merely further describes the abstract idea of claims 1 and 18 without integrating into a practical application, or reciting significantly more.
As to claim 5, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the mental processes of wherein the string includes an identifier of an edge device associated with data and an indication of a type of the data (This merely describes the data being read or heard mentally by a person, and does not affect a person’s ability to read/hear it and understand it.);
wherein the instructions cause the one or more processors to (Again, this merely implements the abstract idea on a general purpose computer.):
decompose the string to determine a first component and a second component of the string, wherein the first component is the identifier of the edge device associated with the data and the second component is the type of the data (A person can read or hear a string and mentally break it down into what’s needed to understand content recited therein.); and
query the knowledgebase for the context with the first component and the second component (A person can look up information in a general knowledgebase or datastore based on information they’ve mentally acquired and mentally identify matching data therein.).
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 6, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the mental processes of wherein the string includes an indication of a requested output presentation, wherein the requested output presentation is at least one of a visual output presentation, a textual output presentation, or an audible output presentation (This merely describes the data being read or heard mentally by a person, and does not affect a person’s ability to read/hear it and understand it.);
wherein the instructions cause the one or more processors to (Again, this merely implements the abstract idea on a general purpose computer.):
decompose the string to determine a component of the string, wherein the component is the indication of the requested output presentation (A person can read or hear a string and mentally break it down into what’s needed to understand content recited therein.); and
compose the response based on the component of the string by:
determining whether the component indicates the visual output presentation, the textual output presentation, or the audible output presentation (A person can mentally determine which response type is indicated.);
composing the response as the visual output presentation in response to a first determination that the component indicates the visual output presentation (A person can mentally formulate a visual response intended to be provided. The claim does not require outputting any response. Even if it did, if recited at a high level of generality, would enable a person to merely provide a response on pen and paper.);
composing the response as the textual output presentation in response to a second determination that the component indicates the textual output presentation (A person can mentally formulate a textual response intended to be provided. The claim does not require outputting any response. Even if it did, if recited at a high level of generality, would enable a person to merely provide a response on pen and paper.); and
composing the response as the audible output presentation in response to a third determination that the component indicates the audible output presentation (A person can mentally formulate an audible response intended to be provided. The claim does not require outputting any response. Even if it did, if recited at a high level of generality, would enable a person to merely say a response.).
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 7, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the mental processes of retrieve historical question data based on an identity of the user (A person can mentally retrieve historical question data they remember a user providing.);
identify one or more presentation preferences of the user based on the historical question data (A person can mentally think of preferences known of the user based thereon.); and
compose the response based on a presentation rule and the one or more presentation preferences (A person can mentally formulate a response based on the other mentally acquired information.).
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 8, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the mental processes of selecting one presentation template from a plurality of presentation templates based on data and a presentation rule (A person can mentally make a selection from information provided to them, e.g. available presentation templates), wherein each of the plurality of presentation templates defines a presentation format (This merely describes the data a person can mentally interpret and decide from.); and
composing the response based on the data and the one presentation template (A person can mentally compose a response based on the data mentally acquired.).
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 9, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites a plurality of nodes, wherein the plurality of nodes represent equipment, spaces, people, and events associated with the building; and
a plurality of edges representing relationships between the equipment, spaces, people, and events.
The claim merely describes the data stored in the knowledgebase without any further recitation as to how it’s specifically used. A person is capable of reading a simple graph with nodes representing entities and edges representing relationships to search and find desired data. Accordingly, the claim merely describes the data used mentally by a person implementing the abstract idea of claim 1 with significantly more and without any practical application.
As to claim 10, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the mental processes of build a query data structure with a plurality of parameters using the context (The structure is recited at a high level of generality and as such, a person can mentally construct a query data structure having multiple parameters using the context previously mentally obtained.); and
query the knowledgebase or the datastore based on the plurality of parameters of the query data structure (A person can mentally use the parameters to look up information in a knowledgebase.).
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 11, the claim is rejected for the same reasons as claim 10 above. In addition, the claim recites wherein the plurality of parameters include:
a first parameter derived from the string; and
a second parameter based on the context.
These features merely describe the data used to implement the abstract idea. A person can mentally understand a process a parameter derived from the string and a parameter passed on context. Accordingly, the claim merely further describes the abstract idea of claim 10 without integrating into a practical application, or reciting significantly more.
As to claim 12, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the knowledgebase is a digital twin that stores the contextual information of the building through a plurality of entities and a plurality of relationships between the plurality of entities.
These features merely describe the data used to implement the abstract idea. A person can mentally understand a process a simple graph of nodes and edges and read that they correspond to features of building. Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 13, the claim is rejected for the same reasons as claim 12 above. In addition, the claim recites the plurality of entities represent equipment, spaces, people, and events associated with the building.
These features merely describe the data used to implement the abstract idea. A person can mentally understand a process a simple graph of nodes and edges and read that they correspond to features of building. Accordingly, the claim merely further describes the abstract idea of claim 12 without integrating into a practical application, or reciting significantly more.
As to claim 14, the claim is rejected for the same reasons as claim 1 above. In addition, the claim recites the mental processes of perform one or more analytic algorithms based on the context to generate one or more analytics results (The analytics is recited at a high level of generality to enable a person to mentally analyze the data to generate “analytics results.”); and
push the one or more analytics results to the user device of the user (A person can use pen and paper to provide results. Additionally, this feature is at best insignificant extra-solution activity of data gathering and outputting not indicative of a practical application or significantly more. See MPEP §2106.05(g).).
Accordingly, the claim merely further describes the abstract idea of claim 1 without integrating into a practical application, or reciting significantly more.
As to claim 15, the claim is rejected for the same reasons as claim 14 above. In addition, the claim recites the mental processes of retrieve at least one of historical question data associated with the user or user contextual data describing the user (A person can mentally look up, read, and retrieve relevant matching information.); and
select the one or more analytic algorithms from a plurality of analytic algorithms based on at least one of the historical question data associated with the user or the user contextual data describing the user (A person can mentally make a selection of a desired analytic algorithm to use mentally based on mentally obtained historical question data and context of a user.).
Accordingly, the claim merely further describes the abstract idea of claim 14 without integrating into a practical application, or reciting significantly more.
As to claim 16, the claim is rejected for the same reasons as claim 15 above. In addition, the claim recites the mental processes of wherein the knowledgebase is a digital twin comprising:
a plurality of nodes, wherein the plurality of nodes represent equipment, spaces, people, and events associated with the building (These features merely describe the data used to implement the abstract idea. A person can mentally understand a process a simple graph of nodes and edges and read that they correspond to features of building.); and
a plurality of edges representing relationships between the equipment, spaces, people, and events (These features merely describe the data used to implement the abstract idea. A person can mentally understand a process a simple graph of nodes and edges and read that they correspond to features of building.);
wherein the context includes at least one of one or more particular nodes of the digital twin or one or more particular edges of the digital twin (These features merely describe the data used to implement the abstract idea. A person can mentally understand a process a simple graph of nodes and edges and read that they correspond to features of building.);
wherein the instructions cause the one or more processors to (Again, this merely implements the abstract idea on a general purpose computer.):
generate a query data structure by causing the query data structure to include one or more parameters based on at least one of the one or more particular nodes of the digital twin or the one or more particular edges of the digital twin (The generation and structure of the query data structure are recited at a high level of generality such that a person can mentally generate a query data structure based on mentally obtained information.).
Accordingly, the claim merely further describes the abstract idea of claim 15 without integrating into a practical application, or reciting significantly more.
As to claim 17, the claim is rejected for the same reasons as claim 16 above. In addition, the claim recites the mental processes of decompose the string to determine a requested information context and a presentation context (A person can read a string and break it down into different contexts to understand the contents of the string.);
build the query data structure based on the requested information context (The generation and structure of the query data structure are recited at a high level of generality such that a person can mentally generate a query data structure based on mentally obtained information.); and
compose the response based on the presentation context (A person can mentally compose a response based on mentally obtained presentation context.).
Accordingly, the claim merely further describes the abstract idea of claim 16 without integrating into a practical application, or reciting significantly more.
As to claim 18, the claim recites the mental processes of method, comprising:
receiving, , a string comprising unstructured data in a natural language from a user device of a user, the string representing a user question (A person can mentally receive natural language text by reading it from a user device.);
interpreting the user question by querying a knowledgebase with the string for context associated with the user question from contextual information of a building stored by the knowledgebase (A person can use the question to look up information in a general knowledgebase to mentally identify related context therein.) and selecting, by the one or more processing circuits using the context, a machine learning model to execute to answer the user question (A person can mentally make a selection, using context they’ve mentally acquired, of machine learning model to use.);
querying the knowledgebase or a datastore based at least in part on the context to identify data for the machine learning model to execute on (A person can look up information in a general knowledgebase or datastore based on context they’ve mentally acquired and mentally identify matching data therein.);
and
responsive to receiving the result from the machine learning service, composinga response based on the result data (A person can compose a response in their mind based on what they’ve received and mentally analyzed.).
This judicial exception is not integrated into a practical application because the features of performing the steps of the abstract idea “by one or more processing circuits,” merely attempts to implement the abstract idea on a general purpose computer. See MPEP §2106.05(f). The features of “transmit a request to a machine learning service including the data to cause the machine learning service to instantiate the machine learning model to execute on the data to produce a result” recite insignificant extra-solution activity of necessary gathering and output. The claim only actively outputs data to effect a result, and then receives a generic output therefrom which is necessary to perform the mental process operations of the abstract idea above. As such, the features are not indicative of integration into a practical application. See MPEP §2106.05(g). Furthermore, the machine learning model is recited at a high level of generality of merely generically executing on the data and producing a generic result. Any broad ‘instantiation’ is required to enable a process to execute. As such, the machine learning model is nothing more than a generic computer component performing its routine function, and the feature is, at best, merely using a computer as a tool to perform an abstract idea. See MPEP §2106.05(f).The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements beyond those discussed regarding integration into a practical application to possibly amount to significantly more. Again, those elements merely attempt to apply the abstract idea on a general purpose computer.
As to claim 20, the claim recites the mental processes of
receiving a string comprising unstructured data in a natural language from a user device of a user, the string representing a user question (A person can mentally receive natural language text by reading it from a user device.);
interpreting the user question by querying a knowledgebase with the string for context associated with the user question from contextual information of a building stored by the knowledgebase (A person can use the question to look up information in a general knowledgebase to mentally identify related context therein.) and selecting, using the context, a machine learning model to execute to answer the user question (A person can mentally make a selection, using context they’ve mentally acquired, of machine learning model to use.);
querying the knowledgebase or a datastore based at least in part on the context to identify data for the machine learning model to execute on (A person can look up information in a general knowledgebase or datastore based on context they’ve mentally acquired and mentally identify matching data therein.);
and
composing a response based on the result data (A person can compose a response in their mind based on what they’ve mentally found.).
This judicial exception is not integrated into a practical application because the features of a “one or more non-transitory storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations” to perform the mental processes merely attempts to implement the abstract idea on a general purpose computer. See MPEP §2106.05(f). The features of “transmit a request to a machine learning service including the data to cause the machine learning service to instantiate the machine learning model to execute on the data to produce a result” recite insignificant extra-solution activity of necessary gathering and output. The claim only actively outputs data to effect a result, and then receives a generic output therefrom which is necessary to perform the mental process operations of the abstract idea above. As such, the features are not indicative of integration into a practical application. See MPEP §2106.05(g). Furthermore, the machine learning model is recited at a high level of generality of merely generically executing on the data and producing a generic result. Any broad ‘instantiation’ is required to enable a process to execute. As such, the machine learning model is nothing more than a generic computer component performing its routine function, and the feature is, at best, merely using a computer as a tool to perform an abstract idea. See MPEP §2106.05(f). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements beyond those discussed regarding integration into a practical application to possibly amount to significantly more. Again, those elements merely attempt to apply the abstract idea on a general purpose computer.
Allowable Subject Matter
Claim 2 would be allowable if rewritten to overcome the rejections under 35 USC §112(a) set forth in this Office action, and to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 02 January 2026 have been fully considered but they are not fully persuasive. For Examiner’s response, see discussion below:
(a) Applicant’s arguments, see pages 9-10, with respect to the double patenting rejections of claims 1-20 have been fully considered and are persuasive. The double patenting rejections of claims 1-20 have been withdrawn.
(b) Applicant’s arguments, see pages 10-11, with respect to the rejections of claims 1, 3-5, 9-14, and 18-20 under 35 USC §102 have been fully considered and are persuasive. The rejections of claims 1, 3-5, 9-14, and 18-20 under 35 USC §102 have been withdrawn in view of Applicant’s amendments to the claims.
(c) Applicant’s arguments, see page 11, with respect to the rejections of claims 7, 8, and 15-17 under 35 USC §103 have been fully considered and are persuasive. The rejections of claims 7, 8, and 15-17 under 35 USC §103 have been withdrawn in view of Applicant’s amendments to the claims.
(d) Applicant’s arguments, see page 11, with respect to the rejections of claims 1 and 3-20 under 35 USC §101 have been fully considered but are not persuasive. As set forth in the updated rejections of claims 1 and 3-20 under 35 USC §101, specifically claims 1, 18, and 20, the claims are still directed to an abstract idea even though not all steps are mental processes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kishimoto et al. (US 2021/0326736 A1) discloses selecting one or a plurality of available machine learning models to achieve a defined objections, and instantiating a machine learning pipeline, including the machine learning model ([0070]-[0072]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES E RICHARDSON whose telephone number is (571)270-1917. The examiner can normally be reached Mon-Fri 9:00-5:30.
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, Sherief Badawi can be reached at (571) 272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/James E Richardson/Primary Examiner, Art Unit 2169