DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This Office Action is in response to the Restriction response filed on 12/09/2025.
3. The IDSs filed on 06/26/2023 and 04/08/2024 are considered and entered into the file.
4. Claims 1-9 and 16-25 are pending. All the pending claims are examined.
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.
5. Claims 1, 3-5, 7-9, 16, 18, 19, 21, 23 and 25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lapointe et al (US 20210117716 A1).
As in the current invention Lapointe et al (“Lapointe”) is directed to CLASSIFYING INDIVIDUAL ELEMENTS OF AN INFRASTRUCTURE MODEL.
AS PER CLAIM 1, Lapointe discloses a method (see flowcharts of Figs. 2 and 6) for labeling elements of an infrastructure model with classes, comprising:
displaying a visualization of the infrastructure model in a user interface of a labeling tool executing on one or more computing devices ([0049] FIG. 7 is a first view 700 of an example new or updated infrastructure model with classification labels (here showing elements classified as walls highlighted), which may be produced as a result of operation of the steps of FIG. 6. FIG. 8 is a second view 800 of an example new or updated infrastructure model with classification labels (here showing elements classified as beams highlighted), which may be produced as a result of operation of the steps of FIG. 6). selecting, in response to user input in the user interface, one or more elements of the infrastructure model to create a selection (for example as shown in Fig. 7, Wall element is shown selected; and similarly as shown in Fig. 8 Beam element is shown selected);
predicting, using a machine learning (ML) model in communication with the labeling tool, one or more additional elements that share similarities with the selected elements ([0016] FIG. 6 is a flow diagram of example inference operations that may be implemented at least in part by the classification service of the design insights cloud service to predict classification labels for individual elements of a new or updated infrastructure model. [0034] The design insights cloud service 136 may include a classification service 138 that is capable of automatically classify individual elements of an infrastructure model by training one or more machine learning algorithms to produce a classification model, and later utilizing the classification model to classify the individual elements of the infrastructure models);
adding the one or more additional elements to the selection ([0049] FIG. 8 is a second view 800 of an example new or updated infrastructure model with classification labels (here showing elements classified as beams highlighted), which may be produced as a result of operation of the steps of FIG. 6).
cycling through at least a set of the elements of the selection, the cycling to repeatedly present in the user interface an element or a group of elements of the set of the elements and solicit user confirmation that the element or the-a group of elements belongs to a class and should remain in the selection, or user input indicating the element or the group of elements do not belong to the class and causing removal of the element or the group of elements from the selection (cycling through a set of elements of Fig. 7, [0049] FIG. 7 is a first view 700 of an example new or updated infrastructure model with classification labels (here showing elements classified as walls highlighted), which may be produced as a result of operation of the steps of FIG. 6. Similarly cycling through a set of elements of FIG. 8, Beam element is shown highlighted or selected) .
assigning each element of the selection to the class; and outputting each of the elements of the selection associated with the assigned class (as shown in fig. 7, the selected element name is a wall assigned class and it is highlighted and/or outputted . Similarly in Fig. 8 the selected element name is a Beam assigned class and highlighted and/or outputted ).
AS PER CLAIM 3, Lapointe further discloses the method of claim 1, further comprising: grouping elements of the set of the elements based on one or more predefined rules that place elements that share characteristics within a same group ([0047] For example, the prediction module may apply a higher prior probability that elements grouped together in the hierarchy (e.g., in a same category) of a user schema have a higher likelihood to belong to the same classification than elements that are not grouped together in the hierarchy (e.g., in a same category) of the user schema, and predictions may be adjusted based thereupon).
AS PER CLAIM 4, Lapointe further discloses the method of claim 3, wherein the shared characteristics include a value of a metadata field, a polygon mesh that is same or within a predetermined threshold of difference, or a bounding box that is same or within a predetermined threshold of difference ([0032]The infrastructure modeling hub services 130 may also maintain locks and associated metadata in the repository 140-144. When a client 120 desires to operate upon an infrastructure model, it may obtain the briefcase 150 from a repository 140-144 closest to the desired state and those accepted changesets 160 from the repository 140-144 that when applied bring that briefcase up to the desired state. [0047] For example, the prediction module may apply a higher prior probability that elements grouped together in the hierarchy (e.g., in a same category) of a user schema have a higher likelihood to belong to the same classification than elements that are not grouped together in the hierarchy (e.g., in a same category) of the user schema, and predictions may be adjusted based thereupon).
AS PER CLAIM 5, Lapointe further discloses the method of claim 1, wherein the cycling cycles through each element or group of elements of the set sequentially, periodically, or randomly (as illustrated in Figs. 7 or 8, a user may navigate/cycling through each element or group of elements of classification labels sequentially, periodically, or randomly. For example in Fig. 7, a Wall element is selected; and in Fig. 8 a Beam element is selected).
AS PER CLAIM 7, Lapointe further discloses the method of claim 1, further comprising: predicting, using the ML model, the class of the element or the group of elements ([0016] FIG. 6 is a flow diagram of example inference operations that may be implemented at least in part by the classification service of the design insights cloud service to predict classification labels for individual elements of a new or updated infrastructure model. [0048] The predicted classification labels are then stored in the new or updated infrastructure model. The new or updated infrastructure model with the classification labels may be used to display a view, update a dashboard, etc. in a user interface of the design insights cloud service 136 or of other software. Analytics may be readily run on the infrastructure model now that is has appropriate classification labels).
AS PER CLAIM 8, Lapointe further discloses the method of claim 7, wherein the outputting outputs a label file that includes each of the elements of the selection associated with the assigned class, the label file is usable to train a new ML model, and the selecting, predicting one or more additional elements, predicting the class, cycling, assigning and outputting are repeated using the new ML model ([0008] In example embodiments, techniques are provided to automatically classify individual elements of an infrastructure model by training one or more machine learning algorithms on classified infrastructure models, producing a classification model that maps features to classification labels, and utilizing the classification model to classify the individual elements of the infrastructure model. [0049] FIG. 7 is a first view 700 of an example new or updated infrastructure model with classification labels (here showing elements classified as walls highlighted), which may be produced as a result of operation of the steps of FIG. 6. FIG. 8 is a second view 800 of an example new or updated infrastructure model with classification labels (here showing elements classified as beams highlighted), which may be produced as a result of operation of the steps of FIG. 6).
AS PER CLAIM 9, Lapointe further discloses the method of claim 1, further comprising: loading, by the labeling tool, a prediction file that includes an ML prediction of classes of elements in the infrastructure model ([0016] FIG. 6 is a flow diagram of example inference operations that may be implemented at least in part by the classification service of the design insights cloud service to predict classification labels for individual elements of a new or updated infrastructure model);
loading, by the labeling tool, the label file ([0049] FIG. 7 is a first view 700 of an example new or updated infrastructure model with classification labels (here showing elements classified as walls highlighted), which may be produced as a result of operation of the steps of FIG. 6).
displaying, in a user interface of the labeling tool, a visualization of the infrastructure model, indications of numbers of elements for one or more classes included in the prediction file, and numbers of elements for one or more classes included in the label file ([0049] FIG. 7 is a first view 700 of an example new or updated infrastructure model with classification labels (here showing elements classified as walls highlighted), which may be produced as a result of operation of the steps of FIG. 6. FIG. 8 is a second view 800 of an example new or updated infrastructure model with classification labels (here showing elements classified as beams highlighted), which may be produced as a result of operation of the steps of FIG. 6); and
updating the displayed indications of numbers of elements for each class based on changes made in the assigning ([0016] FIG. 6 is a flow diagram of example inference operations that may be implemented at least in part by the classification service of the design insights cloud service to predict classification labels for individual elements of a new or updated infrastructure model; [0017] FIG. 7 is a first view of an example new or updated infrastructure model with classification labels (here showing elements classified as walls highlighted), which may be produced as a result of operation of the steps of FIG. 6).
As per storage medium claims 16, 18 and 19, these claims recite similar subject matter as that of method claims 1, 3 and 5, respectively. Thus are rejected under similar citations given to the method clams.
As per computing device claims 21, 23 and 25, these claims recite similar subject matter as that of method claims 1 , 4 and 9, respectively. Thus are rejected under similar citations given to the method clams.
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.
6. Claims 6, 20 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Lapointe in view of Buryak et al US 8635172 B1.
Buryak et al (“Buryak”) is directed to Dynamic Techniques For Evaluating Quality Of Clustering Or Classification System Aimed To Minimize The Number Of Manual Reviews Based On Bayesian Inference And Markov Chain Monte Carlo (MCMC) Techniques.
AS PER CLAIMs 6, 20,and 24 Lapointe fails to discloses a Bayesian inference. As a result Lapointe fails to teach using Bayesian inference, a probability that remaining elements of the selection that were not part of the set subject to the cycling belong to the class; and displaying an indication of the probability in the user interface.
Buryak, on the other hand discloses Bayesian inference, wherein, FIG. 2 is a flowchart diagram illustrating an example technique for determining performance of a machine learning classifier or clustering system (column 4, lines 43-45). At step 110, a first item (document) is selected at random from the selected classifier and presented to the human rater by displaying it at the document review station 14 (FIG. 1). The rater is presented an on-screen display question of whether the item (document) belongs to the class associated with the classifier under test, as at step 111. The rater supplies his or her answer by suitable manipulation of the document review station user interface and the answer (yes/no) is supplied to the Bayesian inference analyzer 20 (FIG. 1), which then updates the precision distribution as illustrated at 112 (for the "yes" case) and at 114 (for the "no" case). As previously discussed, the Bayesian inference analyzer 20 updates the precision distribution by incrementing either the a parameter (step 112) or the .beta. parameter (step 114) of the Beta function programmed into the Bayesian inference analyzer 20. Column 8, lines 28-43).
Before effective filling date of the invention, it would have been obvious to a person of ordinary skill in the art to combine the Bayesian inference of Buryak with the system of Lapointe
The suggestion /motivation for doing so would have been to help predict which other items probabilistically belong to the same group. Therefore, it would have been obvious to combine Buryak with Lapointe Barret to obtain the invention as specified in claims 6, 20 and 24.
Allowable Subject Matter
7. Claims 2, 17 and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TADESSE HAILU whose telephone number is (571)272-4051; and the email address is Tadesse.hailu@USPTO.GOV. The examiner can normally be reached Monday- Friday 9:30-5:30 (Eastern time).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bashore, William L. can be reached (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TADESSE HAILU/ Primary Examiner, Art Unit 2174
Prosecution Insights