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 .
DETAILED ACTION
Status of the Application
The following is a Non-Final Office Action in response to communication received on 1/9/2025. Claims 1-20 are pending in this office action. This is the first action on the merits. The Information Disclosure Statements (IDSs) filed on behalf of this case on 3/31/2025, 4/10/2025, and 5/23/2025 have been considered by the Examiner.
Claim Interpretation
12. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
13. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
14. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
-a processing system, configured to: determine a set of properties; determine a parcel for each property of the set of properties; extracting a parcel feature set for each parcel; determine a parcel class for the parcel for each property of the set of properties based on the respective parcel feature set, using the classification model, wherein the classification model is trained, using qualitative labels, to predict training parcel classes based on training parcel feature sets; grouping properties of the set of properties based on the respective parcel classes (see claim 11)
- the processing system is further configured to: determine an adjacent property associated with a property that is determined to be part of the group; determine a parcel associated with the adjacent property and a parcel associated with the property; and determine the adjacent property and the property are part of the same group when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently (see claim 14)
- the processing system is further configured to determine a set of final groups by iteratively determining parcel classes and groups for each of the properties of the set of properties (see claim 15)
- the processing system is further configured to determine a confidence score associated with the final group using a confidence score model based on properties within the final group and a set of auxiliary features (see claim 16)
- processing system is further configured to: determine a neighboring property neighboring a property of a group; and include the neighboring property as part of the group when a comparison metric satisfies a threshold (see claim 20)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
From review of the specification the following appears to be the corresponding structure, to perform the claimed function:
Computer executing instructions
Paragraph 0153- Alternative embodiments implement the above methods and/or processing modules in non-transitory computer-readable media, storing computer-readable instructions that, when executed by a processing system, cause the processing system to perform the method(s) discussed herein. The instructions can be executed by computer-executable components integrated with the computer-readable medium and/or processing system. The computer-readable medium may include any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, non-transitory computer readable media, or any suitable device. The computer-executable component can include a computing system and/or processing system (e.g., including one or more collocated or distributed, remote or local processors) connected to the non-transitory computer-readable medium, such as CPUs, GPUs, TPUS, microprocessors, or ASICs, but the instructions can alternatively or additionally be executed by any suitable dedicated hardware device.
Algorithm(s):
Determine a set of properties:
Paragraph 0019 : In an illustrative example, the method can include: determining a property (e.g., receiving a set of addresses and/or geolocations, one or more properties, etc.);
Determine a parcel for each property of the set of properties
Paragraph 0033 - . A property identifier (property ID) can include: geographic coordinates, an address, a parcel identifier, a location identifier (e.g., Google Plus Codes™, Geohashes™, Place Key™, etc.), a block/lot identifier, a planning application identifier, a municipal identifier (e.g., determined based on the ZIP, ZIP+.sub.4, city, state, etc.), and/or any other identifier. The property identifier can be used to retrieve property information, such as parcel information (e.g., parcel boundary, parcel location, parcel area, parcel shape, parcel geometry, etc.), property measurements, property descriptions, and/or other property data. The property identifier can additionally or alternatively be used to identify a property component, such as a primary building or secondary building, and/or be otherwise used.
Extracting a parcel feature set for each parcel;
Paragraph 0051- The attributes can be determined from property information (e.g., property measurements, property descriptions, etc.), a database 120 or a third party source (e.g., third-party database, MLS™ database, city permitting database, historical weather and/or hazard database, tax assessor database, etc.), be predetermined, be calculated (e.g., from an extracted value and a scaling factor, etc.), and/or be otherwise determined. In a first example, the attributes can be determined by extracting features from property measurements, wherein the attribute values can be determined based on the extracted feature values. In a second example, a trained attribute model can predict the attribute value directly from property information (e.g., based on property imagery, descriptions, etc.). In a third example, the attributes can be determined by extracting features from a property description (e.g., using a sentiment extractor, keyword extractor, etc.). However, the attributes can be otherwise determined. In examples, property attributes and/or values thereof can defined and/or determined as disclosed in U.S. application Ser. No. 18/092,689 filed 3 Jan. 2023, U.S. application Ser. No. 17/526,769 filed 15 Nov. 2021, U.S. application Ser. No. 17/546,620 filed 9 Dec. 2021, U.S. application Ser. No. 17/529,836 filed 18 Nov. 2021, U.S. application Ser. No. 17/749,385 filed 20 May 2022, U.S. application Ser. No. 18/121,114 filed 14 Mar. 2023, U.S. application Ser. No. 17/858,422 filed 6 Jul. 2022, U.S. application Ser. No. 17/981,903 filed 7 Nov. 2022, U.S. application Ser. No. 17/968,662 filed 18 Oct. 2022, U.S. application Ser. No. 17/841,981 filed 16 Jun. 2022, U.S. application Ser. No. 18/141,033 filed 28 Apr. 2023, U.S. application Ser. No. 18/098,841 filed 19 Jan. 2023, and/or U.S. application Ser. No. 18/100,736 filed 24 Jan. 2023, each of which is incorporated in its entirety by this reference (e.g., wherein features and/or feature values disclosed in the references can correspond to attributes and/or attribute values).
Determine a parcel class for the parcel for each property of the set of properties based on the respective parcel features set, using the classification model, wherein the classification model is trained, using qualitative labels, to predict training parcel classes based on training parcel feature sets;
Paragraph 0110- In a first variant, classifying the one or more parcels can include determining a parcel class using a parcel model, such as a classifier, trained to classify one or more parcels based on shape, similarity to adjacent parcels, area, relationship to objects segmented out of a corresponding measurement, and/or any other suitable parcel parameter. In a first example, the classifier can be trained on parcels labeled with “unit parcel”, “surrounding parcel”, “stand-alone parcel”, and/or other parcel classes, wherein the classifier learns to determine the parcel classification for an unknown parcel (e.g., based on features extracted from the parcel information). The labels for the training data can be manually determined, determined using heuristics (e.g., as discussed below), and/or otherwise determined. In a second example, the classifier can be trained on a set of parcels labeled with parcel-parcel relationships (e.g., this first parcel is associated with this surrounding parcel, while this second parcel is not associated with the surrounding parcel), wherein the classifier learns to determine a parcel's relationship with other parcels (e.g., based on features extracted from the information for the respective parcels). In a third example, the classifier can be trained on a combination of the training information from the first and second examples. However, the classifier can be otherwise structured, trained, and/or used.
Grouping properties of the set of properties based on the respective parcel classes
Paragraph 0121- Determining whether the property is part of a group S200 functions to aggregate the property with other properties of the same group, and/or to determine that a group should exist. S200 can be performed after S100, concurrently with S100, before S300, concurrently with S300, and/or at any other suitable time. The group can be: one group, multiple groups, and/or any other suitable number of groups. Whether the property is part of a group is preferably automatically determined (e.g., by a model), but can additionally and/or alternatively be manually determined (e.g., by an inspector, by a user on an interface, etc.). Whether the property is part of a group can be determined using: a group determination model (e.g., rules, heuristics, a classifier, etc.), a parcel model, and/or any other suitable model and/or methodology. In variants, models and/or methodologies can be tuned and/or adjusted for a use case.Paragraph 0122- S200 can determine: whether a group exists; whether a given property is or should be part of a group (e.g., a binary determination); which group a property should belong in (e.g., which set of properties the property should be included within; which group identifier should be assigned to the property; whether a group encompasses a property; etc.); and/or otherwise determine property membership within a group.Paragraph 0123- In a first variant, S200 can be performed based on the parcel classification for a given property. In this variant, different parcel classifications can be associated with whether or not a property should be part of a group (e.g., a binary determination), wherein a downstream process can be used to determine which group the property should be associated with. For example, if a parcel associated with the property is classified as a unit parcel (e.g., the parcel intersects a building segment extracted from a measurement depicting the property), the property is determined to be part of a group (example shown in FIG. 11). Otherwise, the property is determined to not be part of a group. property membership within a group can be otherwise determined based on the property's parcel's classification.Paragraph 0124- In a second variant, whether the property is part of a group can be determined based on property information similarity between the building segments detected within proximal parcels. In this variant, the building segments can be determined using parcel information (e.g., only a measurement region corresponding to the parcel is segmented), or be determined without using parcel information (e.g., building segmentation is performed without the parcel) and associated with the parcel after segmentation. In an embodiment, if a building segment associated with the parcel has the same appearance and/or features as a building segment associated with an adjacent parcel, the property is determined to be part of a group. Otherwise, the property is determined to not be part of a group. For example, two properties can be associated with the same group when they are located within adjacent or proximal parcels (e.g., separated by less than a threshold distance), and when the features and/or attributes extracted from the respective building segments (e.g., appearance features, geometric features, attribute values, etc.) are separated by less than a threshold distance in feature space (e.g., less than a threshold cosine distance, Euclidean distance, Manhattan distance, Chebychev distance, etc.); example shown in FIG. 5. Otherwise, the property is determined to not be part of a group. In a third embodiment, if a building segment depicting the property or the parcel associated with the property falls within a hole in a surrounding parcel, the property is determined to be part of a group (example shown in FIG. 10). Otherwise, the property is determined to not be part of a group. However, property membership within a group can be otherwise determined based on the property information similarity between properties with proximal parcels.Paragraph 0125- In a third variant, whether the property is part of a group can be determined based on a second parcel that entirely or partially surrounds the parcel associated with the property. For example, if a parcel associated with the property falls within (e.g., wholly encompassed within) a hole of a surrounding parcel, the property is determined to be part of a group associated with the surrounding parcel (example shown in FIG. 9). Otherwise, the property is determined to not be part of a group; example shown in FIG. 6. However, property membership within a group can be otherwise determined based on the property's parcel's relationship with surrounding parcels.Paragraph 0126- In a fourth variant, whether the property is part of a group can be determined based on the property's relationship to a predetermined set of property components (e.g., property components that have a high probability of being shared, such as a pool and/or a tennis court). In a first example, if the property is within a threshold distance (e.g., 100 ft, 1000 ft, 10000 ft, any range therein or value therebetween, etc.) from a component segment for a component of the predetermined set (e.g., determined from the same or different measurement from the building segment, such as a pool and/or a tennis court segment, determined using an object detector; a manually-specified location of the component, etc.), the property is determined to be part of a group associated with the component. In a second example, if a parcel associated with the property is within a threshold distance from a hole of surrounding parcel, wherein the hole of the surrounding parcel is in the shape of a component of the predetermined set, the property is determined to be a part of a group associated with the component. The threshold distance can be manually determined (e.g., by a user) or be automatically determined (e.g., by a model). The threshold distance can be predetermined (e.g., hardcoded, retrieved from a database, etc.) or be dynamically determined (e.g., based on a geographic region, based on a property type, based on a population, etc.). However, property membership within a group can be otherwise determined based on the property's relationship (e.g., building segment relationship, parcel relationship) with a set of predetermined property components.Paragraph 0127- In a fifth variant, a property can be considered part of a group when the property's parcel intersects a contiguous building segment. This can occur when a row of different properties share a common roof, when the roofs of the different properties appear uniform enough to be segmented as a unitary building segment by the building segmentation model, or otherwise occur. The property can be considered part of the group when the respective parcel overlaps less than 50% of the intersected building segment, less than 60% of the intersected building segment, less than 80% of the intersected building segment, and/or is otherwise related to the intersected building segment. However, property membership within a group can be otherwise determined based on the property parcel's relationship with building segments overlapping the property parcel.Paragraph 0128- In a sixth variant, whether the property is part of a group can be manually specified.
-“determine an adjacent property associated with a property that is determined to be part of the group; determining a parcel associated with the adjacent property and a parcel associated with the property (see claim 14)
Paragraph 0124- In a second variant, whether the property is part of a group can be determined based on property information similarity between the building segments detected within proximal parcels. In this variant, the building segments can be determined using parcel information (e.g., only a measurement region corresponding to the parcel is segmented), or be determined without using parcel information (e.g., building segmentation is performed without the parcel) and associated with the parcel after segmentation. In an embodiment, if a building segment associated with the parcel has the same appearance and/or features as a building segment associated with an adjacent parcel, the property is determined to be part of a group. Otherwise, the property is determined to not be part of a group. For example, two properties can be associated with the same group when they are located within adjacent or proximal parcels (e.g., separated by less than a threshold distance), and when the features and/or attributes extracted from the respective building segments (e.g., appearance features, geometric features, attribute values, etc.) are separated by less than a threshold distance in feature space (e.g., less than a threshold cosine distance, Euclidean distance, Manhattan distance, Chebychev distance, etc.); example shown in FIG. 5. Otherwise, the property is determined to not be part of a group. In a third embodiment, if a building segment depicting the property or the parcel associated with the property falls within a hole in a surrounding parcel, the property is determined to be part of a group (example shown in FIG. 10). Otherwise, the property is determined to not be part of a group. However, property membership within a group can be otherwise determined based on the property information similarity between properties with proximal parcels.
Processing system is further configured to determine a set of final groups by iteratively determining parcel classes and groups for each of the properties of the set of properties (see claim 15)
Paragraph 0096- The method can be iteratively performed for individual properties (e.g., a property first is classified as part of a group, then an adjacent property is analyzed for inclusion in the group, etc.); be concurrently performed for multiple properties (e.g., identified in a request, wherein the request can include one or more property identifiers; depicted in a measurement; etc.); and/or performed serially and/or in parallel for any other suitable number of properties.
Wherein the processing system is further configured to determine a confidence score associated with the final group using a confidence score model based on properties within the final group and a set of auxiliary features (see claim 16)
Paragraph 0132- The auxiliary features are preferably determined manually (e.g., by a user on an interface), but can additionally and/or alternatively be determined automatically and/or otherwise determined. During inference, the predicted list of properties and/or parcels of a group and the associated auxiliary features can optionally be ingested by a confidence score model (described above), which outputs a confidence score (e.g., how likely the prediction is correct for the group); example shown in FIG. 12. However, the confidence score model can have any other suitable inputs and/or outputs. The predicted list of properties can be refined (e.g., by adding and/or removing a parcel and/or a property associated with the parcel) to maximize the confidence score and/or otherwise refined. A parcel and/or a property associated with the parcel to be added and/or removed from the list of properties can be determined using heuristics, probabilities, a different model, randomly, manually, and/or otherwise determined. For example, every subset combination of the set of predicted list of properties (e.g., greater than a particular number of properties) and the subset combination's associated auxiliary features are inputted into the confidence score model that outputs a confidence score, wherein the subset combination associated with the highest confidence score is selected.
Processing system is further configured to: determine a neighboring property neighboring a property of a group; and include the neighboring property as part of the group when a comparison metric satisfies a threshold
Paragraph 0135- In a third variant, S300 can include identifying other properties within the group based on a comparison metric (e.g., similarity score), which can include: determining a set of neighboring properties (e.g., within a geofence/range of the property, within a surrounding parcel, within an area of a dividing object such as public roads, fences, hedges, and walkways, etc.) neighboring a reference property; determining a descriptive parameter for each property; determining a comparison metric between the neighboring property and the reference property based on the descriptive parameters; and associating the neighboring property as part of the same group if the respective comparison metric satisfies a threshold (e.g., similarity score exceeds a threshold, distance falls between a threshold, etc.). Additionally or alternatively, a neighboring property can be included within the group when the neighboring property has a descriptive parameter with a comparison metric that falls within a threshold of an aggregate descriptive parameter for the set of properties already within the group (e.g., an average feature vector, a majority feature vector, the most common attribute values, etc.). The reference property can be: the property, an outermost property determined to be within the group, and/or any other suitable property. The descriptive parameter can include: appearance (e.g., an appearance feature vector), geometry (e.g., a geometry feature vector), a set of attribute values for a property attribute (e.g., structural attribute, such as roof shape, roof facet count, roof pitch, etc.), and/or any other suitable parameter. The method can optionally align building segments (e.g., such that street-facing sides are oriented in the same direction) before calculating the descriptive parameter; extract descriptive parameters for a set of transformations, rotations, or poses (e.g., wherein preliminary similarity scores are determined for each transformation, rotation, or pose of the set, and the final similarity score is determined based on the most similar transformation, rotation, or pose); and/or otherwise manipulate the building segments before or after descriptive parameter extraction. The property attribute is preferably a structural attribute (e.g., for a primary structure, accessory structure, neighboring structure, etc.), but can additionally and/or alternatively be a condition-related attribute (e.g., roof condition, etc.), and/or any other suitable attribute. The attribute value for a property (e.g., reference property, neighboring property, etc.) can be determined using a model that ingests a measurement depicting the property and optionally auxiliary information (e.g., parcel data, text descriptors, etc.) and outputs an attribute value for the property; example shown in FIG. 7. The model can be tuned and/or adjusted for a use case or not be tuned and/or adjusted for a use case. The comparison metric can be: a loss function, a distance metric, a similarity metric (e.g., cosine similarity, Manhattan similarity, Mahalanobis similarity, etc.), a dissimilarity metric, and/or any other suitable metric. The threshold can be predetermined (e.g., hardcoded, retrieved from a database 120, etc.) or be dynamically determined. The threshold can be determined based on: a physical distance between properties, a number of properties in an adjacent or otherwise similar group (e.g., same entity, same developer, similar appearance cues, similar parcel, etc.), a geographic region, a climate, a property type, heuristics (e.g., to evaluate whether a set of influential features or attributes are similar), and/or otherwise determined. In a first example, physical distance between properties can have a direct relationship (e.g., the variables increase or decrease together) with the threshold. When the physical distance between two properties is short, the threshold is determined to be lower. In a second example, the number of properties in the group can have an inverse relationship with the threshold. When the number of properties in the group is large, the threshold is determined to be lower. However, the threshold can be otherwise determined.
Applicant’s specification does not disclose the algorithm for claim 14 specifically “and determine the adjacent property and the property are part of the same group when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently”. See corresponding 112 first/a and second/b rejections above.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-8,10-13, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,7,9, 11-12, 17-18, and 22 of U.S. Patent No. 12, 229,845.
Although the claims at issue are not identical, they are not patentably distinct from each other because all the limitations of claims 1-8,10-13, and 20 are found in claims 1,7,9, 11-12, 17-18, and 22 of U.S. Patent No. 12, 229,845.
Specifically:
All of the limitations of claims 1-3 are found in claim 1 of US 1229845.
Claim 4 is found in claims 11 and 18 of US 1229845.
Claim 5 is found in claims 11 and 22 of US 1229845.
Claim 6 is found in claim 7 of US 1229845.
Claim 7 is found in claim 1 of US 1229845, specifically it is noted that claim 7 is mapped to “ determining whether each property of the set of properties is part of a group based on the respective parcel class and the respective building segment”
Claim 8 is found in claim 1 of US 1229845
Claim 10 is found in claim 9 of US 1229845, specficially it is noted the first and set group of claim 9 maps to the set of groups of claim 10
Claim 11 is found in claim 11 of US 1229845, it is noted that “extracting a parcel feature” maps to an extract building segment.
Claim 12 is found in claim 12 of US 1229845,
Claim 13 is found in claim 11 of US 1229845, note “wherein the parcel feature set is extracted based on a parcel boundary” maps to at least “ determine a set of measurements depicting each of a set of properties; extract a building segment for each property of the set of properties from the set of measurements using a segmentation model comprising”
Claim 20 is found in claim 17 of US 1229845
U.S. Patent No. 12, 229,845 independent claims recite more elements and are thus more specific. Therefore the present application is in effect a genus to the species of U.S. Patent No. 12, 229,845. The species anticipates the genus being claimed in the application currently being examined, and therefore a patent to the genus (present application number 19014832)would improperly extend the right to exclude granted by a patent to the species or sub-genus should the genus issue as a patent after the species or sub-genus.
See MPEP 804 cited herein:
2. Anticipation Analysis
A nonstatutory double patenting rejection is appropriate where a claim in an application under examination claims subject matter that is different, but not patentably distinct, from the subject matter claimed in a prior patent or a copending application. The claim under examination is not patentably distinct from the reference claim(s) if the claim under examination is anticipated by the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 1052, 29 USPQ2d 2010, 2015-16 (Fed. Cir. 1993). This type of nonstatutory double patenting situation arises when the claim being examined is, for example, generic to a species or sub-genus claimed in a conflicting patent or application, i.e., the entire scope of the reference claim falls within the scope of the examined claim. In such a situation, a later patent to a genus would, necessarily, extend the right to exclude granted by an earlier patent directed to a species or sub-genus. In this type of nonstatutory double patenting situation, an obviousness analysis is not required for the nonstatutory double patenting rejection. The nonstatutory double patenting rejection in this case should explain the fact that the species or sub-genus claimed in the conflicting patent or application anticipates the claimed genus in the application being examined and, therefore, a patent to the genus would improperly extend the right to exclude granted by a patent to the species or sub-genus should the genus issue as a patent after the species or sub-genus
Claims 9 and 15-19 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,7,9, 11-12, 17-18, and 22 of U.S. Patent No. US 12, 229,845 further in view of Gross (United States Patent Application Publication Number: US 2016/0048934).
Claim 9 is not found in US 1229845 of “wherein properties within the final group are associated with a common entity.
However Gross teaches
wherein properties within the final group are associated with a common entity. (see paragraphs 0225-0226, Examiner’s note: various ways to group information like by zip code or by number like 10 in this examples, further this is based on the common entity like zip code, type, condition, or the user request information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US 1229845 with the aforementioned teachings from Gross with the motivation of providing a way to provide information related to shared features in the final group (see Gross paragraphs 0225-0226), when providing information related to shared features is known (see US 1229845 claims 5-6)
-Claim 15 is not found in US 1229845 of “wherein the processing system is further configured to determine a set of final groups by iteratively determining parcel classes and groups for each of the properties of the set of properties’
However, Gross teaches
wherein the processing system is further configured to determine a set of final groups by iteratively determining parcel classes and groups for each of the properties of the set of properties. (see paragraphs 0138-0139, 0154, and 0156 Examiner’s note: teaches refining a group to determine desired information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US 1229845 with the aforementioned teachings from Gross with the motivation of providing a way to determine a final group by refining constraints (see paragraphs 0138-0139, 0154, and 0156), when merging groups together based on similarity is known (see claim 10 and 19)
-Claim 16 is not found in US 1229845 of “wherein the processing system is further configured to determine confidence score associated with the final group using a confidence score model based on properties within the final group and a set of auxiliary features’
However, Gross teaches
wherein the processing system is further configured to determine a confidence score associated with the final group using a confidence score model based on properties within the final group and a set of auxiliary features (see paragraphs 0162 and 0168, Examiner’s note: using confidence scores).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US 1229845 with the aforementioned teachings from Gross with the motivation of providing a way to provide information on the confidence of the calculation or estimation (see Gross paragraphs 0162 and 0168), when making an estimation is known (e.g. classifying according to a model)(see US 1229845 claim 1)
Claim 17 is found in claim 19 of US 1229845. Claim 18 is found in claim 17 of US 1229845. Claim 19 is found in claims 7 and 17 of US 1229845. None of the limitations in claims 17-19 of the present application (19014832) are not taught by US 1229845. However, these limitations depend off of claims 15, 17, and 18 respectively and therefore are referenced in this section as claim 15 relies upon Gross.
Subject Matter Where Prior Art is not applied
5. Prior art is not applied to claim 14, however the claims are rejected under other grounds (101, 112 a/first, and 112 second/b, see office action below).
As per claim 14, Gross (US 2016/0048934) teaches
wherein the processing system is further configured to: determine an adjacent property associated with a property that is determined to be part of the group; determine a parcel associated with the adjacent property and a parcel associated with the property; (see Figure 30L and paragraphs 0332 and 0349, Examiner’s note: teaches determining properties that are adjacent and providing information related to adjacent properties).
Further Gross teaches each attribute my defined in image processing by curves (see paragraph 0122).
Gross does not expressly teach and determine the adjacent property and the property are part of the same group when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently.
Mrvaniya et al. (US 11,393,194) which is in the art of image analysis property detection teaches removing boundaries based on neighboring property contour perimeter, contour area, convex hull area, a ratio between contour area and or contour perimeter (see column 5 lines 50- column 6 lines 35).
And Saxena et al. (United States Patent Application Publication number: US 2020/02355515) teaches grouping one or more features based on a shape algorithm like a convex hull (see paragraphs 0060 and 0062)
However these while similar concepts does not read on and determine the adjacent property and the property are part of the same group when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently, when viewed in combination of the other elements of the claim and the claim from which the claim depends. Further there is no motivation to combine this teachings from Mrvaniya or Saxena et al. with the aforementioned teachings from Gross without improper hindsight.
Therefore a prior art rejection has not been applied.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) the idea of collecting information, analyzing it and displaying results of the collection analysis, where the specific information relates to property information.
The claims are recited at such a high level of generality the claims recite observations, evaluations, judgements, and opinions that could be performed in the human mind or with use of a physical aid (e.g. pen and paper) to perform the claim limitation therefore the claims recite a mental process.
Further the idea of collecting information, analyzing it and displaying results of the collection analysis, where the specific information relates to property information is a fundamental economic principle or practice, which is a certain method of organizing human activity.
Mental processes and certain methods of organizing human activity are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and or (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
As per claim 1, the claims recite mental process and or human activity steps specifically using a classification model to predict a parcel class, by comparing previous or known information including where the model comprises a qualitive label for each of a set of parcels associated with the set of properties based on features extracted from parcel information for each of the set of parcels, determining a property, determining a parcel class for the parcel using a classification model, and determining whether the property is part of a group based on the parcel class, given the broad recitation in the claim.
The additional elements that the model is “trained” and the known or previous information is “training data” merely results apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 integrate a judicial exception into a practical application or provide significantly more. Further here the claim recites only the idea of a solution or outcome but fails to recite details of how a solution to a problem is accomplished. Rather here Applicant recites a result oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words apply it.
Further the additional elements that the model is “trained” and the known or previous information is “training data” merely results in generally linking it to the field of computers.
As per claim 2, the claims recite mental process and or human activity steps specifically determining a measurement of a property, and extracting a building segment for the property using a segmentation model, where the property is part of a group based on a building segment. There are no additional elements beyond those previously discussed above, given the broad recitation in the claim.
As per claim 3, the claims further define the segmentation that as discussed above is part of the abstract idea. The additional element that further defines the segmentation model is a neural network merely results in apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 integrate a judicial exception into a practical application or provide significantly more. Further here the claim recites only the idea of a solution or outcome but fails to recite details of how a solution to a problem is accomplished. Rather here Applicant recites a result oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words apply it.
Further the additional element that that the model is a “neural network” merely results in generally linking it to the field of computers.
As per claim 4, the claims recite the measurement is from an aerial image. This is a mental process and or human activity step given the broad recitation in the claim. There are no additional elements beyond those previously discussed above.
As per claim 5, the claims define the different type of parcel classes such are mental process and or human activity steps. There are no additional elements beyond those previously discussed above.
As per claim 6, the claims recite mental process and or human activity steps of determining neighboring property that is determined to be part of the group, and determining this based on a comparison of a descriptive parameter of the neighboring property and a parameter of the property. There are no additional elements beyond those previously discussed above given the broad recitation in the claim.
As per claim 7, the claims recite mental process and or human activity steps of determining other properties within the group when the property is determined to be part of the group. There are no additional elements beyond those previously discussed above.
As per claim 8, the claims recite mental process and or human activity steps of determining a final group comprising properties that are determined to be part of the group. There are no additional elements beyond those previously discussed above.
As per claim 9, the claims recite mental process and or human activity steps of properties within the final group are associated with a common entity. There are no additional elements beyond those previously discussed above.
As per claim 10, the claims recite mental process and or human activity steps of merging the group with a set of groups to determine a final group. There are no additional elements beyond those previously discussed above.
As per claim 11, the claims recite mental process and or human activity steps specifically using a classification model to determine a parcel class for each property, by comparing previous or known information including where the model comprises qualitive labels, determining a set of properties, determining a parcel class for each of the set of properties, extracting a feature for each parcel, and grouping properties based on their respective classes, given the broad recitation in the claim.
The additional element that the model is “trained” and the known or previous information is “training data” merely results apply it or generally linking it to the field of computers as discussed above in claim 1.
The additional element that these limitations are being performed by a “processing system” merely results apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 integrate a judicial exception into a practical application or provide significantly more. Further here the claim recites only the idea of a solution or outcome but fails to recite details of how a solution to a problem is accomplished.
Further the additional elements that these human activities or mental process steps are being performed by a processing system merely results in generally linking it to the field of computers.
As per claim 12, the claims recite mental process and or human activity steps of the parcel class is determined based on a relationship between the parcel and a building segment associated with the property. There are no additional elements beyond those previously discussed above.
As per claim 13, the claims recite mental process and or human activity steps of the parcel feature set is extracted based on a parcel boundary. There are no additional elements beyond those previously discussed above.
As per claim 14, the claims recite mental process and or human activity steps of determine an adjacent property associated with a property that is determined to be part of the group, determine a parcel associated with the adjacent property and a parcel associated with the property, and determine the adjacent property and the property are part of the same group, when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently. The additional element that this is being performed by a processing system merely results in apply it or generally linking it to the field of computers as discussed in claim 11 above.
As per claim 15, the claims recite mental process and or human activity steps of determine a set of final groups by iteratively determining parcel classes and groups for each of the properties of the set of properties. The additional element that this is being performed by a processing system merely results in apply it or generally linking it to the field of computers as discussed in claim 11 above.
As per claim 16, the claims recite mental process and or human activity steps of determine a confidence score associated with the final group using a confidence score model based on properties within the final group and a set of auxiliary features. The additional element that this is being performed by a processing system merely results in apply it or generally linking it to the field of computers as discussed in claim 11 above.
As per claim 17, the claims recite mental process and or human activity steps of the final group is determined by merging a first group with a second group. There are no additional elements beyond those previously discussed above
As per claim 18, the claims recite mental process and or human activity steps of the first and second group are merged based on a comparison between a first and second value for summary descriptive parameters for the first and second groups. There are no additional elements beyond those previously discussed above
As per claim 19, the claims recite mental process and or human activity steps of wherein the summary descriptive parameter comprises an average. There are no additional elements beyond those previously discussed above
As per claim 20, the claims recite mental process and or human activity steps of determine a neighboring property neighboring a property of a group and include the neighboring property as part of the group when a comparison metric satisfied a threshold. The additional element that this is being performed by a processing system merely results in apply it or generally linking it to the field of computers as discussed in claim 11 above.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above under the practical application step.
Claim Rejections - 35 USC § 112
8. 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.
9. Claim 14 is 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 per claim 14, Applicant’s specification does not disclose the algorithm for claim 14 specifically the limitation “and determine the adjacent property and the property are part of the same group when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently”. This limitation is interpreted under 112 sixth/f see claim interpretation section below.
The specification does not recite the algorithm for performing this function.
Specifically, Applicant merely recites results not a series of steps for how the claimed determination is made. See MPEP 2181, cited herein: “An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary, Microsoft Press, 5th edition, 2002. Applicant may express the algorithm in any understandable terms including as a mathematical formula, in prose, in a flow chart, or "in any other manner that provides sufficient structure."”
Specifically here Applicant does not disclose how the processing device or computer determines the convexity of the parcel boundary for the combined parcel and the convexity of the parcel boundary for each parcel independently to then perform the determination. No sequence of steps, flowchart, example, etc. is provided. Rather Applicant merely discloses the results of the determination, and what do with the results of the determination. One of ordinary skill in the art would not understand the sequence of steps that perform the function claimed, and therefore convey that the inventors at the time the application was filed had possession of the claimed invention. Therefore Applicant does not recite the algorithm for performing the claimed function, and therefore the claims do not comply with the written description requirement and must be rejected under 112 a/first.
Examiner notes below the sections, where convexity is cited in Applicant’s specification for reference.
Paragraph 0042- One or more property features can be extracted from the property information (and/or a set thereof). A feature can represent aspects of the information itself (e.g., aspects of the measurement). Features can be independent (e.g., do not carry information about and/or are not dependent on the values of other features) or dependent (e.g., determined based on another feature, dependent upon another feature, etc.). Examples of features that can be determined include: geometric features (e.g., aspects of a geometric measurement), appearance-based features (e.g., aspects of an image or appearance measurement), interaction-based features (e.g., how geometries interact with each other, how attributes interact with each other, etc.), and/or other features. Examples of features that can be extracted can include: color components, length, area, circularity, gradient magnitude, gradient direction, points, edges, measurement unit intensity values, convexity gain (e.g., whether the total convexity of two combined geometries is higher than the convexities of the geometries alone), and/or other features. Features can be determined using: image processing, point cloud processing, machine learning techniques (ex. extracted using an encoder, extracted by a neural network or an intermediate layer thereof, etc.), SIFT, using a Gaussian, edge detection, corner detection, blob detection, ridge detection, edge direction, changing intensity, autocorrelation, thresholding, blob extraction, template matching, Hough transform, etc.), and/or any other suitable set of methodologies.
Paragraph 0115- In a fourth embodiment of the second variant, a parcel can be classified as a unit parcel when the overall convexity of the parcel, fit against another parcel (e.g., an adjacent parcel), has a higher convexity than the parcels' individual convexity.
Paragraph 0116- In a fifth embodiment of the second variant, a parcel can be classified based on the feature values and/or attribute values (e.g., using heuristics, a classifier, etc.).Paragraph 0117- However, the parcel can be otherwise determined based on rules and/or heuristics.Paragraph 0118- However, the one or more parcels can be otherwise classified.Paragraph 0119- S100 can optionally include determining parcel information for one or more parcels. The parcel information can be determined for a single parcel, for each parcel of multiple parcels, for combined multiple parcels (e.g., geometric interactions of multiple parcels such as combined shape of multiple parcels), and/or otherwise determined. The parcel information can be associated with one parcel, multiple parcels, and/or any other suitable number of parcels. Parcels can be adjacent to each other or not be adjacent to each other. Parcel information can include: parcel boundary, parcel position, parcel area, parcel shape, parcel perimeter, parcel convexity, other parcel geometry, and/or any other suitable information. In examples, the parcel information includes a convexity of a boundary for a single parcel and/or a convexity of boundary for combined parcels.
Paragraph 0138- In a sixth variant, S300 can include identifying other properties within the group based on parcel information for parcels. In an example, given two parcels (a parcel associated with the property and a parcel associated with a different property), if a convexity of the boundary for the combined parcels is greater than a convexity of the boundary for each parcel independently, the properties associated with the parcels are determined to be part of the same group. In another example, parcel subgroups can be combined when the convexity of the combined parcel subgroups is greater than the convexity of each parcel subgroup independently.
10. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
11. Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “ where the processing system is further configured to…. Determine the adjacent property and the property are part of the same group when a convexity of a parcel boundary for the combined parcels is greater than a convexity of a parcel boundary for each parcel independently” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Specifically the specification does not recite the algorithm for performing this function. Applicant merely recites results not a series of steps for how a determination is made. See MPEP 2181 “An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary, Microsoft Press, 5th edition, 2002. Applicant may express the algorithm in any understandable terms including as a mathematical formula, in prose, in a flow chart, or "in any other manner that provides sufficient structure."”
Specifically here Applicant does not disclose how the processing device or computer determines the convexity of the parcel boundary for the combined parcel and the convexity of the parcel boundary for each parcel independently to then perform the determination. No sequence of steps, flowchart, example, etc. is provided. Rather Applicant merely discloses the results of the determination, and what do with the results of the determination. Therefore Applicant does not recite the algorithm for performing the claimed function, and therefore the claims are indefinite and therefore rejected under 112 second/b (see MPEP 2181). Additionally see above, cited paragraphs 0042, 0115-0119, and 0138 where convexity is cited in Applicant’s specification.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
15. 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.
16. 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.
17. Claim(s) 1-13 and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Gross (United States Patent Application Publication Number: US 2016/0048934).
As per claim 1, Gross teaches A method, comprising: (see paragraph 0004, Examiner’s note: automated tool method).
determining a classification model trained to predict a training parcel class, comprising a qualitative label, for each of a set of training parcels associated with a set of training properties, based on features extracted from parcel information for each of the set of training parcels; (see paragraphs 0122-0123, and 0137, Examiner’s note: teaches a machine learning algorithm which can be a neural network to provide matching properties and reports based on input, where the network is trained on tagged housing or real estate information).
determining a property; determining a parcel class for a parcel associated with the property using the classification model; and determining whether the property is part of a group based on the parcel class (see paragraphs 0141-0147 and 0154-0155, Examiner’s note: providing outputs of properties of interest to the user based on inputs (see paragraphs 0154-0155), where these outputs are considered a group related to the user requested property constraints (see paragraphs 0141-0147 and 0154-0155)).
As per claim 2, Gross teaches
further comprising: determining a measurement depicting the property; and" extracting a building segment for the property from the measurement using a segmentation model, wherein whether the property is part of the group is further determined based on the building segment (see paragraphs 0187-0188, Examiner’s note: measurements that are used to determine the overall structure and used to provide further related information).
As per claim 3, Gross teaches
wherein the segmentation model comprises a neural network (see paragraph 0121, Examiner’s note: objects can be analyzed with neural networks).
As per claim 4, Gross teaches
wherein the measurement comprises an aerial image (see paragraph 0193, 0209, Examiner’s note: aerial drone image data).
As per claim 5, Gross teaches
wherein the parcel class comprises at least one of a unit parcel, a surrounding parcel, or a stand-alone parcel (see paragraphs 0145, 0148, 0156, and Figure 30L, Examiner’s note: these are very broad alternatives and could read on different matching features of the property or real estate like matching properties of a specific roof type, roof color (see paragraph 0156), type of house like craftsman (see paragraph 0148), Different types of facades (see paragraph 0145) or alternatively providing information regarding neighbors (see Figure 30L).
As per claim 6, Gross teaches
further comprising: determining a neighboring property neighboring a property that is determined to be part of the group; and determining whether the neighboring property is part of the group based on a comparison between a descriptive parameter of the neighboring property and a descriptive parameter of the property (see paragraphs 0223 and 0225, Examiner’s note: providing property information based on zip codes).
As per claim 7, Gross teaches
further comprising determining other properties within the group when the property is determined to be part of the group. (see paragraphs 0223 and 0225, Examiner’s note: providing property information based on zip codes).
As per claim 8, Gross teaches
further comprising determining a final group comprising properties that are determined to be part of the group (see paragraphs 0225-0226, Examiner’s note: various ways to group information like by zip code or by number like 10 in this examples).
As per claim 9, Gross teaches
wherein properties within the final group are associated with a common entity. (see paragraphs 0225-0226, Examiner’s note: various ways to group information like by zip code or by number like 10 in this examples, further this is based on the common entity like zip code, type, condition, or the user request information).
As per claim 10, Gross teaches
wherein the final group is determined by merging the group with a set of groups to determine the final group (see paragraphs 0138-0139, 0154, and 0156, Examiner’s note: teaches refining a group to determine desired information).
As per claim 11, Gross teaches A system, comprising: (see paragraph 0004, Examiner’s note: automated system).
a processing system, configured to: (see paragraph 0100, Examiner’s note: software running on a computer to perform functions).
determine a set of properties; determine a parcel for each property of the set of properties; extracting a parcel feature set for each parcel; determine a parcel class for the parcel for each property of the set of properties based on the respective parcel feature set, using the classification model, wherein the classification model is trained, using qualitative labels, to predict training parcel classes based on training parcel feature sets; (see paragraphs 0122-0123, and 0137, Examiner’s note: teaches a machine learning algorithm which can be a neural network to provide matching properties and reports based on input, where the network is trained on tagged housing or real estate information).
grouping properties of the set of properties based on the respective parcel classes. (see paragraphs 0141-0147 and 0154-0155, Examiner’s note: providing outputs of properties of interest to the user based on inputs (see paragraphs 0154-0155), where these outputs are considered a group related to the user requested property constraints (see paragraphs 0141-0147 and 0154-0155)).
As per claim 12, Gross teaches
wherein the parcel class is determined based on a relationship between the parcel and a building segment associated with the property. (see paragraphs 0187-0188, Examiner’s note: measurements that are used to determine the overall structure and used to provide further related information).
As per claim 13, Gross teaches
wherein the parcel feature set is extracted based on a parcel boundary (see paragraphs 0138, 0223, and 0225, Examiner’s note: zip code, city bock, street etc).
As per claim 15, Gross teaches
wherein the processing system is further configured to determine a set of final groups by iteratively determining parcel classes and groups for each of the properties of the set of properties. (see paragraphs 0138-0139, 0154, and 0156 Examiner’s note: teaches refining a group to determine desired information).
As per claim 16, Gross teaches
wherein the processing system is further configured to determine a confidence score associated with the final group using a confidence score model based on properties within the final group and a set of auxiliary features (see paragraphs 0162 and 0168, Examiner’s note: using confidence scores to provide and refine infromation).
As per claim 17, Gross teaches
wherein the final group is determined by merging a first group with a second group. (see paragraphs 0138-0139, 0154, and 0156 Examiner’s note: teaches refining a group to determine desired information).
As per claim 18, Gross teaches
wherein the first group and the second group are merged based on a comparison between a first value for a summary descriptive parameter for the first group and a second value for the summary descriptive parameter for the second group. (see paragraphs 0138-0139, 0154, and 0156 Examiner’s note: teaches refining a group to determine desired information).
As per claim 19, Gross teaches
wherein the summary descriptive parameter comprises an average (see paragraphs 0225, 0227, 0286, 0318, 0463, Examiner’s note: various uses of average to determine parameters including average condition, average leads, and below average).
As per claim 20, Gross teaches
wherein the processing system is further configured to: determine a neighboring property neighboring a property of a group; and include the neighboring property as part of the group when a comparison metric satisfies a threshold (see paragraphs 0230-0231, and 0344, Examiner’s note: returning results for a specific neighborhood (e.g. zip code) when other constraints are met).
Conclusion
18. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Terrazas et al. (United States Patent Application Publication Number: US 2016/0063516) teaches determining a commercial area of interest based on aerial photos (see abstract and Figures 6 and 11)
Kottenstette et al. (United States Patent Application Publication Number: US 2017/0076438) teaches a system for analyzing and remotely sensing property information (see abstract and Figures 5A-6)
Strong et al. (United States Patent Application Publication Number: US 2017/0371897) teaches providing real estate parcels of interest based on image data (see paragraphs 0008, and Figured 6-7)
Okazaki (United States Patent Number: US 10,529,029) teaches a system for identifying property characteristics and maintenance through image analysis (see abstract and Figures 2a-2b, and 5a-5b).
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626