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 . Claims 1-20 are presented in the claim.
Priority
Application claims benefit of priority to Provisional Application No. 63/294,275 filed Dec 28, 2021 is acknowledged.
Information Disclosure Statement
The information disclosure statements submitted on 05/04/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”)
Claim 1, 8 and 15 have the following abstract idea analysis.
Step 1: The claim is directed to “a system, method and CRM”. The claim is directed to the statutory categories accordingly.
Step 2A Prong 1: claims recites an abstract idea limitations of "identifying datasets associated with a party;" and "generating an aggregated dataset associated with the DIDs;"
. The limitation is a mental concept (simple identification) and mental concept (combining). See MPEP 2106.04(a)(2).
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "a machine learning algorithms " or "processors" or "storage media" does not yield eligibility. Claims are still in line with mathematical/mental concepts such as claim 1, 8 and 15 are not specific to a practical application. The additional elements as such are generic recognition models and computers which do not include specialized hardware. See MPEP § 2106.05(f).
Claim 1, 8 and 15 do not include a particular field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying machine learning to a field or data without an advancement in the new field or new machine learning is ineligible. MPEP § 2106.05(h).
Step 2B: The claims do not contain significantly more than their judicial exceptions. models and hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claim lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations.
Regarding claims 2-7, 9-14 and 16-20 merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claim 1, 8 and 15 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-7, 9-14 and 16-20. A certain type of data used may only be applying concepts to a field of use.
With respect to step 2B The claims disclose similar limitations described for the independent claims above and do not provide anything significantly more than mathematical or mental concepts . Claims 2-7, 9-14 and 16-20 recite the additional elements of "legal data associated with one or more hybrid legal documents; workflow data associated with one or more negotiations of one or more hybrid legal documents; accounting data associated with one or more hybrid journal entries; and subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. wherein the aggregated datasets are stored in one or more data stores controlled by the party. wherein the machine learning algorithms comprise one or more types of algorithms, the types of algorithms comprising: supervised learning algorithms; unsupervised learning algorithms; self-supervised learning algorithms; and reinforcement learning algorithms. wherein identifying the datasets associated with the party comprises: receiving the datasets from the party; receiving permission from the party to access the datasets from a data store controlled by the party; or receiving permission from the party to access and obtain the datasets from the data store controlled by the party. wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes: a contract type value; a date value; a contract provision; a payment term; a party characteristic; and a characteristic of the subject of a hybrid legal document. wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document. " These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-7, 9-14 and 16-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101
Claim Rejections - 35 USC § 103
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 of this title, 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.
Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bonat et al. (US 12045843 B2 hereinafter Bonat) in view of Peng (US 11271754 B2)
As to independent claim 1, Bonat teaches a system comprising one or more processors and one or more computer-readable non-transitory storage media coupled to the one or more processors and including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: [software, medium and processors Col. 14 ln. 44-65]
identifying datasets associated with a party; [identifies data associated with entities Col. 1 ln. 44-65 "retrieving at least one data source including privacy policies, contracts, public news, human inputs, and direct examination of websites for the first-level entity"]
generating an aggregated dataset associated [with the DIDs]; [collects all the data at a data store (aggregates) Fig. 2 205 Col. 9 ln. 48-63 "data, such as identified first-level entities and data collection and usage policies, is collected at data store 205"]
generating a training dataset associated with the aggregated dataset; and [builds training sets Col. 4 ln. 42-50] [prepares data for ML environment (training) Col. 10 ln. 7-23 "data processing engines that prepare and/or transform various types, formats, structures, etc., of data for processing in a machine learning environment:"]
using one or more machine learning algorithms to recognize patterns within the training dataset. [NLP/GNN models for identifying patterns and predict relationships Col. 11 ln. 33-44, Col. 6 ln. 12-17 "A privacy policy, or other textual data collection and usage policies, may be processed using NLP in order to identify specific word patterns that indicate that the entity collects, uses, shares, sells, etc., a consumer's data"]
Bonat does not specifically teach identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs.
However, Peng teaches identifying one or more decentralized identifiers (DIDs) associated with the datasets; [acquires data using DID and stores them together in a blockchain Col. 8 ln. 48-64 "controlling authorization of access to data is provided. The method includes sending a first request for a first digital activity decentralized identifier to a decentralized identifier blockchain node that is associated with a decentralized identifier blockchain configured to store records associated with a plurality of decentralized identifiers (DID) of a plurality of users"]
generating an aggregated dataset associated with the DIDs; [adds data together in a blockchain Col. 8 ln. 24-33 " storing data about the digital activity decentralized identifiers and the decentralized identifiers of the users in multiple records in a first decentralized identifier blockchain, wherein each record stores data associated with one of the users; and controlling authorization of access to data that are associated with the users and stored in the consortium blockchains at least in part based on the records stored in the first decentralized identifier blockchain."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat by incorporating the identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs disclosed by Peng because both techniques address the same field of tracking data and by incorporating Peng into Bonat makes it easier for users to track data records while sharing them to authorized users [Peng Col. 15 ln. 48-57]
As to dependent claim 2, the rejection of claim 1 is incorporated Bonat and Peng further teach wherein the aggregated dataset comprises one or more types of data, the types of data comprising: [Bonat various sources have different types of data Col. 4 ln. 42-50 "(8) Embodiments may leverage numerous data sources for gathering data, such as privacy policies, contracts, public news, human inputs, direct examination of websites, etc., to build training sets" ]
legal data associated with one or more hybrid legal documents; [Bonat Col. 7 ln. 54-63 "
legal documents such as contracts, lawsuits, court transcripts"]
workflow data associated with one or more negotiations of one or more hybrid legal documents; [Bonat graphs entities and relationships data Col. 4 ln. 42-50 ]
accounting data associated with one or more hybrid journal entries; and
subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. [Peng user medical data Col. 21-22 ln. 59-21]
As to dependent claim 3, the rejection of claim 1 is incorporated Bonat and Peng further teach wherein the aggregated datasets are stored in one or more data stores controlled by the party.[Peng records held by DID owner (party) Col. 23 ln. 5-12]
As to dependent claim 4, the rejection of claim 1 is incorporated Bonat and Peng further teach wherein the machine learning algorithms comprise one or more types of algorithms, the types of algorithms comprising:
supervised learning algorithms; [Bonat models with labels are supervised Col. 2 ln. 11-15]
unsupervised learning algorithms;
self-supervised learning algorithms; and
reinforcement learning algorithms.
As to dependent claim 5, the rejection of claim 1 is incorporated Bonat and Peng further teach wherein identifying the datasets associated with the party comprises:
receiving the datasets from the party; [Peng user shares records Col. 14 ln. 9-12]
receiving permission from the party to access the datasets from a data store controlled by the party; or [Peng manage authorization for others Col. 14 ln. 25-35]
receiving permission from the party to access and obtain the datasets from the data store controlled by the party. [Peng manage authorization for others Col. 14 ln. 25-35]
As to dependent claim 6, the rejection of claim 1 is incorporated Bonat and Peng further teach wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes:
a contract type value; [Bonat legal docs, privacy policies and type of information Col. 1 ln .44-65 "each edge identifies a type of information"]
a date value; [Bonat text Col. 8 ln. 11-24]
a contract provision;
a payment term;
a party characteristic; and [Bonat restrictions Col. 8 ln. 11-24]
a characteristic of the subject of a hybrid legal document.
As to independent claim 8, Bonat teaches a method, comprising:
identifying datasets associated with a party; [identifies data associated with entities Col. 1 ln. 44-65 "retrieving at least one data source including privacy policies, contracts, public news, human inputs, and direct examination of websites for the first-level entity"]
generating an aggregated dataset associated [with the DIDs]; [collects all the data at a data store (aggregates) Fig. 2 205 Col. 9 ln. 48-63 "data, such as identified first-level entities and data collection and usage policies, is collected at data store 205"]
generating a training dataset associated with the aggregated dataset; and [builds training sets Col. 4 ln. 42-50] [prepares data for ML environment (training) Col. 10 ln. 7-23 "data processing engines that prepare and/or transform various types, formats, structures, etc., of data for processing in a machine learning environment:"]
using one or more machine learning algorithms to recognize patterns within the training dataset. [NLP/GNN models for identifying patterns and predict relationships Col. 11 ln. 33-44, Col. 6 ln. 12-17 "A privacy policy, or other textual data collection and usage policies, may be processed using NLP in order to identify specific word patterns that indicate that the entity collects, uses, shares, sells, etc., a consumer's data"]
Bonat does not specifically teach identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs.
However, Peng teaches identifying one or more decentralized identifiers (DIDs) associated with the datasets; [acquires data using DID and stores them together in a blockchain Col. 8 ln. 48-64 "controlling authorization of access to data is provided. The method includes sending a first request for a first digital activity decentralized identifier to a decentralized identifier blockchain node that is associated with a decentralized identifier blockchain configured to store records associated with a plurality of decentralized identifiers (DID) of a plurality of users"]
generating an aggregated dataset associated with the DIDs; [adds data together in a blockchain Col. 8 ln. 24-33 " storing data about the digital activity decentralized identifiers and the decentralized identifiers of the users in multiple records in a first decentralized identifier blockchain, wherein each record stores data associated with one of the users; and controlling authorization of access to data that are associated with the users and stored in the consortium blockchains at least in part based on the records stored in the first decentralized identifier blockchain."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat by incorporating the identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs disclosed by Peng because both techniques address the same field of tracking data and by incorporating Peng into Bonat makes it easier for users to track data records while sharing them to authorized users [Peng Col. 15 ln. 48-57]
As to dependent claim 9, the rejection of claim 8 is incorporated Bonat and Peng further teach wherein the aggregated dataset comprises one or more types of data, the types of data comprising: [Bonat various sources have different types of data Col. 4 ln. 42-50 "(8) Embodiments may leverage numerous data sources for gathering data, such as privacy policies, contracts, public news, human inputs, direct examination of websites, etc., to build training sets" ]
legal data associated with one or more hybrid legal documents; [Bonat Col. 7 ln. 54-63 "
legal documents such as contracts, lawsuits, court transcripts"]
workflow data associated with one or more negotiations of one or more hybrid legal documents; [Bonat graphs entities and relationships data Col. 4 ln. 42-50 ]
accounting data associated with one or more hybrid journal entries; and
subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. [Peng user medical data Col. 21-22 ln. 59-21]
As to dependent claim 10, the rejection of claim 8 is incorporated Bonat and Peng further teach wherein the aggregated datasets are stored in one or more data stores controlled by the party.[Peng records held by DID owner (party) Col. 23 ln. 5-12]
As to dependent claim 11, the rejection of claim 8 is incorporated Bonat and Peng further teach wherein the machine learning algorithms comprise one or more types of algorithms, the types of algorithms comprising:
supervised learning algorithms; [Bonat models with labels are supervised Col. 2 ln. 11-15]
unsupervised learning algorithms;
self-supervised learning algorithms; and
reinforcement learning algorithms.
As to dependent claim 12, the rejection of claim 8 is incorporated Bonat and Peng further teach wherein identifying the datasets associated with the party comprises:
receiving the datasets from the party; [Peng user shares records Col. 14 ln. 9-12]
receiving permission from the party to access the datasets from a data store controlled by the party; or [Peng manage authorization for others Col. 14 ln. 25-35]
receiving permission from the party to access and obtain the datasets from the data store controlled by the party. [Peng manage authorization for others Col. 14 ln. 25-35]
As to dependent claim 13, the rejection of claim 8 is incorporated Bonat and Peng further teach wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes:
a contract type value; [Bonat legal docs, privacy policies and type of information Col. 1 ln .44-65 "each edge identifies a type of information"]
a date value; [Bonat text Col. 8 ln. 11-24]
a contract provision;
a payment term;
a party characteristic; and [Bonat restrictions Col. 8 ln. 11-24]
a characteristic of the subject of a hybrid legal document.
As to independent claim 15, Bonat teaches one or more computer-readable non-transitory storage media embodying instructions that, when executed by a processor, cause the processor to perform operations comprising: [software, medium and processors Col. 14 ln. 44-65]
identifying datasets associated with a party; [identifies data associated with entities Col. 1 ln. 44-65 "retrieving at least one data source including privacy policies, contracts, public news, human inputs, and direct examination of websites for the first-level entity"]
generating an aggregated dataset associated [with the DIDs]; [collects all the data at a data store (aggregates) Fig. 2 205 Col. 9 ln. 48-63 "data, such as identified first-level entities and data collection and usage policies, is collected at data store 205"]
generating a training dataset associated with the aggregated dataset; and [builds training sets Col. 4 ln. 42-50] [prepares data for ML environment (training) Col. 10 ln. 7-23 "data processing engines that prepare and/or transform various types, formats, structures, etc., of data for processing in a machine learning environment:"]
using one or more machine learning algorithms to recognize patterns within the training dataset. [NLP/GNN models for identifying patterns and predict relationships Col. 11 ln. 33-44, Col. 6 ln. 12-17 "A privacy policy, or other textual data collection and usage policies, may be processed using NLP in order to identify specific word patterns that indicate that the entity collects, uses, shares, sells, etc., a consumer's data"]
Bonat does not specifically teach identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs.
However, Peng teaches identifying one or more decentralized identifiers (DIDs) associated with the datasets; [acquires data using DID and stores them together in a blockchain Col. 8 ln. 48-64 "controlling authorization of access to data is provided. The method includes sending a first request for a first digital activity decentralized identifier to a decentralized identifier blockchain node that is associated with a decentralized identifier blockchain configured to store records associated with a plurality of decentralized identifiers (DID) of a plurality of users"]
generating an aggregated dataset associated with the DIDs; [adds data together in a blockchain Col. 8 ln. 24-33 " storing data about the digital activity decentralized identifiers and the decentralized identifiers of the users in multiple records in a first decentralized identifier blockchain, wherein each record stores data associated with one of the users; and controlling authorization of access to data that are associated with the users and stored in the consortium blockchains at least in part based on the records stored in the first decentralized identifier blockchain."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat by incorporating the identifying one or more decentralized identifiers (DIDs) associated with the datasets; and generating an aggregated dataset associated with the DIDs disclosed by Peng because both techniques address the same field of tracking data and by incorporating Peng into Bonat makes it easier for users to track data records while sharing them to authorized users [Peng Col. 15 ln. 48-57]
As to dependent claim 16, the rejection of claim 15 is incorporated Bonat and Peng further teach wherein the aggregated dataset comprises one or more types of data, the types of data comprising: [Bonat various sources have different types of data Col. 4 ln. 42-50 "(8) Embodiments may leverage numerous data sources for gathering data, such as privacy policies, contracts, public news, human inputs, direct examination of websites, etc., to build training sets" ]
legal data associated with one or more hybrid legal documents; [Bonat Col. 7 ln. 54-63 "
legal documents such as contracts, lawsuits, court transcripts"]
workflow data associated with one or more negotiations of one or more hybrid legal documents; [Bonat graphs entities and relationships data Col. 4 ln. 42-50 ]
accounting data associated with one or more hybrid journal entries; and
subject data associated with one or more subjects of one or more hybrid legal documents, wherein each of the one or more subjects is associated with an asset, a person, an entity, or a service provider. [Peng user medical data Col. 21-22 ln. 59-21]
As to dependent claim 17, the rejection of claim 15 is incorporated Bonat and Peng further teach wherein the aggregated datasets are stored in one or more data stores controlled by the party.[Peng records held by DID owner (party) Col. 23 ln. 5-12]
As to dependent claim 18, the rejection of claim 15 is incorporated Bonat and Peng further teach wherein the machine learning algorithms comprise one or more types of algorithms, the types of algorithms comprising:
supervised learning algorithms; [Bonat models with labels are supervised Col. 2 ln. 11-15]
unsupervised learning algorithms;
self-supervised learning algorithms; and
reinforcement learning algorithms.
As to dependent claim 19, the rejection of claim 15 is incorporated Bonat and Peng further teach wherein identifying the datasets associated with the party comprises:
receiving the datasets from the party; [Peng user shares records Col. 14 ln. 9-12]
receiving permission from the party to access the datasets from a data store controlled by the party; or [Peng manage authorization for others Col. 14 ln. 25-35]
receiving permission from the party to access and obtain the datasets from the data store controlled by the party. [Peng manage authorization for others Col. 14 ln. 25-35]
As to dependent claim 20, the rejection of claim 15 is incorporated Bonat and Peng further teach wherein the training dataset comprises one or more descriptive attributes from the following set of descriptive attributes:
a contract type value; [Bonat legal docs, privacy policies and type of information Col. 1 ln .44-65 "each edge identifies a type of information"]
a date value; [Bonat text Col. 8 ln. 11-24]
a contract provision;
a payment term;
a party characteristic; and [Bonat restrictions Col. 8 ln. 11-24]
a characteristic of the subject of a hybrid legal document.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bonat in view of Peng as applied to the rejection of claim 1 and 8 above, and further in view of Cella et al. (US 20220198562 A1 hereinafter Cella)
As to dependent claim 7, the combination of Bonat and Peng teach all the limitations of claim 1 that is incorporated.
Bonat and Peng do not specifically teach wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document.
However, Cella teaches wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document. [digital ledger with smart contracts including government agreement ¶915, licensing and copyrights ¶918, smart will, estate ¶1832-1833 employment ¶1847, purchases and leases ¶1039]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat and Peng by incorporating the wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document disclosed by Cella because all techniques address the same field of tracking data and by incorporating Cella into Bonat and Peng provides smarter contracts for improved controls of rights facilitating audits and verification [Cella ¶29]
As to dependent claim 14, the combination of Bonat and Peng teach all the limitations of claim 8 that is incorporated.
Bonat and Peng do not specifically teach wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document.
However, Cella teaches wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document. [digital ledger with smart contracts including government agreement ¶915, licensing and copyrights ¶918, smart will, estate ¶1832-1833 employment ¶1847, purchases and leases ¶1039]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the data sharing by Bonat and Peng by incorporating the wherein the aggregated datasets are associated with one or more types of documents, the types of documents comprising: an asset purchase agreement; a copyright license; a lease of real estate property; a lease of mineral rights; an employment agreement; a corporate governance document; a copyright split sheet; a will; or a service provider document disclosed by Cella because all techniques address the same field of tracking data and by incorporating Cella into Bonat and Peng provides smarter contracts for improved controls of rights facilitating audits and verification [Cella ¶29]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Liu et al. (US 20220083972 A1) teaches a blockchain warehouse with IDs (see ¶33)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143