AIA
Claims 1-20 examined. Apriori, “the claims”, means the pending claims.
19036424 filed 01/24/2025 is Con of 17895626, filed 08/25/2022, now U.S. Pat 12243070
Effective date 01/24/2025
PRIORITY
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 121 is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 121 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original non-provisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of the first paragraph of 35 U.S.C. 112. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The limitation that is missing from the application as filed is
CLAIM 3 10 17
3. The method of claim 1, further comprising: quantifying a resource usage of the specific user over a predefined time period, wherein the resource usage is associated with a particular service of the one or more services; and automatically populating the particular graphical element in the user interface such that the particular graphical element comprises a predicted value indicating an amount of predicted resource conservation by performing the at least one recommended action.
Therefore the claimed invention is not supported by 17895626 filed on 08/25/2022. The priority date for the claim limitations of the instant application will be the filing date of the claim adding the language missing from the application as filed. That date is 01/24/2025.
Claim Rejections - 35 USC § 112
35 U.S.C. 112a:
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.
Claim 3 10 17 is/are rejected under 35 U.S.C. 112(a) 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
NEW MATTER REJECTION
The following contains new matter.
CLAIM 3 10 17
3. The method of claim 1, further comprising: quantifying a resource usage of the specific user over a predefined time period, wherein the resource usage is associated with a particular service of the one or more services; and automatically populating the particular graphical element in the user interface such that the particular graphical element comprises a predicted value indicating an amount of predicted resource conservation by performing the at least one recommended action.
WRITTEN DESCRIPTION REJECTION
Claim 3 10 17: 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims dependent on rejected claims and are accordingly rejected.
Claim 3 10 17: rejected under 35 USC 112; the claims recites
CLAIM 3 10 17
3. The method of claim 1, further comprising: quantifying a resource usage of the specific user over a predefined time period, wherein the resource usage is associated with a particular service of the one or more services; and automatically populating the particular graphical element in the user interface such that the particular graphical element comprises a predicted value indicating an amount of predicted resource conservation by performing the at least one recommended action.
However, there is no 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. Further, the invention is merely functional claiming. The claims contain 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed (see MPEP § 2161.01). Here Applicant's claims are directed to generic claims constrained only by the broad steps of the claim. When looking to the specification for an explanation of the steps taken to achieve the claimed determinations, the Applicant appears only to disclose broad generic descriptions and black box diagrams that describe the intended results of the determinations. The generic limitations recited in the claims encompass both disclosed examples and all known methods of performing these functions in the prior art. The claims are rejected because the original disclosure does not support every possible species of the claimed genus. Claims dependent on rejected claims and are accordingly rejected. For computer-implemented inventions, the determination of the sufficiency of disclosure will require an inquiry into both the sufficiency of the disclosed hardware as well as the disclosed software due to the interrelationship and interdependence of computer hardware and software. In order for the written description requirement to be satisfied, the specification must disclose the specific type of microcomputer used in the claimed invention as well as the necessary steps for implementing the claimed function. Further the disclosure must provide sufficient detail such that one skilled in the art would know how to program the microprocessor to perform the necessary steps described in the specification. See MPEP §2161.01- §2163.07(b). The Applicant merely presents broad steps and generic descriptions to achieve the results. Applicant’s claims are rejected because the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate that the inventor possessed the invention or provides details sufficient for a person of ordinary skill in the art to program a general purpose computer to perform the claimed limitations. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b). Claims are dependent on rejected claims and are accordingly rejected.
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
The claims 1-20 are rejected under 35 USC 101 b/c the claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) is/are directed to one or more abstract idea(s). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the abstract idea(s). The rejection follows the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG).
Step 1: (MPEP 2106.03)
The claims 1-20 and dependents are directed to statutory classes (1 method, 8 apparatus, 15 article of manufacture). The claims herein are directed to subject matter which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception -- law of nature, natural phenomenon, abstract idea. (MPEP 2106.04).
CLAIM 1 8 15 similar
1. A method of automatically populating graphical elements on a user interface of a computer system comprising:
O receiving, by the [computer] system, sets of information from a plurality of third-party [server] operators, the sets of information comprising one or more services provided by the plurality of third-party [server] operators
O creating, for each set of information, a respective graphical element for the [user interface], the respective graphical element indicating at least one service provided in a respective set of information received from the plurality of third-party [server] operators
O collecting, via the [user interface], a set of data from a specific user;
O applying a trained [machine-learning model] to the set of data to predict a pattern in activities associated with the specific user, the trained [machine-learning model] trained using training data comprising data collected from a plurality of users
O [automatically populating a particular graphical element in the user interface] based on the pattern in activities associated with the specific user, the particular graphical element comprising at least one recommended action related to the at least one service indicated by the particular graphical element
bold = judicial exception underline = apply it
Claim 1-20: rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites an idea, Certain Methods of Organizing Human Activity. Can the idea be done with pencil and paper by a person? Yes. For small data sets one can do simple machine learning (e.g. regression) mentally or with pencil and paper. One can with pencil and paper or mentally apply a machine learning model after training on sets, identify a pattern and populate a user interface?
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
The claim says one is to take the idea and “apply it” with generic elements generally applied.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements to perform data gathering, generally apply generic elements, and mere display. The additional elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional step automatically MPEP 2106.05 is “iii. Mere automation of manual processes”. See (MPEP 21056.05 “vi. Instructions to display two sets of information on a computer display in a non-interfering manner”).
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claim recites generic additional elements for data gathering (MPEP 2106.05 “Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed”), automatically MPEP 2106.05 (“iii. Mere automation of manual processes”) populating an interface based on an identified pattern in specific user data.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements to gather data and automatically populate an interface for mere display amounts to no more than mere instructions to apply the exception using a generic computer component. See (MPEP 21056.05 “vi. Instructions to display two sets of information on a computer display in a non-interfering manner”). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Double Patenting
OBVIOUSNESS 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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
US Ser 17895626
US Ser 19036424
1. A method of automatically populating graphical elements on a user interface of a computer system comprising: receiving, by the computer system, sets of information from a plurality of third-party server operators, the sets of information comprising one or more services provided by the plurality of third-party server operators; creating, for each set of information, a graphical element for the user interface, the graphical element indicating at least one service provided in a respective set of information received from the plurality of third-party operators; collecting, via the user interface, a first set of data from a plurality of users; training a machine-learning model to determine at least one pattern in activities using the first set of data; collecting, via the user interface, a second set of data from a specific user; applying the machine-learning model to the second set of data to predict a pattern in activities associated with the specific user, wherein the predicted pattern comprises an evolving list of graphical element categories to associate with a marital, parental, or career status of the specific user; generating, by the machine-learning model, a ranking of a set of potential graphical elements based on the predicted pattern; and automatically populating at least two graphical elements from the set of potential graphical elements for the user interface based on the pattern in activities associated with the specific user and arranging the at least two graphical elements in order from a top towards a bottom of the user interface based on the ranking generated by the machine-learning model.
1. A method of automatically populating graphical elements on a user interface of a computer system comprising:
receiving, by the computer system, sets of information from a plurality of third-party server operators, the sets of information comprising one or more services provided by the plurality of third-party server operators;
creating, for each set of information, a respective graphical element for the user interface, the respective graphical element indicating at least one service provided in a respective set of information received from the plurality of third-party server operators;
collecting, via the user interface, a set of data from a specific user;
applying a trained machine-learning model to the set of data to predict a pattern in activities associated with the specific user, the trained machine-learning model trained using training data comprising data collected from a plurality of users;
automatically populating a particular graphical element in the user interface based on the pattern in activities associated with the specific user, the particular graphical element comprising at least one recommended action related to the at least one service indicated by the particular graphical element.
The claims are rejected on the ground of provisional nonstatutory double patenting over claims of 17895626. Both data gathering for ML pattern then automatically populating graphical elements from set of potential graphical elements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for obviousness rejections in this Office Action:
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made
MPEP 2123: “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for ALL they contain.” In re Heck, 699 F.2d 1331 (Fed. Cir. 1983) A reference may be relied upon for ALL that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989).”
Claims rejected under 35 USC 103 over Swindell (US 20180314972 US Pat 11442748) in view of Prisadnikov (US 11386349) in view of Ben-Itzhak (US 20200111121)
CLAIM 1 8 15
CLAIM 4 11 18
CLAIM 6 13 20
CLAIM 7 14
summary
Swindell machine learning for user activities used to populate/target graphical content (Software App) to specific user. Swindell is predicated on user activities (the claimed set). Swindell targeting to a user is predicated on there being a user.
NOT EXPLICIT in Swindell: apply ML with set (population) … (user) BUT Prisadnikov does that. Prisadnikov determines if there is a user.
NOT EXPLICIT in Swindell is third party server operator and graphical element for each set.
Ben-Itzhak shows one can target get something e.g. graphical elements (a simple substitution for Swindell’s SA), & user data is easy to get from many places including third party, & graphical elements associated with set (Ben-Itzshak’s user type/persona category).
In Swindell/Prisadnikov/Ben-Itzhak, the result is graphical elements for set (Swindell) and ML (Prisaknikov) for targeting specific user (Swindell)
The rejection modifies Swindell basically by accounting for the fact that
a) before targeting a user, make sure you actually have a user (Prisadnikov)
b) user data can be had from many sources including third party, and what is targeted wouldn’t have to be an SA (Swindell) but substituted with other e.g. graphical elements (Ben-Itzhak)
CLAIM 1 8 15
O receiving, by the computer system, sets of information from a plurality of third-party server operators, the sets of information comprising one or more services provided by the plurality of third-party server operators
Swindell US 20180314972 Abstract, Fig 4 and corresponding text
NOT EXPLICIT in Swindell is
third party server
Ben-Itzhak US 20200111121 ¶ 25
O creating, for each set of information, a respective graphical element for the user interface, the respective graphical element indicating at least one service provided in a respective set of information received from the plurality of third-party server operators
Ben-Itzhak US 20200111121 ¶ 38 51 52 61 for a user type, there’s a content
NOT EXPLICIT in Swindell is apply ML
O collecting, via the user interface, a set of data from a specific user
Prisadnikov US 11386349 Abstract, Fig 4, 6 and corresponding text
O applying a trained machine-learning model to the set of data to predict a pattern in activities associated with the specific user, the trained machine-learning model trained using training data comprising data collected from a plurality of users
Prisadnikov US 11386349 Abstract, Fig 4, 6 and corresponding text
O applying a trained machine-learning model to the set of data to predict a pattern in activities associated with the specific user, the trained machine-learning model trained using training data comprising data collected from a plurality of users;
Swindell US 20180314972 Abstract, Fig 4 and corresponding text
O automatically populating a particular graphical element in the user interface based on the pattern in activities associated with the specific user, the particular graphical element comprising at least one recommended action related to the at least one service indicated by the particular graphical element
Swindell US 20180314972 Abstract, Fig 4 and corresponding text
PNG
media_image1.png
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The rejection modifies Swindell basically by accounting for the fact that
a) before targeting a user, (and it’s common sense) to make sure you have a user (Prisadnikov)
b) user data can be had from many sources including third party, and what is targeted wouldn’t have to be an SA (Swindell) but could be other things e.g. graphical elements (Ben-Itzhak)
Tailoring an offering to a user is predicated on the fact that there is actually a user. It would have been obvious looking at Swindell tailoring offering to user consider whether one is in fact tailoring to a user and to consult the works of colleagues and find the Teaching, Suggestion and Motivation of Prisadnikov and combine the two with the predictable result and advantage of accounting for whether Swindell’s presentation is to a user not a bot (Prisadnikov).
This is further merely Combining Prior Art Elements According to Known Methods.
Market considerations and Design Forces would prompt modification of Swindell, e.g. when advertiser refuses to pay for display to nobody because the presentation is not to a user but a bot.
User data can come from different places. It would have been obvious looking at Swindell to consult the works of colleagues as to the source of Swindell’s user data and find the Teaching, Suggestion and Motivation of Ben-Itzhak that one can third-party source user data and different content can be matched to user type (the common sense adage “bait the hook to suit the fish”) and combine the two with the predictable result of getting first set and second set data from third party and content targeted to user type. This is further merely Combining Prior Art Elements According to Known Methods
Swindell machine learning for user activities used to populate/target graphical content (Software App) to specific user. Swindell contemplates is based on user activities (the claimed set). Swindell targeting to a user is predicated on there being a user.
NOT EXPLICIT in Swindell: apply ML
Prisadnikov does that. Prisadnikov determines if there is a user or only seems to be.
NOT EXPLICIT in Swindell is third party server operator and graphical element for each set.
Ben-Itzhak shows one can target get something e.g. graphical elements (a simple substitution for Swindell’s SA), & user data is easy to get from many places including third party, & graphical elements associated with set (Ben-Itzshak’s user type/persona category). In the Swindell/Prisadnikov/Ben-Itzhak combination, the result is graphical elements for set (Swindell) (Prisaknikov) for targeting specific user (Swindell)
graphical element use data can be
a time of day,
a graphical element type,
a graphical element category, or
a location of the at least one graphical element on the centralized offer webpage
Swindell US 20180314972 Abstract, Fig 4 and corresponding text
¶ 19-22 24 25 35 37 46 53
SA use data (Swindell Fig 4) in combination with Ben-Itzhak graphic is graphical use data
Motivation to combine provided above
CLAIM 4 11 18
claim 1 8 15, wherein automatically populating the particular graphical element comprises populating a centralized offer webpage with the particular graphical element
which factors impact graphical element selection for the centralized offer webpage
Swindell US 20180314972 Abstract, Fig 4 and corresponding text
SA use data (Swindell Fig 4) in combination with Ben-Itzhak graphic is graphical use data
Motivation to combine provided above
CLAIM 6 13 20
claim 1 8 15, wherein the pattern in activities predicted by the trained machine-learning model comprises a timeline indicating one or more activities predicted to be performed by the specific user
Swindell Fig 1 + text
CLAIM 7 14
claim 1 8 15, wherein the pattern in activities predicted by the trained machine-learning model comprises one or more activities occurring within a particular time window, and wherein the particular graphical element is automatically populated in the user interface prior to the particular time window
Swindell Fig 1 + text, ¶ 24
Claims rejected under 35 USC 103 over Swindell (US 20180314972 US Pat 11442748) in view of Prisadnikov (US 11386349) in view of Ben-Itzhak (US 20200111121) in view of Tang US 20140095273
CLAIM 2 9 16
CLAIM 5 12 19
CLAIM 2 9 16
claim 1 8 15, wherein
O quantifying, based on the pattern in activities, a likelihood of the specific user performing activities related to a particular service of the one or more services
Ben-Itzhak at least ¶ 20 27 37 40 44 46 50 51 61 Fig 6-7 9 + text
Motivation to combine above
NOT EXPLICIT IN Swindell
O automatically populating the particular graphical element by outputting a notification for display to the specific user via the user interface, wherein the notification comprises the at least one recommended action related to the particular service
Tang US 20140095273 at least Fig 3 4 7-10 and corresponding text. Note Fig 8-9 and text
It would have been obvious looking at Swindell to consult the works of colleagues as to what else can be tailored to user besides an SA (Swindell Fig 4) and find the Teaching, Suggestion and Motivation of Tang and combine the two with the predictable result of content targeted to user, content tailor to graphical elements on a centralized offer page.
This is further merely Combining Prior Art Elements According to Known Methods
Market considerations and Design Forces would prompt modification of Swindell since an app or a graphical element are not exclusive alternatives as exemplified by the prevalence of in-app ads.
CLAIM 5 12 19
claim 4 11 18, further comprising: identifying a related graphical element related to the particular graphical element; and outputting a side-by-side comparison of the related graphical element and the particular graphical element on the centralized offer webpage.
Tang US 20140095273 at least Fig 3 4 7-10 and corresponding text. Note Fig 8-9 and text
Motivation to combine provided above
Claims rejected under 35 USC 103 over Swindell (US 20180314972 US Pat 11442748) in view of Prisadnikov (US 11386349) in view of Ben-Itzhak (US 20200111121) in view of
Achin “Predictive Value Of A Feature” US 20180060738
CLAIM 3 10 17
claim 1 8 15, further comprising:
O quantifying a resource usage of the specific user over a predefined time period, wherein the resource usage is associated with a particular service of the one or more services
O automatically populating the particular graphical element in the user interface such that the particular graphical element comprises a predicted value indicating an amount of predicted resource conservation by performing the at least one recommended action
Achin “Predictive Value Of A Feature” US 20180060738 ¶ 94 166 167 289
This is further merely Combining Prior Art Elements According to Known Methods. It would have been obvious to combine Swindell with Achin “Predictive Value Of A Feature” US 20180060738. The prior art included each element claimed, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. One of ordinary skill in the art could have combined the elements as claimed by known methods and that in combination, each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable. Therefore all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods, and the combination would have yielded predictable results.
POC
Pertinent prior art
US PG Pub 20190205998
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BREFFNI BAGGOT
Primary Examiner
Art Unit 3621
/BREFFNI BAGGOT/Primary Examiner, Art Unit 3621
Prosecution Insights