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
The instant application having Application No. 17387090 has a total of 42 claims pending in the application, of which claims 1-20, 32, and 34 have been cancelled.
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 21-31, 33, and 35-41 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-16 of U.S. Patent No. 11106878 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of the instant claims can be met by those of the patent as shown below.
Instant Application
11106878 B2
Examiners Comment
“A method of identifying hypotheses in a corpus of data, the method comprising:
Claim 1: A method of identifying hypotheses in a corpus of data, the method comprising:
Receiving an ontology by one or more computers, the ontology including a plurality of fields and a plurality of choices for each of the fields such that the ontology includes a plurality of ontology vectors that each include one choice for each of the fields of the ontology
Claim 1: receiving an ontology by one or more computers, wherein the ontology includes fields and a plurality of choices for each of the fields such that the ontology includes a plurality of ontology vectors, each ontology vector including one choice for each of the fields;
Receiving the corpus of data by the one or more computers
Claim 1: receiving the corpus of data by the one or more computers;
Populating the ontology by the one or more computers by detecting data in the corpus of data that corresponds to each of the ontology vectors
Claim 1: populating the ontology, by the one or more computers, by detecting data from the corpus of data that correspond to each of the ontology vectors;
Plotting the populated ontology vectors in an ontology space by the one or more computers, each dimension of the ontology space being associated with one or more of the fields of the ontology
Claim 1: plotting the populated ontology vectors in an ontology space by the one or more computers, each dimension of the ontology space being associated with one or more of the fields of the ontology;
Transforming the plotted ontology space into a hypothesis space, by the one or more computers, by Grouping the ontology vectors that describe similar and/or related concepts into hypothesis neighborhoods,
Claim 1: transforming the plotted ontology space into a hypothesis space, by the one or more computers, by grouping the plotted ontology vectors that describe similar and/or related concepts into neighborhoods, wherein each neighborhood represents a hypothesis;
Weighing each of the hypothesis neighborhoods by the one or more computers and
weighing each of the plotted hypotheses by the one or more computers
Applying an optimization algorithm by the one or more computers to rank the hypothesis neighborhoods in accordance with the weight of each hypothesis neighborhood in the hypothesis space by:
Claim 1: applying an optimization algorithm to find the lowest troughs in the hypothesis surface or fittest population members in the population,
Introducing a random variation or mutation into the hypothesis space representing the hypothesis neighborhoods
Claim 10: wherein the optimization algorithm de-weights trivial or uninteresting hypotheses by: introducing a random variation or mutation into the hypothesis space; identifying a local minima in the varied or mutated hypothesis space; and determining an anticipation level of a hypothesis represented by a neighborhood at the local minima.
Determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood
Claim 10: wherein the optimization algorithm de-weights trivial or uninteresting hypotheses by: introducing a random variation or mutation into the hypothesis space; identifying a local minima in the varied or mutated hypothesis space; and determining an anticipation level of a hypothesis represented by a neighborhood at the local minima.
Determining an anticipation level of each hypothesis neighborhood based on a profile of a user
Claim 7: wherein the optimization algorithm weights each of the plotted hypotheses in part by: storing personalized criteria of a user
Claim 10: Determining an anticipation level of a hypothesis represented by a neighborhood at the local minima
De-weighting each hypothesis neighborhood having an anticipation level indicative of anticipated, trivial, or uninteresting hypotheses, that the user expects from the corpus of data without use of the hypothesis space
Claim 10: wherein the optimization algorithm de-weights trivial or uninteresting hypotheses by: introducing a random variation or mutation into the hypothesis space; identifying a local minima in the varied or mutated hypothesis space; and determining an anticipation level of a hypothesis represented by a neighborhood at the local minima.
Claim 7: de-wight hypotheses that are trivial or uninteresting to the user
Wherein a rate of the random variation or mutation is based on the anticipation level of the preceding hypothesis neighborhood
Claim1 0: introducing a random variation or mutation into the hypothesis space
Claim 13: wherein a rate of variation or mutation is determined as a function of the anticipation level
As per claims 22-31, 33, and 35-41, these claims are similarly rejected under obvious-type double patenting with claims 1-16 of 11106878 B2.
Claims 21-31, 33, and 35-41, are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-16 of U.S. Patent No. 10521727 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of the instant claims can be met by those of the patent as shown in claims 1-12.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-31, 33, and 35-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 21 is a process type claim. Claim 41 is a machine type claim. Therefore, claims 21-31, 33, and 35-41 are directed to either a process, machine, manufacture or composition of matter.
As per claim 21,
2A Prong 1:
“including a plurality of fields and a plurality of choices for each of the fields such that the ontology includes a plurality of ontology vectors that each include one choice for each of the fields of the ontology” A user, mentally or with pencil and paper, creates a vector with a set of fields that can be filled as needed.
“populating the ontology … by detecting data in the corpus of data that correspond to each of the ontology vectors” The User, mentally or with pencil and paper, tries to match incoming data to the set-up ontology.
“Plotting the populated ontology vectors in an ontology space … each dimension of the ontology space being associated with one or more of the fields of the ontology” The user mentally or with pencil and paper plots the populated ontology.
“transforming the plotted ontology space into a hypothesis space .. by grouping the ontology vectors that describe similar and/or related concepts into hypothesis neighborhoods” The user mentally or with pencil and paper organizes the ontology vectors into “neighborhoods” based upon the topic/concepts found within.
“weighing each of the hypothesis neighborhoods…” The user mentally or with pencil and paper applies weights to each of the hypotheses
“applying an … algorithm … rank the hypothesis neighborhoods in accordance with the weight of each hypothesis neighborhood in the hypothesis space by” The user mentally or with pencil and paper ranks the hypotheses.
“introducing a random variable or mutation into the hypothesis space representing the hypothesis neighborhoods” The user mentally or with pencil and paper mutates or adds random variables into the various neighborhoods by changing values at random.
“determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood” The user mentally or with pencil and papers measures the fitness of each neighborhood based on the mathematically determining local minima for those neighborhoods.
“determining an anticipation level of each hypothesis neighborhood based on a profile of a user” The User mentally or with pencil and paper determines the anticipation level of each neighborhood based on the user’s interests.
“de-weighting each hypothesis neighborhood having an anticipation level indicative of anticipated trivia or uninteresting hypotheses that the user expects from the corpus of data without use of the hypothesis space” The user mentally or with pencil and paper de-weights the identified trivial or uninteresting hypotheses by changing them to nominal values or zero based on the anticipation level and their own expectations.
“wherein a rate of the random variation or mutation is based on the anticipation level of the preceding hypothesis neighborhood” The user mentally or with pencil and paper makes the random changes based on anticipation level of the neighborhoods
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“one or more computers” (mere instructions to apply the exception using a generic computer component);
“Optimization algorithm” (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) – Examiner’s note: This is a generic off the shelf optimization algorithm with no structure or details that make it beyond a common generic application of an optimization algorithm);
“Receiving an ontology …” , “Receiving the corpus of data” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“one or more computers” (mere instructions to apply the exception using a generic computer component)
“Optimization algorithm” (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) – Examiner’s note: This is a generic off the shelf optimization algorithm with no structure or details that make it beyond a common generic application of an optimization algorithm).
“Receiving an ontology …” , “Receiving the corpus of data” (MPEP 2106.05(d)(II) indicate that merely “transmitting or receiving data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 22, this claim has additional optimization details, but discloses no more than well-known types of algorithms, and therefore is rejected similarly to claim 21.
As per claim 23 and 30-31, 33 , 38, this claim contains additional generic machine learning algorithms like clustering and mental steps similar to claim 21, and is therefore rejected similarly to claim 21.
As per claim 24-29, 35-37, 39 , this claim contains additional mental steps and is rejected similarly to claim 21.
As per claim 40, this claim contains additional mental steps and receiving/transmitting steps as claim 21, and is rejected for similar reasons.
As per claim 41,
2A Prong 1:
“the ontology including a plurality of fields and a plurality of choices for each of the fields such that the ontology includes a plurality of ontology vectors that each include one choice for each of the fields of the ontology,” The user, mentally or with pencil and paper, creates a vector with a set of fields that can be filled as needed.
“populates the ontology by detecting data in the corpus of data that corresponds to each of the ontology vectors ” The User, mentally or with pencil and paper, tries to match incoming data to the set-up ontology.
“Plots the populated ontology vectors in an ontology space” The user mentally or with pencil and paper makes a plot of the ontology vectors.
“transforming the plotted ontology space into a hypothesis space by grouping the plotted ontology vectors that describe similar and/or related concepts into hypothesis neighborhoods” The user mentally or with pencil and paper groups similar/related ontology vectors into “neighborhoods.”
“weighs each of the hypothesis neighborhoods” The user mentally or with pencil and paper applies weights to each of the hypotheses
“applies an … algorithm to rank the hypothesis neighborhoods in accordance with the weight of each hypothesis neighborhood in the hypothesis space by” The user mentally or with pencil and paper ranks the hypotheses.
“introducing a random variable or mutation into the hypothesis space representing the neighborhoods” The user mentally or with pencil and paper mutates or adds random variables into the various neighborhoods by changing values at random.
“determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood” The user mentally or with pencil and papers measures the rate of change of local minimal and assigns a fitness level to the neighborhoods
“Determining an anticipation level of each hypothesis neighborhood based on a profile of a user” The User mentally or with pencil and paper determines the anticipation level of each neighborhood based on the user’s interests.
“de-weighting each hypothesis neighborhood having an anticipation level indicative of anticipated, trivial, or uninteresting hypotheses that the user expects from the corpus of data without use of the hypothesis space” The user mentally or with pencil and paper de-weights the identified trivial or uninteresting hypotheses by changing them to nominal values or zero based on the anticipation level and their own expectations.
“wherein a rate of the random variation or mutation is based on the anticipation level of the preceding hypothesis neighborhoods” The user mentally or with pencil and paper makes the random changes based on anticipation level of the neighborhoods
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“non-transitory computer readable storage media” , “a content server”, “one or more computers” (mere instructions to apply the exception using a generic computer component);
“Optimization algorithm” (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) – Examiner’s note: This is a generic off the shelf optimization algorithm with no structure or details that make it beyond a common generic application of an optimization algorithm);
“Receiving an ontology …” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“non-transitory computer readable storage media” , “a content server”, “one or more computers” (mere instructions to apply the exception using a generic computer component)
“Optimization algorithm” (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) – Examiner’s note: This is a generic off the shelf optimization algorithm with no structure or details that make it beyond a common generic application of an optimization algorithm).
“Receiving an ontology …” (MPEP 2106.05(d)(II) indicate that merely “transmitting or receiving data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 42, this claim contains similar mental steps to claim 41, and is rejected for similar reasons.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-31, 33, and 35-42 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 21, this claim contains the limitation: “de-weighting each hypothesis neighborhood having an anticipation level indicative of anticipated, trivial, or uninteresting hypothesis that the user expects from the corpus of data without use of the hypothesis space.” However, this limitation is not supported by the specification. Anticipation level is only discussed in one paragraph, and only in relation to the anticipation level being based on the profile of the user, and affecting the rate of mutation (see paragraph 0079).
The closest paragraph, 083, discloses checking if a neighborhood is anticipated, or trivial, and skipping them. However, there is no discussion of de-weighting based on anticipation level.
Paragraph 080 discusses de-weighting un-interesting clusters, but makes no mention of anticipation level. It is based on the user’s personal opinion of what is trivial or un-interesting hypotheses. It does not discuss neighborhoods, nor does it discuss anticipation level, or any required level or threshold of anticipated in order to make the decision.
This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 22-31, 33, 35-40 and 42, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a).
As per claim 21, this claim calls for “wherein a rate of the random variation or mutation is based on the anticipation level of the preceding hypothesis neighborhood.” This limitation is not supported by the specification. Mutation is only discussed in paragraphs 0078-0079, and it states that the rate of mutation – NOT random variations, can be modified based on anticipation level of the neighborhood, but does not talk about preceding hypothesis neighborhoods. At best, the specification describes the rate of change of mutations being based on the hypothesis neighborhood it is being put in, but not about preceding or different neighborhoods, and does not apply at all to random variation at all. This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 22-31, 33, 35-40 and 42, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a).
As per claim 21, this claim calls for the limitation: “determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood.” However, this limitation is not supported by the specification. The rate of change is only discussed in paragraph 079, and does not designate any sort of local minima. Further, as discussed below in the rejection under U.S.C. 112(b) how can a “local minima” have a rate of change? A local minima is the local minimum value, not a slope, it does not ascend or descend, it does not have a slope. Since this limitation is not supported by the specification, the claim is rejected under U.S.C. 112(a) for new matter.
As per claims 22-31, 33, 35-40 and 42, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a).
As per claim 23, this claim calls for “ranking the fittest population members of a population having population members that each represent a hypothesis neighborhood and each have a fitness proportional to the weight of the hypothesis neighborhood.”
First, the Specification at no time describes a population member being a neighborhood. Population is described in paragraph 079, which states that a fitness can be determined for a population member. The only other paragraph that discusses it is paragraph 0103, which only makes mention of neighborhoods in regards to troughs in simulated annealing. The population in paragraph 0103 is merely referred to generically and makes no mention of neighborhoods. This causes the claim to contain new matter, and therefore be rejected under U.S.C. 112(a).
As per claim 27, this claim calls for “the weight of each hypothesis neighborhood is based at least in part on the weight of the fields of the ontology vectors in each hypothesis neighborhood.” However, this limitation is not supported by the specification. Fields are discussed in paragraph 0060, 0062, 0064, 072, 073, 091, and 0102, but these paragraphs do not discuss fields having weights or neighborhoods, let along the weighting of the neighborhoods being based on weights of fields of the ontology.
The closest paragraph is 071 which states that fields can have multiple choices, and the system is configured to support weighing or biasing certain events, but at best this describes weighting events, not fields. There is no discussion of the weights of neighborhoods being based on weights of fields, or fields having weights at all. Therefore this is considered new matter, and the claim rejected under U.S.C. 112(a).
As per claim 38, this claim calls for “wherein the hypothesis neighborhoods are ranked based on the path through the multi-dimensional space by which each hypothesis neighborhood was discovered by the optimization algorithm.” This limitation is not supported by the specification. Paths are only discussed in paragraph 0046, and are only discussed in relation to ranking hypothesis, not hypothesis neighborhoods. There is no discussion in the specification of ranking hypothesis neighborhoods based on paths, and therefore the claim is rejected under U.S.C. 112(a) for new matter.
As per claim 39, this claim calls for “wherein the hypothesis neighborhoods are ranked in a stateless manner based on the positions of each hypothesis neighborhood in the multi-dimensional space.” This limitation is not supported by the specification. Stateless determinations are only discussed in paragraph 0046, and are only discussed in relation to ranking hypothesis, not hypothesis neighborhoods. There is no discussion in the specification of ranking hypothesis neighborhoods in a stateless manner, and therefore the claim is rejected under U.S.C. 112(a) for new matter.
As per claim 40, this claim calls for “outputting at least some of the ranked hypothesis neighborhoods for display to a user.” This limitation is not supported by the specification. The specification at no time discloses displaying ranked hypothesis neighborhoods. The only mention of displaying is in paragraphs 053 and 055, none of which discuss neighborhoods. Figures 6-16 all denote some method of displaying individual hypothesis, but make no mention of hypothesis neighborhoods. At no time do any of these figures or paragraphs disclose displaying hypothesis neighborhoods, and therefore the claim is rejected under U.S.C. 112(a) for new matter.
As per claim 41, this claim contains the limitation: “de-weighting each hypothesis neighborhood having an anticipation level indicative of anticipated, trivial, or uninteresting hypothesis that the user expects from the corpus of data without use of the hypothesis space.” However, this limitation is not supported by the specification. Anticipation level is only discussed in one paragraph, and only in relation to the anticipation level being based on the profile of the user, and affecting the rate of mutation (see paragraph 0079).
The closest paragraph, 083, discloses checking if a neighborhood is anticipated, or trivial, and skipping them. However, there is no discussion of de-weighting based on anticipation level.
Paragraph 080 discusses de-weighting un-interesting clusters, but makes no mention of anticipation level. It is based on the user’s personal opinion of what is trivial or un-interesting hypotheses. It does not discuss neighborhoods, nor does it discuss anticipation level, or any required level or threshold of anticipated in order to make the decision.
This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claim 41, this claim calls for “wherein a rate of the random variation or mutation is based on the anticipation level of the preceding hypothesis neighborhood.” This limitation is not supported by the specification. Mutation is only discussed in paragraphs 0078-0079, and it states that the rate of mutation – NOT random variations, can be modified based on anticipation level of the neighborhood, but does not talk about preceding hypothesis neighborhoods. At best, the specification describes the rate of change of mutations being based on the hypothesis neighborhood it is being put in, but not about preceding or different neighborhoods, and does not apply at all to random variation at all. This causes the limitation to be new matter, and therefore rejected under U.S.C. 112(a).
As per claim 41, this claim calls for the limitation: “determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood.” However, this limitation is not supported by the specification. The rate of change is only discussed in paragraph 079, and does not designate any sort of local minima. Further, as discussed below in the rejection under U.S.C. 112(b) how can a “local minima” have a rate of change? A local minima is the local minimum value, not a slope, it does not ascend or descend, it does not have a slope. Since this limitation is not supported by the specification, the claim is rejected under U.S.C. 112(a) for new matter.
As per claim 42, this claim calls for “wherein the rate of change of the local minima representing the hypothesis neighborhood is based on a slope of descent or ascent of the local minima representing the hypothesis neighborhood.” However, this limitation is not supported by the specification. The rate of change is only discussed in paragraph 079, and does not designate any sort of local minima. Further, as discussed below in the rejection under U.S.C. 112(b) how can a “local minima” have a rate of change? A local minima is the local minimum value, not a slope, it does not ascend or descend, it does not have a slope. Since this limitation is not supported by the specification, the claim is rejected under U.S.C. 112(a) for new matter.
Claim Rejections - 35 USC § 112
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.
Claims 21-31, 33, and 35-42 are 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.
As per claim 21, this claim calls for “determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood.” How can a “local minima” have a rate of change? A local minimum represents the local lowest value in the area. It does not have a value that changes over time, other than discovering another local minimum. Local minimum values do not have a rate of change, as they merely represent the local minimum in that area. This cases the claim to be confusing, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 22-31, 33, 35-40 and 42, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a).
As per claim 41, this claim calls for “determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood.” How can a “local minima” have a rate of change? A local minimum represents the local lowest value in the area. It does not have a value that changes over time, other than discovering another local minimum. Local minimum values do not have a rate of change, as they merely represent the local minimum in that area. This cases the claim to be confusing, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claim 42, this claim calls for “wherein the rate of change of the local minima representing the hypothesis neighborhood is based on a slope of descent or ascent of the local minima representing the hypothesis neighborhood.” ow can a “local minima” have a rate of change, or a slope? A local minimum represents the local lowest value in the area. It does not have a value that changes over time, other than discovering another local minimum. It does not have a slope that ascends or descends, it is merely a single point.. This cases the claim to be confusing, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
Allowable Subject Matter
Independent claims 21 and 41 and their respective dependent claims (22-31, 33, 35-40 and 42) would be allowable over the prior art if the rejections under U.S.C. 101 are overcome, a terminal disclaimer is filed for the double patenting rejections, and the limitations rejected under U.S.C. 112(a) can be shown to be supported by the specification. No prior art will be applied to the claims, as the use of a fitness level of each hypothesis neighborhood being based on a rate of change of a local minima representing the hypothesis neighborhood by an optimization algorithm to rank hypothesis neighborhoods in accordance with their weight would not be obvious to one of ordinary skill in the art at the time of filing.
Response to Arguments
In pg.15, the Applicants argue in regards to the rejection under U.S.C. 101,
Populating an ontology by detecting data in the corpus. Claim 21 recites "receiving an ontology by one or more computers, the ontology including a plurality of fields and a plurality of choices for each of the fields such that the ontology includes a plurality of ontology vectors that each include one choice for each of the fields of the ontology" and "populating the ontology, by the one or more computers, by detecting data in the corpus of data that correspond to each of the ontology vectors." This is the only claimed step that the specification acknowledges "may be implemented by humans exclusively" and even this step is described in the specification as being implementable "by a computer with human supervision or completely implemented by machines via entity extraction", 6 for example by "parallel computing in which plural machines code the data independently. Entity extraction from corpora of data comprising emails, phone records, text messages, social network activities, GPS data, news articles, and other digital information is an AI/natural language processing task. No human could practically detect and code data from such a corpus (potentially including millions of documents) into ontology vectors using pencil and paper.
In response, the Examiner maintains the rejection as shown above. The use of generic computer equipment to execute an abstract idea is not enough to make the claim significantly more than the abstract idea. Merely stating in the specification that a computer is used to execute certain aspects of the claim, or data comes from extracting information from other generic computer systems is not enough to cause the claims to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.15-16, the Applicant further argues in regards to the rejection under U.S.C. 101,
Plotting populated ontology vectors in an N-dimensional ontology space. The specification explains that even with a modest ontology having three fields with 100 choices each, the ontology space includes 1003 === 1,000,000 distinct events. 8 When the number of dimensions grows beyond three-to 4, 5, or even 100 or more-the specification states that "the ontology space becomes so complex that a human analyst will find it difficult-to-impossible intuitively understand the ontology space."⁹ A human mind is not equipped to plot vectors in an N-dimensional space of this magnitude. The suggestion that a user could "mentally or with pencil and paper" plot a multi-dimensional space containing potentially millions of vectors is precisely the kind of impractical characterization that the 2025 Memo instructs examiners to avoid.
In response, the Examiner maintains the rejection as shown above. The question is not how long it would take a human to perform the action, or how many humans would be needed to perform the action, but whether the claim contains an abstract idea. First, the claim at no time limits the choices to huge values. N could just as easily be 2 as it could be 10,000. Even if the claim DID disclose a requirement of millions of values, human beings could work together to process and consider those values. The use of generic computer hardware to quickly process large volumes of data does not cause the claims to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.16, the Applicant further argues in regards to the rejection under U.S.C. 101,
Transforming the ontology space into a hypothesis space by grouping similar/related concepts. Claim 21 requires "transforming the plotted ontology space into a hypothesis space, by the one or more computers, by grouping the ontology vectors that describe similar and/or related concepts into hypothesis neighborhoods." Dependent claims 30 and 31 further specify that this grouping uses "one or more clustering techniques" including "hierarchies, filters and thresholds, topic models, or conditional random fields." Topic models and conditional random fields are well-recognized Al/machine-learning techniques. They are computational methods that require iterative probabilistic inference over large datasets, not simple observations or judgments a person could perform mentally. No human analyst could practically apply topic modeling or conditional random fields to cluster a million-entry ontology space using pencil and paper.
In response, the Examiner maintains the rejection as shown above. Similarly to generic computer hardware, the use of generic well-known machine learning/AI algorithms is not enough to make the claims significantly more than the abstract idea. Applicant merely claims generic “optimization algorithms” and well-known named algorithms like clustering, generic “topic models” or known mathematics such as “conditional random fields.” Merely adding in generic algorithms that contain no additional details or limitations beyond generic, off the shelf algorithms does not cause the claims to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.16, Applicant further argues in regards to the rejection under U.S.C. 101,
Under the 2025 Memo, those limitations resolve the Prong One inquiry. Because the pre-optimization steps "encompass AI in a way that cannot be practically performed in the human mind," they "do not fall within [the mental processes] grouping." The 2025 Memo's analysis is consistent with the distinction between claims that recite a judicial exception and claims that merely involve a judicial exception. The 2025 Memo provides Example 39 (training a neural network for facial detection) as an illustration: the claim limitation "training the neural network in a first stage using the first training set" does not recite a judicial exception because, even though "training the neural network" involves a broad array of techniques that "may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols." By analogy, the claim limitations of "populating the ontology by detecting data in the corpus," "plotting the populated ontology vectors in an ontology space," and "grouping the ontology vectors that describe similar and/or related concepts into hypothesis neighborhoods" do not set forth or describe mathematical formulas-they describe Al-driven computational processes that involve but do not recite mathematical concepts.
In response, the Examiner maintains the rejection as shown above. The Steps Applicant describes at no time call for or require AI or machine learning of any kind. They merely call for organizing data, relating it together, and grouping them based on those relations. There is no call for AI for these processes, let alone specific AI processes that would be anything more than generic, off the shelf AI algorithms, and therefore the rejection is maintained as shown above.
In pg.17-18, the Applicant argues in regards to the rejection under U.S.C. 101,
Dismissing the claimed optimization algorithm as merely "adding the words 'apply it' with the judicial exception" under MPEP 2106.05(f), the Examiner characterizes it as "a generic off the shelf optimization algorithm with no structure or details that make it beyond a common generic application of an optimization algorithm." However, the assertion that the Applicant "provided no detail about the 'optimization algorithm' beyond named algorithms (claim 22)" is factually incorrect as applied to the amended claims, which recite four distinct sub-steps that define how the optimization algorithm operates:
introducing a random variation or mutation into the hypothesis space representing the hypothesis neighborhoods; determining a fitness level of each hypothesis neighborhood based on a rate of change of a local minima representing the hypothesis neighborhood; determining an anticipation level of each hypothesis neighborhood based on a profile of a user; and de-weighting each hypothesis neighborhood having an anticipation level indicative of anticipated, trivial, or uninteresting hypotheses that the user expects from the corpus of data without use of the hypothesis space, wherein a rate of the random variation or mutation is based on the anticipation level of the preceding hypothesis neighborhood. That is not a "generic, off the shelf" optimization algorithm. 10 Instead, the claimed algorithm departs from generic optimization processes in at least two critical, domain-specific respects:
In response, the Examiner maintains the rejection as shown above. As stated in the rejection, each of those steps can be performed mentally or with a pencil and paper. Merely attributing them to a generic “optimization algorithm” does not cause them to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.18, the Applicant further argues in regards to the rejection under U.S.C. 101,
First, the algorithm incorporates an anticipation level determined from a user profile ---- a step that has no counterpart in generic simulated annealing, Monte Carlo, or genetic algorithms. That feature tailors the optimization to the specific domain of hypothesis generation by incorporating user-specific knowledge about what hypotheses the user would already expect from the data. The specification explains that the anticipation assessment allows the algorithm to "direct the search in a different direction, one more likely to produce unanticipated, non-trivial hypotheses."
In response, the Examiner maintains the rejection as shown above. Once again, each of these steps can be performed mentally or with pencil and paper. Merely attributing them to generic, off the shelf machine learning algorithms does not cause the claims to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.17-18, Applicant makes similar arguments in regards to various other mental steps or actions capable of being performed with pencil and paper, and the rejection is maintained for similar reasons given above.
In pg.19-20, the Applicant further argues in regards to the rejection under U.S.C. 101,
Here, the specification expressly identifies a technological problem with prior art data analysis techniques: "Current practices in identifying information of interest from a large amount of data includes the use of keyword searches to look for specific information, the use of Bayesian classifiers to divide information, and the use of logistic regression to look for risk factors of predefined or desired outcomes. These practices, by their nature, however cannot identify surprises, latest developments, or novel plots because these searches rely on a human conceived and defined set of interests or knowledge that a computer-aided search treats as a priori knowledge." The specification then describes a specific technical solution: "a computer implemented method that allows the data itself to define a space of possible hypotheses, which optionally merges and groups similar hypotheses, and then weights and selects a subset of relevant hypotheses for further consideration by a human analyst." That approach "uses a theoretical and physical basis to implement hypothesis generation" through simulated annealing, which "provides an understood, validated theoretical construct by which the problem of hypothesis generation can be solved."
In response, the Examiner maintains the rejection as shown above. “Hypothesis generation” or the consider of large amounts of data to make decisions is not a technology. Applicant at no time discloses any particular type of technology that would move beyond the abstract idea, and therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 21-31, 33, and 35-42 have been considered but are either moot in view of the new rejections, conclusory, or repetitions of the above arguments with the rejections maintained for similar reasons given above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BEN M RIFKIN/Primary Examiner, Art Unit 2123
Prosecution Insights